diff --git a/__init__.py b/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/analysis/.timefit.py.swp b/analysis/.timefit.py.swp new file mode 100644 index 0000000..8b58eef Binary files /dev/null and b/analysis/.timefit.py.swp differ diff --git a/analysis/Data Exploration Memcached.ipynb b/analysis/Data Exploration Memcached.ipynb index 6bdb758..4e10d8d 100644 --- a/analysis/Data Exploration Memcached.ipynb +++ b/analysis/Data Exploration Memcached.ipynb @@ -826,7 +826,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.6" + "version": "3.8.3" }, "toc": { "base_numbering": 1, diff --git a/analysis/energy_time_fit.py b/analysis/energy_time_fit.py new file mode 100644 index 0000000..00092a3 --- /dev/null +++ b/analysis/energy_time_fit.py @@ -0,0 +1,138 @@ +import sys +sys.path.append('../bayesopt') + +import read_agg_data +import torch +import torch.nn as nn +import torch.autograd as auto +import torch.optim as optim + +import numpy as np +import matplotlib.pylab as plt + +import pdb + +plt.ion() + +def inference(d, n_iter, lr, workload, sys, print_freq=10): + # p_busy_min = 20 + p_static = { + 'c1':1.5, + 'c3':0.5, + 'c4':0.25, + 'c7':0, + 'busy': 10 + } + chosen_sleep = 'c7' + + # p_q = p_static[chosen_sleep] + # p_detect = p_static[chosen_sleep] + + #starts randomly + max_time = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True) + alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True) + beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True) + p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True) + p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True) + p_q = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True) + p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True) + itr_suppress = torch.rand(1, requires_grad=True) + + qps = d[:,3] + energy = d[:,0]/(qps*20) + itr = d[:,1] + dvfs = d[:,2] + time = d[:,4] + interarrival_time = 1/qps*10**6 + + current_loss_time = -100 + fixed_max_time = -100 + fixed_alpha = -100 + + criterion = nn.MSELoss() + optimizer_time = optim.Adam([max_time, alpha], lr=lr) + optimizer_energy = optim.Adam([max_time, alpha, beta, p_detect, p_q], lr=lr) + # optimizer = optim.Adam([max_time, alpha, beta, p_detect, p_q], lr=lr) + + print(f'---------------FOR TIME LOSS {workload} {sys} lr = {lr}---------------') + + for _ in range(n_iter): + p_busy = (p_static_busy + p_busy_min*dvfs**(2+beta)) + t_busy = (max_time / dvfs**(1+alpha)) + pred_time = itr_suppress*itr + t_busy + loss_time = criterion(pred_time/time, torch.ones((1,pred_time.shape[1])).double()) + # loss = loss_energy + loss_time + + optimizer_time.zero_grad() + loss_time.backward(retain_graph=True) + optimizer_time.step() + + if(current_loss_time == -100): + current_loss_time = loss_time.item() + else: + if(current_loss_time >= loss_time.item()): + current_loss_time = loss_time.item() + fixed_max_time = max_time.item() + fixed_alpha = alpha.item() + + if _ % print_freq == 0: + print(max_time.item(), alpha.item(), itr_suppress.item(), loss_time.item()) + + print(f'---------------FOR ENERGY LOSS {workload} {sys} lr = {lr} max_time = {fixed_max_time} alpha = {fixed_alpha}---------------') + + for _ in range(n_iter): + t_busy_energy = (fixed_max_time / dvfs**(1+fixed_alpha)) + t_q_energy = (interarrival_time - itr - t_busy_energy) + pred_energy = (p_detect * itr_suppress*itr) + (p_busy * t_busy_energy) + (p_q * t_q_energy) + loss_energy = criterion(pred_energy/energy, torch.ones((1,pred_energy.shape[1])).double()) + + optimizer_energy.zero_grad() + loss_energy.backward(retain_graph=True) + optimizer_energy.step() + + if _ % print_freq == 0: + print(max_time.item(), alpha.item(), beta.item(), p_detect.item(), p_q.item(), itr_suppress.item(), loss_energy.item()) + + return pred_energy, pred_time + +def run(n_iter=2000, lr = 1e-2): + #read linux_mcd.csv + for workload in ['mcd']: + df_comb, _, _ = read_agg_data.start_analysis(workload) #DATA + df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: int(x, base=16)) + df_comb = df_comb[(df_comb['itr']!=1) | (df_comb['dvfs']!=65535)] #filter out linux dynamic + df_comb['dvfs'] = df_comb['dvfs'].astype(float) / df_comb['dvfs'].min() + df_comb = df_comb[df_comb['QPS'] == 400000] + + for sys in ['ebbrt_tuned']: + df = df_comb[(df_comb['sys']==sys)].copy() + df = df[['joules_mean','itr', 'dvfs', 'QPS', 'read_99th_mean']] + d = df.values + d = torch.tensor(d) + plt.plot(d[:,0], d[:,1], 'p') + + for lr in [lr]: + pred_energy, pred_time = inference(d, n_iter, lr, workload, sys, print_freq=1000) + df[f'pre_energy lr={lr}'] = pred_energy.view(245, 1).detach().numpy() + df[f'pre_time lr={lr}'] = pred_time.view(245, 1).detach().numpy() + + for pred_name in ['energy', 'time']: + if pred_name == 'energy': + pred = pred_energy + qps = d[:,3] + yvalue = d[:,0]/(qps*20) + else: + pred = pred_time + yvalue = d[:,4] + fig, ax = plt.subplots() + plt.title(f'predict:{pred_name} workload={workload} system={sys} lr={lr} QPS=400000') + plt.xlabel(u"predictions") + plt.ylabel(pred_name) + scatter = ax.scatter(pred.detach().numpy()[0], yvalue, marker = 'o', s = d[:,1], c = d[:,2], alpha=0.3) + legend1 = ax.legend(*scatter.legend_elements(),loc="upper left", title="dvfs") + ax.add_artist(legend1) + handles, labels = scatter.legend_elements(prop="sizes", alpha=0.6) + legend2 = plt.legend(handles, labels, loc="lower right", title="itr") + ax.add_artist(legend2) + plt.savefig(f'plots/energy_time_fit/randomp_{pred_name}_{workload}_{sys}_{lr}.png') + plt.close() \ No newline at end of file diff --git a/analysis/experiment.py b/analysis/experiment.py new file mode 100644 index 0000000..5de4c6c --- /dev/null +++ b/analysis/experiment.py @@ -0,0 +1,47 @@ +import sys +sys.path.append('../bayesopt') + +import read_agg_data +import torch +import torch.nn as nn +import torch.autograd as auto +import torch.optim as optim +import math + +import numpy as np +import matplotlib.pylab as plt + +import pdb + +plt.ion() + +def inference(n_iter, lr, print_freq=10): + x = torch.rand(1, requires_grad=True) + + func_tensor = torch.Tensor([1] * 10) + # min_tensor = torch.Tensor([-1] * 10) + + # criterion = nn.MSELoss() + optimizer = optim.Adam([x], lr=lr) + + for _ in range(n_iter): + # func = 5 * x + func = torch.Tensor(math.sin(x)) + pdb.set_trace() + # loss = criterion(min_tensor, func) + + optimizer.zero_grad() + func.backward() + optimizer.step() + + if _ % print_freq == 0: + print(x.item()) + + return x + +def run(n_iter=2000, + lr=1e-1): + + pred = inference(n_iter, lr, print_freq=500) + + return pred \ No newline at end of file diff --git a/analysis/mcd_ebbrt_tuned.png b/analysis/mcd_ebbrt_tuned.png new file mode 100644 index 0000000..d582632 Binary files /dev/null and b/analysis/mcd_ebbrt_tuned.png differ diff --git a/analysis/nn_models.py b/analysis/nn_models.py new file mode 100644 index 0000000..2068445 --- /dev/null +++ b/analysis/nn_models.py @@ -0,0 +1,164 @@ +import torch +import torch.nn as nn +import torch.optim as optim +from torch.utils.data import Dataset, DataLoader + +import numpy as np +import matplotlib.pylab as plt +from sklearn.model_selection import train_test_split + +plt.ion() + +device = 'cuda' if torch.cuda.is_available() else 'cpu' + +#TODO: pass train_df and test_df to create Dataset objects +class Dataset(Dataset): + def __init__(self, df, transform=None): + self.df = df + self.N_cols = df.shape[1] + + def __len__(self): + return len(self.df) + + def __getitem__(self, ix): + x = np.array(self.df.iloc[ix]) + + #TODO: map features and labels appropriately + features = x[:(self.N_cols-1)] + label = x[[-1]] + + return (torch.from_numpy(features).float(), torch.from_numpy(label)) + +#TODO: create an instance of Net with N_inputs = number of features, N_outputs = 2(time, energy), N_hidden_layers=1 or 2, N_hidden_nodes=128 +#TODO: activation = nn.ReLU(), output_activation = None (TODO: maybe change output_activation to exp() to impose positivity) +class Net(nn.Module): + def __init__(self, N_inputs, N_outputs, N_hidden_layers, N_hidden_nodes, activation, output_activation): + super(Net, self).__init__() + + self.N_inputs = N_inputs + self.N_outputs = N_outputs + + self.N_hidden_layers = N_hidden_layers + self.N_hidden_nodes = N_hidden_nodes + + self.layer_list = nn.ModuleList([]) #use just as a python list + for n in range(N_hidden_layers): + if n==0: + self.layer_list.append(nn.Linear(N_inputs, N_hidden_nodes)) + else: + self.layer_list.append(nn.Linear(N_hidden_nodes, N_hidden_nodes)) + + self.output_layer = nn.Linear(N_hidden_nodes, N_outputs) + + self.activation = activation + self.output_activation = output_activation + + def forward(self, inp): + out = inp + for layer in self.layer_list: + out = layer(out) + out = self.activation(out) + + out = self.output_layer(out) + if self.output_activation is not None: + pred = self.output_activation(out) + else: + pred = out + + return pred + + +def train_model(train_dl, test_dl, model, criterion, N_epochs, print_freq, lr=1e-3, optimizer='adam'): + '''Loop over dataset in batches, compute loss, backprop and update weights + ''' + + model.train() #switch to train model (for dropout, batch normalization etc.) + + model = model.to(device) + if optimizer=='adam': + optimizer = optim.Adam(model.parameters(), lr=lr) + print("Using adam") + elif optimizer=='sgd': + optimizer = optim.SGD(model.parameters(), lr=lr) + print("Using sgd") + else: + raise ValueError("Please use either adam or sgd") + + loss_dict = {} + for epoch in range(N_epochs): #loop over epochs i.e. sweeps over full data + curr_loss = 0 + N = 0 + + for idx, (features, labels) in enumerate(train_dl): #loop over batches = random samples from train dataset + #move features and labels to GPU if needed + features = features.to(device) + labels = labels.to(device) + + preds = model(features) #make predictions + loss = criterion(preds.squeeze(), labels.squeeze().float()) #compute loss between predictions and labels + + curr_loss += loss.item() #accumulate loss + N += len(labels) #accumulate number of data points seen in this epoch + + #backprop and updates + optimizer.zero_grad() + loss.backward() + optimizer.step() + + if epoch % print_freq == 0 or epoch==N_epochs-1: + val_loss = validate(test_dl, model, criterion) #get model perf metrics from test set + + loss_dict[epoch] = val_loss + + print(f'Iter = {epoch} Train Loss = {curr_loss / N} val_loss = {val_loss}') + + return model, loss_dict + +def validate(test_dl, model, criterion): + '''Loop over test dataset and compute loss and accuracy + ''' + model.eval() #switch to eval model + + loss = 0 + N = 0 + + preds_all, labels_all = torch.tensor([]), torch.tensor([]) + + with torch.no_grad(): #no need to keep variables for backprop computations + for idx, (features, labels) in enumerate(test_dl): #loop over batches from test set + features = features.to(device) + labels = labels.to(device).float() + + preds = model(features) + + preds_all = torch.cat((preds_all, preds.to('cpu')), 0) + labels_all = torch.cat((labels_all, labels.to('cpu')), 0) + + loss += criterion(preds.squeeze(), labels.squeeze()) #cumulative loss + N += len(labels) + + #avg_precision = average_precision_score(labels_all.squeeze().numpy(), preds_all.squeeze().numpy()) + + return loss / N + +def run(): + df = ... #TODO: read csv file + + #Split into train-test randomly + df_train, df_test = train_test_split(df, train_size=0.7) + + #Create Dataset objects + ds_torch_train = Dataset(df_train) + ds_torch_test = Dataset(df_test) + + #Create Dataloader objects (to sample batches of rows) + batch_size = 32 + dl_torch_train = DataLoader(ds_torch_train, batch_size=batch_size, num_workers=0) + dl_torch_test = DataLoader(ds_torch_test, batch_size=batch_size, num_workers=0) + + #criterion i.e. loss function + criterion = nn.MSELoss() + + #init and train model + model = Net(...) #TODO: appropriate arguments + model, loss_dict = train_model(...) #TODO: arguments \ No newline at end of file diff --git a/analysis/nsdi23_model_mcd.ipynb b/analysis/nsdi23_model_mcd.ipynb new file mode 100644 index 0000000..2901571 --- /dev/null +++ b/analysis/nsdi23_model_mcd.ipynb @@ -0,0 +1,1049 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('../bayesopt')\n", + "\n", + "import read_agg_data\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.autograd as auto\n", + "import torch.optim as optim\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as plt\n", + "import pandas as pd\n", + "import math\n", + "\n", + "import pdb\n", + "\n", + "dvfs_dict = {\n", + " \"0xc00\" : 1.2,\n", + " \"0xd00\" : 1.3,\n", + " \"0xe00\" : 1.4,\n", + " \"0xf00\" : 1.5,\n", + " \"0x1000\" : 1.6,\n", + " \"0x1100\" : 1.7,\n", + " \"0x1200\" : 1.8,\n", + " \"0x1300\" : 1.9,\n", + " \"0x1400\" : 2.0,\n", + " \"0x1500\" : 2.1,\n", + " \"0x1600\" : 2.2,\n", + " \"0x1700\" : 2.3,\n", + " \"0x1800\" : 2.4,\n", + " \"0x1900\" : 2.5,\n", + " \"0x1a00\" : 2.6,\n", + " \"0x1b00\" : 2.7,\n", + " \"0x1c00\" : 2.8,\n", + " \"0x1d00\" : 2.9,\n", + " \"0xffff\" : 3.0,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3073\n", + "[200000 400000 600000 0]\n", + "Index(['sys', 'i', 'itr', 'dvfs', 'rapl', 'read_5th', 'read_10th', 'read_50th',\n", + " 'read_90th', 'read_95th', 'read_99th', 'measure_QPS', 'target_QPS',\n", + " 'time', 'joules', 'rx_desc', 'rx_bytes', 'tx_desc', 'tx_bytes',\n", + " 'instructions', 'cycles', 'ref_cycles', 'llc_miss', 'c1', 'c1e', 'c3',\n", + " 'c6', 'c7', 'num_interrupts', 'QPS'],\n", + " dtype='object')\n", + "[400000 600000 200000]\n" + ] + } + ], + "source": [ + "#df_comb, _, _ = read_agg_data.start_analysis('mcd') #DATA\n", + "#df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: int(x, base=16))\n", + "\n", + "df_comb = pd.read_csv('/home/handong/jupyter/jupyter-notebooks/nic-tuning-experiments/bayesopt/summary_data/mcd_combined.csv', sep=' ')\n", + "print(df_comb.shape[0])\n", + "df_comb['QPS'] = df_comb['target_QPS']\n", + "\n", + "print(df_comb['QPS'].unique())\n", + "df_comb = df_comb[(df_comb['i'] == 1) & (df_comb['rapl'] == 135)]\n", + "#df_comb = df_comb[(df_comb['rapl'] == 135)]\n", + "df_comb = df_comb[df_comb['read_99th'] <= 500]\n", + "\n", + "df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: dvfs_dict[x])\n", + "df_comb = df_comb[(df_comb['itr']!=1) & (df_comb['dvfs']!=65535)] #filter out linux dynamic\n", + "print(df_comb.columns)\n", + "\n", + "# df_comb['dvfs'] = df_comb['dvfs'].astype(float) / df_comb['dvfs'].min()\n", + "# print(df_comb['dvfs'].unique())\n", + "# df_comb['itr'] = df_comb['itr'].astype(float) / df_comb['itr'].min()\n", + "# print(df_comb['itr'].unique())\n", + "#print(10**6)\n", + "print(df_comb['QPS'].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 50 100 200 300 400 350]\n", + "1675.5\n", + "******* ebbrt_tuned 50 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "174 0.000209 50 1.7 5221529 1.899239e+11 103.8\n", + "262 0.000223 50 1.9 5221089 1.844129e+11 103.4\n", + "351 0.000240 50 2.1 5220371 1.796836e+11 103.3\n", + "440 0.000258 50 2.3 5214101 1.763346e+11 103.9\n", + "526 0.000277 50 2.5 5220459 1.737004e+11 103.8\n", + "616 0.000296 50 2.7 5223052 1.701433e+11 103.0\n", + "706 0.000321 50 2.9 5216163 1.690650e+11 102.8\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 50 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "4 0.000168 50 1.3 6036916 3.025599e+11 104.1\n", + "92 0.000180 50 1.5 6035703 2.803556e+11 104.9\n", + "179 0.000194 50 1.7 6033477 2.668666e+11 105.3\n", + "268 0.000208 50 1.9 6032140 2.624055e+11 105.4\n", + "357 0.000225 50 2.1 6033866 2.551110e+11 105.1\n", + "446 0.000240 50 2.3 6031643 2.471547e+11 105.9\n", + "532 0.000261 50 2.5 6032598 2.480025e+11 106.6\n", + "622 0.000280 50 2.7 6031159 2.420433e+11 105.7\n", + "712 0.000304 50 2.9 6032217 2.407263e+11 106.0\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 50 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "10 0.000171 50 1.3 6206452 3.935791e+11 102.8\n", + "98 0.000185 50 1.5 6205251 3.610531e+11 102.1\n", + "185 0.000199 50 1.7 6204518 3.390249e+11 104.1\n", + "274 0.000215 50 1.9 6204134 3.256803e+11 104.0\n", + "363 0.000232 50 2.1 6203612 3.133541e+11 106.2\n", + "452 0.000250 50 2.3 6202987 3.085547e+11 106.8\n", + "538 0.000270 50 2.5 6202883 3.022569e+11 105.9\n", + "628 0.000292 50 2.7 6202776 2.975200e+11 109.1\n", + "718 0.000316 50 2.9 6201800 2.962684e+11 108.5\n", + "\n", + "2106.63\n", + "******* linux_tuned 50 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1486 0.000213 50 1.3 5218634 4.599466e+11 138.9\n", + "1930 0.000213 50 1.3 5220109 4.599841e+11 138.7\n", + "1548 0.000232 50 1.5 5177475 4.049907e+11 124.2\n", + "2002 0.000232 50 1.5 5176745 4.045315e+11 125.9\n", + "1610 0.000269 50 1.7 5155688 3.624321e+11 117.0\n", + "2075 0.000251 50 1.7 5172205 3.641128e+11 117.0\n", + "2147 0.000291 50 1.9 5153643 3.313907e+11 113.4\n", + "2219 0.000298 50 2.1 5141375 3.035007e+11 108.0\n", + "2291 0.000323 50 2.3 5118452 2.799074e+11 106.0\n", + "2395 0.000353 50 2.5 5094243 2.611035e+11 105.6\n", + "2483 0.000384 50 2.7 5090058 2.452500e+11 105.2\n", + "2571 0.000415 50 2.9 5072154 2.326829e+11 106.1\n", + "\n", + "2400.58\n", + "******* linux_tuned 50 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1934 0.000223 50 1.3 6048093 7.310257e+11 341.0\n", + "2007 0.000225 50 1.5 6039895 6.615798e+11 198.8\n", + "2079 0.000244 50 1.7 6030084 5.970014e+11 151.8\n", + "1675 0.000265 50 1.9 6024476 5.529146e+11 130.2\n", + "2151 0.000264 50 1.9 6024084 5.472257e+11 128.8\n", + "1737 0.000287 50 2.1 6017531 5.003071e+11 118.6\n", + "2223 0.000287 50 2.1 6015240 4.986567e+11 120.1\n", + "2296 0.000310 50 2.3 6012185 4.605418e+11 115.4\n", + "2399 0.000339 50 2.5 6010602 4.298558e+11 111.6\n", + "2487 0.000367 50 2.7 6006830 4.001675e+11 107.7\n", + "2575 0.000400 50 2.9 6005187 3.763980e+11 105.7\n", + "\n", + "2654.84\n", + "******* linux_tuned 50 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2155 0.000283 50 1.9 6190548 7.257716e+11 275.9\n", + "2227 0.000309 50 2.1 6192252 6.796291e+11 194.6\n", + "2316 0.000337 50 2.3 6192616 6.428080e+11 153.5\n", + "2404 0.000365 50 2.5 6191938 5.980526e+11 134.7\n", + "2492 0.000397 50 2.7 6192273 5.626668e+11 124.0\n", + "2580 0.000429 50 2.9 6192620 5.246938e+11 115.5\n", + "\n", + "1675.5\n", + "******* ebbrt_tuned 100 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "15 0.000313 100 1.3 3043321 1.924407e+11 154.5\n", + "104 0.000334 100 1.5 3043262 1.844762e+11 154.3\n", + "191 0.000355 100 1.7 3043290 1.771736e+11 153.6\n", + "280 0.000379 100 1.9 3043897 1.732380e+11 153.4\n", + "369 0.000406 100 2.1 3042843 1.674202e+11 153.3\n", + "457 0.000433 100 2.3 3043840 1.639538e+11 153.5\n", + "544 0.000466 100 2.5 3043557 1.625835e+11 152.7\n", + "634 0.000501 100 2.7 3044127 1.586730e+11 152.8\n", + "724 0.000539 100 2.9 3044273 1.575179e+11 152.2\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 100 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "21 0.000321 100 1.3 3120627 2.911510e+11 154.6\n", + "196 0.000369 100 1.7 3120523 2.644230e+11 154.6\n", + "286 0.000396 100 1.9 3120497 2.553182e+11 154.6\n", + "375 0.000426 100 2.1 3120517 2.490231e+11 154.2\n", + "462 0.000458 100 2.3 3120466 2.429661e+11 154.3\n", + "550 0.000496 100 2.5 3120522 2.435881e+11 153.4\n", + "640 0.000532 100 2.7 3120526 2.366776e+11 154.4\n", + "730 0.000577 100 2.9 3120506 2.346554e+11 154.0\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 100 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "27 0.000336 100 1.3 3124708 3.844774e+11 155.3\n", + "114 0.000362 100 1.5 3124642 3.581078e+11 155.0\n", + "202 0.000391 100 1.7 3124646 3.440160e+11 155.3\n", + "292 0.000419 100 1.9 3124642 3.267733e+11 154.7\n", + "381 0.000451 100 2.1 3124631 3.169717e+11 155.9\n", + "467 0.000485 100 2.3 3124653 3.116417e+11 156.0\n", + "556 0.000525 100 2.5 3124663 3.072970e+11 154.8\n", + "646 0.000566 100 2.7 3124617 3.016410e+11 154.6\n", + "736 0.000615 100 2.9 3124608 3.011797e+11 155.6\n", + "\n", + "2106.63\n", + "******* linux_tuned 100 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1942 0.000362 100 1.3 3016621 4.188141e+11 184.6\n", + "2015 0.000392 100 1.5 3015677 3.737492e+11 174.9\n", + "2087 0.000423 100 1.7 3018319 3.377049e+11 170.9\n", + "2159 0.000457 100 1.9 3017132 3.094803e+11 169.5\n", + "2231 0.000497 100 2.1 3017215 2.865768e+11 168.7\n", + "2335 0.000536 100 2.3 3009006 2.681600e+11 169.4\n", + "2423 0.000583 100 2.5 3007258 2.543001e+11 170.2\n", + "2511 0.000633 100 2.7 2997316 2.437554e+11 171.3\n", + "2599 0.000685 100 2.9 2994394 2.349044e+11 172.5\n", + "\n", + "2400.58\n", + "******* linux_tuned 100 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1946 0.000394 100 1.3 3115897 6.878025e+11 295.9\n", + "2019 0.000427 100 1.5 3116209 6.142148e+11 225.1\n", + "2091 0.000459 100 1.7 3116996 5.384308e+11 188.5\n", + "2163 0.000496 100 1.9 3117158 4.852459e+11 180.0\n", + "2235 0.000539 100 2.1 3117256 4.445204e+11 171.5\n", + "2339 0.000582 100 2.3 3117344 4.099733e+11 169.2\n", + "2427 0.000632 100 2.5 3117357 3.816575e+11 166.7\n", + "2515 0.000686 100 2.7 3117286 3.580680e+11 165.6\n", + "2603 0.000745 100 2.9 3117360 3.378497e+11 164.7\n", + "\n", + "2654.84\n", + "******* linux_tuned 100 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2095 0.000503 100 1.7 3123123 7.270530e+11 392.3\n", + "2167 0.000548 100 1.9 3123803 6.772242e+11 258.6\n", + "2239 0.000595 100 2.1 3124066 6.221400e+11 206.6\n", + "2343 0.000644 100 2.3 3124129 5.734289e+11 188.8\n", + "2431 0.000697 100 2.5 3124197 5.262300e+11 177.7\n", + "2519 0.000751 100 2.7 3124214 4.819835e+11 170.4\n", + "2607 0.000815 100 2.9 3124318 4.537178e+11 167.7\n", + "\n", + "1675.5\n", + "******* ebbrt_tuned 200 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "33 0.000606 200 1.3 1561019 1.734411e+11 249.6\n", + "120 0.000642 200 1.5 1561047 1.644802e+11 250.1\n", + "208 0.000682 200 1.7 1560999 1.602762e+11 249.6\n", + "298 0.000722 200 1.9 1561030 1.547365e+11 249.7\n", + "386 0.000769 200 2.1 1561047 1.485075e+11 249.3\n", + "473 0.000822 200 2.3 1561003 1.465697e+11 249.7\n", + "562 0.000878 200 2.5 1560969 1.443002e+11 249.1\n", + "652 0.000935 200 2.7 1561051 1.411333e+11 249.5\n", + "742 0.000999 200 2.9 1561046 1.390771e+11 249.1\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 200 400000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "39 0.000635 200 1.3 1562466 2.697584e+11 249.7\n", + "126 0.000673 200 1.5 1562481 2.621141e+11 262.6\n", + "214 0.000726 200 1.7 1562444 2.439520e+11 248.8\n", + "304 0.000757 200 1.9 1562470 2.284070e+11 250.3\n", + "392 0.000801 200 2.1 1562464 2.331850e+11 257.3\n", + "479 0.000853 200 2.3 1562467 2.306333e+11 262.2\n", + "568 0.000916 200 2.5 1562460 2.317443e+11 255.8\n", + "658 0.001002 200 2.7 1562459 2.183752e+11 249.9\n", + "748 0.001074 200 2.9 1562468 2.143033e+11 248.7\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 200 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "45 0.000664 200 1.3 1562473 3.654273e+11 274.8\n", + "132 0.000703 200 1.5 1562471 3.502044e+11 270.5\n", + "220 0.000748 200 1.7 1562468 3.326593e+11 265.0\n", + "310 0.000806 200 1.9 1562468 3.219903e+11 258.4\n", + "398 0.000840 200 2.1 1562477 3.065463e+11 265.7\n", + "485 0.000899 200 2.3 1562462 3.100330e+11 270.5\n", + "574 0.000964 200 2.5 1562458 3.063907e+11 264.2\n", + "664 0.001053 200 2.7 1562463 3.064447e+11 258.5\n", + "754 0.001091 200 2.9 1562469 3.003311e+11 268.5\n", + "\n", + "2106.63\n", + "******* linux_tuned 200 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1954 0.000688 200 1.3 1559224 3.796443e+11 296.9\n", + "2027 0.000741 200 1.5 1559379 3.409071e+11 291.8\n", + "2099 0.000798 200 1.7 1559190 3.099416e+11 285.2\n", + "2171 0.000858 200 1.9 1559272 2.868034e+11 276.6\n", + "2243 0.000930 200 2.1 1559222 2.660824e+11 276.5\n", + "2347 0.001003 200 2.3 1558627 2.507451e+11 275.3\n", + "2435 0.001085 200 2.5 1557789 2.370932e+11 276.3\n", + "2523 0.001177 200 2.7 1556529 2.285748e+11 276.4\n", + "2611 0.001270 200 2.9 1557824 2.189206e+11 276.0\n", + "\n", + "2400.58\n", + "******* linux_tuned 200 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1958 0.000764 200 1.3 1562641 6.153520e+11 342.5\n", + "2031 0.000823 200 1.5 1562632 5.384820e+11 317.0\n", + "2103 0.000887 200 1.7 1562706 4.786562e+11 302.6\n", + "2175 0.000960 200 1.9 1562695 4.367089e+11 290.2\n", + "2247 0.001038 200 2.1 1562708 4.033413e+11 293.7\n", + "2351 0.001123 200 2.3 1562699 3.782067e+11 285.2\n", + "2439 0.001218 200 2.5 1562731 3.527546e+11 283.0\n", + "2527 0.001304 200 2.7 1561956 3.340450e+11 288.4\n", + "2615 0.001429 200 2.9 1562750 3.128069e+11 275.0\n", + "\n", + "2654.84\n", + "******* linux_tuned 200 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2107 0.000979 200 1.7 1562786 6.640448e+11 397.4\n", + "2179 0.001064 200 1.9 1562795 6.200477e+11 331.6\n", + "2251 0.001146 200 2.1 1562810 5.502158e+11 310.7\n", + "2355 0.001239 200 2.3 1562805 5.177743e+11 298.8\n", + "2443 0.001347 200 2.5 1562804 4.783754e+11 291.3\n", + "2531 0.001455 200 2.7 1562809 4.431551e+11 282.7\n", + "2619 0.001551 200 2.9 1562781 4.175727e+11 295.9\n", + "\n", + "1675.5\n", + "******* ebbrt_tuned 300 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "51 0.000903 300 1.3 1041618 1.673578e+11 357.1\n", + "138 0.000959 300 1.5 1041598 1.654854e+11 355.4\n", + "226 0.001013 300 1.7 1041607 1.571900e+11 355.5\n", + "316 0.001066 300 1.9 1041613 1.512917e+11 356.8\n", + "404 0.001137 300 2.1 1041609 1.448051e+11 354.9\n", + "491 0.001208 300 2.3 1041611 1.450336e+11 356.1\n", + "580 0.001280 300 2.5 1041616 1.400301e+11 354.9\n", + "670 0.001376 300 2.7 1041523 1.382381e+11 356.1\n", + "760 0.001471 300 2.9 1041607 1.402800e+11 354.3\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 300 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "57 0.000943 300 1.3 1041656 2.507369e+11 366.4\n", + "144 0.000997 300 1.5 1041646 2.352696e+11 362.2\n", + "232 0.001062 300 1.7 1041654 2.267574e+11 360.1\n", + "322 0.001134 300 1.9 1041659 2.217302e+11 350.5\n", + "410 0.001187 300 2.1 1041649 2.224460e+11 356.6\n", + "497 0.001251 300 2.3 1041640 1.928122e+11 360.4\n", + "586 0.001363 300 2.5 1041646 2.028735e+11 350.5\n", + "676 0.001420 300 2.7 1041653 1.985223e+11 356.5\n", + "766 0.001577 300 2.9 1041656 2.066888e+11 351.4\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 300 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "63 0.000984 300 1.3 1041651 3.409537e+11 383.5\n", + "150 0.001049 300 1.5 1041649 3.249638e+11 360.1\n", + "238 0.001106 300 1.7 1041643 3.014450e+11 379.0\n", + "327 0.001172 300 1.9 1041648 2.892195e+11 376.4\n", + "416 0.001264 300 2.1 1041644 2.675962e+11 361.3\n", + "503 0.001333 300 2.3 1041655 2.728385e+11 360.5\n", + "592 0.001418 300 2.5 1041645 2.714869e+11 364.0\n", + "682 0.001550 300 2.7 1041664 2.711051e+11 354.4\n", + "772 0.001618 300 2.9 1041651 2.721008e+11 364.3\n", + "\n", + "2106.63\n", + "******* linux_tuned 300 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1966 0.001021 300 1.3 1041592 3.691559e+11 401.6\n", + "2039 0.001096 300 1.5 1041574 3.273570e+11 396.1\n", + "2111 0.001178 300 1.7 1041542 2.987906e+11 391.8\n", + "2183 0.001265 300 1.9 1041462 2.748956e+11 388.5\n", + "2255 0.001367 300 2.1 1041416 2.579214e+11 388.2\n", + "2359 0.001476 300 2.3 1041232 2.460202e+11 386.5\n", + "2447 0.001586 300 2.5 1040942 2.279152e+11 387.2\n", + "2535 0.001723 300 2.7 1040159 2.231041e+11 385.9\n", + "2623 0.001862 300 2.9 1040332 2.150551e+11 386.0\n", + "\n", + "2400.58\n", + "******* linux_tuned 300 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1970 0.001129 300 1.3 1041887 5.864127e+11 458.6\n", + "2043 0.001210 300 1.5 1041884 5.087747e+11 435.5\n", + "2115 0.001293 300 1.7 1041891 4.531473e+11 429.7\n", + "2187 0.001403 300 1.9 1041876 4.226638e+11 404.3\n", + "2259 0.001530 300 2.1 1041883 3.868485e+11 395.6\n", + "2363 0.001637 300 2.3 1041857 3.606601e+11 394.9\n", + "2451 0.001792 300 2.5 1041901 3.443151e+11 390.5\n", + "2539 0.001937 300 2.7 1041884 3.218515e+11 384.0\n", + "2627 0.002091 300 2.9 1041881 3.097599e+11 391.8\n", + "\n", + "2654.84\n", + "******* linux_tuned 300 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2119 0.001421 300 1.7 1041838 6.089765e+11 480.6\n", + "2191 0.001507 300 1.9 1041851 5.559836e+11 469.4\n", + "2263 0.001621 300 2.1 1041855 5.121567e+11 441.5\n", + "2367 0.001745 300 2.3 1041904 4.860125e+11 433.8\n", + "2455 0.001912 300 2.5 1041789 4.524298e+11 417.5\n", + "2543 0.002068 300 2.7 1041882 4.266260e+11 405.5\n", + "2631 0.002174 300 2.9 1041872 4.091905e+11 405.0\n", + "\n", + "1675.5\n", + "******* ebbrt_tuned 350 200000\n", + "Empty DataFrame\n", + "Columns: [joules_per_interrupt, itr, dvfs, num_interrupts, ref_cycles, read_99th]\n", + "Index: []\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1836.46\n", + "******* ebbrt_tuned 350 400000\n", + "Empty DataFrame\n", + "Columns: [joules_per_interrupt, itr, dvfs, num_interrupts, ref_cycles, read_99th]\n", + "Index: []\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 350 600000\n", + "Empty DataFrame\n", + "Columns: [joules_per_interrupt, itr, dvfs, num_interrupts, ref_cycles, read_99th]\n", + "Index: []\n", + "\n", + "2106.63\n", + "******* linux_tuned 350 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1978 0.001184 350 1.3 892879 3.570502e+11 449.6\n", + "2051 0.001275 350 1.5 892890 3.224655e+11 443.8\n", + "2123 0.001368 350 1.7 892913 2.880056e+11 441.6\n", + "2195 0.001468 350 1.9 892689 2.704350e+11 439.8\n", + "2267 0.001585 350 2.1 892793 2.461656e+11 437.7\n", + "2371 0.001702 350 2.3 892838 2.379652e+11 437.0\n", + "2459 0.001831 350 2.5 892558 2.210142e+11 436.8\n", + "2547 0.001982 350 2.7 890542 2.140299e+11 437.2\n", + "2635 0.002131 350 2.9 892014 2.075557e+11 437.6\n", + "\n", + "2400.58\n", + "******* linux_tuned 350 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2055 0.001391 350 1.5 893011 4.871073e+11 494.6\n", + "2127 0.001507 350 1.7 893052 4.437976e+11 478.3\n", + "2199 0.001604 350 1.9 892856 4.091595e+11 477.3\n", + "2271 0.001721 350 2.1 892963 3.782915e+11 474.3\n", + "2375 0.001856 350 2.3 893001 3.538089e+11 453.2\n", + "2463 0.002023 350 2.5 893014 3.372406e+11 453.5\n", + "2551 0.002255 350 2.7 893050 3.162472e+11 433.7\n", + "2639 0.002437 350 2.9 893040 2.952957e+11 433.2\n", + "\n", + "2654.84\n", + "******* linux_tuned 350 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2203 0.001780 350 1.9 893015 5.455264e+11 491.1\n", + "2275 0.001926 350 2.1 893030 5.028107e+11 481.3\n", + "2379 0.002068 350 2.3 893027 4.667815e+11 471.4\n", + "2467 0.002237 350 2.5 893023 4.478798e+11 469.7\n", + "2555 0.002428 350 2.7 893039 4.174783e+11 457.4\n", + "2643 0.002644 350 2.9 893024 4.042622e+11 447.9\n", + "\n", + "1675.5\n", + "******* ebbrt_tuned 400 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "69 0.001201 400 1.3 781239 1.599319e+11 443.6\n", + "156 0.001265 400 1.5 781235 1.412006e+11 444.4\n", + "244 0.001342 400 1.7 781243 1.463764e+11 443.6\n", + "333 0.001420 400 1.9 781241 1.442308e+11 444.2\n", + "422 0.001488 400 2.1 781238 1.349614e+11 444.9\n", + "509 0.001570 400 2.3 781237 1.250480e+11 444.1\n", + "598 0.001681 400 2.5 781241 1.333756e+11 443.1\n", + "688 0.001812 400 2.7 781240 1.349334e+11 443.8\n", + "778 0.001923 400 2.9 781245 1.292604e+11 442.8\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 400 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "75 0.001267 400 1.3 781240 2.735703e+11 444.2\n", + "162 0.001346 400 1.5 781248 2.611021e+11 444.6\n", + "250 0.001425 400 1.7 781247 2.500339e+11 445.5\n", + "339 0.001516 400 1.9 781247 2.397122e+11 441.9\n", + "428 0.001607 400 2.1 781241 2.396535e+11 443.3\n", + "515 0.001677 400 2.3 781238 2.176450e+11 457.4\n", + "604 0.001780 400 2.5 781236 2.123568e+11 445.6\n", + "694 0.001960 400 2.7 781235 2.193248e+11 443.5\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 400 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "81 0.001323 400 1.3 781240 3.617910e+11 474.5\n", + "168 0.001417 400 1.5 781237 3.475433e+11 471.2\n", + "256 0.001498 400 1.7 781241 3.135289e+11 458.1\n", + "345 0.001624 400 1.9 781248 3.191303e+11 440.6\n", + "434 0.001689 400 2.1 781240 2.783233e+11 469.3\n", + "520 0.001843 400 2.3 781246 3.045776e+11 445.7\n", + "610 0.001945 400 2.5 781247 2.601970e+11 458.4\n", + "700 0.002096 400 2.7 781242 2.692949e+11 452.3\n", + "789 0.002208 400 2.9 781235 2.763436e+11 474.8\n", + "\n", + "2106.63\n", + "******* linux_tuned 400 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2063 0.001450 400 1.5 781318 3.150755e+11 493.1\n", + "2135 0.001542 400 1.7 781360 2.784672e+11 491.5\n", + "2207 0.001669 400 1.9 781324 2.666548e+11 488.1\n", + "2279 0.001799 400 2.1 780971 2.503521e+11 487.4\n", + "2383 0.001941 400 2.3 781330 2.320813e+11 485.4\n", + "2471 0.002103 400 2.5 781220 2.191376e+11 485.2\n", + "2559 0.002254 400 2.7 780778 2.117097e+11 485.7\n", + "2647 0.002446 400 2.9 779923 2.031581e+11 484.5\n", + "\n", + "2400.58\n", + "******* linux_tuned 400 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2211 0.001886 400 1.9 781397 4.142691e+11 488.5\n", + "2387 0.002144 400 2.3 781376 3.511999e+11 500.0\n", + "2475 0.002367 400 2.5 781409 3.312238e+11 488.0\n", + "2563 0.002517 400 2.7 781419 3.193373e+11 489.3\n", + "2651 0.002699 400 2.9 781391 2.899172e+11 491.6\n", + "\n", + "2654.84\n", + "******* linux_tuned 400 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2391 0.002447 400 2.3 781414 4.945755e+11 478.6\n", + "\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(df_comb['itr'].unique())\n", + "for itr in [50, 100, 200, 300, 350, 400]:\n", + " for sys in ['ebbrt_tuned', 'linux_tuned']:\n", + " for qps in [200000, 400000, 600000]:\n", + " df = df_comb[(df_comb['sys']==sys) & (df_comb['QPS'] == qps)].copy()\n", + " #print(df.shape[0])\n", + " print(df['joules'].max())\n", + " df['joules_per_interrupt'] = df['joules']/df['num_interrupts']\n", + " df = df[['joules_per_interrupt','itr', 'dvfs', 'num_interrupts', 'ref_cycles', 'read_99th']]\n", + " #print(df.shape[0])\n", + " #print('')\n", + " \n", + " dfi = df[df['itr']==itr]\n", + " #dfi = dfi.drop_duplicates(subset = [\"itr\", \"dvfs\"])\n", + " #dfi['joules_mean'] = dfi['joules_mean']/dfi['joules_mean'].max()\n", + " #print(dfi.diff())\n", + " print('*******', sys, itr, qps)\n", + " print(dfi.sort_values(by=['dvfs']))\n", + " #print(dfi.sort_values(by=['dvfs']).diff())\n", + " print('')\n", + " plt.plot(dfi['dvfs'], dfi['joules_per_interrupt'])\n", + " #print(dfi)" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "def inference_time(d, n_iter, lr, workload, sys, fzeta, falpha, fphi, print_freq=1000):\n", + " # p_busy_min = 20\n", + " p_static = {\n", + " 'c1':1.5, \n", + " 'c3':0.5,\n", + " 'c4':0.25,\n", + " 'c7':34, # 34 Watts\n", + " 'busy': 10\n", + " }\n", + " chosen_sleep = 'c7'\n", + "\n", + " p_q = p_static[chosen_sleep]/10**6 # joules/us idle\n", + " # p_detect = p_static[chosen_sleep]\n", + "\n", + " #starts\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(fzeta[0], fzeta[1]), requires_grad=True)\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(falpha[0], falpha[1]), requires_grad=True)\n", + " phi = torch.tensor(torch.Tensor(1,1).uniform_(fphi[0], fphi[1]), requires_grad=True)\n", + " \n", + " #p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " #p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " #p_q = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " #p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True) \n", + " #AA = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " #BB = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " \n", + " #df[['joules','itr', 'dvfs', 'QPS', read_99th, 'num_interrupts']]\n", + " qps = d[:,3]\n", + " ninterrupts = d[:,5]\n", + " energy = (d[:,0]/ninterrupts).log() ## joules/num_interrupts\n", + " #energy = (d[:,0]/(qps).log()\n", + " itr = d[:,1]\n", + " dvfs = d[:,2]\n", + " time = d[:,4]\n", + " \n", + " #interarrival_time = (1/qps)*10**6\n", + " criterion = nn.MSELoss()\n", + " optimizer_time = optim.Adam([zeta, alpha, phi], lr=lr)\n", + "\n", + " for i in range(n_iter): \n", + " t_busy = (zeta / dvfs**(1+alpha)) ## as dvfs increases, max_time should get smaller\n", + " pred_time = (phi*itr) + t_busy ## itr_suppress reflects where pkt is in queue\n", + " \n", + " loss_time = criterion(pred_time/time, torch.ones((1,pred_time.shape[1])).double())\n", + " if i % print_freq == 0:\n", + " print(f'MSE_loss_time={loss_time.item()} loss_time={round(math.sqrt(loss_time.item()), 5)} us'\n", + " +f' zeta={zeta.item()} alpha={alpha.item()} phi={phi.item()}')\n", + " \n", + " optimizer_time.zero_grad()\n", + " loss_time.backward(retain_graph=True)\n", + " optimizer_time.step()\n", + " \n", + " return pred_time" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [], + "source": [ + "def run_time(df_comb, n_iter=2000, lr=1, rqps=400000, rtail='99', \n", + " msys=['ebbrt_tuned'], mpred=['energy', 'time'], \n", + " fzeta=[0, 500], falpha=[-2.0, 2.0], fphi=[-2.0, 2.0]):\n", + " df_comb = df_comb[df_comb['QPS'] == rqps]\n", + "\n", + " i=1\n", + " \n", + " for sys in msys:\n", + " df = df_comb[(df_comb['sys']==sys)].copy()\n", + " rt = 'read_'+rtail+'th'\n", + " df = df[['joules','itr', 'dvfs', 'QPS', rt, 'num_interrupts']]\n", + " tnum = df.shape[0]\n", + " d = df.values\n", + " d = torch.tensor(d)\n", + " print('SYS', sys)\n", + " #pred_energy, max_time, alpha, beta, p_detect, p_q = inference_energy(d, n_iter, lr, 'mcd', sys, print_freq=1000)\n", + " pred_time = inference_time(d, n_iter, lr, 'mcd', sys, fzeta, falpha, fphi)\n", + " \n", + " df[f'pre_time lr={lr}'] = pred_time.view(tnum, 1).detach().numpy()\n", + " \n", + " for pred_name in mpred:\n", + " pred = pred_time\n", + " yvalue = d[:,4]\n", + "\n", + " #fig, ax = plt.subplots()\n", + " ax = plt.subplot(1, len(msys)*len(mpred), i)\n", + " \n", + " if sys == 'ebbrt_tuned':\n", + " plt.title(f'EbbRT @ {int(rqps/1000)}K QPS', fontsize=20)\n", + " else:\n", + " plt.title(f'Linux @ {int(rqps/1000)}K QPS', fontsize=20)\n", + " \n", + "\n", + " plt.ylabel('Measured 99% Tail (us)', fontsize=20)\n", + " plt.xlabel('Predicted 99% Tail (us)', fontsize=20)\n", + " \n", + " tmax = yvalue.max().item()\n", + " plt.plot(np.linspace(0, tmax, 10), np.linspace(0, tmax, 10))\n", + " \n", + " print('measurement', yvalue.mean(), yvalue.std())\n", + " \n", + " scatter = ax.scatter(pred.detach().numpy()[0], yvalue, marker = 'o', \n", + " s = d[:,1], c = d[:,2], alpha=0.5)\n", + " \n", + " legend1 = ax.legend(*scatter.legend_elements(),loc=\"upper left\", title=\"dvfs\")\n", + " ax.add_artist(legend1)\n", + " handles, labels = scatter.legend_elements(prop=\"sizes\", alpha=0.6)\n", + " legend2 = plt.legend(handles, labels, loc=\"lower right\", title=\"itr\")\n", + " ax.add_artist(legend2)\n", + " \n", + " plt.grid(True)\n", + " plt.tight_layout()\n", + " i += 1\n", + " \n", + " plt.subplots_adjust(wspace=0.3, hspace=0)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "MSE_loss_time=0.01630640304216516 loss_time=0.1277 us zeta=78.29601287841797 alpha=-0.7872987389564514 phi=1.0714240074157715\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(fzeta[0], fzeta[1]), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(falpha[0], falpha[1]), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " phi = torch.tensor(torch.Tensor(1,1).uniform_(fphi[0], fphi[1]), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=9.50516126360735e-05 loss_time=0.00975 us zeta=55.91225051879883 alpha=-0.9653683304786682 phi=0.984230637550354\n", + "MSE_loss_time=9.505178126392609e-05 loss_time=0.00975 us zeta=55.90272521972656 alpha=-0.965545117855072 phi=0.9842432141304016\n", + "MSE_loss_time=0.00033707154737110925 loss_time=0.01836 us zeta=55.88916015625 alpha=-0.946825385093689 phi=0.9684661030769348\n", + "measurement tensor(268.5023, dtype=torch.float64) tensor(125.2022, dtype=torch.float64)\n", + "SYS linux_tuned\n", + "MSE_loss_time=0.017415632957421587 loss_time=0.13197 us zeta=56.4552116394043 alpha=-0.44703954458236694 phi=1.0780853033065796\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(fzeta[0], fzeta[1]), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(falpha[0], falpha[1]), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " phi = torch.tensor(torch.Tensor(1,1).uniform_(fphi[0], fphi[1]), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.0007443848565628899 loss_time=0.02728 us zeta=86.70996856689453 alpha=-0.4729021489620209 phi=1.095038652420044\n", + "MSE_loss_time=0.0007443848555897859 loss_time=0.02728 us zeta=86.71083068847656 alpha=-0.47289028763771057 phi=1.095038652420044\n", + "MSE_loss_time=0.0007443852619864989 loss_time=0.02728 us zeta=86.71282196044922 alpha=-0.47282707691192627 phi=1.0950162410736084\n", + "measurement tensor(274.5043, dtype=torch.float64) tensor(138.1329, dtype=torch.float64)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = 16, 6\n", + "df_comb = df_comb[df_comb['itr'] != 350]\n", + "\n", + "run_time(df_comb, n_iter=4000, lr=1, rqps=200000, rtail='99', \n", + " mpred=['time'], msys=['ebbrt_tuned', 'linux_tuned'], \n", + " fzeta=[55, 88], falpha=[-0.99, -0.3], fphi=[0.98, 1.1])" + ] + }, + { + "cell_type": "code", + "execution_count": 107, + "metadata": {}, + "outputs": [], + "source": [ + "def inference_energy(d, n_iter, lr, workload, sys, zeta, alpha, phi, fbeta, fgamma, fdelta, print_freq=1000):\n", + " p_static = {\n", + " 'c1':1.5, \n", + " 'c3':0.5,\n", + " 'c4':0.25,\n", + " 'c7':34, # 34 Watts\n", + " 'busy': 10\n", + " }\n", + " chosen_sleep = 'c7'\n", + "\n", + " p_q = p_static[chosen_sleep]/10**6 # joules/us idle\n", + " # p_detect = p_static[chosen_sleep]\n", + "\n", + " #starts randomly\n", + "# beta = torch.tensor(torch.Tensor(1,1).uniform_(-7.0, -4.0), requires_grad=True)\n", + "# gamma = torch.tensor(torch.Tensor(1,1).uniform_(-0.22, -0.17), requires_grad=True)\n", + "# phi = torch.tensor(torch.Tensor(1,1).uniform_(0.98, 1.0), requires_grad=True)\n", + " #zeta = torch.tensor(torch.Tensor(1,1).uniform_(56.45, 86.71), requires_grad=True)\n", + " \n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(fdelta[0], fdelta[1]), requires_grad=True)\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(fgamma[0], fgamma[1]), requires_grad=True)\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(fbeta[0], fbeta[1]), requires_grad=True)\n", + " mu = torch.tensor(torch.Tensor(1,1).uniform_(-2.0, 2.0), requires_grad=True)\n", + " #phi = torch.tensor(torch.Tensor(1,1).uniform_(fbeta[0], fbeta[1]), requires_grad=True)\n", + "\n", + " #df[['joules','itr', 'dvfs', 'QPS', read_99th, 'num_interrupts']]\n", + " qps = d[:,3]\n", + " ninterrupts = d[:,5]\n", + " #energy = (d[:,0]/ninterrupts).log() ## joules/num_interrupts\n", + " energy = (d[:,0]/ninterrupts)\n", + " #energy = (d[:,0]/(qps).log()\n", + " itr = d[:,1]\n", + " dvfs = d[:,2]\n", + " time = d[:,4]\n", + " \n", + " #interarrival_time = (1/qps)*10**6\n", + " #fixed_phi = phi.item()\n", + " criterion = nn.MSELoss()\n", + " optimizer_energy = optim.Adam([gamma, delta, beta, mu], lr=lr)\n", + " \n", + " for i in range(n_iter):\n", + " #p_busy = (p_q*dvfs**(2+beta))\n", + " #t_busy_energy = (max_time / dvfs**(1+beta))\n", + " #t_q_energy = itr#(fixed_itr_suppress*itr)\n", + " #t_q_energy = (interarrival_time - (fixed_itr_suppress*itr) - t_busy_energy)\n", + " \n", + " #pred_energy = (p_q * t_q_energy) + (p_busy * t_busy_energy)\n", + " #pred_energy = AA*(fixed_itr_suppress*itr)**gamma + BB*dvfs**beta\n", + " #loss_energy = criterion(pred_energy/energy, torch.ones((1,pred_energy.shape[1])).double())\n", + " #pred_energy = ((fixed_itr_suppress*itr*p_q)*((20*(10**6))/(fixed_itr_suppress*itr))) + (dvfs*beta)\n", + " \n", + " ## sigmetrics'22 equations\n", + " #pred_energy = gamma * ((fixed_phi*itr) * (dvfs**beta)) #+ (AA*(dvfs**beta))\n", + " #pred_energy = (gamma*(fixed_phi*itr))*(dvfs**beta) #+ (AA*(dvfs**beta))\n", + " #pred_energy = gamma+(phi+np.log(itr))+(beta*np.log(dvfs))\n", + " \n", + " ## nsdi'23 equations\n", + " #pred_energy = P_work*max_time/dvfs**(1+alpha) + P*itr_suppress*itr\n", + " pred_energy = gamma*(dvfs**(1+beta)) * zeta/dvfs**(1+alpha) + (delta*phi*itr)*(dvfs**mu)\n", + " \n", + " #pred_energy = (*itr + t_busy_energy)*p_q\n", + " loss_energy = criterion(pred_energy, energy)\n", + " #round(math.sqrt(loss_time.item()), 5)\n", + " if i % print_freq == 0:\n", + " print(f'loss_energy={round(math.sqrt(loss_energy.item()), 5)} gamma={gamma.item()} beta={beta.item()} delta={delta.item()} mu={mu.item()}')\n", + " \n", + " optimizer_energy.zero_grad()\n", + " loss_energy.backward(retain_graph=True)\n", + " optimizer_energy.step()\n", + " \n", + " return pred_energy" + ] + }, + { + "cell_type": "code", + "execution_count": 108, + "metadata": {}, + "outputs": [], + "source": [ + "def run_energy(df_comb, n_iter=2000, lr=1, rqps=400000, rtail='99', \n", + " msys=['ebbrt_tuned'], mpred=['energy', 'time'], zeta=0.0, alpha=0.0, phi=0.0,\n", + " fbeta=[-7.0, -4.0], fgamma=[-0.22, -0.17], fdelta=[0.98, 1.0]): \n", + " \n", + " df_comb = df_comb[df_comb['QPS'] == rqps]\n", + " i=1\n", + " \n", + " for sys in msys:\n", + " df = df_comb[(df_comb['sys']==sys)].copy()\n", + " rt = 'read_'+rtail+'th'\n", + " df = df[['joules','itr', 'dvfs', 'QPS', rt, 'num_interrupts']]\n", + " tnum = df.shape[0]\n", + " d = df.values\n", + " d = torch.tensor(d)\n", + " print('SYS', sys)\n", + " pred_energy = inference_energy(d, n_iter, lr, 'mcd', sys, zeta, alpha, phi, fbeta, fgamma, fdelta, print_freq=1000)\n", + " df[f'pre_energy lr={lr}'] = pred_energy.view(tnum, 1).detach().numpy()\n", + " \n", + " for pred_name in mpred:\n", + " pred = pred_energy\n", + " qps = d[:,3]\n", + " #yvalue = (d[:,0]/d[:,5]).log()\n", + " yvalue = (d[:,0]/d[:,5])\n", + "\n", + " #fig, ax = plt.subplots()\n", + " ax = plt.subplot(1, len(msys)*len(mpred), i)\n", + " \n", + " if sys == 'ebbrt_tuned':\n", + " plt.title(f'EbbRT @ {int(rqps/1000)}K QPS', fontsize=20)\n", + " else:\n", + " plt.title(f'Linux @ {int(rqps/1000)}K QPS', fontsize=20)\n", + " \n", + " #plt.title(f'pred:{pred_name} mcd sys={sys} lr={lr} tail={rtail} \\n QPS={rqps} max_time={round(max_time,2)} \\n alpha={round(alpha,2)} beta={round(beta.item(),2)} \\n p_detect={round(p_detect.item(),2)}')\n", + " plt.ylabel('Measured Energy (J)', fontsize=20)\n", + " plt.xlabel('Predicted Energy (J)', fontsize=20)\n", + " \n", + "\n", + " tmax = yvalue.max().item()\n", + " tmin = yvalue.min().item()\n", + " #print(yvalue.min(), yvalue.max(), tmin, tmax)\n", + " plt.plot(np.linspace(tmin, tmax, 10), np.linspace(tmin, tmax, 10))\n", + " \n", + " print('measurement', yvalue.mean(), yvalue.std())\n", + " \n", + " scatter = ax.scatter(pred.detach().numpy()[0], yvalue, marker = 'o', \n", + " s = d[:,1], c = d[:,2], alpha=0.5)\n", + " \n", + " \n", + " legend1 = ax.legend(*scatter.legend_elements(),loc=\"upper left\", title=\"dvfs\")\n", + " ax.add_artist(legend1)\n", + " handles, labels = scatter.legend_elements(prop=\"sizes\", alpha=0.6)\n", + " legend2 = plt.legend(handles, labels, loc=\"lower right\", title=\"itr\")\n", + " ax.add_artist(legend2)\n", + " \n", + " plt.grid(True)\n", + " plt.tight_layout()\n", + " i += 1\n", + " \n", + " plt.subplots_adjust(wspace=0.3, hspace=0)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 109, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "loss_energy=2143.17726 gamma=2.3681387901306152 beta=-2.020653247833252 delta=3.927431583404541 mu=0.9007556438446045\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(fdelta[0], fdelta[1]), requires_grad=True)\n", + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(fgamma[0], fgamma[1]), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(fbeta[0], fbeta[1]), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " mu = torch.tensor(torch.Tensor(1,1).uniform_(-2.0, 2.0), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([43])) that is different to the input size (torch.Size([1, 43])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=0.39192 gamma=0.8214063048362732 beta=-11.220684051513672 delta=-0.04115524888038635 mu=-6.268528461456299\n", + "loss_energy=0.27088 gamma=0.5716808438301086 beta=-11.247697830200195 delta=-0.028223305940628052 mu=-6.268528461456299\n", + "loss_energy=0.14127 gamma=0.2992950975894928 beta=-11.266242980957031 delta=-0.014684107154607773 mu=-6.268528461456299\n", + "loss_energy=0.04717 gamma=0.0997881218791008 beta=-11.272489547729492 delta=-0.004871781915426254 mu=-6.268528461456299\n", + "loss_energy=0.00757 gamma=0.015567498281598091 beta=-11.273193359375 delta=-0.0007408984820358455 mu=-6.268528461456299\n", + "loss_energy=0.00093 gamma=0.0003249791043344885 beta=-11.273193359375 delta=6.538396519317757e-06 mu=-6.268528461456299\n", + "loss_energy=0.00086 gamma=-0.00040861446177586913 beta=-11.273193359375 delta=4.2511121137067676e-05 mu=-6.268528461456299\n", + "loss_energy=0.00086 gamma=-0.0004127329448238015 beta=-11.273193359375 delta=4.271307261660695e-05 mu=-6.268528461456299\n", + "loss_energy=0.00086 gamma=-0.0004127334104850888 beta=-11.273193359375 delta=4.271309444447979e-05 mu=-6.268528461456299\n", + "loss_energy=0.00086 gamma=-0.0004127338179387152 beta=-11.273193359375 delta=4.2713116272352636e-05 mu=-6.268528461456299\n", + "loss_energy=0.00086 gamma=-0.0004127340216655284 beta=-11.273193359375 delta=4.271312718628906e-05 mu=-6.268528461456299\n", + "loss_energy=0.00086 gamma=-0.0004127341089770198 beta=-11.273193359375 delta=4.271313446224667e-05 mu=-6.268528461456299\n", + "loss_energy=0.00086 gamma=-0.0004127341671846807 beta=-11.273193359375 delta=4.271313810022548e-05 mu=-6.268528461456299\n", + "loss_energy=0.00086 gamma=-0.0004127342253923416 beta=-11.273193359375 delta=4.271313446224667e-05 mu=-6.268528461456299\n", + "loss_energy=0.00086 gamma=-0.0004127342253923416 beta=-11.273193359375 delta=4.2713141738204286e-05 mu=-6.268528461456299\n", + "loss_energy=0.00086 gamma=-0.00041273425449617207 beta=-11.273193359375 delta=4.271313810022548e-05 mu=-6.268528461456299\n", + "loss_energy=0.00087 gamma=-0.0008971088100224733 beta=-14.395645141601562 delta=5.0452978030079976e-05 mu=-7.080131530761719\n", + "loss_energy=0.00087 gamma=-0.0008971088100224733 beta=-14.395645141601562 delta=5.0452978030079976e-05 mu=-7.080131530761719\n", + "loss_energy=0.00087 gamma=-0.0008971088100224733 beta=-14.395645141601562 delta=5.0452978030079976e-05 mu=-7.080131530761719\n", + "measurement tensor(0.0009, dtype=torch.float64) tensor(0.0005, dtype=torch.float64)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = 16, 6\n", + "df_comb = df_comb[df_comb['itr'] != 350]\n", + "\n", + "#zeta=56.4552116394043 alpha=-0.44703954458236694 phi=1.0780853033065796\n", + "run_energy(df_comb, n_iter=20000, lr=1, rqps=200000, rtail='99', \n", + " mpred=['time'], msys=['ebbrt_tuned'], zeta=56.45, alpha=-0.44, phi=1.07,\n", + " fbeta=[-4.0, 4.0], fgamma=[-4.0, 4.0], fdelta=[-4.0, 4.0])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/analysis/plots/energy_time_fit/energy_mcd_ebbrt_tuned_1.png b/analysis/plots/energy_time_fit/energy_mcd_ebbrt_tuned_1.png new file mode 100644 index 0000000..c1c4d6e Binary files /dev/null and b/analysis/plots/energy_time_fit/energy_mcd_ebbrt_tuned_1.png differ diff --git a/analysis/plots/energy_time_fit/randomp_energy_mcd_ebbrt_tuned_1.png b/analysis/plots/energy_time_fit/randomp_energy_mcd_ebbrt_tuned_1.png new file mode 100644 index 0000000..bdace3a Binary files /dev/null and b/analysis/plots/energy_time_fit/randomp_energy_mcd_ebbrt_tuned_1.png differ diff --git a/analysis/plots/energy_time_fit/randomp_time_mcd_ebbrt_tuned_1.png b/analysis/plots/energy_time_fit/randomp_time_mcd_ebbrt_tuned_1.png new file mode 100644 index 0000000..bccf174 Binary files /dev/null and b/analysis/plots/energy_time_fit/randomp_time_mcd_ebbrt_tuned_1.png differ diff --git a/analysis/plots/energy_time_fit/time_mcd_ebbrt_tuned_1.png b/analysis/plots/energy_time_fit/time_mcd_ebbrt_tuned_1.png new file mode 100644 index 0000000..d5534fd Binary files /dev/null and b/analysis/plots/energy_time_fit/time_mcd_ebbrt_tuned_1.png differ diff --git a/analysis/plots/timefit/100_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/100_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..bc2d1a3 Binary files /dev/null and b/analysis/plots/timefit/100_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/10_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/10_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..4fd1c1d Binary files /dev/null and b/analysis/plots/timefit/10_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/200_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/200_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..22bbe7d Binary files /dev/null and b/analysis/plots/timefit/200_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/20_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/20_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..ac78e52 Binary files /dev/null and b/analysis/plots/timefit/20_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/2_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/2_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..7b6721d Binary files /dev/null and b/analysis/plots/timefit/2_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/300_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/300_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..98fdb4c Binary files /dev/null and b/analysis/plots/timefit/300_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/30_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/30_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..6ea99b6 Binary files /dev/null and b/analysis/plots/timefit/30_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/350_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/350_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..06bddaf Binary files /dev/null and b/analysis/plots/timefit/350_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/400_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/400_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..33b7bb4 Binary files /dev/null and b/analysis/plots/timefit/400_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/40_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/40_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..d2475ae Binary files /dev/null and b/analysis/plots/timefit/40_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/50_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/50_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..2ad0836 Binary files /dev/null and b/analysis/plots/timefit/50_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/diff_itr/100_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/diff_itr/100_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..2d3fbc3 Binary files /dev/null and b/analysis/plots/timefit/diff_itr/100_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/diff_itr/10_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/diff_itr/10_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..a72b31f Binary files /dev/null and b/analysis/plots/timefit/diff_itr/10_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/diff_itr/200_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/diff_itr/200_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..95f00ef Binary files /dev/null and b/analysis/plots/timefit/diff_itr/200_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/diff_itr/20_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/diff_itr/20_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..bacd865 Binary files /dev/null and b/analysis/plots/timefit/diff_itr/20_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/diff_itr/2_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/diff_itr/2_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..8dd8e01 Binary files /dev/null and b/analysis/plots/timefit/diff_itr/2_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/diff_itr/300_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/diff_itr/300_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..7f69f20 Binary files /dev/null and b/analysis/plots/timefit/diff_itr/300_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/diff_itr/30_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/diff_itr/30_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..4582245 Binary files /dev/null and b/analysis/plots/timefit/diff_itr/30_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/diff_itr/400_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/diff_itr/400_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..e728d82 Binary files /dev/null and b/analysis/plots/timefit/diff_itr/400_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/diff_itr/40_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/diff_itr/40_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..df6cfce Binary files /dev/null and b/analysis/plots/timefit/diff_itr/40_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/diff_itr/50_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png b/analysis/plots/timefit/diff_itr/50_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png new file mode 100644 index 0000000..0a7a464 Binary files /dev/null and b/analysis/plots/timefit/diff_itr/50_mcd_ebbrt_tuned_read_99th_mean_400000_0.1.png differ diff --git a/analysis/plots/timefit/fitparams b/analysis/plots/timefit/fitparams new file mode 100644 index 0000000..2a0ac97 Binary files /dev/null and b/analysis/plots/timefit/fitparams differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_0.01.png b/analysis/plots/timefit/mcd_ebbrt_tuned_0.01.png new file mode 100644 index 0000000..ad0413b Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_0.01.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_0.1.png b/analysis/plots/timefit/mcd_ebbrt_tuned_0.1.png new file mode 100644 index 0000000..aab5607 Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_0.1.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_1.png b/analysis/plots/timefit/mcd_ebbrt_tuned_1.png new file mode 100644 index 0000000..824e22b Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_1.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_10.png b/analysis/plots/timefit/mcd_ebbrt_tuned_10.png new file mode 100644 index 0000000..bcb1a52 Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_10.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_read_10th_mean_200000_0.01.png b/analysis/plots/timefit/mcd_ebbrt_tuned_read_10th_mean_200000_0.01.png new file mode 100644 index 0000000..76a9cc1 Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_read_10th_mean_200000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_read_10th_mean_400000_0.01.png b/analysis/plots/timefit/mcd_ebbrt_tuned_read_10th_mean_400000_0.01.png new file mode 100644 index 0000000..6ac63c0 Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_read_10th_mean_400000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_read_10th_mean_600000_0.01.png b/analysis/plots/timefit/mcd_ebbrt_tuned_read_10th_mean_600000_0.01.png new file mode 100644 index 0000000..ef23143 Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_read_10th_mean_600000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_read_50th_mean_200000_0.01.png b/analysis/plots/timefit/mcd_ebbrt_tuned_read_50th_mean_200000_0.01.png new file mode 100644 index 0000000..6a42288 Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_read_50th_mean_200000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_read_50th_mean_400000_0.01.png b/analysis/plots/timefit/mcd_ebbrt_tuned_read_50th_mean_400000_0.01.png new file mode 100644 index 0000000..2e5456d Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_read_50th_mean_400000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_read_50th_mean_600000_0.01.png b/analysis/plots/timefit/mcd_ebbrt_tuned_read_50th_mean_600000_0.01.png new file mode 100644 index 0000000..8d13d5d Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_read_50th_mean_600000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_read_5th_mean_200000_0.01.png b/analysis/plots/timefit/mcd_ebbrt_tuned_read_5th_mean_200000_0.01.png new file mode 100644 index 0000000..ab65f3b Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_read_5th_mean_200000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_read_5th_mean_400000_0.01.png b/analysis/plots/timefit/mcd_ebbrt_tuned_read_5th_mean_400000_0.01.png new file mode 100644 index 0000000..f6b6b46 Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_read_5th_mean_400000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_read_5th_mean_600000_0.01.png b/analysis/plots/timefit/mcd_ebbrt_tuned_read_5th_mean_600000_0.01.png new file mode 100644 index 0000000..4d7ece3 Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_read_5th_mean_600000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_read_90th_mean_200000_0.01.png b/analysis/plots/timefit/mcd_ebbrt_tuned_read_90th_mean_200000_0.01.png new file mode 100644 index 0000000..55b186b Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_read_90th_mean_200000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_read_90th_mean_400000_0.01.png b/analysis/plots/timefit/mcd_ebbrt_tuned_read_90th_mean_400000_0.01.png new file mode 100644 index 0000000..29a610a Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_read_90th_mean_400000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_read_90th_mean_600000_0.01.png b/analysis/plots/timefit/mcd_ebbrt_tuned_read_90th_mean_600000_0.01.png new file mode 100644 index 0000000..bd96c98 Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_read_90th_mean_600000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_read_99th_mean_200000_0.01.png b/analysis/plots/timefit/mcd_ebbrt_tuned_read_99th_mean_200000_0.01.png new file mode 100644 index 0000000..6af606e Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_read_99th_mean_200000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_read_99th_mean_400000_0.01.png b/analysis/plots/timefit/mcd_ebbrt_tuned_read_99th_mean_400000_0.01.png new file mode 100644 index 0000000..0d10f10 Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_read_99th_mean_400000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_read_99th_mean_400000_1.png b/analysis/plots/timefit/mcd_ebbrt_tuned_read_99th_mean_400000_1.png new file mode 100644 index 0000000..8bbf36e Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_read_99th_mean_400000_1.png differ diff --git a/analysis/plots/timefit/mcd_ebbrt_tuned_read_99th_mean_600000_0.01.png b/analysis/plots/timefit/mcd_ebbrt_tuned_read_99th_mean_600000_0.01.png new file mode 100644 index 0000000..0bbb1dc Binary files /dev/null and b/analysis/plots/timefit/mcd_ebbrt_tuned_read_99th_mean_600000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_linux_tuned_read_10th_mean_200000_0.01.png b/analysis/plots/timefit/mcd_linux_tuned_read_10th_mean_200000_0.01.png new file mode 100644 index 0000000..ca38a13 Binary files /dev/null and b/analysis/plots/timefit/mcd_linux_tuned_read_10th_mean_200000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_linux_tuned_read_10th_mean_400000_0.01.png b/analysis/plots/timefit/mcd_linux_tuned_read_10th_mean_400000_0.01.png new file mode 100644 index 0000000..3312fbe Binary files /dev/null and b/analysis/plots/timefit/mcd_linux_tuned_read_10th_mean_400000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_linux_tuned_read_10th_mean_600000_0.01.png b/analysis/plots/timefit/mcd_linux_tuned_read_10th_mean_600000_0.01.png new file mode 100644 index 0000000..5d3ef3d Binary files /dev/null and b/analysis/plots/timefit/mcd_linux_tuned_read_10th_mean_600000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_linux_tuned_read_50th_mean_200000_0.01.png b/analysis/plots/timefit/mcd_linux_tuned_read_50th_mean_200000_0.01.png new file mode 100644 index 0000000..49b0fc7 Binary files /dev/null and b/analysis/plots/timefit/mcd_linux_tuned_read_50th_mean_200000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_linux_tuned_read_50th_mean_400000_0.01.png b/analysis/plots/timefit/mcd_linux_tuned_read_50th_mean_400000_0.01.png new file mode 100644 index 0000000..13b6342 Binary files /dev/null and b/analysis/plots/timefit/mcd_linux_tuned_read_50th_mean_400000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_linux_tuned_read_50th_mean_600000_0.01.png b/analysis/plots/timefit/mcd_linux_tuned_read_50th_mean_600000_0.01.png new file mode 100644 index 0000000..0bacd8b Binary files /dev/null and b/analysis/plots/timefit/mcd_linux_tuned_read_50th_mean_600000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_linux_tuned_read_5th_mean_200000_0.01.png b/analysis/plots/timefit/mcd_linux_tuned_read_5th_mean_200000_0.01.png new file mode 100644 index 0000000..34694ab Binary files /dev/null and b/analysis/plots/timefit/mcd_linux_tuned_read_5th_mean_200000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_linux_tuned_read_5th_mean_400000_0.01.png b/analysis/plots/timefit/mcd_linux_tuned_read_5th_mean_400000_0.01.png new file mode 100644 index 0000000..9ab1680 Binary files /dev/null and b/analysis/plots/timefit/mcd_linux_tuned_read_5th_mean_400000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_linux_tuned_read_5th_mean_600000_0.01.png b/analysis/plots/timefit/mcd_linux_tuned_read_5th_mean_600000_0.01.png new file mode 100644 index 0000000..dc293eb Binary files /dev/null and b/analysis/plots/timefit/mcd_linux_tuned_read_5th_mean_600000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_linux_tuned_read_90th_mean_200000_0.01.png b/analysis/plots/timefit/mcd_linux_tuned_read_90th_mean_200000_0.01.png new file mode 100644 index 0000000..4fc65ac Binary files /dev/null and b/analysis/plots/timefit/mcd_linux_tuned_read_90th_mean_200000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_linux_tuned_read_90th_mean_400000_0.01.png b/analysis/plots/timefit/mcd_linux_tuned_read_90th_mean_400000_0.01.png new file mode 100644 index 0000000..3ddac25 Binary files /dev/null and b/analysis/plots/timefit/mcd_linux_tuned_read_90th_mean_400000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_linux_tuned_read_90th_mean_600000_0.01.png b/analysis/plots/timefit/mcd_linux_tuned_read_90th_mean_600000_0.01.png new file mode 100644 index 0000000..9935e01 Binary files /dev/null and b/analysis/plots/timefit/mcd_linux_tuned_read_90th_mean_600000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_linux_tuned_read_99th_mean_200000_0.01.png b/analysis/plots/timefit/mcd_linux_tuned_read_99th_mean_200000_0.01.png new file mode 100644 index 0000000..bb0b263 Binary files /dev/null and b/analysis/plots/timefit/mcd_linux_tuned_read_99th_mean_200000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_linux_tuned_read_99th_mean_400000_0.01.png b/analysis/plots/timefit/mcd_linux_tuned_read_99th_mean_400000_0.01.png new file mode 100644 index 0000000..e57629b Binary files /dev/null and b/analysis/plots/timefit/mcd_linux_tuned_read_99th_mean_400000_0.01.png differ diff --git a/analysis/plots/timefit/mcd_linux_tuned_read_99th_mean_600000_0.01.png b/analysis/plots/timefit/mcd_linux_tuned_read_99th_mean_600000_0.01.png new file mode 100644 index 0000000..46424c3 Binary files /dev/null and b/analysis/plots/timefit/mcd_linux_tuned_read_99th_mean_600000_0.01.png differ diff --git a/analysis/powerlaw.py b/analysis/powerlaw.py index b6c2c1a..d1b00b4 100644 --- a/analysis/powerlaw.py +++ b/analysis/powerlaw.py @@ -24,6 +24,8 @@ def inference(d, n_iter, lr, print_freq=10): x = d[:,0] y = d[:,1] + print(type(d)) + print(type(x)) criterion = nn.MSELoss() optimizer = optim.Adam([A, B, p], lr=lr) diff --git a/analysis/run_ebbrt_netpipe.ipynb b/analysis/run_ebbrt_netpipe.ipynb new file mode 100644 index 0000000..93c85b1 --- /dev/null +++ b/analysis/run_ebbrt_netpipe.ipynb @@ -0,0 +1,1349 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('../bayesopt')\n", + "\n", + "import read_agg_data\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.autograd as auto\n", + "import torch.optim as optim\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as plt\n", + "import pandas as pd\n", + "\n", + "import pdb\n", + "import math\n", + "\n", + "dvfs_dict = {\n", + " \"0xC00\" : 1.2,\n", + " \"0xD00\" : 1.3,\n", + " \"0xE00\" : 1.4,\n", + " \"0xF00\" : 1.5,\n", + " \"0x1000\" : 1.6,\n", + " \"0x1100\" : 1.7,\n", + " \"0x1200\" : 1.8,\n", + " \"0x1300\" : 1.9,\n", + " \"0x1400\" : 2.0,\n", + " \"0x1500\" : 2.1,\n", + " \"0x1600\" : 2.2,\n", + " \"0x1700\" : 2.3,\n", + " \"0x1800\" : 2.4,\n", + " \"0x1900\" : 2.5,\n", + " \"0x1A00\" : 2.6,\n", + " \"0x1B00\" : 2.7,\n", + " \"0x1C00\" : 2.8,\n", + " \"0x1D00\" : 2.9,\n", + " \"0xffff\" : 3.0,\n", + "}\n", + "\n", + "JOULE_CONVERSION = 0.00001526 #counter * constant -> JoulesOB\n", + "TIME_CONVERSION_khz = 1./(2899999*1000)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 12 16 20 24 28 32 36 40 60 80 100]\n", + "[ 64 8192 65536 524288]\n", + "Index(['itr', 'msg', 'dvfs', 'rapl', 'tput', 'lat', 'PK0_JOULE', 'PK1_JOULE',\n", + " 'START_RDTSC', 'END_RDTSC', 'joules', 'time', 'sys'],\n", + " dtype='object')\n", + "******* 6 8192\n", + " joules msg itr dvfs time\n", + "2649 4.370693 8192 6 1.2 0.232333\n", + "2493 4.542444 8192 6 1.3 0.238059\n", + "2337 5.119379 8192 6 1.4 0.261364\n", + "2181 5.309122 8192 6 1.5 0.263917\n", + "2025 5.169234 8192 6 1.6 0.250664\n", + "1869 4.218078 8192 6 1.7 0.219205\n", + "1713 4.262027 8192 6 1.8 0.220848\n", + "1557 4.382398 8192 6 1.9 0.220543\n", + "1401 4.432221 8192 6 2.0 0.222492\n", + "1245 4.419250 8192 6 2.1 0.223286\n", + "1089 4.418564 8192 6 2.2 0.223600\n", + "933 4.454364 8192 6 2.3 0.224988\n", + "781 4.380780 8192 6 2.4 0.223695\n", + "625 4.451052 8192 6 2.5 0.226300\n", + "469 4.359157 8192 6 2.6 0.224094\n", + "313 4.296392 8192 6 2.7 0.224996\n", + "157 4.387677 8192 6 2.8 0.224931\n", + "1 4.293432 8192 6 2.9 0.222755\n", + "******* 8 8192\n", + " joules msg itr dvfs time\n", + "2653 5.471076 8192 8 1.2 0.291000\n", + "2497 5.560363 8192 8 1.3 0.291964\n", + "2341 5.562575 8192 8 1.4 0.284428\n", + "2185 5.549574 8192 8 1.5 0.277227\n", + "2029 5.772767 8192 8 1.6 0.280371\n", + "1873 4.970778 8192 8 1.7 0.260411\n", + "1717 4.939418 8192 8 1.8 0.256071\n", + "1561 4.918954 8192 8 1.9 0.254268\n", + "1405 4.831102 8192 8 2.0 0.248729\n", + "1249 4.776624 8192 8 2.1 0.244631\n", + "1093 5.145504 8192 8 2.2 0.255401\n", + "937 5.084357 8192 8 2.3 0.253053\n", + "785 5.194886 8192 8 2.4 0.253862\n", + "629 5.085029 8192 8 2.5 0.246491\n", + "473 5.036914 8192 8 2.6 0.243652\n", + "317 5.527798 8192 8 2.7 0.253858\n", + "161 5.427601 8192 8 2.8 0.250429\n", + "5 5.560073 8192 8 2.9 0.252118\n", + "******* 12 8192\n", + " joules msg itr dvfs time\n", + "2657 5.469520 8192 12 1.2 0.291004\n", + "2501 6.155732 8192 12 1.3 0.323214\n", + "2345 6.151016 8192 12 1.4 0.313255\n", + "2189 6.116376 8192 12 1.5 0.304613\n", + "2033 6.495450 8192 12 1.6 0.316695\n", + "1877 6.364031 8192 12 1.7 0.296194\n", + "1721 6.235999 8192 12 1.8 0.285017\n", + "1565 6.226553 8192 12 1.9 0.281570\n", + "1409 5.953399 8192 12 2.0 0.279169\n", + "1253 5.459052 8192 12 2.1 0.266952\n", + "1097 5.375015 8192 12 2.2 0.263801\n", + "941 5.270606 8192 12 2.3 0.259972\n", + "789 5.299676 8192 12 2.4 0.259890\n", + "633 5.221163 8192 12 2.5 0.259137\n", + "477 5.241124 8192 12 2.6 0.259441\n", + "321 5.151441 8192 12 2.7 0.259047\n", + "165 5.023348 8192 12 2.8 0.259346\n", + "9 5.097298 8192 12 2.9 0.259791\n", + "******* 16 8192\n", + " joules msg itr dvfs time\n", + "2661 5.883096 8192 16 1.2 0.313215\n", + "2505 6.328139 8192 16 1.3 0.332744\n", + "2349 6.165071 8192 16 1.4 0.314620\n", + "2193 6.445168 8192 16 1.5 0.320664\n", + "2037 4.996323 8192 16 1.6 0.266076\n", + "1881 6.667033 8192 16 1.7 0.309225\n", + "1725 4.459384 8192 16 1.8 0.247879\n", + "1569 4.480809 8192 16 1.9 0.247329\n", + "1413 4.489614 8192 16 2.0 0.247318\n", + "1257 4.493521 8192 16 2.1 0.247458\n", + "1101 4.492971 8192 16 2.2 0.247524\n", + "945 4.522408 8192 16 2.3 0.247603\n", + "637 4.528420 8192 16 2.5 0.247717\n", + "481 4.499625 8192 16 2.6 0.247609\n", + "325 4.505729 8192 16 2.7 0.247827\n", + "169 4.480092 8192 16 2.8 0.247461\n", + "13 4.548869 8192 16 2.9 0.247778\n", + "******* 20 8192\n", + " joules msg itr dvfs time\n", + "2665 5.484826 8192 20 1.2 0.307050\n", + "2509 5.507060 8192 20 1.3 0.307851\n", + "2353 5.491357 8192 20 1.4 0.307471\n", + "2197 5.503290 8192 20 1.5 0.307225\n", + "2041 5.512813 8192 20 1.6 0.307329\n", + "1885 5.496301 8192 20 1.7 0.307449\n", + "1729 5.491662 8192 20 1.8 0.307488\n", + "1573 5.495859 8192 20 1.9 0.307489\n", + "1417 5.503092 8192 20 2.0 0.307278\n", + "1261 5.503656 8192 20 2.1 0.307337\n", + "1105 5.515956 8192 20 2.2 0.307303\n", + "949 5.540235 8192 20 2.3 0.307365\n", + "793 5.538815 8192 20 2.4 0.307406\n", + "641 5.519939 8192 20 2.5 0.307367\n", + "485 5.536572 8192 20 2.6 0.307240\n", + "329 5.528088 8192 20 2.7 0.307272\n", + "173 5.508326 8192 20 2.8 0.307352\n", + "17 5.625599 8192 20 2.9 0.307051\n", + "******* 24 8192\n", + " joules msg itr dvfs time\n", + "2669 4.424378 8192 24 1.2 0.247887\n", + "2513 4.439592 8192 24 1.3 0.248466\n", + "2357 4.436998 8192 24 1.4 0.248833\n", + "2201 4.442034 8192 24 1.5 0.248613\n", + "2045 4.457446 8192 24 1.6 0.249509\n", + "1889 4.443132 8192 24 1.7 0.248626\n", + "1733 4.449450 8192 24 1.8 0.248240\n", + "1577 4.445482 8192 24 1.9 0.248954\n", + "1421 4.441103 8192 24 2.0 0.248601\n", + "1265 4.466145 8192 24 2.1 0.249436\n", + "1109 4.454867 8192 24 2.2 0.248543\n", + "953 4.438386 8192 24 2.3 0.248494\n", + "797 4.448779 8192 24 2.4 0.249418\n", + "645 4.483724 8192 24 2.5 0.250045\n", + "489 4.465015 8192 24 2.6 0.248445\n", + "333 4.443392 8192 24 2.7 0.248405\n", + "177 4.454211 8192 24 2.8 0.248503\n", + "21 4.494925 8192 24 2.9 0.248673\n", + "******* 28 8192\n", + " joules msg itr dvfs time\n", + "2673 5.128917 8192 28 1.2 0.287658\n", + "2517 5.105432 8192 28 1.3 0.287340\n", + "2361 5.111948 8192 28 1.4 0.287438\n", + "2205 5.108987 8192 28 1.5 0.287214\n", + "2049 5.127116 8192 28 1.6 0.287260\n", + "1893 5.112970 8192 28 1.7 0.287419\n", + "1737 5.131541 8192 28 1.8 0.287299\n", + "1581 5.113565 8192 28 1.9 0.287377\n", + "1425 5.132915 8192 28 2.0 0.287277\n", + "1269 5.131175 8192 28 2.1 0.287478\n", + "1113 5.117441 8192 28 2.2 0.287397\n", + "957 5.121272 8192 28 2.3 0.287325\n", + "801 5.119089 8192 28 2.4 0.287461\n", + "649 5.144070 8192 28 2.5 0.287198\n", + "493 5.138072 8192 28 2.6 0.287540\n", + "337 5.128489 8192 28 2.7 0.287176\n", + "181 5.151410 8192 28 2.8 0.287537\n", + "25 5.183319 8192 28 2.9 0.287287\n", + "Index(['itr', 'msg', 'dvfs', 'rapl', 'tput', 'lat', 'PK0_JOULE', 'PK1_JOULE',\n", + " 'START_RDTSC', 'END_RDTSC', 'joules', 'time', 'sys'],\n", + " dtype='object')\n" + ] + } + ], + "source": [ + "#df_comb, _, _ = read_agg_data.start_analysis('mcd') #DATA\n", + "#df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: int(x, base=16))\n", + "\n", + "df_comb = pd.read_csv('/home/handong/jupyter/jupyter-notebooks/nic-tuning-experiments/bayesopt/summary_data/ebbrt_netpipe_old.csv', sep=' ')\n", + "df_comb['joules'] = df_comb['PK1_JOULE']\n", + "df_comb['time'] = (df_comb['END_RDTSC']* TIME_CONVERSION_khz) - (df_comb['START_RDTSC']* TIME_CONVERSION_khz)\n", + "df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: dvfs_dict[x])\n", + "df_comb = df_comb[(df_comb['rapl'] == 135)]\n", + "df_comb = df_comb[(df_comb['itr']!=1) & (df_comb['dvfs']!=65535)] #filter out linux dynamic\n", + "df_comb['sys'] = 'ebbrt_tuned'\n", + "\n", + "print(df_comb['itr'].unique())\n", + "print(df_comb['msg'].unique())\n", + "print(df_comb.columns)\n", + "\n", + "\n", + "df = df_comb[['joules', 'msg', 'itr', 'dvfs', 'time']]\n", + "for itr in [6, 8, 12, 16, 20, 24, 28]:\n", + " for msg in [8192]:\n", + " dfi = df[(df['itr']==itr) & (df['msg']==msg)]\n", + " print('*******', itr, msg)\n", + " print(dfi.sort_values(by=['dvfs']))\n", + " \n", + "#print(df.sort_values(by=['dvfs']))\n", + "\n", + "# df_comb = df_comb[(df_comb['i'] == 1) & (df_comb['rapl'] == 135)]\n", + "#df_comb = df_comb[(df_comb['rapl'] == 135)]\n", + "\n", + "# df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: dvfs_dict[x])\n", + "# \n", + "\n", + "# print(df_comb['time'].min())\n", + "# print(df_comb.columns)\n", + "# print(df_comb['itr'].unique())\n", + "# print(df_comb['dvfs'].unique())\n", + "# print(df_comb['msg'].unique())\n", + "# print(df_comb['sys'].unique())\n", + "# print(df_comb.shape[0])\n", + "\n", + "# df_comb['dvfs'] = df_comb['dvfs'].astype(float) / df_comb['dvfs'].min()\n", + "# print(df_comb['dvfs'].unique())\n", + "# df_comb['itr'] = df_comb['itr'].astype(float) / df_comb['itr'].min()\n", + "# print(df_comb['itr'].unique())\n", + "#print(10**6)\n", + "\n", + "print(df_comb.columns)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [], + "source": [ + "def inference_time(d, n_iter, lr, workload, sys, print_freq=10): \n", + " #starts randomly\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + " #beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + " \n", + " itr = d[:,1]\n", + " dvfs = d[:,2]\n", + " time = (d[:,3]/5000)\n", + " msgsize = d[:,4]\n", + " \n", + " criterion = nn.MSELoss()\n", + " optimizer_time = optim.Adam([max_time, alpha, gamma, delta], lr=lr)\n", + "\n", + " for i in range(n_iter): \n", + " t_busy = (torch.exp(max_time) / dvfs**(1+alpha)) ## dvfs impact on processing\n", + " \n", + " #pred_time = itr_suppress*itr + t_busy\n", + " #pred_time = ((2*((itr*itr_suppress)**beta))/(10**6)) + (gamma*(2*((msgsize*8)/(10**10)))) + (2*t_busy)\n", + " #pred_time = (gamma*itr*itr_suppress)*(dvfs**beta)\n", + " #pred_time = ((2*(((itr**beta))))/(10**6)) + (2*t_busy) \n", + " \n", + " #pred_time = ((2*(((itr**beta))))/(10**6)) + (2*t_busy) + (2*((msgsize*8)/(10**10)))\n", + " beta = gamma*dvfs+delta\n", + " pred_time = ((2*(((itr**beta))))/(10**6)) + (2*t_busy) + (2*((msgsize*8)/(10**10)))\n", + " \n", + " #pred_time = A(itr)**beta*(dvfs**gamma)\n", + " #pred_time = 2*itr**(alpha*dvfs)\n", + " \n", + " #import pdb\n", + " #pdb.set_trace()\n", + " \n", + " #loss_time = criterion(pred_time/time, torch.ones((1,pred_time.shape[1])).double())\n", + " loss_time = criterion(pred_time, time)\n", + " \n", + " if i % 1000 == 0:\n", + " print(f'MSE_loss_time={loss_time.item()} loss_time={round(math.sqrt(loss_time.item())*10**6, 5)} us '\n", + " + f'max_time={max_time.item()} alpha={alpha.item()} gamma={gamma.item()} delta={delta.item()}')\n", + " \n", + " optimizer_time.zero_grad()\n", + " loss_time.backward()\n", + " optimizer_time.step()\n", + "\n", + "\n", + " return pred_time" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [], + "source": [ + "def inference_energy(d, n_iter, lr, workload, sys, print_freq=10):\n", + " #starts randomly\n", + " #max_time = torch.tensor(torch.Tensor(1,1).uniform_(-5, 5), requires_grad=True)\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " #gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " #delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " \n", + " energy = (d[:,0]/5000) #(d[:,0]/ninterrupts).log() ## joules/num_interrupts\n", + " itr = d[:,1]\n", + " dvfs = d[:,2]\n", + " time = (d[:,3]/5000)\n", + " msgsize = d[:,4]\n", + " \n", + " criterion = nn.MSELoss()\n", + " optimizer_energy = optim.Adam([alpha, beta], lr=lr)\n", + " \n", + " for i in range(n_iter):\n", + " #p_busy = (p_q*dvfs**(2+beta))\n", + " #t_busy_energy = (max_time / dvfs**(1+beta))\n", + " #t_q_energy = itr#(fixed_itr_suppress*itr)\n", + " #t_q_energy = (interarrival_time - (fixed_itr_suppress*itr) - t_busy_energy)\n", + " \n", + " #pred_energy = (p_q * t_q_energy) + (p_busy * t_busy_energy)\n", + " #pred_energy = AA*(fixed_itr_suppress*itr)**gamma + BB*dvfs**beta\n", + " #loss_energy = criterion(pred_energy/energy, torch.ones((1,pred_energy.shape[1])).double())\n", + " #pred_energy = ((fixed_itr_suppress*itr*p_q)*((20*(10**6))/(fixed_itr_suppress*itr))) + (dvfs*beta)\n", + " #pred_energy = ((alpha*(itr**gamma))*(delta*(dvfs**beta))) #+ (AA*(dvfs**beta))\n", + " \n", + " pred_energy = (alpha*itr*((msgsize*8)/(10**10)))+(dvfs*beta)\n", + " #pred_energy = alpha*(itr+dvfs)\n", + " #pred_energy = alpha+np.log(itr)+np.log(dvfs)\n", + " \n", + " #pred_energy = (gamma*(fixed_itr_suppress*itr))*(dvfs**beta) #+ (AA*(dvfs**beta)) \n", + " #pred_energy = 2*(gamma+(np.log(itr)))+(2*(beta*np.log(dvfs)))\n", + " \n", + " loss_energy = criterion(pred_energy, energy)\n", + "\n", + " if i % 1000 == 0:\n", + " print(f'MSE_loss_energy={loss_energy.item()} alpha={alpha.item()} beta={beta.item()}')\n", + " \n", + " optimizer_energy.zero_grad()\n", + " loss_energy.backward(retain_graph=True)\n", + " optimizer_energy.step()\n", + " return pred_energy" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [], + "source": [ + "def run(df_comb, n_iter=2000, lr=1, rmsg=64, msys=['ebbrt_tuned'], mpred=['energy', 'time']): \n", + " df_comb = df_comb[df_comb['msg'] == rmsg]\n", + "\n", + " for sys in msys:\n", + " df = df_comb[(df_comb['sys']==sys)].copy()\n", + " #df = df[['joules','itr', 'dvfs', 'QPS', 'read_99th', 'num_interrupts']]\n", + " print(df['itr'].unique())\n", + " df = df[['joules', 'itr', 'dvfs', 'time', 'msg']]\n", + " \n", + " tnum = df.shape[0]\n", + " d = df.values\n", + " d = torch.tensor(d)\n", + " print('SYS', sys)\n", + " \n", + " #pred_energy, max_time, alpha, beta, p_detect, p_q = inference_energy(d, n_iter, lr, 'mcd', sys, print_freq=1000)\n", + " #pred_energy, pred_time = inference(d, n_iter, lr, 'netpipe', sys, print_freq=1000)\n", + " for pred_name in mpred:\n", + " if pred_name == 'energy':\n", + " pred_energy = inference_energy(d, n_iter, lr, 'netpipe', sys, print_freq=1000)\n", + " pred = pred_energy\n", + " #yvalue = (d[:,0]/d[:,4]).log()\n", + " yvalue = (d[:,0]/5000)\n", + " #yvalue = d[:,0]\n", + " else:\n", + " pred_time = inference_time(d, n_iter, lr, 'netpipe', sys, print_freq=1000)\n", + " pred = pred_time\n", + " yvalue = d[:,3]/5000\n", + "\n", + " fig, ax = plt.subplots()\n", + " plt.title(f'pred:{pred_name} mcd sys={sys} lr={lr} \\n MSG={rmsg}')\n", + " #plt.title(f'pred:{pred_name} mcd sys={sys} lr={lr} tail={rtail} \\n QPS={rqps} max_time={round(max_time,2)} \\n alpha={round(alpha,2)} beta={round(beta.item(),2)} \\n p_detect={round(p_detect.item(),2)}')\n", + " plt.xlabel(u\"predictions\")\n", + " plt.ylabel(f'{pred_name}')\n", + " print('yvalue', yvalue.shape)\n", + " \n", + " \n", + " #if pred_name == 'time':\n", + " tmax = yvalue.max().item()\n", + " plt.plot(np.linspace(0, tmax, 10), np.linspace(0, tmax, 10))\n", + " \n", + " scatter = ax.scatter(pred.detach().numpy(), yvalue, marker = 'o', \n", + " s = d[:,1], c = d[:,2], alpha=0.3)\n", + " #scatter = ax.scatter(pred.detach().numpy(), yvalue, marker = 'o', alpha=0.3)\n", + " plt.ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(0,0))\n", + " plt.ticklabel_format(axis=\"x\", style=\"sci\", scilimits=(0,0))\n", + " \n", + " legend1 = ax.legend(*scatter.legend_elements(),loc=\"upper left\", title=\"dvfs\")\n", + " ax.add_artist(legend1)\n", + " handles, labels = scatter.legend_elements(prop=\"sizes\", alpha=0.6)\n", + " legend2 = plt.legend(handles, labels, loc=\"lower right\", title=\"itr\")\n", + " ax.add_artist(legend2)" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 12 16 20 24 28 32 36 40 60 80 100]\n", + "SYS ebbrt_tuned\n", + "MSE_loss_energy=5.168452341132003 alpha=-1.708956241607666 beta=1.078780174255371\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([233])) that is different to the input size (torch.Size([1, 233])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_energy=3.601953520378848e-07 alpha=0.22872960567474365 beta=0.0013634448405355215\n", + "MSE_loss_energy=3.601894864483013e-07 alpha=0.23033452033996582 beta=0.0013620688114315271\n", + "MSE_loss_energy=9.201323573500481e-07 alpha=0.23068849742412567 beta=0.001715848920866847\n", + "MSE_loss_energy=5.721064796726022e-07 alpha=0.2301170527935028 beta=0.0011444255942478776\n", + "MSE_loss_energy=3.6020374264592136e-07 alpha=0.23033663630485535 beta=0.0013638535747304559\n", + "MSE_loss_energy=3.6018948767386886e-07 alpha=0.2303348183631897 beta=0.0013620519312098622\n", + "MSE_loss_energy=3.601894871860436e-07 alpha=0.2303348034620285 beta=0.0013620556565001607\n", + "yvalue torch.Size([233])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=8000, lr=1, rmsg=65536, mpred=['energy'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 12 16 20 24 28 32 36 40 60 80 100]\n", + "SYS ebbrt_tuned\n", + "MSE_loss_time=1.8762807855597806e-09 loss_time=43.31606 us max_time=-11.823007583618164 alpha=0.6161473989486694 gamma=0.19456696510314941 delta=0.12956058979034424\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([233])) that is different to the input size (torch.Size([1, 233])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=6.975892937025525e-11 loss_time=8.35218 us max_time=-10.534247398376465 alpha=-0.48536813259124756 gamma=0.0714179053902626 delta=0.5456116199493408\n", + "MSE_loss_time=6.318177332257112e-11 loss_time=7.9487 us max_time=-10.662113189697266 alpha=-0.6914904713630676 gamma=0.04345206543803215 delta=0.6028088927268982\n", + "MSE_loss_time=6.124368005504947e-11 loss_time=7.82583 us max_time=-10.736156463623047 alpha=-0.7997615933418274 gamma=0.026221372187137604 delta=0.6396529078483582\n", + "MSE_loss_time=6.0595445851343e-11 loss_time=7.78431 us max_time=-10.779688835144043 alpha=-0.862259566783905 gamma=0.016334425657987595 delta=0.6607226133346558\n", + "MSE_loss_time=6.036541486460858e-11 loss_time=7.76952 us max_time=-10.80585765838623 alpha=-0.8994534611701965 gamma=0.010489935986697674 delta=0.6731500029563904\n", + "MSE_loss_time=6.028115501681533e-11 loss_time=7.76409 us max_time=-10.821769714355469 alpha=-0.9219419360160828 gamma=0.006973694544285536 delta=0.6806159019470215\n", + "MSE_loss_time=6.024972604921868e-11 loss_time=7.76207 us max_time=-10.831500053405762 alpha=-0.935651957988739 gamma=0.00483663659542799 delta=0.685148298740387\n", + "MSE_loss_time=6.023785227500771e-11 loss_time=7.7613 us max_time=-10.837480545043945 alpha=-0.9440523982048035 gamma=0.0035308673977851868 delta=0.6879181265830994\n", + "MSE_loss_time=6.02333121275369e-11 loss_time=7.76101 us max_time=-10.841169357299805 alpha=-0.9492366909980774 gamma=0.0027251981664448977 delta=0.6896246671676636\n", + "yvalue torch.Size([233])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=10000, lr=.1, rmsg=65536, mpred=['time'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40]\n", + "SYS linux_tuned\n", + "MSE_loss_energy=10.401746502436177 alpha=0.26566147804260254 beta=1.5014312267303467\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([340])) that is different to the input size (torch.Size([1, 340])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_energy=3.262613680156139e-07 alpha=-1.1357345581054688 beta=0.0009362245909869671\n", + "MSE_loss_energy=3.0685046480679154e-07 alpha=-0.9034810662269592 beta=0.0009217746555805206\n", + "MSE_loss_energy=2.754881005294182e-07 alpha=-0.5081182718276978 beta=0.0008971766801550984\n", + "MSE_loss_energy=2.3110499279456789e-07 alpha=0.10404948145151138 beta=0.0008590901270508766\n", + "MSE_loss_energy=1.7641721148758873e-07 alpha=0.9801543951034546 beta=0.0008045823778957129\n", + "MSE_loss_energy=1.2128311679971452e-07 alpha=2.108976125717163 beta=0.0007343515753746033\n", + "MSE_loss_energy=8.071682083613856e-08 alpha=3.3447418212890625 beta=0.0006574671133421361\n", + "MSE_loss_energy=6.308644321431112e-08 alpha=4.339838027954102 beta=0.0005955544183962047\n", + "MSE_loss_energy=6.190181196842276e-08 alpha=4.802731990814209 beta=0.0005897650262340903\n", + "yvalue torch.Size([340])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=10000, lr=.1, rmsg=8192, mpred=['energy'], msys=['linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 149, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40]\n", + "SYS linux_tuned\n", + "MSE_loss_time=4.697942430351674e-09 loss_time=68.54154 us max_time=-10.81550121307373 alpha=0.755046010017395 gamma=0.5304393768310547 delta=-0.1696922779083252\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([340])) that is different to the input size (torch.Size([1, 340])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=1.650716033856304e-10 loss_time=12.84802 us max_time=-9.71119213104248 alpha=-0.10054003447294235 gamma=0.0830567255616188 delta=0.8295048475265503\n", + "MSE_loss_time=1.6479061136481811e-10 loss_time=12.83708 us max_time=-9.7344331741333 alpha=-0.1372804492712021 gamma=0.07618862390518188 delta=0.8443202376365662\n", + "MSE_loss_time=1.6478467414845924e-10 loss_time=12.83685 us max_time=-9.737837791442871 alpha=-0.14262999594211578 gamma=0.07518447935581207 delta=0.8464860320091248\n", + "MSE_loss_time=1.6478453963215062e-10 loss_time=12.83684 us max_time=-9.738335609436035 alpha=-0.14341333508491516 gamma=0.0750381276011467 delta=0.8468016386032104\n", + "MSE_loss_time=1.6478453681808504e-10 loss_time=12.83684 us max_time=-9.738377571105957 alpha=-0.14348241686820984 gamma=0.07502569258213043 delta=0.8468275666236877\n", + "MSE_loss_time=1.6478453677116028e-10 loss_time=12.83684 us max_time=-9.738378524780273 alpha=-0.14348465204238892 gamma=0.07502537220716476 delta=0.8468281626701355\n", + "MSE_loss_time=1.6478453677116025e-10 loss_time=12.83684 us max_time=-9.738378524780273 alpha=-0.14348465204238892 gamma=0.07502536475658417 delta=0.8468281626701355\n", + "MSE_loss_time=1.6478453677116028e-10 loss_time=12.83684 us max_time=-9.738378524780273 alpha=-0.14348465204238892 gamma=0.07502537220716476 delta=0.8468281626701355\n", + "MSE_loss_time=1.6478453677116025e-10 loss_time=12.83684 us max_time=-9.738378524780273 alpha=-0.14348465204238892 gamma=0.07502536475658417 delta=0.8468281626701355\n", + "yvalue torch.Size([340])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=10000, lr=.1, rmsg=65536, mpred=['time'], msys=['linux_tuned'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40]\n", + "SYS linux_tuned\n", + "MSE_loss_energy=3.1736209974731158 alpha=1.6539652347564697 beta=-0.8273105621337891\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([340])) that is different to the input size (torch.Size([1, 340])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_energy=8.609881749776227e-07 alpha=2.1432385444641113 beta=0.0013114969478920102\n", + "MSE_loss_energy=4.824321421179702e-07 alpha=1.61918044090271 beta=0.001572378329001367\n", + "MSE_loss_energy=3.5453072235186565e-07 alpha=1.2246168851852417 beta=0.0017687961226329207\n", + "MSE_loss_energy=3.4125223926614626e-07 alpha=1.071757197380066 beta=0.0018448913469910622\n", + "MSE_loss_energy=3.4100761104064225e-07 alpha=1.0487926006317139 beta=0.0018563230987638235\n", + "MSE_loss_energy=3.410073301453634e-07 alpha=1.047995924949646 beta=0.0018567200750112534\n", + "MSE_loss_energy=1.2136040151154726e-06 alpha=1.048421859741211 beta=0.002291131531819701\n", + "MSE_loss_energy=3.410102254985726e-07 alpha=1.0479880571365356 beta=0.001857515424489975\n", + "MSE_loss_energy=3.410073301154088e-07 alpha=1.047987937927246 beta=0.0018567241495475173\n", + "yvalue torch.Size([340])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=10000, lr=.1, rmsg=65536, mpred=['energy'], msys=['linux_tuned'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[28 30 32 34 36 38 40]\n", + "SYS linux_tuned\n", + "MSE_loss_energy=5.199010363610722 alpha=1.655785083770752 beta=-1.607767105102539\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([29])) that is different to the input size (torch.Size([1, 29])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_energy=3.3289138809710734e-05 alpha=2.7812421321868896 beta=-0.013115008361637592\n", + "MSE_loss_energy=1.4360415481617989e-05 alpha=1.9579185247421265 beta=-0.0049598123878240585\n", + "MSE_loss_energy=3.8635257924247674e-06 alpha=1.1497951745986938 beta=0.003044822718948126\n", + "MSE_loss_energy=1.4008444447896256e-06 alpha=0.6610794067382812 beta=0.00788565631955862\n", + "MSE_loss_energy=1.232328208547477e-06 alpha=0.5111297965049744 beta=0.009370938874781132\n", + "MSE_loss_energy=1.2306223463538695e-06 alpha=0.49479347467422485 beta=0.009532756172120571\n", + "MSE_loss_energy=1.2306216016418088e-06 alpha=0.4944464862346649 beta=0.009536191821098328\n", + "MSE_loss_energy=1.2306216417763603e-06 alpha=0.49444490671157837 beta=0.009536066092550755\n", + "MSE_loss_energy=1.2306610980935978e-06 alpha=0.49444931745529175 beta=0.009540589526295662\n", + "yvalue torch.Size([29])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=10000, lr=.1, rmsg=524288, mpred=['energy'], msys=['linux_tuned'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 10 12 16 20 24 28]\n", + "SYS ebbrt_tuned\n", + "MSE_loss_time=9.3518833865048e-10 loss_time=30.58085 us max_time=-10.931262969970703 alpha=0.8639646768569946 gamma=-0.49780237674713135 delta=-0.21662914752960205\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([80])) that is different to the input size (torch.Size([1, 80])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=4.7445300777785e-11 loss_time=6.88805 us max_time=-10.515900611877441 alpha=-0.5818349123001099 gamma=-0.4936071038246155 delta=-0.2227908968925476\n", + "MSE_loss_time=4.236176593303981e-11 loss_time=6.50859 us max_time=-10.630703926086426 alpha=-0.748201847076416 gamma=-0.5089138746261597 delta=-0.23197980225086212\n", + "MSE_loss_time=4.217741654004542e-11 loss_time=6.49441 us max_time=-10.652167320251465 alpha=-0.7784810662269592 gamma=-0.5206874012947083 delta=-0.2379435896873474\n", + "MSE_loss_time=4.21571846459416e-11 loss_time=6.49286 us max_time=-10.65610408782959 alpha=-0.7841393947601318 gamma=-0.531475305557251 delta=-0.24318993091583252\n", + "MSE_loss_time=4.2143893041436884e-11 loss_time=6.49183 us max_time=-10.656660079956055 alpha=-0.7850796580314636 gamma=-0.5417852997779846 delta=-0.2481829822063446\n", + "MSE_loss_time=4.2131668979084197e-11 loss_time=6.49089 us max_time=-10.656660079956055 alpha=-0.7851954102516174 gamma=-0.5517299771308899 delta=-0.25301480293273926\n", + "MSE_loss_time=4.2120232537629125e-11 loss_time=6.49001 us max_time=-10.656532287597656 alpha=-0.7851487398147583 gamma=-0.5613399744033813 delta=-0.25770172476768494\n", + "MSE_loss_time=4.2109499922781713e-11 loss_time=6.48918 us max_time=-10.656356811523438 alpha=-0.785022497177124 gamma=-0.5706393718719482 delta=-0.2622584402561188\n", + "MSE_loss_time=4.209941524421303e-11 loss_time=6.48841 us max_time=-10.656184196472168 alpha=-0.7848891615867615 gamma=-0.5796449184417725 delta=-0.2666912376880646\n", + "yvalue torch.Size([80])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=10000, lr=.1, rmsg=8192, mpred=['time'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 10 12 14 16 20 24 28 30]\n", + "SYS ebbrt_tuned\n", + "MSE_loss_energy=1.8716230579680093 alpha=-0.8316636085510254 beta=0.874535083770752\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([49])) that is different to the input size (torch.Size([1, 49])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_energy=6.246585413241833e-07 alpha=-1.3500171899795532 beta=0.0026435942854732275\n", + "MSE_loss_energy=2.92997325904283e-07 alpha=-0.7278295755386353 beta=0.002298724604770541\n", + "MSE_loss_energy=1.1255511871187007e-07 alpha=-0.123292937874794 beta=0.001963638933375478\n", + "MSE_loss_energy=7.152672242858627e-08 alpha=0.2361350953578949 beta=0.001764413551427424\n", + "MSE_loss_energy=6.885477372127496e-08 alpha=0.34346234798431396 beta=0.0017049235757440329\n", + "MSE_loss_energy=6.88297931080648e-08 alpha=0.35467055439949036 beta=0.0016987109556794167\n", + "MSE_loss_energy=6.882978363556487e-08 alpha=0.3548932373523712 beta=0.001698593609035015\n", + "MSE_loss_energy=4.516908398239483e-07 alpha=0.35528793931007385 beta=0.0020928415469825268\n", + "MSE_loss_energy=6.883143400136191e-08 alpha=0.35489335656166077 beta=0.0016977684572339058\n", + "MSE_loss_energy=6.882978354508891e-08 alpha=0.3548942506313324 beta=0.001698586973361671\n", + "MSE_loss_energy=3.676757241138263e-05 alpha=0.35874953866004944 beta=0.005558536853641272\n", + "MSE_loss_energy=6.883012188926645e-08 alpha=0.3548939824104309 beta=0.001698216306976974\n", + "MSE_loss_energy=4.077934111563847e-07 alpha=0.3545233905315399 beta=0.0013276224490255117\n", + "MSE_loss_energy=6.884899453212272e-08 alpha=0.3548913896083832 beta=0.0016957942862063646\n", + "yvalue torch.Size([49])\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYIAAAElCAYAAADp4+XfAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOydeXhU1fn4P+/sk30PIQmEnRCWACKiBcWiInWpilC0WkWrVttqa23t8mvd+q221UpbrbVqW3fQal1xl4IIKvse2SGBhCRkz0xmO78/7g0OIRskk5DkfJ5nnrlzz/beOzPnveec97yvKKXQaDQaTd/F0t0CaDQajaZ70YpAo9Fo+jhaEWg0Gk0fRysCjUaj6eNoRaDRaDR9HK0INBqNpo+jFUEPRET2iMiM7pajJyAiZ4lIYXfLcTyIyBIRub6FtBwRUSJi62q5Ik1b16Z/95FDKwKNppcjIneJyLPtzNuiEupNiMiPRKRYRKpE5CkRcbaS93ERKRCRkIhc04VidhlaEXQzPfXJrqfK3dfoK9/T8VyniJwH3Al8HcgBBgN3t1JkPXAzsKYDIp7UaEUQAcwh7M9FZIuIVIjIP0XEZaadJSKFIvIzESkG/ikiFhG5U0R2iki5iCwSkaSw+q4Skb1m2i/baLvFusKG3t8RkX0iUhZeXzvLXici+4CPRMQqIg+a9ewWke83Du1F5HIRWd1EtttF5L8tyL1ERO4TkU9FpFZE3hCRZBF5TkSqReQLEckJy58nIu+LyGERKRGRX5jn3SLyL/O+bwEmtXKvRET+JCKHzCfDDSIyWkQmmXXawvJeJiLrzONTRWSVKVeJiDzU2nfSQtunmddaKSLrReSsJlmGiMjnplyvhf8eTOaLyAEROSgit4fVe5eIvCwiz4pINXAT8Atgrnlf17ci02+BqcBfzbx/lWamayRs1CAi14jIJyLyR/Oe7xaR88PyxovIk6acReZ3bDXTrGa5MhHZBXzjOO5f0+u8pr1lge8ATyqlNiulKoB7WyuvlHpEKfUh4D2ONnoWSin96uQXsAfYBGQDScBy4D4z7SwgADwAOAE3cBuwEsgyz/0deMHMPwqoBaaZaQ+Z5WeY6V8DKsPabq2uHEAB/zDbHQc0ALnHUfZpINosfxOwxcyfCHxg5rGZ5Q831m3WsRa4rIV7tgTYAQwB4s16vwRmmPU9DfzTzBsLHARuB1zm58lm2v3AMvO+Z5vfQ2ELbZ4HrAYSAAFygQwzbQtwfljeV4HbzeMVwFXmcQxwWli+ylZed5p5MoFyYBbGw9g55ufUsHtRBIw27/V/gGebfA8vmGljgNKw38NdgB/4plm32zz3bDt/u0uA68M+N7Znay4PRgfqB74LWIHvAQcAMdP/i/E7igbSgM+BG820m4BtfPU/+bhpW838r1q7zivauP8DzLLrgblh9aaY7Sa3cW8+Aa7p7v4lIn1WdwvQG1/mD/amsM+zgJ3m8VmAD3CFpW8Fvh72OcP8kduAXwMvhqVFm+VntNB2a3U1/qmzwtI/B751HGUHh6V/1PinNj/PCP8jA38Dfmse5wEVgLMFuZcAvwz7/CCwOOzzhcA683gesLaFenYBM8M+30DLiuBsDGVzGmBpkvYz4DnzOAmo5yslsRRjKiHlBH8fPwOeaXLuXeA7Yffi/rC0UeZ3bg37HkaGpf8e4wkXjA5yaZO67yKyimBHWFqUmb8fkI7xoOEOS58HfBz2+wn/n5zbtK1m/lczWrrO47j/O5v8RuxmuzltlOu1ikBPDUWO/WHHe4H+YZ9LlVLhw8yBwKvmNEElRoccxPgj9Q+vSylVh/H02BKt1dVIcdhxPcZTbXvLhl9X/yafw48B/g1cISICXAUsUko1tCJ7Sdixp5nPjXJmY/yZm6OpTHtbakwp9RHwV+ARoESMRcE4M/lZ4EIRiQHmAMuUUgfNtOuA4cA2c8rqglauqTkGApc33mfzXn8NQ/E20vQa7BhPri2l928hrSs48ntSStWbhzEY12kHDoZd598xRgZwHN9VC5zoddYCcWGfG49rTrC+Ho9WBJEjO+x4AMZwuZGmLl/3Y0xDJIS9XEqpIowpkCN1iUgUkNxKu63V1RbtKRsu+0GMaaFGwq8ZpdRKjCfZqRjD9mfaIUN72I8xhdQcR90vjHvfIkqpPyulJmKMWIYDd5jnizCmgC7BUGLPhJXZrpSah9GhPQC8LCLRAObcekuvX4TJ/0yT+xytlLo/TLSm1+AHylpJb+33dTwuhpvmrTPfo8LO9WtnXfsxRgQpYdcZp5TKM9OP67tqS1YRubKN+99Y/2aMadFGxgElSqnWHrB6NVoRRI5bRCTLXOT7BbCwlbyPAb8VkYEAIpIqIhebaS8DF4jI10TEAdxD699ba3W1xfGWXQTcKiKZIpKAMeXRlKcxnroDSqlP2ilHW7wJ9BOR20TEKSKxIjI5TKafi0iiiGQBP2ipEnNReLKI2DE6PC/GCChc9p9izMO/Glbu2yKSqpQKYcw901hOKRXTyuv/zLyNo43zzAVTlxhGBOFK9dsiMspU/PcALyulwmX7fyISJSJ5wLW0/vsqAXJEpD3/9xIMKxrM6ynFWK/4tinrfFpWwkdhjqDeAx4UkTgxjBGGiMiZZpZFwA/N/0kihiXPCaOUeq6N+7/PzPo0cJ15fxOBXwH/aqleEXGIYewhgN38vnpV39mrLuYk43mMP8Eu83VfK3kXAK8D74lIDcaC7WQApdRm4BazvoMY8+xHNkiJyFQRqW1PXe3geMv+w7zGDRgLwW9jLGSHd1jPYCx6dtZoAKVUDcYC64UY0xLbgelm8t0YUwy7TdlaazfOvIYKs0w58Mew9Fcxp8vMKblGZgKbzfu+AGONpd0WJUqp/cDFGA8IpRhPzndw9P/xGYzOqRhjQfyHTar5H8bi+ofAH5VS77XS5Evme7mItGUCuQCYbVoA/dk8911TvnKMkdOnbdQRztWAA2PxvQLjwaZxCuwfGGsj6zFMM185jnpPGKXUOxjrKh9jfO97gd80povI4rDRGxi/Iw9wOvC4eTytK2TtKhpX9jWdiIjswVhM+6C7ZelKTLPBx5RSA8POuYFDwASl1PZuE+4EEZGdGAvifeq71PQt9IhAc8KIYbM/S4x9A5kYT1WvNsn2PeCLHqoELsOYh/6ou2XRaCJJn9h1qIkYgjEVsxBjuPwWhrmrkWiMjATD1rtHISJLMMw2rzLXAnoNTaYSwzlfKbWsS4XRnBToqSGNRqPp4+ipIY1Go+njaEWg0Wg0fRytCDQ9HjGcojV1EmcTw5mcCjuXJyLvmaaRlSKyWkRmhaXHishDYjgNrBPDMd/LInLqCcp1qxhO2OpEZKuIDDfPnyWGS+PwzU7fCSv3LxHxNUlvdNSWIiLLxXAKWCkiK0TkjCbtDhaRN0WkRgyHbr8/Efk1fQetCDS9hUrg/LDPszDs1sN5A3gfw2VGGoZtfjWAGP7oP8LYPHYBxh6DXOBFs67jQgzvnNdheNSMMesM3xl8oMlmp383qeL3TdIb92bUAvOBVAxHfw8AbzQqQXPT4fvmtfTD2PndrlgEmr6LthrS9Baewdi89Ib5+WqMHaT3gfEkDQwC/qGU8pl5loeVvwqj0zwrbPNYHcYGqJePRxBz1+lvMByUbTFPt+Qb6bgwN64VhLUTxFAISRj7Na7BUDLhrrE3dEbbmt6LHhFoegv/BaaJSILp7mIq8FpYejnGTtxnReSbIpLepPwM4N0mO4iPQYyYBZUtvB41s2WZr9Eist+cHrq7iVuCNHM6a7cYMRGimzR1sxixFlab+xmOkQPDJcbrwBNKqUNm0mnAHnN3bJkYsQPGtHZNGk2PVARihJY7JCKbOqGugeafbZ2IbBaRmzpDRk2X48UYDcwFvoXRQR5x+6AMO+npGK6MH8TwiLlURIaZWVII86IpIvlm514tIgVh9Yxt4iwu/HWzma3RZ9C5GFNN0zHcL19nnt8G5GO4WjgbmIgRZ6KRPwPDMKav/h/wr6brAEqpsRjTV1dguEduJMu8/j9jePd8C3jNnDLSaJqlR+4jEJFpGHOlTyulRnewLgfGfWgQw+XwJuB0pdSBNopqThLMBeFhGB3f7zA2sf0MwzfTdqWUNFMmG8NvTIJSaoqILAS8SqnvNMk3A+OJO+c45BmP4TvnLKXU/8xztwNfU0pd0kz+04C3lFLNepUVkceAOqXU7S2kb8Xwd7ReRF4D4pRS0800wVg/maaUajE6maZv0yNHBEqppRjRr44ghlfDd8yn+2UiMrKddfnCfOQ76aH3RAMYkckyMBaDW/V0ajp+ewTDIR4YztvObWaK5ijMUWNLbo4fM7MVYLjfbu9TlsJQXieabucrj6EbjqNdjQboXZ3e48APTN/yPwEebSP/EUQk25xz3Q88oEcDPRNz+udC4CLVZKgrhlvqu0VkqBjukFMwrG9WmlmexhhBvCpG3GKrGK6HT2nSRl4rbo5vMvPUY7jd+KlpkpqF4cHzTVOWs0RkgBhkY4TXPLKeISKzRSTGlPNc4NsYU12NsY6/JoZrZLeI/AxD8X1mFn8WOE1EZpgmp7dhWCtt7YRbrOml9AqrIXNK53TgJWMkDBhP94jIpRj+3JtSpJQ6D448HY4Vkf7Af0XkZaVUSTNlNCc5ptvu5vBhhF38AGM9oBbDDfEPzHJeEZmO4TvpLTNPGbAKI0LZ8fJ9jIeTAxhTM/8AnjLTJgDPYVj7lGMsdIe7Pb4VeBJjFLAb+K5SaomZ5sSY/x+MEaxmI/CNxocXpVSBiHwbI7ZEGsYU1UVhllIazTH0yDUCABHJAd5USo0WI7xggVIqo/VS7ar3nxjztcdlMqjRaDQ9lV4xNaSUqgZ2i8jlYCyQici4Noph5s0Sw2c+YkQrOgPTTluj0Wj6Aj1SEYjICxjxZEeISKGIXAdciRF+bj1GTNL2hmfMBT4zy/0PI9rTxkjIrdFoNCcjPXZqSKPRaDSdQ8RGBGIEeP5cRNabJnd3N5PnLBGpMjdzrRORXzdXl0aj0WgiRySthhqAs5VStSJiBz4RkcVKqZVN8i1TSl3Q3kpTUlJUTk5OZ8qp0Wg0vZ7Vq1eXKaVSm0uLmCIw7bgbQ+LZzVeH56FycnJYtWpVR6vRaDSaPoWI7G0pLaKLxeamnHUYXhHfV0p91ky2Keb00WIRyWuhnhtEZJWIrCotLY2kyBqNRtPniKgiUEoFlVL5GI6wThWRpn6B1gADlVLjgL9gbKxprp7HlVKnKKVOSU1tdmSj0Wg0mhOkS8xHlVKVwBJgZpPz1UqpWvP4bcBubv3XaDQaTRcRsTUCEUkF/EqpSnPD1gyMaErhefoBJUopJUY4QAvGlvvjwu/3U1hYiNfrbTtzD8DlcpGVlYXdbu9uUTQaTR8gklZDGcC/TcdXFmCRUurNRn//SqnHgNnA90QkAHgwXOke94JyYWEhsbGx5OTkEOZrqEeilKK8vJzCwkIGDRrU3eJoNJo+QCSthjYA45s5/1jY8V+Bv3a0La/X2yuUAICIkJycjF4U12h6P0o1oAI7IVQDlmTENpijA9l1Db3C+yjQK5RAI73pWjQaTfMo5UN5PwRVCbgg8CUqVAqO07q8D+g1ikCj0Wi6i1p/GaUNO1EqhEP155//q+V704eSEuNsuVDwIIQqEKvhNFmpeAjsBnsuSEIXSW7QI53OdQV33XUXf/zjH1tMX7ZsGXl5eeTn5+PxeLpQMo1GczJR4ytlZ81yvIFq1u2tZfajG3lq+R4+2V7WajkjRMRXXbCIgAiogJEeqkAF9qCCxSgViuQl6BHBifLcc8/xk5/8hGuvvba7RdFoNJ2AUooiz2EOeatxWKzkxKQRY3O1We5Qw3YsKopnl1l44VMvaXEWHrgSvjkms9VyYklCEUQpPyJ2VKgOsIMllpBvAwQ2mkFKBSyp4DwTI8R656NHBGH89re/ZcSIEcyYMYOCggI8Hg+nnnrqkfQ9e/YwduxYnnjiCRYtWsQ999zDlVdeycGDB5k2bRr5+fmMHj2aZcuWdeNVaDSaE2F7zUE+Lf2SA/WH2V5TwsfFm6kNtG2SvrfMz4+fCfDcci/njHHw+PVx5GaHaMsAUqzJ4DgdVAUqVAISQJzTQdUbSkDSEWsGYukHwUOowK7OutRj0CMCk9WrV/Piiy+ydu1aAoEAEyZMYOLEifh8Pnbt2sXgwYNZuHAhc+bM4frrr+eTTz7hggsuYPbs2Tz44IOcd955/PKXvyQYDFJfX9/dl6PR9FmqGxpYfbCICo+HJLebiRmZxDpbmasHAqEgGyv3k+aKw2pa7ZQ31LCr5hBjEwc0W0YpxdMr9vJ/b9fhsIf4zaXRTBvpoDZQRpLjKyvGoPITCPlwWNzHWARZ7INRtmxQDSBuRKyowB5QgljC8kocBA+AfeSJ35hW0IrAZNmyZVxyySVERUUBcNFFFwEwZ84cFi1axJ133snChQtZuHDhMWUnTZrE/Pnz8fv9fPOb3yQ/P79LZddoNAbegJ8Pd+0kEAoR63BQWlfHh7t3cv7Q4ThtLXd3ARUE1BElAGC32GgI+pvNX1Lt5Y6XN7D0y1LOGp7KHRfEEHLsoi5YS7JzIBnuPJRSlDbsoMRTgCKE3eImKyqfWPvRbnJE7CBhm0fFZUSrDkd5wdL6VFNH0FNDYTRnsjV37lwWLVrEl19+iYgwbNiwY/JMmzaNpUuXkpmZyVVXXcXTTz/dFeJqNJomlNd7qPP7SHK7sVutJLmjqPX5KPe0Pkp3WuzE26Op9NUBEFQh6vwNpLvjjsn79saDnPfwUj7fXc69F+fxz2snkZc2itHxsxiT8A2yovOxio1qfzEHPJtxWxOIsaViwcbe2s/xh9qYbrKkgSUVFTqICtWhguUgCrEd2/d0FloRmEybNo1XX30Vj8dDTU0Nb7zxBgBDhgzBarVy7733Mnfu3GbL7t27l7S0NL773e9y3XXXsWbNmq4UXaPRtEJ7TPJFhNNSh+G2OjnkraK8oYa8xGyyo75yfVbt9fPjReu4+bk1DEiK4q0fTuWqKV9NAYlYjpr6qfDtxynRWMQKgN3iIkSQusDhNmSxIM4zwT4RLNFgH4K4zkcssSdw9e1DTw2ZTJgwgblz55Kfn8/AgQOZOnXqkbS5c+dyxx13sHv37mbLLlmyhD/84Q/Y7XZiYmL0iECj6SaSo9xE2x1UeDzEOBzU+BqIsjtIdke1WTbG5uLsfnk0BANYLRYclq+6x892lfPjRes5WOXhh2cP5QdfH4bd2vpztAUrqkkIFgXIMfM+xyLiQOwjI7YmcEx7PS1m8SmnnKKaBqbZunUrubm53SRRZOiN16TRdAXVDQ2sOlBoLBZHRXFKOxaLW6IhEOTB9wr4x9LdJMdZuGJGLGcO6c/ohGyc1tadQtb6y9hZuxy3JQGbxUFDsJYQQUbEnY3NEhkz0NYQkdVKqVOaS9MjAo1G0y78Pj/eugYcLjtO94l1rF1BnNPJ2YOGdLieL0tquPXFdWw9WM1po5xcMz0dt8PC3rpS/KEgp6W2PmcfY09hQNQEij1b8QSqiLYmkhk1tluUQFtoRaDRaNqktLCcVe+uIxgIohSMmZpLTl52d4t13Cil8AYC2CwW7FZrs3lCIcU/P93DA+9sI8Zp4/pZcXxtRNIRi6IUZyxFnsN4gz5c1tY79URnNgmOTEKEsMrJ292evJJpNJqTAm99A1+8s5aoWDfOKCcBf5D1SzYTnxJLYnrX+sTpCJVeDysK93O4vh6rRRiVksbo9H5YwlaTD1Z5+MlL61m+o5wZuWn89pI8llduPGpev3FxONTOaXURC9YO2uUopUDVgdgR6fzRmFYEGo2mVeqq6gkGQzijjA7IZrdisVqoqajrMYogEArx0Z5diIJ+MbEEQiHWlhwk2uFkSFISAK+vP8CvXt1IIKS4/9IxzJ2UjYgwwJ/C3royUp2xiAjlDbWkOuOIsnXN9JgKHUY1rABVDQqUfThiH4d04ghDKwKNRtMqdocNFVKEQgqLxXwaDoWwO3tO91FeX4/H56dfjGGCabNYSHJFUVBeSoorll+/vonX1h1g/IAE/jQnn5yU6CNlxyQMIBAKUuSpACDVGcekxDRUYB9YYhBL0pG8KlQPoVLACtYMRJqffmovhqvqjwEbYkk3nM/5t6JwII4xHao7nJ7zTWo0mm4hNimGgaOy2LNpHw63E7/XR0pmEmnZPT+8eMGBeu59eSmHahq4/ZzhfO+sIdiamIU6rXZOSx2OJ+BDoXBb6lHe91AYu5GV40wstixUqBblfQ/wgAqBdQA4p3Ys0EyoDJT3iKtqEQvKkgqBbaAVgUaj6SpEhDFTc0nNSqaqtJqo+Cgyh/bDauvY025XkhwVRZTDTpXXS7zLhccX4PkVxawoqGNwajSvfO90xmUb01xKhTjs20edvxyXLZ4U5yAsYsUlVahQJcpfbPgCsvZDhWoh8CXYskwfQb6v4gsECyFUAdbkI3L4Q158IQ8Oixu7pW3PpqgQx+77tQAKpVSnBbDRikCj0bSJxWKh/5B+9B/Sr7tFOS6q/R78oQBxdjdn5wxhReF+1u4/zEsrKiip9DP31FR+eE4aKVFf7Qko8W6n2LMVpyWaCn8hvmAdma5+qIb3QVkheAgsbpRKNBdwG+9Jy52yUooSbwGHvNvNfIp09wjSnMNa78ytqSBWlPIg4jYXjUvBNrJTo5hpFxOdyPz580lLS2P06NHNpu/fv5/p06eTm5tLXl4eCxYs6GIJNZqeiVKqTbfOTfmy+gDvHVzPkpLNfFi8CYsF9hZZ+Pt7pYSCFhZcmcm8s4ooC2xke81SGoKGn6EK3z6ibUk4rTHEWFM47NuHCpaAciDWdLCkGP6AVCVY+4N9FCpUafoIcqJCxajgAbBmgyURgJrAIYo9W4m2JhFjSybKmsjB+i3UBcpbvQYRJzimgfKaMpSAJRuxN9/HnCgRGxGIiAtYCjjNdl5WSv2mSR4BFgCzgHrgGqVUxB31lBaWU/DFDipLq0lIjWPEpKGkZiW3XbANrrnmGr7//e9z9dVXN5tus9l48MEHmTBhAjU1NUycOJFzzjmHUaNGdbhtjaa3srX0EOtLihGEU/r3Z0jSV/9Vb8CPPxgi2uE4ygzUE/CxsXI/Kc5YrGLhy9IqvvXqCjbvr+drI2zccl4Ah+NLom39sIqd2kAp9cEKnNZo7OLGF6rHarUTUD5sFqfRybMBFSwFEcQ1zQgsoxpQ3o9RIWMhGcdERFwgVrD0O7I+UOkrwmGJPvLZIlbsFjeVviJi7K2vtVhs/VDWiw2rIRyIJaZT7y9EdmqoAThbKVUrInbgExFZrJRaGZbnfGCY+ZoM/M18jxilheWseP0LouKjSExPwFPrYcXrXzDlokkdVgbTpk1jz549LaZnZGSQkWHMH8bGxpKbm0tRUZFWBBpNCxTX1vDFgSLSo2NQSvFp4X4S3W6S3FFsOlTC+uKDIEKiy8VZOYOJshtTPEEztKMF4ZMtNfz7o8MIwk8vjGLaqAYaQorCuj0kh0KkOTMIqRB2Mebss6LHsrtmJfX+YqLVTjKcKaAGIK4ZqNBhqgLR7KqoxBssJcdZRj9bGRZrfyP0pH8duGcfG3egOb9DKtRuqyIRG0hS2xlPkIgpAmWM42rNj3bz1XRsdzHwtJl3pYgkiEiGUupgpOQq+GIHUfFRRMcZTqga3wu+2NEpo4L2smfPHtauXcvkyRHVexpNj6a2wYdVLNgsjU/SQp3PT6m3mJd2f0GaK5Z4WzSfHt7NlrrdXDl8MhlRiUTZHLhC0fzx9SI27PQxqJ+Nh+aMIzp6K0WeOvbXVRFUNir9tXhDJYxLmEi0zfj/u6xxDI+fjr9hE7ZAFRZrBvjWgPsiDvmdfFJagNNqwyZWNnv30OCsYFB8f8AKBDi2m4NEZxblvt0ElRur2AmEfASVj0RH5GIMHA8RXSMQEauIrAMOAe8rpT5rkiUT2B/2udA8FzEqS6txx7iPOueOcVNZWh3JZo+itraWyy67jIcffpi4uGP9nWs0GoN4l4uQCuENBPD4/SilcNosLDtUQFCFqAhU83nVNhIdUXj9QVaWb6ch6OeT7eXc/VwxW3b7+c6Z/XjxhtMY378fSc4R7Ks7SJTNR7prBOmuSZR6Mom2ZVEfrKDCV4g3UIZF+XBY4w0X0soHCArh87LPUGo7wh6cVj/RjmGUNXioa9gHoRKwjW72KT/alkx21AR8oXpqA2UElIeB0acQZUvs+pvaDBG1GlJKBYF8EUkAXhWR0UqpTWFZmlv2PkadisgNwA0AAwY0HzauvSSkxuGp9RwZCQB4aj0kpHZNh+z3+7nsssu48sorufTSS7ukTY3mZMUfDFLr8xFlt6NQlHs8uG02kky30anR0XxtYA7riw8iCNNzBhHltGMVsGHFiuBTfnzBEEkuQxnc/cYWnl9ZyLC0GJ66ZhLDMxwU129lb22IWMdgom3jibbaKSivoaahiNqgh5jgSvonlOO0+IkNbSTNMQCn63Swj4FgKTgmcKjhIIf920h0pOAP1VLp20CiYzylTGagNZloV6q5ltA8Sc4BJDgyjXUHcRyJU3Ay0CXmo0qpShFZAswEwhVBIRDuuSoLONBM+ceBx8FwQ90RWUZMGsqK178AjJGAp9ZDfVU9487M60i17UIpxXXXXUdubi4//vGPI96eRnMy4w34eX/XTmoaGhABpQz/PQo4I2sAg03XD4MSEhmU8NWTs1KK0clZVDfspLCmikRJwKu8WOtdPLu4hgOHDzP/jEH8dOYIXHYru2s/ozZQhijBH/KS4IhneeEe7Fix2yDNEUux50ssliTGJNmwhaAm6MUZ3IXFfdGRdqvqPibWnoQ/JDitUfhDlTQEqwgQhds5BLG6m17iMVjEikPaztfVRGxqSERSzZEAIuIGZgDbmmR7HbhaDE4DqiK5PgCQmpXMlIsm4YpyUlFSiSvK2SkLxQDz5s1jypQpFBQUkJWVxZNPPgnArFmzOHDgAMuXL+eZZ57ho48+Ij8/n/z8fN5+++0Ot6vR9EQO1tZS6fWQHh3Dodo69ldVkhETS5LLzcbSkhbLiQgTkwbxnRFn8NPxMwitF1sAACAASURBVLl93Hk4Dw/m76/VEwpYePa6yfz6wlG47MYTdyDkx4YDm8VJUPkZEZOFTdmw2YQ4WxS5MdkkOeMorqvCTyxBrFjxgG3oUe1axMaA6GQagn6q/V7qAw0c9tUxNKYfcfaTr3M/HiI5IsgA/i3GhJkFWKSUelNEbgJQSj0GvI1hOroDw3z02gjKc4TUrOSILAy/8MILzZ5v7Oz79+9/3LbQGk1vxWG1ohR4AwEArBYLIaWo9/tJch/bsQZCQcobanDbnMTZ3SQ4otlXXs+PFq1j9d4KLhibwU9nDWG/r5iPi0sYGdefjKhEMqNGs7fuC0IEGRg9nvoGBzmujCN+hwCE4VTKKqqDHqJtp5MWnY/Fnn5U+xnuXOqDnzI8Lp5yXzWi0hmTeBoZ7p7vaiOSVkMbgPHNnH8s7FgBt0RKBo1Gc/LSPyaWCRn92VNZwRkDBhAKKXZUHCbe6WJSZtZReUMqxCel2yjz1iAC01Jz+XhTNXe/sRmLRVjwrXxmjU3nvQMbUCgs4uOD4s+ZmDSC4XGnkBt/zpG6XJYQboedGl8DsQ7Dg2ilx8qw2OmMSuiH3eJudv4+xp7C8NgzqQ2UMRgr8Y7+J2WQmRNBu5jQaPooPq/PsMLp4mhjtT4f1Q1ektxuRqelMzrtqyfvSZlZiAhVXi8bS4px2mwMSkjErwKUN9SQ7o5nX2U1P3x+Ayu2VzNlcDJ/nDOOzAQ31X4PDSE/qa44qny7Cak6Dnn3kOpKJ9X1VcQyq8XCWQMHsXTvXopra1BARkwMp2YNbDP8pNsWj9sWH6lb021oRaDR9EG+XLOTgs93opQiZ3Q2o88YicUSeY8z1Q0NLN7xJYFQkGi7g/OHDsdp+6obEpEjeZQypoP2V1VxZk4OSY5YPt5Wyksf1+L1wa++kcv8MwYdcY3tttpxWGxU++vxhwSFH4fVgk2OfWpPckdx0YiRVHm9WC1CnLMdDuB6MVoRaDR9jIqSSrau3E5y/yREhF3r95KWnUK/nJZNHzutbY8HXzBI/5hYiutqqW5oINV2dDd0qLaWQChEhjmHX1RTyeG6Bt77NMgLn9cwLD2av8ybwMh+R5t82y02pqblsqFyHw0ymBFxQ+nvTiXe0byjPIsIic2sRfRFtCLQaPoYPq8fi8WC1fS7b7Nb8dZ5u6TtRLcLh9XKwdoaYhwO4pzHTku57DZCShFSip21B1hdVMYji8s4VBXghmmDuf3c4ThbcIEd74hiatrISF9Gr0MrAo2mjxGbFIPVZqHmcC0WqwUVUiSkdc28d5zTxayhw6n2NZDkch81LdRI/9g4clNS2XiomDfXVbDlSydx0SH+/p1xnDsyq5laNR1FKwKNpo8RFevm9Isn8eXqXYQCIfKn55GQeuKKIBQKUXO4FqvNSkxC9DHpSikKysoo99STm5pKkjuK2GZGAo1YREhxJvLf5bvZVGhj4nAHV56VxJkD01ss01l4PB527dpFMBiMeFuRwmq1MnjwYNzHMe2lFYFG0wdJSI3n1JnHWHcfN1Vl1Xzx7jo8NV5UKITD7SAuORa708bA3CxSs1M4VFfHZ0X7ibLbKamr5dLclnfxK6V4/vN93PfmVhw2C3+el8/pI+OIsbnatOjpDHbt2kVKSgqpqaldsnje2YRCIUpLS9m1axd5ee33lqAVQScyf/583nzzTdLS0ti0aVOzeXJycoiNjcVqtWKz2Vi1alUXS6npC4RCoYh3ZKFQiC/eWQcCKZlJFO0oZv3SrWQN60fW8P4UbV/FmKkjiR2aiogQVApHKzIdqvFy53828tG2Q0wdlsIfZo+jX3zXWvMEg8EeqwTAiCSXmppKSUnLO7Obo08qgoM11WwoKaHcW0+yK4qx6elkxHbc6VxbgWka+fjjj0lJ6fm7ETUnH0optqwoYNeGfSSkxXHKefm4opxHwho27mxvK8xhMBjEam3dKVrN4Vo8tR5SMpPx+wIc2FFMv4Gp1FbWEZMQjTvGxbbPd3LOyCzOyB7AYa+X4UnN7+h/d3MxP39lI3UNAe66cBRXT8k5Yhba1fRUJdDIicjf5xTBwZpqPti9i1iHg1R3NHV+Hx/s3sWMQYM7rAzaCkyj0USa8oMV7Fy3h+TMZCpLqvj4xU9QIXBHO0nJSmJ/wUGsVgtjzxpF/8HHmlWWHTjM2g824PX4SM1KJn/6aFxRzc/nW22GiwgAf4MfUCAKu912JD0UDNFQ38CQpGSGNFNHbUOAe97YzKJVheT1j+PhufkMS49tJqcmkvRs1XcCbCgpIdbhINbhxCJCrMNJrMPBhuMcSp0oIsK5557LxIkTefzxx7ukTU3vp/pwDfu2FVFdXoNSChGoq65nz+b9xKfEUlNZx7v//Ji45FjccW7WvL8BT63nqDrqazx89tZqbE47Kf2TOHyggnUfNz/FCRCTEE2/nFTKig5jMzv9qtIaMswA935fAJvdijum+emd1XsPM2vBMl5eXcjNZw3h1ZvP6JVKYPx4Yy2moKCAv//9790sTfP0uRFBubeeVPfRlg3RdgelnrouaX/58uX079+fQ4cOcc455zBy5EimTZvWJW1reif1NR4+eeUzAv4gIkJKVjJlRYdxuO2kZadgtVmxWi0E/EGsNgs2MZ7kGzy+o4I01VbWEQqpIyOAxH4JlBaWt7rekH/2GLav3sneLYVkDE6jvsaLzWGjoqSKgD/A+LNHY7Mf3c34AiEWfPglf1uyk8xENwtvnMKknMiFYexu1q5dC8DOnTt58cUXufHGG4/J4/f7sdsjvxjeEn1OESS7oqjz+444mwKo8/tIdkW1Uqrz6N+/PwBpaWlccsklfP7551oRaDpEfXU9QX+Q1KxkSgvLGTIuh1PPH08wGGLF66soKyynweMjY2g6lYeqUCFFdLz7GFNPu9OOCilCIYXFInjrGnC5Ha3OOTucdvJOH0ne6cYmropDVRzcWYxYhIzB6ceYpe44VMNtC9exqaiaOadk8f8uGEWsq/s6wK4gKiqK+vp6fv7zn7Nr1y5GjhzJFVdcQWJiIm+//TYNDQ3U19ezcuXKtiuLEH1OEYxNT+eD3bsAYyRQ5/dR4/MxOTPyG1Xq6uoIhULExsZSV1fHe++9x69//euIt6vp3cSlxBGbHENZ0WGi46NISIvH7rBjB8745qlUllRic9iwO+3s31aExWZl4KisY57UE1LjGDJuIDvX70Usgohw2gUTj0uWxLR4EpvZnKaU4t+f7uF3i7cR5bDy2LcnMnN0864feiu/+93v+MMf/sDHH38MwF/+8hfWrFnDxo0bSUuLvHuP1uhziiAjNo4ZgwazoaSEUk8dya4oJmdmdYrV0Lx581iyZAllZWVkZWVx9913c9111zFr1iyeeOIJvF4vl1xyCQCBQIArrriCmTNndrhdTd/G4bTztUsmU1tZR1RcFA6n/ai0tAGpRz6PmjKixXpEhLzTR9J/aAb+Bj8xCdFExXbcF09JtZefvLSeZdvLOGtEKr+fPZa02L7t5K2RqVOndrsSgD6oCMBQBp3R8TelrcA0AOvXr+/0djV9k6qyaqrLa0jJTMId4+7Q7uBwmnuiP1He3niQX7y6Ea8/yH3fHM2Vkwe0abral4iOPnYndnfQJxWBRtPTqa/x8MmrxgJxbEI0Z33rjJPK/r3a6+eu1zbzytoixmXF86e5+QxOjelusbqVuLg4amtru1uMZtGKQKPpgfi8PgK+IPEpsdRW1hEMBLE4Tg5FsHJXObcvWk9xtZcffn0YPzh7KHbrySFbdzJp0iRsNhsjRozgyiuvJDExsbtFOoJWBBpNDyQ+JY7hEwdzYFcJY88chd3R/ZY3DYEgD733JY8v28XApCheumkKEwacPJ1dd1FfXw+A0+lkxYoV3SxN82hFoNH0QESE3NOGk3va8O4WBYCCYsMsdOvBauadOoBffSOXaKfuXnoK+pvSaDQnTCikeGr5bn7/TgFxbhtPXH0KM0ZF3l20pnOJmCIQkWzgaaAfEAIeV0otaJLnLOA1YLd56hWl1D2Rkkmj0XQeByo9/OSl9Xy6s5wZuencf9kYUmJajjOgOXmJ5IggANyulFojIrHAahF5Xym1pUm+ZUqpCyIoh0aj6WReW1fE//vvJgIhxf2XjmHupGxtFtqDiZgiUEodBA6axzUishXIBJoqAo1G00Ooqvfzq9c28cb6A0wYkMCf5uYzMPnksIXXnDhdYtMlIjnAeOCzZpKniMh6EVksIs2G1BGRG0RklYisKi0tjaCkHWP+/PmkpaUxevToFvNUVlYye/ZsRo4cSW5u7klrRaDRNGX5jjLOe3gpizce5PZzhrPoxilaCfQSIr5YLCIxwH+A25RS1U2S1wADlVK1IjIL+C8wrGkdSqnHgccBTjnlFNVRmUo8lWypKqLCV0eiI5pR8ZmkuxM6Wm27AtPceuutzJw5k5dffhmfz3fEtEyjOVnx+oP8/p0Cnlq+m8Gp0bxy9emMzer4/0Vz8hDREYGI2DGUwHNKqVeapiulqpVStebx24BdRCIauqvEU8my0m14Qz6SnTF4Qz6WlW6jxFPZ4bqnTZtGUlLL7nSrq6tZunQp1113HQAOh4OEBP2H0py8bD5QxUV//YSnlu/m6ikDeesHU7US6IVETBGIsXL0JLBVKfVQC3n6mfkQkVNNecojJRPAlqoiom1OYmwuLCLE2FxE25xsqSqKZLOAERg7NTWVa6+9lvHjx3P99ddTV9c1cRA0muMhGFL8bclOvvnIcirr/fzr2kncc/Fo3I7Ww1dqOoeysjJmzpzJoEGDGDx4MB9++GFE24vkiOAM4CrgbBFZZ75michNInKTmWc2sElE1gN/Br6lGoOqRogKXx1R1qNN3KKsTip8ke+QA4EAa9as4Xvf+x5r164lOjqa+++/P+LtajTHw/7D9cx7fCUPvLONGbnpvHvbNM4a0f0eMk9WvF4v27dvx+v1dlqdN954I+eddx67d+9my5YtjBs3rtPqbo5IWg19ArRqT6aU+ivw10jJ0ByJjmjqgw3E2L5yg1sfbCDREflFr6ysLLKyspg8eTIAs2fP1opAc9KglOKVNUX85vXNAPzx8nFcNiFTm4W2gtfr5eabb2bHjh0MHTqURx99FJerYy62KyoqWLlyJS+99BIALperw3W2RZ/zBDUqPpO6QAO1AS8hpagNeKkLNDAqPjPibffr14/s7GwKCgoA+PDDDxk1alTE29Vo2qKizsfNz63h9pfWMyojjsW3TmX2xCytBNpg//797Nixg6ysLHbs2MH+/fs7XOe2bdtITk5mzpw55ObmMnfuXKqrm9rZdC59ThGkuxOYmjoSl8VBeUMtLouDqakjO8VqaN68eUyZMoWCggKysrJ48sknAZg1axYHDhwAjKhEV155JWPHjmXdunX84he/6HC7Gk1HWFJwiPMeXsoHW0v42cyRvHDDaWQndU3o1p5OdnY2Q4cOpbCwkKFDh5Kdnd3hOgOBAFu2bOGWW25h69atREdHRzySYZ/0NZTuTuiUjr8p7QlMk5+fz6pVqzq9bY3mePH4gvxu8VaeXrGXYWkxPHXNJEZndl5Qmr6Ay+Xi0UcfZf/+/WRnZ3fKFE5OTg7p6elMnz4dgLlz5/K73/2uw/W2Rp9UBBpNX2djYRW3LVzLztI65p8xiJ/OHIHLri2CTgSXy8WwYcdsfzphsrOzycjIYMOGDYwdO5b33nuPkSNHdlr9zaEVgUbThwgEQ/xtyU4WfLidlBgnz10/mTOGRnTrjuYE+Mtf/sIVV1yBz+dj4MCBPP/88xFtTysCjaaPsLe8jh8tXMeafZVcOK4/9108mvio7g9oozmWKVOmsGnTpi5rTysCjaaXo5Ri4Rf7uefNLVgtwoJv5XNxfuSt5DQ9B60INJpeTFltAz9/ZSPvbylhyuBkHpwzjv4J7u4WS3OSoRWBRtNL+XBrCT/7zwaqPQF+9Y1c5p8xCItF7wvQHItWBBpNL6PeF+DeN7fywuf7GNkvlmevn8zIfnHdLZbmJEYrAo2mF7F2XwU/WriOvYfruXHaYH587nCcNm0WqmmdPrezOJK0FpimoKCA/Pz8I6+4uDgefvjhbpBS0xvxB0P86f0vmf3YCvxBxQvfPY2fz8rVSkDTLvrkiKDGV0qJtwBPsBK3NYF01whiHakdrre1wDQjRoxg3bp1AASDQTIzM7nkkks63KZGs6u0lh8tXMf6wiouHZ/JXRfnEefSZqGa9tPnFEGNr5RdtZ/isEQTZU3EF/Kwq/ZTBsec3mFlMG3aNPbs2dNmvg8//JAhQ4YwcODADrWn6dsopXjus33c99YWnDYrj1wxgW+MzehusTQ9kD43NVTiLcBhicZpjUbEgtMajcMSTYm3oMtkePHFF5k3b16XtafpfRyq8TL/X1/wq/9uYlJOEu/eNk0rgV7Efffdx7Bhwxg6dCj33ntvxNvrc4rAE6zEYTnajtphceMJdjxUZXvw+Xy8/vrrXH755V3Snqb38e7mYmY+vIxPd5Zz14Wj+Pe1p9IvPrL+6jUts3XrVl5++WW2bt3aKfWtWrWKf//736xZs4atW7eyePHiiO8y7nNTQ25rAr6QB6f1q0A0vpAHt7Vr4rAuXryYCRMmkJ6e3iXtaXoPtQ0B7n59My+tLiSvfxwLvpXP0LTY7harT7N161ZuueUW/H4/drudRx55hNzc3A7VuXHjRiZMmEBsrPHdnnHGGSxcuLBZI5TOos+NCNJdI/CF6mgI1qFUiIZgHb5QHemuEV3S/gsvvKCnhTTHzao9hzl/wVL+s6aQW6YP4dWbz9BK4CRg8+bN+P1+srOzCQQCbN68ucN15ufn89lnn1FSUkJNTQ3vv/9+pwS8aY0+pwhiHakMjjkdu8VFfbACu8XVKQvF0HZgmvr6et5//30uvfTSDrel6Rv4AiH+8O425vx9BQCLbpzCHeeNxGHrc3/dk5K8vDzsdjuFhYXYbDby8vI6XOf48eO57bbbmD59OtOnTycvLw+bLbKTN31uaggMZdAZHX9T2hOYpry8vNPb1fROdhyq4baF69hUVM2cU7L49YV5xDj75F/2pCU3N5dHHnmEzZs3k5eX1+FpoUZuu+02brvtNgC+//3vd0rks9aI2K9KRLKBp4F+QAh4XCm1oEkeARYAs4B64Bql1JpIyaTR9ARCIcXTK/bwu8XbiHbaeOzbE5k5ul93i6Vpgdzc3E5TAI0UFRWRmZnJ9u3beeutt/j88887tf6mRPLxIgDcrpRaIyKxwGoReV8ptSUsz/nAMPM1Gfib+a7R9EmKq7zc8fJ6lm0vY/qIVB6YPZa0WG0R1Ne4+OKLqaiowGazsWDBAlJTO38GI5yIKQKl1EHgoHlcIyJbgUwgXBFcDDytlFLAShFJEJEMs6xG06d4a8NBfvHqRnyBEPd9czRXTh6AMWjW9DW6Oq55l0w4ikgOMB74rElSJhC+HF5onjtKEYjIDcANAAMGDIiUmBpNt1Dt9fOb1zbz6toixmXF86e5+QxOjelusTR9iIgrAhGJAf4D3KaUqm6a3EwRdcwJpR4HHgc45ZRTjknXaHoqK3eVc/ui9RRXe7n168P4/tlDsVu1RZCma4moIhARO4YSeE4p9UozWQqB8OXwLOBAJGXSaE4GGgJBHnrvSx5ftouBSVG8fNMUxg9I7G6xNH2USFoNCfAksFUp9VAL2V4Hvi8iL2IsElfp9QFNb2dbcTW3vbiObcU1XDF5AL/6Ri5RDm0Wquk+2vXrE5HvYzzVVxxH3WcAVwEbRWSdee4XwAAApdRjwNsYpqM7MMxHrz2O+jWaHkUopHhq+W5+/04BcW4bT37nFL6eq12NaLqf9j6G9AO+EJE1wFPAu6alT4sopT6h+TWA8DwKuKWdMpz0zJ8/nzfffJO0tLQWnUT96U9/4oknnkBEGDNmDP/85z9xubR5YG/nQKWH2xetZ8WucmbkpnP/ZWNIiXF2t1gaDdBOFxNKqV9h2Po/CVwDbBeR/xORIRGULWKEAsWEPO8Tqn/ReA8Ud0q911xzDe+8806L6UVFRfz5z39m1apVbNq0iWAwyIsvvtgpbWtOXl5bV8R5Dy9lfWElD1w2hn9cPVErAc1JRbvNE8yn92LzFQASgZdF5PcRki0ihALF4PsAlAckxXj3fdApymDatGkkJSW1micQCODxeAgEAtTX19O/f/8Ot6s5Oamq9/ODF9Zy64vrGJYWw+JbpzJ3kt4boDn5aJciEJEfishq4PfAcmCMUup7wETgsgjK1/n4NwJxiCUGEQtiiQHizPORJTMzk5/85CcMGDCAjIwM4uPjOffccyPerqbrWb6jjPMeXsrijQe5/ZzhLLpxCgOTo9suqNEAc+bMISkpiWHDhh05t3PnTiZPnszgwYMZOnQo9913X6e1194RQQpwqVLqPKXUS0opP4BSKgRc0GnSdAWqHCTq6HMSZZyPMBUVFbz22mvs3r2bAwcOUFdXx7PPPhvxdjVdh9cf5J43tnDlE58R5bTyys2n84OvD8Om9wb0SoLBIM8//zx33HEHzz//PMFgsFPqnT9/Pm+88cZR52w2Gw899BC7du1i1apVPPHEE6xZ0zmu2dq7WPwwgIiEz3vUKKX8SqnOCcvTVUgyqHqQsJ2bqt44H2E++OADBg0adMRvyKWXXsqnn37Kt7/97Yi3rYk8mw9UcduL69h+qJbvTBnInefn4nZYu1ssTQRZuHAhjz76KDExMaxYsQIR6ZR4IzNnzqSg4OjwuQMHDjwS5zwhIYGhQ4eyb98+JkyY0OH22vuYsgYoBb4EtpvHu0VkjYhM7LAUXYl9DFCNCtWiVAgVqgWqzfORZcCAAaxcuZL6+nqUUnz44Yed7rVQ0/UEQ4q/LdnJNx9ZTpXHz7/nn8rdF4/WSqAPsHbtWmJiYkhKSiImJqbTntDboqCggM2bN3PmmWd2Sn3tVQTvALOUUilKqWQMr6GLgJuBRztFki7CYusHjhkgblBlxrtjhnG+g7QVmGby5MnMnj2bCRMmMGbMGEKhEDfccEOH29V0H/sP1zPv8ZU88M42ZuSm8+5t0zhzeGQ9RWpOHsaPH09tbS2HDx+mpqamU57O26KqqopLL72UBx54gMTEztmN3t6poVOUUjc1flBKvSci/6eU+rGI9Dg7OIutH3RCx9+U9gSmufvuu7n77rs7vW1N16KU4j9rirjrdSM04YOXj+PSCZnaIqiPMXfuXESENWvWMGHCBObMmRPR9hoaGrjgggu4/PLLufrqqzut3vYqgsMi8jOg0eh9LlAhIlaMoDMaTZ/hcJ2PX766kcWbijk1J4kH54wjOymq7YKaXofVamXevHldEoc8FAoxb948hg8fzl133dWpdbdXEVwB/Ab4r/n5E/OcFYisCtRoTiKWFBzijpc3UFnv487zR/LdqYOxWvQoQNO5XHjhhaxcuZKKigrS09P5+c9/zqhRo3j11VcZNmwYI0eOBODee+/l8ssv73B7bSoC86n/YaVUS6YtOzosRSeglOo1w/I2vHdougGPL8j/vb2VZ1buZXh6DP+6dhJ5/eO7WyxNL6Wp6Wgjkeob2lQESqmgiKSKiEMp5YuIFB3E5XJRXl5OcnJyj1cGSinKy8u1/6GTiA2Fldy2cB27Suu47muDuOO8Ebjs2iJI03to79TQHmC5iLwO1DWebMW9dJeSlZVFYWEhpaWl3S1Kp+ByucjKyupuMfo8gWCIvy3ZyYIPt5Ma6+S56ydzxtCU7hZLo+l02qsIDpgvCxAbOXFODLvdzqBBg7pbDE0vYm95HT9auI41+yq5aFx/7r14NPFR9u4WS6OJCO1SBEqpuwFEJFopVddWfo2mp6KUYuEX+7nnzS3YLMKCb+VzcX5md4ul0USU9gammYLhgjoGGCAi44AblVI3R1I4jaYrKatt4M7/bOSDrSWcPiSZP14+jv4J7u4WS6OJOMfja+g8jNCSKKXWi8i0iEml0XQxH2wp4c5XNlDtDfCrb+Qy/4xBWLRZqKaP0O5AqUqp/U0scjrHzZ5G043UNQS4762tvPD5PnIz4nju+nxG9DvplsE0mojSXkWwX0ROB5SIOIAfAj3L66hG04Q1+yr48cJ17D1cz41nDubH5wzHadNmoZq+R3sVwU3AAiATKATeoxfFGtb0LfzBEH/5aAePfLyDfnEuXvzuaUweHHk35BpNe5kzZw4ffPABycnJbN++/cj5zMxMoqOjsVgs2Gy2FmOjHy/ttRoqA67slBY1mm5kZ2ktP164jvWFVVw6IZO7LsojzqXNQjUnRkVFBffffz+bN28mLy+PO++8s1M8gs6fP59bb72Va6655pi0//3vf2RkZHS4jXDaG6oyVUR+ISKPi8hTja82yjwlIodEpFmVJSJniUiViKwzX78+kQvQaNqDUopnVu7lG39ext7D9Tx65QQempOvlYCmQ9x///2sWLECl8vFihUruP/++zul3pkzZ5KS0nWbF9s7NfQasAz4gPYvEv8L+CvwdCt5limlelaoS02P41CNl5++vIElBaVMG57KH2aPJT1Ou/DQdJzNmzeTlpaGw+EgLS2NzZs3R7zNr3/964gI8+fP5/bbb++UOturCKKUUj87noqVUktFJOe4JdJoOpF3NhXz81c2UO8Lcs/FeVx12sAe749Kc/KQl5fHihUrSEtL49ChQ0yZMiWi7S1fvpycnByKioo4++yzycvLY+bMmR2ut70Ryt4UkVkdbu1YpojIehFZLCJ5LWUSkRtEZJWIrOot/oQ0kaXG6+eOl9Zz07OryUx089YPp3L1lBytBP5/e3ceFtV1PnD8+zIouIMRUVBQA4iouKHGJQtJGpdq3I0mbWqSxiapiUvTX61N2uxNmhZNmk2bvW2icS2hZm1I1Gx1ww0huCQBIQSjLIKgzJzfHzNYRJCRmXEGeD/Pw+PM3HPPfecIIosDBwAAHatJREFU894z99xzlFstXryYESNGUF5ezogRI1i8eLFHj9ejRw/AftF4woQJfP75526p19kewXzgtyJyCjgNCGCMMe1dOPYOINIYc8KRZDYA0bUVNMasAFYAJCQk6BzN6ry2fn2MhavSyC08ybzEKO65JpqW/s6e8yjlvODgYJ544omLcqzi4mJsNhtBQUEUFxfz0Ucfcd9997mlbmcTQQfso4Z6GmMeEpEIwKXL1saY4mqPN4rIcyLSyTFCSakLdqrSxrIPv+KFTw7SLbg1q+8YwZDIjt4OS6kLVtvCNGPGjGHy5MkAWK1Wpk+fzrRp09xyPGcTwbPYl6S8GngIKAHWAkMbemAR6QLkG2OMiAzD/jXVDw2tTzVvWfklLFiVxr7cYm5I6M79E+NoG+D0jfNK+ZS6FqbJzMz0yPGc/UsZbowZLCI7AYwxxx13GNdJRN4ErgI6iUgO9qUuWzj2fwGYDtwpIpXASWCW0aW51AWy2Qyvff41j7+TQZsAf5b/dAhj+nbxdlhKNSrOJoLTjiUrDdjvK6CeReuNMeddzdkY8wz24aVKNch3ReX8es0uNmcd5erYzjwxLZ6QdgHeDkupRsfZRPA0sB7oLCKPYj+bd89VCqUaIGV3Lr9bv5dTlTYendKPG4dF6IggpRrI2Skm/iki24FrsI8YmmyM0Unn1EVXdPI0DyTvY/3OIwzoHsTSmQPoFdLW22Ep1ahdyDTUGUCGB2NR6rw+P/gDv3orjfySChZcG828xCj8LTosVClX6bAK5fMqKq385f2v+NvmQ/S4pA1r7hjBoAjXJ/ZSStlpIlA+LeO7YhasTCPjuxJuGh7B737ch9Yt9ddWKXfSvyjlk2w2w0tbDvPke5m0b9WCl+ckcHVsqLfDUqpJ0i9Ylc85UniSG1/8gkc37ueq3iG8t+ByTQLKZ1mtVo4fP47V6r7Ve2fOnEnHjh2Jjj571p2HHnqIqKgooqOjmThxImVlZW45niYC5TOMMWzYeYSxyzaxJ6eIP02LZ/lPh3BJW703QPmm9PR0pk2bxpQpU5g2bRrp6eluqffWW2895+7iw4cPs3z5cnbt2kVWVhZWq5WXXnrJLcfTRKB8QmHZKe5+cycLVqURE9qOd+Zfwcyh3fXeAOWzrFYrS5YsoaKigrCwMCoqKliyZIlbegZ1LUxjtVopLS3l9OnTnDx5km7durl8LNBEoHzAlqyjjF22mXf3fsevx/TmrV+MIOKS1t4OS6nzKi4uprCw8MzSlMHBwRQWFlJcXFzPng3Ts2dP5s2bR48ePejcuTPt27dnypQpbqlbE4HymvLTVh58ex8/eelL2gRYWH/XKH6ZGIXFT3sByve1b9+eoKAgjh8/DtjXLw4KCqJ9e1dm569bQUEBKSkpHDhwgO+++46ysjKef/55t9StiUB5xd4jRUz86xZe+fRr5ozsQcrdl9O/Wwdvh6WU0ywWC4899hgBAQHk5uYSEBDAY489hsVi8cjxUlJSiIyMJCwsjICAACZPnsxnn33mlrp1+Ki6qKw2w/JNB1n6wVcEt27Ja7cO48qYEG+HpVSDxMXFsXbtWoqLi2nfvr3HkgDYVyfbvn07JSUltGnTho8++ojBgwe7pW5NBOqiyT5WxqK30tj69XHG9+/Co5P7E9zmvLOZK+XzLBbLmesE7lLbwjQLFixg4sSJxMfH4+/vT79+/Vi0aJFbjqeJQHmcMYY123N48O10BEiaOYApg8J1RJBSdahrYZqlS5eydOlStx9PE4HyqGOlp1iybg/v7vuOYT07kjRzAN2CdUSQUr5EE4HymNTM7/m/NbspLDvF4nGx3H55Lx0RpJQP0kSg3O7kKSuPbdzP37/4hpjQtrx2yzDiwjwzpE4p5TpNBMqtdmUXsnBVGoeOlvLz0T25d0xvAlt4biSFUsp1mgiUW1RabTz38UGe/k8WIe0CeOPnwxkZde4t8kop3+OxRCAiLwMTgO+NMf1q2S7AU8B4oAyYY4zZ4al4lOd880MpC1alsfPbQq4fEMbDk/rRoXULb4ellHKSJ3sErwLPAK/XsX0cEO34GQ487/hXNRLGGFZuzebhlHT8/YSnZg1k0sBwb4ellLpAHksExphNItLjPEUmAa8bYwzwhYgEiUhXY0yep2JS7nP0RAWL1+7hw/35jLz0Ev48YwBhQa28HZZSqgG8OddQOJBd7XmO47VziMhcEdkmItsKCgouSnCqbh+m5zNm6SY2ZRVw34/78I/bhmsSUM1Wfn4+aWlp5Ofnu6W+gwcPMnz4cHr16kVUVBSPPPLImW11LVjjKm8mgtoGlJvaChpjVhhjEowxCSEhOi+Nt5RWVPLbdbv5+evb6Nw+kLfnjebnl/fCT+8NUM1USkoKs2fPZtGiRcyePZuUlBSX6/T39ycpKYlDhw6xbds2XnzxRXbssF8+rW3BGnfwZiLIAbpXe94NyPVSLKoeO749zo+f3szKrdn84spebPjlSHp3aeftsJTymvz8fJKSkggODqZLly4EBweTlJTkcs8gMjKSUaNGARAUFERUVBTffvstUPeCNa7yZiJIBm4Wu8uAIr0+4HtOW20kvZ/J9Oc/47TVsPL2y/jtuD4E+Ou9Aap5y8uzf1wFBASc9W/V6+6QmZnJvn37uPLKK91WZ208OXz0TeAqoJOI5AB/AFoAGGNeADZiHzp6APvw0Vs8FYtqmIMFJ1i0Ko1dOUVMHRzOA9f3pX2gDgtVCqBr164AVFRUEBAQQEVFxVmvu6qoqIipU6fyxBNPuH1205o8OWpodj3bDfBLTx1fNZwxhn98+S2P/judwBYWnrtpMOP7u+eXW6mmIjQ0lEWLFpGUlHTmtUWLFhEaGupy3RUVFUyYMIEZM2Zw8803u1xfffTOYnWW74vL+b+1u/k4s4ArYkJ4cno8oe0DvR2WUj5pwoQJDB06lLy8PLp27eqWJGCz2Zg9ezYxMTE88MADrgfpBF2qUp3x7t48xizbxOcHf+DB6/vy2i1DNQkoVY/Q0FAGDhzoliQA8OGHH7J+/Xo2b95MbGwssbGxrF69GrAvWDN69GgOHz5MaGgoy5Ytc8sxtUegKCk/zYNvp7Nmew79wzuw9IaBRHVu6+2wlGqWrrvuOuzfnJ/LE0NHQRNBs7f162MsXJVGbuFJ5iVGcc810bT0146iUs2JJoJm6lSljaUffsULnxyke3BrVt8xgiGRHb0dllLKCzQRNENf5ZewYGUa6XnF3JDQnfsnxtE2QH8VlGqu9K+/GbHZDK9+9jWPv5tB2wB/lv90CGP6dvF2WEopL9NE0Ex8V1TOr9fsYnPWUa6O7cwT0+IJaRfg7bCUUj5AE0EzkLI7l9+t38upShuPTunHjcMisK8LpJRSmgiatKKTp/nDv/ayIS2XAd2DWDpzAL1CdFioUupsmgiaqM8OHuXet3aRX1LBgmujmZcYhb9Fh4Uqpc6lnwxNTEWllUf/nc5NL35JQAsLa+8cyYJrYzQJKOUBNpuN7du3k5KSwvbt27HZbC7Xeb6FaQAqKyvp06cPiYmJLh+rivYImpD9ecUsXJVGxncl/OSyCJaM70PrlvpfrJQn2Gw2kpKSSE5Oxs/PD6vVyqRJk1i0aBF+fg0/8apamGbUqFEUFhYycOBAxo8fz+DBgwF45JFHiI6OpqSkxF1vRXsETYHNZlix6SCTnvmUoydO8cqcoTwyub8mAaU8aOfOnSQnJ9O1a1fCwsIICwsjOTmZnTt3ulTv+RamOXToEO+99x633367y/FXp58UjdyRwpP86q00vjh0jOviQvnj1P5c0laHhSrlaXl5efj5+WGx2Bdpslgs+Pn5eXRhmrvuuosnn3yS4uJitx0DtEfQaBlj2LDzCGOXbWJPThF/mh7P8p8O0SSg1EXStWtXrFYrVqsVAKvVis1m89jCNCtXriQkJITRo0e7pf7qtEfQCBWWneJ3G/by7915JEQGkzRzIBGXtPZ2WEo1K4MGDWLSpElnrhHYbDauv/56Bg0a5HLdtS1Ms2XLFt5//33Cw8OpqKjgxIkTTJ48mQ0bNrh8PKlrulNflZCQYLZt2+btMLxmc1YB967exQ8nTrHwRzHcceWlWPz05jCl3GH37t3Ex8c7Xd5ms7Fz584zC9MMGjTIpQvFVXVOnz6d4OBgXnrppVrLbNy4kSeffJLU1NRat9f2PkRkuzEmobby2iNoJMpPW3n8nQxe/exrojq35aWfDaVfeAdvh6VUs+bn58eQIUPcWmfVwjTR0dHExsYC8PDDDzNjxgy3Hqc6TQSNwN4jRSxYlcaB708wZ2QPFo+LJbCFxdthKaU84HwL01QZP34848ePd9sxPXqxWETGikimiBwQkcW1bL9KRIpEJM3x83tPxtPYWG2GZ1MPMOW5TykpP83rtw7jgev7ahJQSrmVx3oEImIBngV+BOQAW0Uk2RiTXqPoZmPMBE/F0VhlHytj4ao0tn1znB/378qjU/oR1Lqlt8NSSjVBnvxqaBhwwBhzCEBEVgKTgJqJQFVjjGH19hweTN6HnwhLbxjA5IHhOluoUspjPJkIwoHsas9zgOG1lBshIruAXOBeY8w+D8bk0344UcGS9Xt4b18+w3t25C8zB9AtWIeFKqU8y5OJoLZT2JpXQHYAkcaYEyIyHtgARJ9TkchcYC5ARESEu+P0CakZ3/PrNbspPnmaJeNjuW10Lx0WqpS6KDx5sTgH6F7teTfsZ/1nGGOKjTEnHI83Ai1EpFPNiowxK4wxCcaYhJCQEA+GfPGVnarkvg17uOXVrVzSpiX/mjeKuVfovQFKqYvHkz2CrUC0iPQEjgCzgBurFxCRLkC+McaIyDDsiekHD8bkU9KyC1m0Ko3DP5Ry++U9+dV1vXVEkFLqovNYIjDGVIrIPOA9wAK8bIzZJyJ3OLa/AEwH7hSRSuAkMMs0tludG6DSauPZ1IM8/VEWoe0C+OfPhzPy0nM6QkopdVF49IYyx9c9G2u89kK1x88Az3gyBl9z+GgpC1elkZZdyOSBYTw4qR8dWrXwdlhKqQYoLy8nNTWVzMxMevfuTWJiIoGBgS7VefDgQW688UYKCgrw8/Njzpw53HfffezevZuZM2eeKZeTk8NvfvMb7r//flffht5ZfLEYY3jzv9k8nJJOC4vw19mDmDggzNthKaUaqLy8nPnz57N//34sFguVlZVs2LCBp556yqVkcL6FaTIyMgD7KmVdunThhhtucMt70URwERSUVLB47W7+k/E9o6Iu4c8zBtC1Qytvh6WUckFqair79+8nPNx+n48xhvT0dFJTUxk3blyD642MjCQyMhI4e2GaqhXKAN5++20iIiKIiYlx+X2ArkfgcR+k5zN22SY2HzjK7yfE8fdbh2sSUKoJyMzMxGKxnLnZU0Tw9/cnKyvLrceovjBNlTfffNOtk9Bpj8BDSisqeejtdFZtyyaua3venDWQmNB23g5LKeUmvXv3prKyEmPMmR5BZWUl0dHn3ArVIDUXpqlSXl7OBx98QFJSkluOA5oIPGL7N8dZuCqN7ONl3HnVpSy8NoaW/tr5UqopSUxMZMOGDaSnp+Pv709lZSVxcXEkJia6XHdtC9NUWbduHX379qVbt24uH6eKJgI3Om218fR/sng29QBhQa1YNXcEw3p29HZYSikPCAwM5KmnniI1NZWsrCyio6PdMmrIZrMxe/ZsYmJieOCBB87Z/sYbb5w1esgdNBG4yYHvT7BwVRp7jhQxfUg3/jAxjnaBOixUqaYsMDCQcePGuXRxuKbzLUxTUlLCli1beO2119x2PNBE4DJjDH//4hse27ifVi0sPH/TYMb1d8/i1Uqp5ud8C9O0a9eOwsJCtx9TE4EL8ovL+fWa3Wz6qoArY0J4cno8ndu71i1USqmLTRNBA72zJ4/frt9D+WkrD0/qy08ui9Q1A5RSjZImggtUUn6aB5LTWbsjh/huHUiaOZCozm29HZZSSjWYJoIL8N/Dx1i4Ko28opPcc3UUd18TTQuLDgtVSjVumgicUFFpZekHWSzfdJCIjq1ZfcdIhkQG17+jUko1ApoI6vFVfgnzV6axP6+YWUO7c/+EONoEaLMppZoO/USrg81meOWzr3ni3QzaBfjzt5sT+FFcqLfDUkopt9NEUIu8opPcu3oXnx74gWtiO/P4tHhC2gV4OyyllPIIvdJZQ/KuXMYs3cSObwr549T+vPizBE0CSqlaFRQUsHz5cubPn8/y5cspKChwuc6ysjLi4+Pp3bs3UVFRLFy48My2tWvX0rNnTyIiIliyZInLx6qiPQKHopOn+f2/9vKvtFwGdg9i2Q0D6dGpjbfDUkr5qIKCAubOncuxY8do06YNaWlpvPvuu6xYsYKQkJAG1xsYGMjmzZvp0KEDFRUVDB06lI8++ogrrriCBQsW8P7779OzZ08GDBjA9OnTz1qnoKG0RwB8duAoY5dtImV3Hot+FMOaO0ZoElBKnde6des4duwY4eHhBAUFER4ezrFjx1i3bp1L9fr5+dGhQwcATp06RWVlJSLCJ598Qo8ePejTpw+BgYFMmzaNNWvWuOOtNO9EUH7ayiMp6dz44pe0amFh3Z0jueeaaPz13gClVD3S09Np0+bsE8Y2bdqQnp7uct2VlZXExsYSGhrKVVddRWJiItnZ2YSF/W952+7du3PkyBGXjwXNOBHszytm0jOf8uKWw/zksghS7hnNgO5B3g5LKdVIxMXFUVpaetZrpaWlxMXFuVy3v78/GRkZfPvtt2zfvp1t27bVOhGdu6a18WgiEJGxIpIpIgdEZHEt20VEnnZs3y0irn/ZVQ+rzbD8k4NMeuZTjpWd4pVbhvLI5P60bqmXS5RSzps6dSodO3bkyJEjFBYWcuTIETp27MjUqVPddoxOnTpx+eWXn1mjODc398y2mj0EV3js009ELMCzwI+AHGCriCQbY6r3m8YB0Y6f4cDzjn89Iud4Gb96axdfHj7GmL6h/HFqPB3btPTU4ZRSTVhISAgrVqxg3bp1pKenExcXx9SpU126UAyQm5tLy5Yt6dSpE6WlpXz88cfce++9XHHFFRw+fJiMjAx69OjB2rVreeONN9zyXjx5GjwMOGCMOQQgIiuBSUD1RDAJeN3Y+zxfiEiQiHQ1xuS5O5iPM7/n7jd2YjOGP02PZ8aQbjpbqFLKJSEhIfziF79wa53Z2dnMmTMHq9WKMYbJkycza9YsAJYuXcrYsWOxWq3cdNNNDBkyxC3H9GQiCAeyqz3P4dyz/drKhANnJQIRmQvMBYiIiGhQMD07tWFQZDCPTOpHxCWtG1SHUkp52vDhw9m/f3+t22bMmMGMGTPcfkxPXiOo7XS75tUOZ8pgjFlhjEkwxiQ0tNsVeUkbXr91mCYBpZSqwZOJIAfoXu15NyC3AWWUUkp5kCcTwVYgWkR6ikhLYBaQXKNMMnCzY/TQZUCRJ64PKKWUs2w2m7dDcElD4vfYNQJjTKWIzAPeAyzAy8aYfSJyh2P7C8BGYDxwACgDbvFUPEopVR+LxUJBQQEhISH4+TW+26xsNhsFBQVYLJYL2k9qu0nBlyUkJJht27Z5OwylVBN08uRJDh06hNVq9XYoDWaxWOjVqxetWrU663UR2W6MSahtH72LSimlHFq1akXfvn29HcZF1/j6PkoppdxKE4FSSjVzmgiUUqqZa3QXi0WkAPimgbt3Ao66MRxPaiyxapzupXG6l8b5P5HGmFrvyG10icAVIrKtrqvmvqaxxKpxupfG6V4ap3P0qyGllGrmNBEopVQz19wSwQpvB3ABGkusGqd7aZzupXE6oVldI1BKKXWu5tYjUEopVYMmAqWUauaaZCIQkbEikikiB0RkcS3bRUSedmzfLSKDfTTOq0SkSETSHD+/91KcL4vI9yKyt47tvtKe9cXpK+3ZXURSRWS/iOwTkfm1lPF6mzoZp9fbVEQCReS/IrLLEeeDtZTxhfZ0Jk7vtKcxpkn9YJ/y+iDQC2gJ7ALiapQZD7yDfYW0y4AvfTTOq4AUH2jTK4DBwN46tnu9PZ2M01fasysw2PG4HfCVj/6OOhOn19vU0UZtHY9bAF8Cl/lgezoTp1fasyn2CIYBB4wxh4wxp4CVwKQaZSYBrxu7L4AgEenqg3H6BGPMJuDYeYr4Qns6E6dPMMbkGWN2OB6XAPuxr9Vdndfb1Mk4vc7RRiccT1s4fmqOgvGF9nQmTq9oiokgHMiu9jyHc395nSnjac7GMMLRlXxHRHx1flxfaE9n+VR7ikgPYBD2s8PqfKpNzxMn+ECbiohFRNKA74EPjDE+2Z5OxAleaM+mmAikltdqZl1nyniaMzHswD4/yADgr8AGj0fVML7Qns7wqfYUkbbAWmCBMaa45uZadvFKm9YTp0+0qTHGaowZiH3d82Ei0q9GEZ9oTyfi9Ep7NsVEkAN0r/a8G5DbgDKeVm8Mxpjiqq6kMWYj0EJEOl28EJ3mC+1ZL19qTxFpgf3D9Z/GmHW1FPGJNq0vTl9qU0cMhcDHwNgam3yiPavUFae32rMpJoKtQLSI9BSRlsAsILlGmWTgZsdIgsuAImNMnq/FKSJdREQcj4dh///64SLH6QxfaM96+Up7OmJ4CdhvjEmqo5jX29SZOH2hTUUkRESCHI9bAdcCGTWK+UJ71hunt9qzyS1VaYypFJF5wHvYR+a8bIzZJyJ3OLa/AGzEPorgAFAG3OKjcU4H7hSRSuAkMMs4hhZcTCLyJvbRDJ1EJAf4A/YLXT7Tnk7G6RPtCYwCfgrscXxfDLAEiKgWqy+0qTNx+kKbdgVeExEL9g/Ot4wxKb72N+9knF5pT51iQimlmrmm+NWQUkqpC6CJQCmlmjlNBEop1cxpIlBKqWZOE4FSSnmR1DNZ4gXWFSki2x0T1p0ZhVgfTQRKXQDH7JApjsfXSy2zxlYrGyQid1V7HiYiay5GnKpReZVzb4BrqDxgpOPu5eHAYhEJq28nTQRKYZ8D5kL3McYkG2MeP0+RIOCuauVzjTHTGxKfarpqmyxRRC4VkXcdZ/ebRSTWybpOGWMqHE8DcPIzXhOBavJEpIeIZIjIa2Kfi36NiLQWka9F5PcisgWYISLXicjnIrJDRFY75tipWjciw1FuarV654jIM47HoSKyXuyThe0SkZHA48Cljm76k4449jrKB4rIKyKyR0R2ikhitTrXOT4EskTkT47XLSLyqojsdeyz8OK2orrIVgB3G2OGAPcCzzm7o9jXkdiNfZK9J4wx9U6l0eTuLFaqDr2B24wxn4rIy/zvTL3cGDNa7PO5rAOuNcaUishvgEWOD+K/AVdjvyt1VR31Pw18YoyZ4uhdtAUWA/0c3fSqGTyr/BLAGNPfcbb3vojEOLYNxD7TZwWQKSJ/BToD4caYfo66glxsD+WjHCcgI4HVjtkmwH52j4hMBR6qZbcjxpgxAMaYbCDe8ZXQBhFZY4zJP98xNRGo5iLbGPOp4/E/gHscj6s+2C8D4oBPHX98LYHPgVjgsDEmC0BE/gHMraX+q4GbwT7DJFAkIsHniWc09tklMcZkiMg3QFUi+I8xpshxvHQgEtgH9HIkhX8D7zv/1lUj4wcUVp1AVOeY+K+2SQrPYYzJFZF9wOXAea9N6VdDqrmoOZdK1fNSx7+CfX74gY6fOGPMbXXs6w61TYtcpaLaYyvgb4w5DgzAPmPlL4EXPRCT8gGOqb4Pi8gMOLPM5gBn9hWRbo4J7XCciIwCMuvbTxOBai4iRGSE4/FsYEuN7V8Ao0QkCsBxDSEG++yQPUXk0mr71uY/wJ2OfS0i0h4owb7EY202ATc5ysdgn8itzj9Yx1dXfsaYtcD92JfkVE2AY7LEz4HeIpIjIrdh/924TUR2Ye8NOrt6YR/gS8d+nwB/NsbsqW8n/WpINRf7gZ+JyHIgC3geuLtqozGmQETmAG+KSIDj5fuMMV+JyFzg3yJyFHsCqbmYCMB8YIXjj9gK3GmM+VxEPnVcIH4HeLZa+eeAF0RkD1AJzDHGVFT7TrimcOAVEak6efvthTaA8k3GmLpOLi54SKkx5gMg/kL309lHVZPnuEibUnWhVSl1Nv1qSCmlmjntESilVDOnPQKllGrmNBEopVQzp4lAKaWaOU0ESinVzGkiUEqpZu7/AeR3ZvKx85tRAAAAAElFTkSuQmCC\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=15000, lr=.1, rmsg=65536, mpred=['energy'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 10 12 14 16 20 24 28 30]\n", + "SYS ebbrt_tuned\n", + "MSE_loss_time=6.258170179356339e-10 loss_time=25.01634 us max_time=-11.112141609191895 alpha=-0.20667457580566406 gamma=0.20616579055786133 delta=0.14654159545898438\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([49])) that is different to the input size (torch.Size([1, 49])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=3.8352846966180634e-11 loss_time=6.19297 us max_time=-10.703995704650879 alpha=-0.4967239797115326 gamma=0.32468709349632263 delta=0.23750276863574982\n", + "MSE_loss_time=3.739892863921175e-11 loss_time=6.11547 us max_time=-10.730485916137695 alpha=-0.5888569355010986 gamma=0.3261134624481201 delta=0.22593876719474792\n", + "MSE_loss_time=3.6967499875839745e-11 loss_time=6.08009 us max_time=-10.750848770141602 alpha=-0.6498305797576904 gamma=0.3184581995010376 delta=0.23383992910385132\n", + "MSE_loss_time=3.6707352629978575e-11 loss_time=6.05866 us max_time=-10.767232894897461 alpha=-0.6932665705680847 gamma=0.3067343533039093 delta=0.25083649158477783\n", + "MSE_loss_time=3.651504617565465e-11 loss_time=6.04277 us max_time=-10.781044006347656 alpha=-0.7266898155212402 gamma=0.29346156120300293 delta=0.2716030478477478\n", + "MSE_loss_time=3.63587941477605e-11 loss_time=6.02983 us max_time=-10.793082237243652 alpha=-0.7542036175727844 gamma=0.2799374759197235 delta=0.2933439016342163\n", + "MSE_loss_time=3.6227245503133124e-11 loss_time=6.01891 us max_time=-10.803948402404785 alpha=-0.7780500650405884 gamma=0.2668508291244507 delta=0.31466519832611084\n", + "MSE_loss_time=3.6115336238321904e-11 loss_time=6.0096 us max_time=-10.813849449157715 alpha=-0.7994301319122314 gamma=0.2544863820075989 delta=0.3348894417285919\n", + "MSE_loss_time=3.6019766740820316e-11 loss_time=6.00165 us max_time=-10.823001861572266 alpha=-0.8189905881881714 gamma=0.2429746687412262 delta=0.3537555932998657\n", + "yvalue torch.Size([49])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=10000, lr=.1, rmsg=65536, mpred=['time'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40]\n", + "SYS linux_tuned\n", + "MSE_loss_energy=18.681472224736677 alpha=-0.0990293025970459 J\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([680])) that is different to the input size (torch.Size([1, 680])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_energy=3.349001683366263e-07 alpha=5.4724056099075824e-05 J\n", + "MSE_loss_energy=3.3489944894610644e-07 alpha=5.466543007059954e-05 J\n", + "MSE_loss_energy=3.3489944894458e-07 alpha=5.466537913889624e-05 J\n", + "MSE_loss_energy=3.3489944894409114e-07 alpha=5.4665350035065785e-05 J\n", + "MSE_loss_energy=3.34899448943987e-07 alpha=5.466534275910817e-05 J\n", + "MSE_loss_energy=3.348994489439332e-07 alpha=5.466533184517175e-05 J\n", + "MSE_loss_energy=3.3489944894389087e-07 alpha=5.466532820719294e-05 J\n", + "MSE_loss_energy=1.0592082571550955e-06 alpha=2.1330277377273887e-05 J\n", + "MSE_loss_energy=3.440480151089373e-07 alpha=5.838388460688293e-05 J\n", + "MSE_loss_energy=3.349623944952624e-07 alpha=5.497341408045031e-05 J\n", + "MSE_loss_energy=7.164122409006158e-07 alpha=3.072250183322467e-05 J\n", + "MSE_loss_energy=4.36903829727795e-06 alpha=-2.3160711862146854e-05 J\n", + "MSE_loss_energy=3.348995113745342e-07 alpha=5.4675008868798614e-05 J\n", + "MSE_loss_energy=3.3489944900058766e-07 alpha=5.466504080686718e-05 J\n", + "MSE_loss_energy=3.349039223646735e-07 alpha=5.474726276588626e-05 J\n", + "MSE_loss_energy=4.061640660713825e-07 alpha=6.500710878754035e-05 J\n", + "MSE_loss_energy=3.348994494259767e-07 alpha=5.466617221827619e-05 J\n", + "MSE_loss_energy=0.0005069936531984957 alpha=-0.0008173314854502678 J\n", + "MSE_loss_energy=3.350801006186595e-07 alpha=5.4144624300533906e-05 J\n", + "MSE_loss_energy=3.348994490512316e-07 alpha=5.466493166750297e-05 J\n", + "MSE_loss_energy=0.00018620293333671006 alpha=0.0005828178254887462 J\n", + "MSE_loss_energy=3.3517760300184166e-07 alpha=5.401921953307465e-05 J\n", + "MSE_loss_energy=3.3489944900273695e-07 alpha=5.466563015943393e-05 J\n", + "MSE_loss_energy=0.0022667512125380484 alpha=0.0018989448435604572 J\n", + "MSE_loss_energy=3.3505803472038723e-07 alpha=5.5153177527245134e-05 J\n", + "MSE_loss_energy=3.348994522280068e-07 alpha=5.4663098126184195e-05 J\n", + "MSE_loss_energy=3.34899449064474e-07 alpha=5.4665750212734565e-05 J\n", + "MSE_loss_energy=3.348994628415158e-07 alpha=5.4660758905811235e-05 J\n", + "MSE_loss_energy=3.348996230872932e-07 alpha=5.464915375341661e-05 J\n", + "yvalue torch.Size([680])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=30000, lr=.01, rmsg=65536, mpred=['energy'], msys=['linux_tuned'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40]\n", + "SYS linux_tuned\n", + "MSE_loss_time=2.607992334315835e-09 loss_time=51.06851 us\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([1020])) that is different to the input size (torch.Size([1, 1020])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=1.4321045374656487e-10 loss_time=11.96706 us\n", + "MSE_loss_time=1.1229138646877116e-10 loss_time=10.59676 us\n", + "MSE_loss_time=9.833010400099115e-11 loss_time=9.91615 us\n", + "MSE_loss_time=9.200676189518122e-11 loss_time=9.59202 us\n", + "MSE_loss_time=8.793716094145902e-11 loss_time=9.37748 us\n", + "MSE_loss_time=8.473511251011704e-11 loss_time=9.20517 us\n", + "MSE_loss_time=8.21104176869666e-11 loss_time=9.06148 us\n", + "MSE_loss_time=7.996481491071716e-11 loss_time=8.9423 us\n", + "MSE_loss_time=7.822415766882217e-11 loss_time=8.84444 us\n", + "MSE_loss_time=7.682671397251105e-11 loss_time=8.76508 us\n", + "MSE_loss_time=7.571198302318803e-11 loss_time=8.70126 us\n", + "MSE_loss_time=7.48307368101092e-11 loss_time=8.65048 us\n", + "MSE_loss_time=7.413629515511132e-11 loss_time=8.61024 us\n", + "MSE_loss_time=7.359265064749525e-11 loss_time=8.57862 us\n", + "MSE_loss_time=7.316875728823207e-11 loss_time=8.55387 us\n", + "MSE_loss_time=7.283833685100317e-11 loss_time=8.53454 us\n", + "MSE_loss_time=7.258367014843486e-11 loss_time=8.51961 us\n", + "MSE_loss_time=7.238630113228117e-11 loss_time=8.50801 us\n", + "MSE_loss_time=7.223392324688401e-11 loss_time=8.49905 us\n", + "MSE_loss_time=7.211650266127755e-11 loss_time=8.49214 us\n", + "MSE_loss_time=7.202590025360469e-11 loss_time=8.48681 us\n", + "MSE_loss_time=7.195628706055052e-11 loss_time=8.48271 us\n", + "MSE_loss_time=7.190357369142107e-11 loss_time=8.4796 us\n", + "MSE_loss_time=7.186187512867872e-11 loss_time=8.47714 us\n", + "MSE_loss_time=7.183138556239405e-11 loss_time=8.47534 us\n", + "MSE_loss_time=7.18069740090079e-11 loss_time=8.4739 us\n", + "MSE_loss_time=7.178859771846951e-11 loss_time=8.47282 us\n", + "MSE_loss_time=7.177498299049814e-11 loss_time=8.47201 us\n", + "MSE_loss_time=7.176399229696884e-11 loss_time=8.47136 us\n", + "yvalue torch.Size([1020])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df_comb = pd.read_csv('/home/handong/jupyter/jupyter-notebooks/nic-tuning-experiments/bayesopt/summary_data/netpipe_combined.csv', sep=' ')\n", + "#df_comb = df_comb[(df_comb['i'] == 1) & (df_comb['rapl'] == 135)]\n", + "df_comb = df_comb[(df_comb['rapl'] == 135)]\n", + "\n", + "df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: dvfs_dict[x])\n", + "df_comb = df_comb[(df_comb['itr']!=1) & (df_comb['dvfs']!=65535)] #filter out linux dynamic\n", + "\n", + "run_energy(df_comb, n_iter=30000, lr=.01, rmsg=8192, mpred=['time'], msys=['linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 10 12 14 16 20 24 28 30]\n", + "SYS ebbrt_tuned\n", + "loss_time=2.57520754163816e-06 max_time=-11.216626167297363 alpha=0.3909003734588623 gamma=0.9856512546539307 delta=0.5502802133560181\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([49])) that is different to the input size (torch.Size([1, 49])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_time=4.969348230600252e-10 max_time=-11.678744316101074 alpha=0.705072283744812 gamma=0.5869538187980652 delta=0.15162181854248047\n", + "loss_time=3.1589909805032055e-10 max_time=-11.555351257324219 alpha=0.6717889904975891 gamma=0.5569562315940857 delta=0.1252904087305069\n", + "loss_time=2.596477904439758e-10 max_time=-11.356971740722656 alpha=0.6064764261245728 gamma=0.545141339302063 delta=0.11924216151237488\n", + "loss_time=1.9554310192149585e-10 max_time=-11.130919456481934 alpha=0.5296098589897156 gamma=0.5335561037063599 delta=0.11535559594631195\n", + "loss_time=1.3196472427766278e-10 max_time=-10.906689643859863 alpha=0.4496612846851349 gamma=0.5181466341018677 delta=0.10872402787208557\n", + "loss_time=8.627199477662976e-11 max_time=-10.721221923828125 alpha=0.37631598114967346 gamma=0.5000279545783997 delta=0.09867574274539948\n", + "loss_time=6.440469141453763e-11 max_time=-10.59963607788086 alpha=0.31640923023223877 gamma=0.483280211687088 delta=0.08744902908802032\n", + "loss_time=5.6820670550361005e-11 max_time=-10.535999298095703 alpha=0.2690556049346924 gamma=0.4716368019580841 delta=0.07784360647201538\n", + "loss_time=5.4216592815039454e-11 max_time=-10.509061813354492 alpha=0.22995366156101227 gamma=0.4655805826187134 delta=0.07032199203968048\n", + "loss_time=5.286372026998652e-11 max_time=-10.50101375579834 alpha=0.195633664727211 gamma=0.4635092616081238 delta=0.06384226679801941\n", + "loss_time=5.1800428024282384e-11 max_time=-10.501510620117188 alpha=0.16407504677772522 gamma=0.46370741724967957 delta=0.05754728242754936\n", + "loss_time=5.083597624839159e-11 max_time=-10.505500793457031 alpha=0.1342552751302719 gamma=0.46502685546875 delta=0.051180534064769745\n", + "loss_time=4.9938671417926936e-11 max_time=-10.510814666748047 alpha=0.10571003705263138 gamma=0.46680256724357605 delta=0.04482342302799225\n", + "loss_time=4.91025316692642e-11 max_time=-10.516536712646484 alpha=0.07820577919483185 gamma=0.4686674177646637 delta=0.03866315633058548\n", + "loss_time=4.832613422323807e-11 max_time=-10.522258758544922 alpha=0.051686327904462814 gamma=0.47043007612228394 delta=0.03281768411397934\n", + "loss_time=4.760580725417142e-11 max_time=-10.52798080444336 alpha=0.02610282227396965 gamma=0.47202613949775696 delta=0.027391334995627403\n", + "loss_time=4.693878430241124e-11 max_time=-10.53353500366211 alpha=0.0014243152691051364 gamma=0.47339460253715515 delta=0.022414973005652428\n", + "loss_time=4.6322023707385866e-11 max_time=-10.538923263549805 alpha=-0.022347141057252884 gamma=0.4745330810546875 delta=0.017890719696879387\n", + "loss_time=4.575165545118472e-11 max_time=-10.544201850891113 alpha=-0.04523705691099167 gamma=0.47545745968818665 delta=0.013821450062096119\n", + "yvalue torch.Size([49])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=20000, lr=0.01, rmsg=65536, mpred=['time'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 10 12 14 18 20 22 24 26 28 30 32 34 36 38 40 16]\n", + "SYS ebbrt_tuned\n", + "loss_time=1.3230961388258921e-08 max_time=-11.893648147583008 alpha=0.6270890235900879 gamma=0.18047595024108887 delta=-0.09962725639343262\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([45])) that is different to the input size (torch.Size([1, 45])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_time=1.8958488275455196e-09 max_time=-11.03542423248291 alpha=0.3844453692436218 gamma=0.24624042212963104 delta=0.8157742619514465\n", + "loss_time=4.0103061025381426e-10 max_time=-9.965204238891602 alpha=0.015366299077868462 gamma=0.09874476492404938 delta=0.8807123303413391\n", + "loss_time=1.2521801763705126e-10 max_time=-9.682907104492188 alpha=-0.24912947416305542 gamma=0.09442934393882751 delta=0.7038712501525879\n", + "loss_time=8.856843543575778e-11 max_time=-9.702617645263672 alpha=-0.4068114459514618 gamma=0.1354701966047287 delta=0.6141543984413147\n", + "loss_time=6.996599469468223e-11 max_time=-9.724239349365234 alpha=-0.5223428010940552 gamma=0.16338804364204407 delta=0.55540931224823\n", + "loss_time=6.025628725733556e-11 max_time=-9.740906715393066 alpha=-0.6078701615333557 gamma=0.18102142214775085 delta=0.5154772996902466\n", + "loss_time=5.5122739095906116e-11 max_time=-9.75363540649414 alpha=-0.6712750792503357 gamma=0.19214054942131042 delta=0.4882328510284424\n", + "loss_time=5.2386910228608144e-11 max_time=-9.763300895690918 alpha=-0.7183417081832886 gamma=0.19906334578990936 delta=0.4697312116622925\n", + "loss_time=5.0919445579404864e-11 max_time=-9.770689964294434 alpha=-0.7533528804779053 gamma=0.2032381147146225 delta=0.4573560655117035\n", + "yvalue torch.Size([45])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/matplotlib/collections.py:988: UserWarning: Collection without array used. Make sure to specify the values to be colormapped via the `c` argument.\n", + " warnings.warn(\"Collection without array used. Make sure to \"\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=10000, lr=0.01, rmsg=524288, mpred=['time'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/analysis/run_mcd.ipynb b/analysis/run_mcd.ipynb new file mode 100644 index 0000000..887fb1d --- /dev/null +++ b/analysis/run_mcd.ipynb @@ -0,0 +1,1802 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('../bayesopt')\n", + "\n", + "import read_agg_data\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.autograd as auto\n", + "import torch.optim as optim\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as plt\n", + "import pandas as pd\n", + "import math\n", + "\n", + "import pdb\n", + "\n", + "dvfs_dict = {\n", + " \"0xc00\" : 1.2,\n", + " \"0xd00\" : 1.3,\n", + " \"0xe00\" : 1.4,\n", + " \"0xf00\" : 1.5,\n", + " \"0x1000\" : 1.6,\n", + " \"0x1100\" : 1.7,\n", + " \"0x1200\" : 1.8,\n", + " \"0x1300\" : 1.9,\n", + " \"0x1400\" : 2.0,\n", + " \"0x1500\" : 2.1,\n", + " \"0x1600\" : 2.2,\n", + " \"0x1700\" : 2.3,\n", + " \"0x1800\" : 2.4,\n", + " \"0x1900\" : 2.5,\n", + " \"0x1a00\" : 2.6,\n", + " \"0x1b00\" : 2.7,\n", + " \"0x1c00\" : 2.8,\n", + " \"0x1d00\" : 2.9,\n", + " \"0xffff\" : 3.0,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3073\n", + "[200000 400000 600000 0]\n", + "Index(['sys', 'i', 'itr', 'dvfs', 'rapl', 'read_5th', 'read_10th', 'read_50th',\n", + " 'read_90th', 'read_95th', 'read_99th', 'measure_QPS', 'target_QPS',\n", + " 'time', 'joules', 'rx_desc', 'rx_bytes', 'tx_desc', 'tx_bytes',\n", + " 'instructions', 'cycles', 'ref_cycles', 'llc_miss', 'c1', 'c1e', 'c3',\n", + " 'c6', 'c7', 'num_interrupts', 'QPS'],\n", + " dtype='object')\n", + "[400000 600000 200000]\n" + ] + } + ], + "source": [ + "#df_comb, _, _ = read_agg_data.start_analysis('mcd') #DATA\n", + "#df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: int(x, base=16))\n", + "\n", + "df_comb = pd.read_csv('/home/handong/jupyter/jupyter-notebooks/nic-tuning-experiments/bayesopt/summary_data/mcd_combined.csv', sep=' ')\n", + "print(df_comb.shape[0])\n", + "df_comb['QPS'] = df_comb['target_QPS']\n", + "\n", + "print(df_comb['QPS'].unique())\n", + "df_comb = df_comb[(df_comb['i'] == 1) & (df_comb['rapl'] == 135)]\n", + "#df_comb = df_comb[(df_comb['rapl'] == 135)]\n", + "df_comb = df_comb[df_comb['read_99th'] <= 500]\n", + "\n", + "df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: dvfs_dict[x])\n", + "df_comb = df_comb[(df_comb['itr']!=1) & (df_comb['dvfs']!=65535)] #filter out linux dynamic\n", + "print(df_comb.columns)\n", + "\n", + "# df_comb['dvfs'] = df_comb['dvfs'].astype(float) / df_comb['dvfs'].min()\n", + "# print(df_comb['dvfs'].unique())\n", + "# df_comb['itr'] = df_comb['itr'].astype(float) / df_comb['itr'].min()\n", + "# print(df_comb['itr'].unique())\n", + "#print(10**6)\n", + "print(df_comb['QPS'].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 50 100 200 300 400 350]\n", + "1675.5\n", + "******* ebbrt_tuned 50 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "174 0.000209 50 1.7 5221529 1.899239e+11 103.8\n", + "262 0.000223 50 1.9 5221089 1.844129e+11 103.4\n", + "351 0.000240 50 2.1 5220371 1.796836e+11 103.3\n", + "440 0.000258 50 2.3 5214101 1.763346e+11 103.9\n", + "526 0.000277 50 2.5 5220459 1.737004e+11 103.8\n", + "616 0.000296 50 2.7 5223052 1.701433e+11 103.0\n", + "706 0.000321 50 2.9 5216163 1.690650e+11 102.8\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 50 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "4 0.000168 50 1.3 6036916 3.025599e+11 104.1\n", + "92 0.000180 50 1.5 6035703 2.803556e+11 104.9\n", + "179 0.000194 50 1.7 6033477 2.668666e+11 105.3\n", + "268 0.000208 50 1.9 6032140 2.624055e+11 105.4\n", + "357 0.000225 50 2.1 6033866 2.551110e+11 105.1\n", + "446 0.000240 50 2.3 6031643 2.471547e+11 105.9\n", + "532 0.000261 50 2.5 6032598 2.480025e+11 106.6\n", + "622 0.000280 50 2.7 6031159 2.420433e+11 105.7\n", + "712 0.000304 50 2.9 6032217 2.407263e+11 106.0\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 50 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "10 0.000171 50 1.3 6206452 3.935791e+11 102.8\n", + "98 0.000185 50 1.5 6205251 3.610531e+11 102.1\n", + "185 0.000199 50 1.7 6204518 3.390249e+11 104.1\n", + "274 0.000215 50 1.9 6204134 3.256803e+11 104.0\n", + "363 0.000232 50 2.1 6203612 3.133541e+11 106.2\n", + "452 0.000250 50 2.3 6202987 3.085547e+11 106.8\n", + "538 0.000270 50 2.5 6202883 3.022569e+11 105.9\n", + "628 0.000292 50 2.7 6202776 2.975200e+11 109.1\n", + "718 0.000316 50 2.9 6201800 2.962684e+11 108.5\n", + "\n", + "2106.63\n", + "******* linux_tuned 50 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1486 0.000213 50 1.3 5218634 4.599466e+11 138.9\n", + "1930 0.000213 50 1.3 5220109 4.599841e+11 138.7\n", + "1548 0.000232 50 1.5 5177475 4.049907e+11 124.2\n", + "2002 0.000232 50 1.5 5176745 4.045315e+11 125.9\n", + "1610 0.000269 50 1.7 5155688 3.624321e+11 117.0\n", + "2075 0.000251 50 1.7 5172205 3.641128e+11 117.0\n", + "2147 0.000291 50 1.9 5153643 3.313907e+11 113.4\n", + "2219 0.000298 50 2.1 5141375 3.035007e+11 108.0\n", + "2291 0.000323 50 2.3 5118452 2.799074e+11 106.0\n", + "2395 0.000353 50 2.5 5094243 2.611035e+11 105.6\n", + "2483 0.000384 50 2.7 5090058 2.452500e+11 105.2\n", + "2571 0.000415 50 2.9 5072154 2.326829e+11 106.1\n", + "\n", + "2400.58\n", + "******* linux_tuned 50 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1934 0.000223 50 1.3 6048093 7.310257e+11 341.0\n", + "2007 0.000225 50 1.5 6039895 6.615798e+11 198.8\n", + "2079 0.000244 50 1.7 6030084 5.970014e+11 151.8\n", + "1675 0.000265 50 1.9 6024476 5.529146e+11 130.2\n", + "2151 0.000264 50 1.9 6024084 5.472257e+11 128.8\n", + "1737 0.000287 50 2.1 6017531 5.003071e+11 118.6\n", + "2223 0.000287 50 2.1 6015240 4.986567e+11 120.1\n", + "2296 0.000310 50 2.3 6012185 4.605418e+11 115.4\n", + "2399 0.000339 50 2.5 6010602 4.298558e+11 111.6\n", + "2487 0.000367 50 2.7 6006830 4.001675e+11 107.7\n", + "2575 0.000400 50 2.9 6005187 3.763980e+11 105.7\n", + "\n", + "2654.84\n", + "******* linux_tuned 50 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2155 0.000283 50 1.9 6190548 7.257716e+11 275.9\n", + "2227 0.000309 50 2.1 6192252 6.796291e+11 194.6\n", + "2316 0.000337 50 2.3 6192616 6.428080e+11 153.5\n", + "2404 0.000365 50 2.5 6191938 5.980526e+11 134.7\n", + "2492 0.000397 50 2.7 6192273 5.626668e+11 124.0\n", + "2580 0.000429 50 2.9 6192620 5.246938e+11 115.5\n", + "\n", + "1675.5\n", + "******* ebbrt_tuned 100 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "15 0.000313 100 1.3 3043321 1.924407e+11 154.5\n", + "104 0.000334 100 1.5 3043262 1.844762e+11 154.3\n", + "191 0.000355 100 1.7 3043290 1.771736e+11 153.6\n", + "280 0.000379 100 1.9 3043897 1.732380e+11 153.4\n", + "369 0.000406 100 2.1 3042843 1.674202e+11 153.3\n", + "457 0.000433 100 2.3 3043840 1.639538e+11 153.5\n", + "544 0.000466 100 2.5 3043557 1.625835e+11 152.7\n", + "634 0.000501 100 2.7 3044127 1.586730e+11 152.8\n", + "724 0.000539 100 2.9 3044273 1.575179e+11 152.2\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 100 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "21 0.000321 100 1.3 3120627 2.911510e+11 154.6\n", + "196 0.000369 100 1.7 3120523 2.644230e+11 154.6\n", + "286 0.000396 100 1.9 3120497 2.553182e+11 154.6\n", + "375 0.000426 100 2.1 3120517 2.490231e+11 154.2\n", + "462 0.000458 100 2.3 3120466 2.429661e+11 154.3\n", + "550 0.000496 100 2.5 3120522 2.435881e+11 153.4\n", + "640 0.000532 100 2.7 3120526 2.366776e+11 154.4\n", + "730 0.000577 100 2.9 3120506 2.346554e+11 154.0\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 100 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "27 0.000336 100 1.3 3124708 3.844774e+11 155.3\n", + "114 0.000362 100 1.5 3124642 3.581078e+11 155.0\n", + "202 0.000391 100 1.7 3124646 3.440160e+11 155.3\n", + "292 0.000419 100 1.9 3124642 3.267733e+11 154.7\n", + "381 0.000451 100 2.1 3124631 3.169717e+11 155.9\n", + "467 0.000485 100 2.3 3124653 3.116417e+11 156.0\n", + "556 0.000525 100 2.5 3124663 3.072970e+11 154.8\n", + "646 0.000566 100 2.7 3124617 3.016410e+11 154.6\n", + "736 0.000615 100 2.9 3124608 3.011797e+11 155.6\n", + "\n", + "2106.63\n", + "******* linux_tuned 100 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1942 0.000362 100 1.3 3016621 4.188141e+11 184.6\n", + "2015 0.000392 100 1.5 3015677 3.737492e+11 174.9\n", + "2087 0.000423 100 1.7 3018319 3.377049e+11 170.9\n", + "2159 0.000457 100 1.9 3017132 3.094803e+11 169.5\n", + "2231 0.000497 100 2.1 3017215 2.865768e+11 168.7\n", + "2335 0.000536 100 2.3 3009006 2.681600e+11 169.4\n", + "2423 0.000583 100 2.5 3007258 2.543001e+11 170.2\n", + "2511 0.000633 100 2.7 2997316 2.437554e+11 171.3\n", + "2599 0.000685 100 2.9 2994394 2.349044e+11 172.5\n", + "\n", + "2400.58\n", + "******* linux_tuned 100 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1946 0.000394 100 1.3 3115897 6.878025e+11 295.9\n", + "2019 0.000427 100 1.5 3116209 6.142148e+11 225.1\n", + "2091 0.000459 100 1.7 3116996 5.384308e+11 188.5\n", + "2163 0.000496 100 1.9 3117158 4.852459e+11 180.0\n", + "2235 0.000539 100 2.1 3117256 4.445204e+11 171.5\n", + "2339 0.000582 100 2.3 3117344 4.099733e+11 169.2\n", + "2427 0.000632 100 2.5 3117357 3.816575e+11 166.7\n", + "2515 0.000686 100 2.7 3117286 3.580680e+11 165.6\n", + "2603 0.000745 100 2.9 3117360 3.378497e+11 164.7\n", + "\n", + "2654.84\n", + "******* linux_tuned 100 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2095 0.000503 100 1.7 3123123 7.270530e+11 392.3\n", + "2167 0.000548 100 1.9 3123803 6.772242e+11 258.6\n", + "2239 0.000595 100 2.1 3124066 6.221400e+11 206.6\n", + "2343 0.000644 100 2.3 3124129 5.734289e+11 188.8\n", + "2431 0.000697 100 2.5 3124197 5.262300e+11 177.7\n", + "2519 0.000751 100 2.7 3124214 4.819835e+11 170.4\n", + "2607 0.000815 100 2.9 3124318 4.537178e+11 167.7\n", + "\n", + "1675.5\n", + "******* ebbrt_tuned 200 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "33 0.000606 200 1.3 1561019 1.734411e+11 249.6\n", + "120 0.000642 200 1.5 1561047 1.644802e+11 250.1\n", + "208 0.000682 200 1.7 1560999 1.602762e+11 249.6\n", + "298 0.000722 200 1.9 1561030 1.547365e+11 249.7\n", + "386 0.000769 200 2.1 1561047 1.485075e+11 249.3\n", + "473 0.000822 200 2.3 1561003 1.465697e+11 249.7\n", + "562 0.000878 200 2.5 1560969 1.443002e+11 249.1\n", + "652 0.000935 200 2.7 1561051 1.411333e+11 249.5\n", + "742 0.000999 200 2.9 1561046 1.390771e+11 249.1\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 200 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "39 0.000635 200 1.3 1562466 2.697584e+11 249.7\n", + "126 0.000673 200 1.5 1562481 2.621141e+11 262.6\n", + "214 0.000726 200 1.7 1562444 2.439520e+11 248.8\n", + "304 0.000757 200 1.9 1562470 2.284070e+11 250.3\n", + "392 0.000801 200 2.1 1562464 2.331850e+11 257.3\n", + "479 0.000853 200 2.3 1562467 2.306333e+11 262.2\n", + "568 0.000916 200 2.5 1562460 2.317443e+11 255.8\n", + "658 0.001002 200 2.7 1562459 2.183752e+11 249.9\n", + "748 0.001074 200 2.9 1562468 2.143033e+11 248.7\n", + "\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1962.71\n", + "******* ebbrt_tuned 200 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "45 0.000664 200 1.3 1562473 3.654273e+11 274.8\n", + "132 0.000703 200 1.5 1562471 3.502044e+11 270.5\n", + "220 0.000748 200 1.7 1562468 3.326593e+11 265.0\n", + "310 0.000806 200 1.9 1562468 3.219903e+11 258.4\n", + "398 0.000840 200 2.1 1562477 3.065463e+11 265.7\n", + "485 0.000899 200 2.3 1562462 3.100330e+11 270.5\n", + "574 0.000964 200 2.5 1562458 3.063907e+11 264.2\n", + "664 0.001053 200 2.7 1562463 3.064447e+11 258.5\n", + "754 0.001091 200 2.9 1562469 3.003311e+11 268.5\n", + "\n", + "2106.63\n", + "******* linux_tuned 200 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1954 0.000688 200 1.3 1559224 3.796443e+11 296.9\n", + "2027 0.000741 200 1.5 1559379 3.409071e+11 291.8\n", + "2099 0.000798 200 1.7 1559190 3.099416e+11 285.2\n", + "2171 0.000858 200 1.9 1559272 2.868034e+11 276.6\n", + "2243 0.000930 200 2.1 1559222 2.660824e+11 276.5\n", + "2347 0.001003 200 2.3 1558627 2.507451e+11 275.3\n", + "2435 0.001085 200 2.5 1557789 2.370932e+11 276.3\n", + "2523 0.001177 200 2.7 1556529 2.285748e+11 276.4\n", + "2611 0.001270 200 2.9 1557824 2.189206e+11 276.0\n", + "\n", + "2400.58\n", + "******* linux_tuned 200 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1958 0.000764 200 1.3 1562641 6.153520e+11 342.5\n", + "2031 0.000823 200 1.5 1562632 5.384820e+11 317.0\n", + "2103 0.000887 200 1.7 1562706 4.786562e+11 302.6\n", + "2175 0.000960 200 1.9 1562695 4.367089e+11 290.2\n", + "2247 0.001038 200 2.1 1562708 4.033413e+11 293.7\n", + "2351 0.001123 200 2.3 1562699 3.782067e+11 285.2\n", + "2439 0.001218 200 2.5 1562731 3.527546e+11 283.0\n", + "2527 0.001304 200 2.7 1561956 3.340450e+11 288.4\n", + "2615 0.001429 200 2.9 1562750 3.128069e+11 275.0\n", + "\n", + "2654.84\n", + "******* linux_tuned 200 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2107 0.000979 200 1.7 1562786 6.640448e+11 397.4\n", + "2179 0.001064 200 1.9 1562795 6.200477e+11 331.6\n", + "2251 0.001146 200 2.1 1562810 5.502158e+11 310.7\n", + "2355 0.001239 200 2.3 1562805 5.177743e+11 298.8\n", + "2443 0.001347 200 2.5 1562804 4.783754e+11 291.3\n", + "2531 0.001455 200 2.7 1562809 4.431551e+11 282.7\n", + "2619 0.001551 200 2.9 1562781 4.175727e+11 295.9\n", + "\n", + "1675.5\n", + "******* ebbrt_tuned 300 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "51 0.000903 300 1.3 1041618 1.673578e+11 357.1\n", + "138 0.000959 300 1.5 1041598 1.654854e+11 355.4\n", + "226 0.001013 300 1.7 1041607 1.571900e+11 355.5\n", + "316 0.001066 300 1.9 1041613 1.512917e+11 356.8\n", + "404 0.001137 300 2.1 1041609 1.448051e+11 354.9\n", + "491 0.001208 300 2.3 1041611 1.450336e+11 356.1\n", + "580 0.001280 300 2.5 1041616 1.400301e+11 354.9\n", + "670 0.001376 300 2.7 1041523 1.382381e+11 356.1\n", + "760 0.001471 300 2.9 1041607 1.402800e+11 354.3\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 300 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "57 0.000943 300 1.3 1041656 2.507369e+11 366.4\n", + "144 0.000997 300 1.5 1041646 2.352696e+11 362.2\n", + "232 0.001062 300 1.7 1041654 2.267574e+11 360.1\n", + "322 0.001134 300 1.9 1041659 2.217302e+11 350.5\n", + "410 0.001187 300 2.1 1041649 2.224460e+11 356.6\n", + "497 0.001251 300 2.3 1041640 1.928122e+11 360.4\n", + "586 0.001363 300 2.5 1041646 2.028735e+11 350.5\n", + "676 0.001420 300 2.7 1041653 1.985223e+11 356.5\n", + "766 0.001577 300 2.9 1041656 2.066888e+11 351.4\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 300 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "63 0.000984 300 1.3 1041651 3.409537e+11 383.5\n", + "150 0.001049 300 1.5 1041649 3.249638e+11 360.1\n", + "238 0.001106 300 1.7 1041643 3.014450e+11 379.0\n", + "327 0.001172 300 1.9 1041648 2.892195e+11 376.4\n", + "416 0.001264 300 2.1 1041644 2.675962e+11 361.3\n", + "503 0.001333 300 2.3 1041655 2.728385e+11 360.5\n", + "592 0.001418 300 2.5 1041645 2.714869e+11 364.0\n", + "682 0.001550 300 2.7 1041664 2.711051e+11 354.4\n", + "772 0.001618 300 2.9 1041651 2.721008e+11 364.3\n", + "\n", + "2106.63\n", + "******* linux_tuned 300 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1966 0.001021 300 1.3 1041592 3.691559e+11 401.6\n", + "2039 0.001096 300 1.5 1041574 3.273570e+11 396.1\n", + "2111 0.001178 300 1.7 1041542 2.987906e+11 391.8\n", + "2183 0.001265 300 1.9 1041462 2.748956e+11 388.5\n", + "2255 0.001367 300 2.1 1041416 2.579214e+11 388.2\n", + "2359 0.001476 300 2.3 1041232 2.460202e+11 386.5\n", + "2447 0.001586 300 2.5 1040942 2.279152e+11 387.2\n", + "2535 0.001723 300 2.7 1040159 2.231041e+11 385.9\n", + "2623 0.001862 300 2.9 1040332 2.150551e+11 386.0\n", + "\n", + "2400.58\n", + "******* linux_tuned 300 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1970 0.001129 300 1.3 1041887 5.864127e+11 458.6\n", + "2043 0.001210 300 1.5 1041884 5.087747e+11 435.5\n", + "2115 0.001293 300 1.7 1041891 4.531473e+11 429.7\n", + "2187 0.001403 300 1.9 1041876 4.226638e+11 404.3\n", + "2259 0.001530 300 2.1 1041883 3.868485e+11 395.6\n", + "2363 0.001637 300 2.3 1041857 3.606601e+11 394.9\n", + "2451 0.001792 300 2.5 1041901 3.443151e+11 390.5\n", + "2539 0.001937 300 2.7 1041884 3.218515e+11 384.0\n", + "2627 0.002091 300 2.9 1041881 3.097599e+11 391.8\n", + "\n", + "2654.84\n", + "******* linux_tuned 300 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2119 0.001421 300 1.7 1041838 6.089765e+11 480.6\n", + "2191 0.001507 300 1.9 1041851 5.559836e+11 469.4\n", + "2263 0.001621 300 2.1 1041855 5.121567e+11 441.5\n", + "2367 0.001745 300 2.3 1041904 4.860125e+11 433.8\n", + "2455 0.001912 300 2.5 1041789 4.524298e+11 417.5\n", + "2543 0.002068 300 2.7 1041882 4.266260e+11 405.5\n", + "2631 0.002174 300 2.9 1041872 4.091905e+11 405.0\n", + "\n", + "1675.5\n", + "******* ebbrt_tuned 350 200000\n", + "Empty DataFrame\n", + "Columns: [joules_per_interrupt, itr, dvfs, num_interrupts, ref_cycles, read_99th]\n", + "Index: []\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 350 400000\n", + "Empty DataFrame\n", + "Columns: [joules_per_interrupt, itr, dvfs, num_interrupts, ref_cycles, read_99th]\n", + "Index: []\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 350 600000\n", + "Empty DataFrame\n", + "Columns: [joules_per_interrupt, itr, dvfs, num_interrupts, ref_cycles, read_99th]\n", + "Index: []\n", + "\n", + "2106.63\n", + "******* linux_tuned 350 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1978 0.001184 350 1.3 892879 3.570502e+11 449.6\n", + "2051 0.001275 350 1.5 892890 3.224655e+11 443.8\n", + "2123 0.001368 350 1.7 892913 2.880056e+11 441.6\n", + "2195 0.001468 350 1.9 892689 2.704350e+11 439.8\n", + "2267 0.001585 350 2.1 892793 2.461656e+11 437.7\n", + "2371 0.001702 350 2.3 892838 2.379652e+11 437.0\n", + "2459 0.001831 350 2.5 892558 2.210142e+11 436.8\n", + "2547 0.001982 350 2.7 890542 2.140299e+11 437.2\n", + "2635 0.002131 350 2.9 892014 2.075557e+11 437.6\n", + "\n", + "2400.58\n", + "******* linux_tuned 350 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2055 0.001391 350 1.5 893011 4.871073e+11 494.6\n", + "2127 0.001507 350 1.7 893052 4.437976e+11 478.3\n", + "2199 0.001604 350 1.9 892856 4.091595e+11 477.3\n", + "2271 0.001721 350 2.1 892963 3.782915e+11 474.3\n", + "2375 0.001856 350 2.3 893001 3.538089e+11 453.2\n", + "2463 0.002023 350 2.5 893014 3.372406e+11 453.5\n", + "2551 0.002255 350 2.7 893050 3.162472e+11 433.7\n", + "2639 0.002437 350 2.9 893040 2.952957e+11 433.2\n", + "\n", + "2654.84\n", + "******* linux_tuned 350 600000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2203 0.001780 350 1.9 893015 5.455264e+11 491.1\n", + "2275 0.001926 350 2.1 893030 5.028107e+11 481.3\n", + "2379 0.002068 350 2.3 893027 4.667815e+11 471.4\n", + "2467 0.002237 350 2.5 893023 4.478798e+11 469.7\n", + "2555 0.002428 350 2.7 893039 4.174783e+11 457.4\n", + "2643 0.002644 350 2.9 893024 4.042622e+11 447.9\n", + "\n", + "1675.5\n", + "******* ebbrt_tuned 400 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "69 0.001201 400 1.3 781239 1.599319e+11 443.6\n", + "156 0.001265 400 1.5 781235 1.412006e+11 444.4\n", + "244 0.001342 400 1.7 781243 1.463764e+11 443.6\n", + "333 0.001420 400 1.9 781241 1.442308e+11 444.2\n", + "422 0.001488 400 2.1 781238 1.349614e+11 444.9\n", + "509 0.001570 400 2.3 781237 1.250480e+11 444.1\n", + "598 0.001681 400 2.5 781241 1.333756e+11 443.1\n", + "688 0.001812 400 2.7 781240 1.349334e+11 443.8\n", + "778 0.001923 400 2.9 781245 1.292604e+11 442.8\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 400 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "75 0.001267 400 1.3 781240 2.735703e+11 444.2\n", + "162 0.001346 400 1.5 781248 2.611021e+11 444.6\n", + "250 0.001425 400 1.7 781247 2.500339e+11 445.5\n", + "339 0.001516 400 1.9 781247 2.397122e+11 441.9\n", + "428 0.001607 400 2.1 781241 2.396535e+11 443.3\n", + "515 0.001677 400 2.3 781238 2.176450e+11 457.4\n", + "604 0.001780 400 2.5 781236 2.123568e+11 445.6\n", + "694 0.001960 400 2.7 781235 2.193248e+11 443.5\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 400 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "81 0.001323 400 1.3 781240 3.617910e+11 474.5\n", + "168 0.001417 400 1.5 781237 3.475433e+11 471.2\n", + "256 0.001498 400 1.7 781241 3.135289e+11 458.1\n", + "345 0.001624 400 1.9 781248 3.191303e+11 440.6\n", + "434 0.001689 400 2.1 781240 2.783233e+11 469.3\n", + "520 0.001843 400 2.3 781246 3.045776e+11 445.7\n", + "610 0.001945 400 2.5 781247 2.601970e+11 458.4\n", + "700 0.002096 400 2.7 781242 2.692949e+11 452.3\n", + "789 0.002208 400 2.9 781235 2.763436e+11 474.8\n", + "\n", + "2106.63\n", + "******* linux_tuned 400 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2063 0.001450 400 1.5 781318 3.150755e+11 493.1\n", + "2135 0.001542 400 1.7 781360 2.784672e+11 491.5\n", + "2207 0.001669 400 1.9 781324 2.666548e+11 488.1\n", + "2279 0.001799 400 2.1 780971 2.503521e+11 487.4\n", + "2383 0.001941 400 2.3 781330 2.320813e+11 485.4\n", + "2471 0.002103 400 2.5 781220 2.191376e+11 485.2\n", + "2559 0.002254 400 2.7 780778 2.117097e+11 485.7\n", + "2647 0.002446 400 2.9 779923 2.031581e+11 484.5\n", + "\n", + "2400.58\n", + "******* linux_tuned 400 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2211 0.001886 400 1.9 781397 4.142691e+11 488.5\n", + "2387 0.002144 400 2.3 781376 3.511999e+11 500.0\n", + "2475 0.002367 400 2.5 781409 3.312238e+11 488.0\n", + "2563 0.002517 400 2.7 781419 3.193373e+11 489.3\n", + "2651 0.002699 400 2.9 781391 2.899172e+11 491.6\n", + "\n", + "2654.84\n", + "******* linux_tuned 400 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2391 0.002447 400 2.3 781414 4.945755e+11 478.6\n", + "\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(df_comb['itr'].unique())\n", + "for itr in [50, 100, 200, 300, 350, 400]:\n", + " for sys in ['ebbrt_tuned', 'linux_tuned']:\n", + " for qps in [200000, 400000, 600000]:\n", + " df = df_comb[(df_comb['sys']==sys) & (df_comb['QPS'] == qps)].copy()\n", + " #print(df.shape[0])\n", + " print(df['joules'].max())\n", + " df['joules_per_interrupt'] = df['joules']/df['num_interrupts']\n", + " df = df[['joules_per_interrupt','itr', 'dvfs', 'num_interrupts', 'ref_cycles', 'read_99th']]\n", + " #print(df.shape[0])\n", + " #print('')\n", + " \n", + " dfi = df[df['itr']==itr]\n", + " #dfi = dfi.drop_duplicates(subset = [\"itr\", \"dvfs\"])\n", + " #dfi['joules_mean'] = dfi['joules_mean']/dfi['joules_mean'].max()\n", + " #print(dfi.diff())\n", + " print('*******', sys, itr, qps)\n", + " print(dfi.sort_values(by=['dvfs']))\n", + " #print(dfi.sort_values(by=['dvfs']).diff())\n", + " print('')\n", + " plt.plot(dfi['dvfs'], dfi['joules_per_interrupt'])\n", + " #print(dfi)" + ] + }, + { + "cell_type": "code", + "execution_count": 135, + "metadata": {}, + "outputs": [], + "source": [ + "def inference(d, n_iter, lr, workload, sys, print_freq=10):\n", + " # p_busy_min = 20\n", + " p_static = {\n", + " 'c1':1.5, \n", + " 'c3':0.5,\n", + " 'c4':0.25,\n", + " 'c7':34, # 34 Watts\n", + " 'busy': 10\n", + " }\n", + " chosen_sleep = 'c7'\n", + "\n", + " p_q = p_static[chosen_sleep]/10**6 # joules/us idle\n", + " # p_detect = p_static[chosen_sleep]\n", + "\n", + " #starts randomly\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(50, 100), requires_grad=True)\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " \n", + " #p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " #p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " #p_q = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " #p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " phi = torch.tensor(torch.Tensor(1,1).uniform_(0.98, 1.0), requires_grad=True)\n", + " \n", + " #AA = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " #BB = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " \n", + " \n", + " #df[['joules','itr', 'dvfs', 'QPS', read_99th, 'num_interrupts']]\n", + " qps = d[:,3]\n", + " ninterrupts = d[:,5]\n", + " energy = (d[:,0]/ninterrupts).log() ## joules/num_interrupts\n", + " #energy = (d[:,0]/(qps).log()\n", + " itr = d[:,1]\n", + " dvfs = d[:,2]\n", + " time = d[:,4]\n", + " \n", + " #interarrival_time = (1/qps)*10**6\n", + "\n", + " current_loss_time = -100\n", + " fixed_zeta = -100\n", + " fixed_alpha = -100\n", + " fixed_phi = -100\n", + " \n", + " criterion = nn.MSELoss()\n", + " optimizer_time = optim.Adam([zeta, alpha, phi], lr=lr)\n", + " optimizer_energy = optim.Adam([gamma], lr=lr)\n", + " # optimizer = optim.Adam([max_time, alpha, beta, p_detect, p_q], lr=lr)\n", + "\n", + " for i in range(n_iter): \n", + " t_busy = (zeta / dvfs**(1+alpha)) ## as dvfs increases, max_time should get smaller\n", + " pred_time = (phi*itr) + t_busy ## itr_suppress reflects where pkt is in queue\n", + " \n", + " loss_time = criterion(pred_time/time, torch.ones((1,pred_time.shape[1])).double())\n", + " if i % 1000 == 0:\n", + " print(f'MSE_loss_time={loss_time.item()} loss_time={round(math.sqrt(loss_time.item()), 5)} us'\n", + " +f' zeta={zeta.item()} alpha={alpha.item()} phi={phi.item()}')\n", + " \n", + " optimizer_time.zero_grad()\n", + " loss_time.backward(retain_graph=True)\n", + " optimizer_time.step()\n", + "\n", + " if(current_loss_time == -100):\n", + " current_loss_time = loss_time.item()\n", + " else:\n", + " if(current_loss_time >= loss_time.item()):\n", + " current_loss_time = loss_time.item()\n", + " \n", + " zeta.requires_grad = False\n", + " alpha.requires_grad = False\n", + " phi.requires_grad = False\n", + " \n", + " for i in range(n_iter):\n", + " #p_busy = (p_q*dvfs**(2+beta))\n", + " #t_busy_energy = (max_time / dvfs**(1+beta))\n", + " #t_q_energy = itr#(fixed_itr_suppress*itr)\n", + " #t_q_energy = (interarrival_time - (fixed_itr_suppress*itr) - t_busy_energy)\n", + " \n", + " #pred_energy = (p_q * t_q_energy) + (p_busy * t_busy_energy)\n", + " #pred_energy = AA*(fixed_itr_suppress*itr)**gamma + BB*dvfs**beta\n", + " #loss_energy = criterion(pred_energy/energy, torch.ones((1,pred_energy.shape[1])).double())\n", + " #pred_energy = ((fixed_itr_suppress*itr*p_q)*((20*(10**6))/(fixed_itr_suppress*itr))) + (dvfs*beta)\n", + " \n", + " ## sigmetrics'22 equations\n", + " #pred_energy = (gamma*(fixed_itr_suppress*itr))*(dvfs**beta) #+ (AA*(dvfs**beta))\n", + " #pred_energy = gamma+(np.log(fixed_phi)+np.log(itr))+(beta*np.log(dvfs))\n", + " \n", + " t_busy = (zeta / dvfs**(1+alpha)) ## as dvfs increases, max_time should get smaller\n", + " #pred_energy = (gamma*phi*itr) * (t_busy**beta) ## itr_suppress reflects where pkt is in queue\n", + " #pred_energy = gamma+(np.log(phi)+np.log(itr))+(beta*np.log(t_busy))\n", + " pred_energy = gamma+(np.log(phi)+np.log(itr))+np.log(t_busy)\n", + " \n", + " #pred_energy = (*itr + t_busy_energy)*p_q\n", + " loss_energy = criterion(pred_energy, energy)\n", + " #loss_energy = criterion(pred_energy/energy, torch.ones((1,pred_energy.shape[1])).double())\n", + " \n", + " if i % 1000 == 0:\n", + " print(f'loss_energy={loss_energy.item()} loss_energy={math.sqrt(loss_energy.item())}J gamma={gamma.item()} beta={beta.item()}')\n", + " #print(pred_energy)\n", + " \n", + " optimizer_energy.zero_grad()\n", + " loss_energy.backward(retain_graph=True)\n", + " optimizer_energy.step()\n", + " \n", + " return pred_energy, pred_time" + ] + }, + { + "cell_type": "code", + "execution_count": 136, + "metadata": {}, + "outputs": [], + "source": [ + "def run_energy(df_comb, n_iter=2000, lr=1, rqps=400000, rtail='99', msys=['ebbrt_tuned'], mpred=['energy', 'time']): \n", + " df_comb = df_comb[df_comb['QPS'] == rqps]\n", + "\n", + " i=1\n", + " \n", + " for sys in msys:\n", + " df = df_comb[(df_comb['sys']==sys)].copy()\n", + " rt = 'read_'+rtail+'th'\n", + " df = df[['joules','itr', 'dvfs', 'QPS', rt, 'num_interrupts']]\n", + " tnum = df.shape[0]\n", + " d = df.values\n", + " d = torch.tensor(d)\n", + " print('SYS', sys)\n", + " #pred_energy, max_time, alpha, beta, p_detect, p_q = inference_energy(d, n_iter, lr, 'mcd', sys, print_freq=1000)\n", + " pred_energy, pred_time = inference(d, n_iter, lr, 'mcd', sys, print_freq=1000)\n", + " \n", + " df[f'pre_energy lr={lr}'] = pred_energy.view(tnum, 1).detach().numpy()\n", + " df[f'pre_time lr={lr}'] = pred_time.view(tnum, 1).detach().numpy()\n", + " \n", + " for pred_name in mpred:\n", + " if pred_name == 'energy':\n", + " pred = pred_energy\n", + " qps = d[:,3]\n", + " yvalue = (d[:,0]/d[:,5]).log()\n", + " #yvalue = (d[:,0]/(qps*20)).log()\n", + " else:\n", + " pred = pred_time\n", + " yvalue = d[:,4]\n", + "\n", + " #fig, ax = plt.subplots()\n", + " ax = plt.subplot(1, len(msys)*len(mpred), i)\n", + " \n", + " if sys == 'ebbrt_tuned':\n", + " plt.title(f'EbbRT @ {int(rqps/1000)}K QPS', fontsize=20)\n", + " else:\n", + " plt.title(f'Linux @ {int(rqps/1000)}K QPS', fontsize=20)\n", + " \n", + " #plt.title(f'pred:{pred_name} mcd sys={sys} lr={lr} tail={rtail} \\n QPS={rqps} max_time={round(max_time,2)} \\n alpha={round(alpha,2)} beta={round(beta.item(),2)} \\n p_detect={round(p_detect.item(),2)}')\n", + " if pred_name == 'energy':\n", + " plt.ylabel('Measured Energy (J)', fontsize=20)\n", + " plt.xlabel('Predicted Energy (J)', fontsize=20)\n", + " else:\n", + " plt.ylabel('Measured 99% Tail (us)', fontsize=20)\n", + " plt.xlabel('Predicted 99% Tail (us)', fontsize=20)\n", + " \n", + " if pred_name == \"time\":\n", + " tmax = yvalue.max().item()\n", + " plt.plot(np.linspace(0, tmax, 10), np.linspace(0, tmax, 10))\n", + " else:\n", + " tmax = yvalue.max().item()\n", + " tmin = yvalue.min().item()\n", + " print(yvalue.min(), yvalue.max(), tmin, tmax)\n", + " plt.plot(np.linspace(tmin, tmax, 10), np.linspace(tmin, tmax, 10))\n", + " \n", + " print('measurement', yvalue.mean(), yvalue.std())\n", + " \n", + " scatter = ax.scatter(pred.detach().numpy()[0], yvalue, marker = 'o', \n", + " s = d[:,1], c = d[:,2], alpha=0.5)\n", + " \n", + " \n", + " legend1 = ax.legend(*scatter.legend_elements(),loc=\"upper left\", title=\"dvfs\")\n", + " ax.add_artist(legend1)\n", + " handles, labels = scatter.legend_elements(prop=\"sizes\", alpha=0.6)\n", + " legend2 = plt.legend(handles, labels, loc=\"lower right\", title=\"itr\")\n", + " ax.add_artist(legend2)\n", + " \n", + " plt.grid(True)\n", + " plt.tight_layout()\n", + " i += 1\n", + " \n", + " plt.subplots_adjust(wspace=0.3, hspace=0)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 137, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "MSE_loss_time=0.0175951974051566 loss_time=0.13265 us zeta=97.37847900390625 alpha=0.5281484127044678 phi=0.9970217347145081\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(50, 100), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " phi = torch.tensor(torch.Tensor(1,1).uniform_(0.98, 1.0), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=9.505161263635196e-05 loss_time=0.00975 us zeta=55.91223907470703 alpha=-0.9653685092926025 phi=0.9842305779457092\n", + "MSE_loss_time=0.004173763231366338 loss_time=0.0646 us zeta=55.1298713684082 alpha=-0.7211835980415344 phi=1.1045502424240112\n", + "MSE_loss_time=9.505178169265197e-05 loss_time=0.00975 us zeta=55.9028205871582 alpha=-0.9655426144599915 phi=0.9842404723167419\n", + "MSE_loss_time=9.505412505741003e-05 loss_time=0.00975 us zeta=55.87580108642578 alpha=-0.9660513997077942 phi=0.9842823147773743\n", + "MSE_loss_time=9.527049401398247e-05 loss_time=0.00976 us zeta=55.908973693847656 alpha=-0.9659504890441895 phi=0.9847217202186584\n", + "MSE_loss_time=9.603051019607593e-05 loss_time=0.0098 us zeta=55.715545654296875 alpha=-0.9698610305786133 phi=0.9855356216430664\n", + "MSE_loss_time=9.505390700263061e-05 loss_time=0.00975 us zeta=55.8790283203125 alpha=-0.9660152196884155 phi=0.9842856526374817\n", + "MSE_loss_time=9.909119467827093e-05 loss_time=0.00995 us zeta=55.686527252197266 alpha=-0.9707880616188049 phi=0.9816576242446899\n", + "MSE_loss_time=9.508867483118556e-05 loss_time=0.00975 us zeta=55.9111328125 alpha=-0.9656029939651489 phi=0.984432578086853\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([43])) that is different to the input size (torch.Size([1, 43])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=260.42515802081175 loss_energy=16.137693702038458J gamma=-0.2597215175628662 beta=-1.5135533809661865\n", + "loss_energy=0.039838215869334136 loss_energy=0.19959512987378758J gamma=-16.396181106567383 beta=-1.5135533809661865\n", + "loss_energy=0.039838215869334136 loss_energy=0.19959512987378758J gamma=-16.396181106567383 beta=-1.5135533809661865\n", + "loss_energy=0.039838215869334136 loss_energy=0.19959512987378758J gamma=-16.396181106567383 beta=-1.5135533809661865\n", + "loss_energy=0.03983821587199546 loss_energy=0.19959512988045439J gamma=-16.39617919921875 beta=-1.5135533809661865\n", + "loss_energy=0.039838215869334136 loss_energy=0.19959512987378758J gamma=-16.396181106567383 beta=-1.5135533809661865\n", + "loss_energy=0.03983821587199546 loss_energy=0.19959512988045439J gamma=-16.39617919921875 beta=-1.5135533809661865\n", + "loss_energy=0.039838215881932745 loss_energy=0.199595129905348J gamma=-16.396177291870117 beta=-1.5135533809661865\n", + "loss_energy=0.03983823427772767 loss_energy=0.19959517598811768J gamma=-16.396316528320312 beta=-1.5135533809661865\n", + "loss_energy=0.039838215873948764 loss_energy=0.19959512988534756J gamma=-16.396183013916016 beta=-1.5135533809661865\n", + "tensor(-8.4708, dtype=torch.float64) tensor(-6.2539, dtype=torch.float64) -8.470832824802478 -6.253898190426139\n", + "measurement tensor(-7.2562, dtype=torch.float64) tensor(0.6583, dtype=torch.float64)\n", + "measurement tensor(268.5023, dtype=torch.float64) tensor(125.2022, dtype=torch.float64)\n", + "SYS linux_tuned\n", + "MSE_loss_time=0.07139623726646026 loss_time=0.2672 us zeta=65.60674285888672 alpha=0.6171145439147949 phi=0.9825926423072815\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(50, 100), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " phi = torch.tensor(torch.Tensor(1,1).uniform_(0.98, 1.0), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.0007443899282742461 loss_time=0.02728 us zeta=86.7108154296875 alpha=-0.4730145335197449 phi=1.0951101779937744\n", + "MSE_loss_time=0.0007443857964867125 loss_time=0.02728 us zeta=86.71493530273438 alpha=-0.47274622321128845 phi=1.095015525817871\n", + "MSE_loss_time=0.0007703081870007161 loss_time=0.02775 us zeta=86.71686553955078 alpha=-0.47906753420829773 phi=1.1009948253631592\n", + "MSE_loss_time=0.0007443848553616532 loss_time=0.02728 us zeta=86.71080780029297 alpha=-0.4728899598121643 phi=1.0950381755828857\n", + "MSE_loss_time=0.0007445422353535158 loss_time=0.02729 us zeta=86.73098754882812 alpha=-0.47239911556243896 phi=1.095672369003296\n", + "MSE_loss_time=0.0008414099121944376 loss_time=0.02901 us zeta=86.84049987792969 alpha=-0.49194201827049255 phi=1.1036627292633057\n", + "MSE_loss_time=0.0007443848572348704 loss_time=0.02728 us zeta=86.71092987060547 alpha=-0.4728822112083435 phi=1.0950380563735962\n", + "MSE_loss_time=0.0007445699512489281 loss_time=0.02729 us zeta=86.73016357421875 alpha=-0.4736815392971039 phi=1.0953534841537476\n", + "MSE_loss_time=0.0007443851573803043 loss_time=0.02728 us zeta=86.71074676513672 alpha=-0.47286951541900635 phi=1.0950177907943726\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([47])) that is different to the input size (torch.Size([1, 47])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=232.996005016283 loss_energy=15.264206661870213J gamma=-0.8651120662689209 beta=0.5470118522644043\n", + "loss_energy=0.14727514223518617 loss_energy=0.383764435865527J gamma=-16.124494552612305 beta=0.5470118522644043\n", + "loss_energy=0.14727514223518617 loss_energy=0.383764435865527J gamma=-16.124494552612305 beta=0.5470118522644043\n", + "loss_energy=0.14727514223582547 loss_energy=0.3837644358663599J gamma=-16.124492645263672 beta=0.5470118522644043\n", + "loss_energy=0.14727514223582547 loss_energy=0.3837644358663599J gamma=-16.124492645263672 beta=0.5470118522644043\n", + "loss_energy=0.14727514223582547 loss_energy=0.3837644358663599J gamma=-16.124492645263672 beta=0.5470118522644043\n", + "loss_energy=0.14727514223582547 loss_energy=0.3837644358663599J gamma=-16.124492645263672 beta=0.5470118522644043\n", + "loss_energy=0.14727514223518617 loss_energy=0.383764435865527J gamma=-16.124494552612305 beta=0.5470118522644043\n", + "loss_energy=0.1532357344749789 loss_energy=0.3914533618133569J gamma=-16.04728889465332 beta=0.5470118522644043\n", + "loss_energy=0.14798377082875985 loss_energy=0.38468658779421966J gamma=-16.09787368774414 beta=0.5470118522644043\n", + "tensor(-8.4557, dtype=torch.float64) tensor(-6.0134, dtype=torch.float64) -8.455712029851307 -6.013406955163207\n", + "measurement tensor(-7.2106, dtype=torch.float64) tensor(0.7405, dtype=torch.float64)\n", + "measurement tensor(274.5043, dtype=torch.float64) tensor(138.1329, dtype=torch.float64)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = 16, 6\n", + "#plt.rc('xtick', labelsize=20) # fontsize of the tick labels \n", + "df_comb = df_comb[df_comb['itr'] != 350]\n", + "\n", + "run_energy(df_comb, n_iter=10000, lr=1, rqps=200000, rtail='99', \n", + " mpred=['energy', 'time'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "MSE_loss_time=0.06428868199896047 loss_time=0.25355 us zeta=69.71638488769531 alpha=1.7010917663574219 phi=0.9856076836585999\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(50, 100), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " phi = torch.tensor(torch.Tensor(1,1).uniform_(0.98, 1.0), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.006262682373148072 loss_time=0.07914 us zeta=55.326629638671875 alpha=-0.8354910612106323 phi=0.9299457669258118\n", + "MSE_loss_time=0.00023153381538950496 loss_time=0.01522 us zeta=55.731143951416016 alpha=-1.0066064596176147 phi=0.9879273176193237\n", + "MSE_loss_time=0.000818704627994386 loss_time=0.02861 us zeta=55.438114166259766 alpha=-1.0268614292144775 phi=0.9523638486862183\n", + "MSE_loss_time=0.0002389242585516224 loss_time=0.01546 us zeta=55.60151672363281 alpha=-1.0020681619644165 phi=0.9863394498825073\n", + "loss_energy=114685945736773.44 loss_energy=10709152.428496545J gamma=-1.673163652420044 beta=0.811272382736206\n", + "loss_energy=0.9982641954168231 loss_energy=0.999131720753987J gamma=4.333056926727295 beta=-5.19451904296875\n", + "loss_energy=0.9982641954168231 loss_energy=0.999131720753987J gamma=4.333056926727295 beta=-5.19451904296875\n", + "loss_energy=0.9982641954168231 loss_energy=0.999131720753987J gamma=4.333056926727295 beta=-5.19451904296875\n", + "loss_energy=0.9982641954168231 loss_energy=0.999131720753987J gamma=4.333056926727295 beta=-5.19451904296875\n", + "measurement tensor(0.0009, dtype=torch.float64) tensor(0.0005, dtype=torch.float64)\n", + "measurement tensor(261.6047, dtype=torch.float64) tensor(125.9583, dtype=torch.float64)\n", + "SYS linux_tuned\n", + "MSE_loss_time=0.07925624017338471 loss_time=0.28152 us zeta=87.306640625 alpha=0.4364926815032959 phi=0.9997515678405762\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(50, 100), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " phi = torch.tensor(torch.Tensor(1,1).uniform_(0.98, 1.0), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.006352369817046986 loss_time=0.0797 us zeta=235.30235290527344 alpha=0.608741283416748 phi=1.0919305086135864\n", + "MSE_loss_time=0.006340326886862715 loss_time=0.07963 us zeta=242.487060546875 alpha=0.651350200176239 phi=1.0915770530700684\n", + "MSE_loss_time=0.006341289329767649 loss_time=0.07963 us zeta=242.59823608398438 alpha=0.6514240503311157 phi=1.0930256843566895\n", + "MSE_loss_time=0.00639518015542074 loss_time=0.07997 us zeta=242.79283142089844 alpha=0.655550479888916 phi=1.1042712926864624\n", + "loss_energy=1.638241009959204e+17 loss_energy=404751900.5463969J gamma=0.9399478435516357 beta=1.631840467453003\n", + "loss_energy=1.0364653171094214 loss_energy=1.0180694068232388J gamma=-5.065530776977539 beta=-4.373667240142822\n", + "loss_energy=1.0364653171094214 loss_energy=1.0180694068232388J gamma=-5.065530776977539 beta=-4.373667240142822\n", + "loss_energy=1.0364653171094214 loss_energy=1.0180694068232388J gamma=-5.065530776977539 beta=-4.373667240142822\n", + "loss_energy=1.0364653171094214 loss_energy=1.0180694068232388J gamma=-5.065530776977539 beta=-4.373667240142822\n", + "measurement tensor(0.0010, dtype=torch.float64) tensor(0.0007, dtype=torch.float64)\n", + "measurement tensor(283.1814, dtype=torch.float64) tensor(129.9831, dtype=torch.float64)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=5000, lr=1, rqps=400000, rtail='99', \n", + " mpred=['energy', 'time'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "MSE_loss_time=0.17272639873153672 loss_time=0.4156 us zeta=249.36131286621094 alpha=0.16319942474365234 phi=0.28351062536239624\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.0006018540708879737 loss_time=0.02453 us zeta=52.222633361816406 alpha=-1.0374584197998047 phi=1.0343842506408691\n", + "MSE_loss_time=0.0006010455509550724 loss_time=0.02452 us zeta=51.72865295410156 alpha=-1.0478835105895996 phi=1.0350453853607178\n", + "MSE_loss_time=0.0006349301062736475 loss_time=0.0252 us zeta=51.689640045166016 alpha=-1.0512018203735352 phi=1.04254150390625\n", + "MSE_loss_time=0.0006010455733388009 loss_time=0.02452 us zeta=51.728668212890625 alpha=-1.0478899478912354 phi=1.035050630569458\n", + "MSE_loss_time=0.00569797683705437 loss_time=0.07548 us zeta=51.591453552246094 alpha=-1.0982624292373657 phi=1.1221656799316406\n", + "MSE_loss_time=0.0006012386977086421 loss_time=0.02452 us zeta=51.70746994018555 alpha=-1.0500953197479248 phi=1.035141110420227\n", + "MSE_loss_time=0.0006010455676814547 loss_time=0.02452 us zeta=51.72853469848633 alpha=-1.047888159751892 phi=1.0350511074066162\n", + "MSE_loss_time=0.000602646105768946 loss_time=0.02455 us zeta=51.686798095703125 alpha=-1.0486127138137817 phi=1.0368787050247192\n", + "MSE_loss_time=0.0006011207458396684 loss_time=0.02452 us zeta=51.728939056396484 alpha=-1.0481948852539062 phi=1.035345435142517\n", + "MSE_loss_time=0.0006010618018086115 loss_time=0.02452 us zeta=51.72428512573242 alpha=-1.0482245683670044 phi=1.035162329673767\n", + "MSE_loss_time=0.0019638890396602234 loss_time=0.04432 us zeta=51.47254180908203 alpha=-1.0311903953552246 phi=0.989669919013977\n", + "MSE_loss_time=0.0006010455508674764 loss_time=0.02452 us zeta=51.728599548339844 alpha=-1.0478845834732056 phi=1.035045862197876\n", + "MSE_loss_time=0.0006010473331956158 loss_time=0.02452 us zeta=51.72866439819336 alpha=-1.0479329824447632 phi=1.035091757774353\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([45])) that is different to the input size (torch.Size([1, 45])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=140.40116966362092 loss_energy=11.8490999516259J gamma=-0.07656121253967285 beta=-0.6371498107910156\n", + "loss_energy=0.0022611150704443046 loss_energy=0.04755118369130578J gamma=-12.857898712158203 beta=0.6829619407653809\n", + "loss_energy=0.0022611150704446403 loss_energy=0.047551183691309305J gamma=-12.857898712158203 beta=0.6829620003700256\n", + "loss_energy=0.0022611150704446403 loss_energy=0.047551183691309305J gamma=-12.857898712158203 beta=0.6829620003700256\n", + "loss_energy=0.0022611150709574046 loss_energy=0.04755118369670102J gamma=-12.85789966583252 beta=0.68296217918396\n", + "loss_energy=0.0022611150707249985 loss_energy=0.047551183694257274J gamma=-12.85789966583252 beta=0.6829624176025391\n", + "loss_energy=0.0026922616291300957 loss_energy=0.05188700828849256J gamma=-12.845952033996582 beta=0.695052444934845\n", + "loss_energy=0.0022611171988025057 loss_energy=0.04755120607095582J gamma=-12.85787296295166 beta=0.6829898357391357\n", + "loss_energy=0.0022614020232037345 loss_energy=0.04755420089964434J gamma=-12.857589721679688 beta=0.6832727789878845\n", + "loss_energy=0.002263772976431909 loss_energy=0.04757912332559217J gamma=-12.858841896057129 beta=0.6820195317268372\n", + "loss_energy=0.00226111591549116 loss_energy=0.047551192576960256J gamma=-12.857882499694824 beta=0.6829795241355896\n", + "loss_energy=0.0022744293239424715 loss_energy=0.04769097738506175J gamma=-12.86000919342041 beta=0.6808520555496216\n", + "loss_energy=0.0022641293088105367 loss_energy=0.047582867807757626J gamma=-12.858902931213379 beta=0.6819580793380737\n", + "loss_energy=0.0022611151036184504 loss_energy=0.047551184040131436J gamma=-12.857895851135254 beta=0.682965874671936\n", + "tensor(-8.6718, dtype=torch.float64) tensor(-6.1155, dtype=torch.float64) -8.671807528583088 -6.115486644714664\n", + "measurement tensor(-7.2364, dtype=torch.float64) tensor(0.7498, dtype=torch.float64)\n", + "measurement tensor(270.9156, dtype=torch.float64) tensor(132.8082, dtype=torch.float64)\n", + "SYS linux_tuned\n", + "MSE_loss_time=0.15737716040717736 loss_time=0.39671 us zeta=285.3316955566406 alpha=-0.5010113716125488 phi=0.01248311996459961\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.011878883507503232 loss_time=0.10899 us zeta=323.7709045410156 alpha=0.41723474860191345 phi=1.0539549589157104\n", + "MSE_loss_time=0.009886563153462856 loss_time=0.09943 us zeta=394.76812744140625 alpha=0.6238011717796326 phi=1.0387611389160156\n", + "MSE_loss_time=0.008362449305227391 loss_time=0.09145 us zeta=477.6034851074219 alpha=0.8293798565864563 phi=1.0268357992172241\n", + "MSE_loss_time=0.008671396778044961 loss_time=0.09312 us zeta=561.08837890625 alpha=0.9947963953018188 phi=1.0721476078033447\n", + "MSE_loss_time=0.012014343480092327 loss_time=0.10961 us zeta=635.4136352539062 alpha=1.181275486946106 phi=1.1522527933120728\n", + "MSE_loss_time=0.006772502670620497 loss_time=0.0823 us zeta=692.1300659179688 alpha=1.2539427280426025 phi=1.0140047073364258\n", + "MSE_loss_time=0.010029874649365176 loss_time=0.10015 us zeta=727.7911987304688 alpha=1.3897606134414673 phi=0.9555983543395996\n", + "MSE_loss_time=0.0067057084241711916 loss_time=0.08189 us zeta=746.5087280273438 alpha=1.3454385995864868 phi=1.0130200386047363\n", + "MSE_loss_time=0.0067038519260281875 loss_time=0.08188 us zeta=754.550048828125 alpha=1.3584855794906616 phi=1.012992262840271\n", + "MSE_loss_time=0.006706108036388552 loss_time=0.08189 us zeta=758.0946655273438 alpha=1.3669278621673584 phi=1.0118769407272339\n", + "MSE_loss_time=0.0067036479432578166 loss_time=0.08188 us zeta=759.215576171875 alpha=1.366018533706665 phi=1.0129821300506592\n", + "MSE_loss_time=0.006703655457714383 loss_time=0.08188 us zeta=759.4853515625 alpha=1.3664532899856567 phi=1.0129793882369995\n", + "MSE_loss_time=0.0067618046962619055 loss_time=0.08223 us zeta=759.4154663085938 alpha=1.3752285242080688 phi=1.004847526550293\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([28])) that is different to the input size (torch.Size([1, 28])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=136.87393951645694 loss_energy=11.699313634417061J gamma=0.5920922756195068 beta=-1.064333438873291\n", + "loss_energy=0.0024915203426911212 loss_energy=0.04991513140011875J gamma=-12.735400199890137 beta=0.9075253009796143\n", + "loss_energy=0.0024915203419033543 loss_energy=0.04991513139222769J gamma=-12.735404014587402 beta=0.9075297117233276\n", + "loss_energy=0.0024915203419033543 loss_energy=0.04991513139222769J gamma=-12.735404014587402 beta=0.9075297117233276\n", + "loss_energy=0.0024915203419033543 loss_energy=0.04991513139222769J gamma=-12.735404014587402 beta=0.9075297117233276\n", + "loss_energy=0.002491520343418476 loss_energy=0.04991513140740467J gamma=-12.735404014587402 beta=0.9075311422348022\n", + "loss_energy=0.002491520342392886 loss_energy=0.04991513139713133J gamma=-12.735404968261719 beta=0.9075299501419067\n", + "loss_energy=0.002491520341889518 loss_energy=0.04991513139208909J gamma=-12.735404968261719 beta=0.9075308442115784\n", + "loss_energy=0.0024915203420158537 loss_energy=0.0499151313933546J gamma=-12.735404968261719 beta=0.9075303673744202\n", + "loss_energy=0.002491520341890641 loss_energy=0.04991513139210034J gamma=-12.735404968261719 beta=0.9075307250022888\n", + "loss_energy=0.002491520341890641 loss_energy=0.04991513139210034J gamma=-12.735404968261719 beta=0.9075307250022888\n", + "loss_energy=0.0024915203444111226 loss_energy=0.04991513141734801J gamma=-12.735404014587402 beta=0.9075315594673157\n", + "loss_energy=0.002491520341887533 loss_energy=0.04991513139206921J gamma=-12.735404968261719 beta=0.9075307846069336\n", + "loss_energy=0.002491520341887533 loss_energy=0.04991513139206921J gamma=-12.735404968261719 beta=0.9075307846069336\n", + "tensor(-8.1694, dtype=torch.float64) tensor(-6.0129, dtype=torch.float64) -8.169370425804964 -6.012855916943453\n", + "measurement tensor(-7.0168, dtype=torch.float64) tensor(0.6565, dtype=torch.float64)\n", + "measurement tensor(296.4500, dtype=torch.float64) tensor(119.2272, dtype=torch.float64)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=14000, lr=1, rqps=600000, rtail='99', \n", + " mpred=['energy', 'time'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "MSE_loss_time=3.466123850823403 loss_time=1.86175 us zeta=429.5527648925781 alpha=-0.9666502475738525 phi=0.6700219511985779\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.03406095864768174 loss_time=0.18456 us zeta=405.8588562011719 alpha=7.589137554168701 phi=1.3116512298583984\n", + "MSE_loss_time=0.03397614584678404 loss_time=0.18433 us zeta=390.543212890625 alpha=7.480998992919922 phi=1.3117531538009644\n", + "MSE_loss_time=0.03376778497923761 loss_time=0.18376 us zeta=358.89666748046875 alpha=7.001809120178223 phi=1.3084983825683594\n", + "MSE_loss_time=0.03259997937539418 loss_time=0.18055 us zeta=268.2585754394531 alpha=5.143280506134033 phi=1.2869969606399536\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([45])) that is different to the input size (torch.Size([1, 45])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=167.2809325538804 loss_energy=12.933713022712404J gamma=1.1304125785827637 beta=-0.8106768131256104\n", + "loss_energy=0.002261115071200098 loss_energy=0.04755118369925294J gamma=-12.85789680480957 beta=0.6829594373703003\n", + "loss_energy=0.0022611150704615482 loss_energy=0.047551183691487094J gamma=-12.857898712158203 beta=0.6829618215560913\n", + "loss_energy=0.0022611150704615482 loss_energy=0.047551183691487094J gamma=-12.857898712158203 beta=0.6829618215560913\n", + "loss_energy=0.002261115070408559 loss_energy=0.047551183690929914J gamma=-12.85789966583252 beta=0.6829630136489868\n", + "measurement tensor(270.9156, dtype=torch.float64) tensor(132.8082, dtype=torch.float64)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=5000, lr=1, rqps=600000, rtail='99', \n", + " mpred=['time'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "MSE_loss_time=0.250746345310389 loss_time=0.50075 us zeta=219.5358428955078 alpha=-0.13427114486694336 phi=0.16302889585494995\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.00011815393441662156 loss_time=0.01087 us zeta=40.25274658203125 alpha=-0.861304759979248 phi=0.5298420786857605\n", + "MSE_loss_time=0.00019645266694098368 loss_time=0.01402 us zeta=38.915367126464844 alpha=-0.893722653388977 phi=0.5386803150177002\n", + "MSE_loss_time=0.0004034163373217096 loss_time=0.02009 us zeta=38.81593322753906 alpha=-0.9112141132354736 phi=0.5424319505691528\n", + "MSE_loss_time=0.000111404641078033 loss_time=0.01055 us zeta=38.83777618408203 alpha=-0.8948989510536194 phi=0.5319880247116089\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([43])) that is different to the input size (torch.Size([1, 43])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=125.50113366436524 loss_energy=11.202728849006622J gamma=-1.395688772201538 beta=1.1223609447479248\n", + "loss_energy=0.007790184376058229 loss_energy=0.08826202114192848J gamma=-12.280386924743652 beta=0.6863613724708557\n", + "loss_energy=0.007790184375574674 loss_energy=0.08826202113918916J gamma=-12.280388832092285 beta=0.6863637566566467\n", + "loss_energy=0.007790184375574674 loss_energy=0.08826202113918916J gamma=-12.280388832092285 beta=0.6863637566566467\n", + "loss_energy=0.007790184375574674 loss_energy=0.08826202113918916J gamma=-12.280388832092285 beta=0.6863637566566467\n", + "SYS linux_tuned\n", + "MSE_loss_time=0.17849212517629381 loss_time=0.42248 us zeta=101.657470703125 alpha=0.49157261848449707 phi=0.2722965478897095\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.00034110931106035394 loss_time=0.01847 us zeta=63.819984436035156 alpha=-0.47737956047058105 phi=0.6017702221870422\n", + "MSE_loss_time=0.0003411219797596367 loss_time=0.01847 us zeta=63.81975173950195 alpha=-0.47725775837898254 phi=0.6016944646835327\n", + "MSE_loss_time=0.0006163266914316118 loss_time=0.02483 us zeta=63.96173858642578 alpha=-0.5066607594490051 phi=0.6098025441169739\n", + "MSE_loss_time=0.00034111043485742995 loss_time=0.01847 us zeta=63.82070541381836 alpha=-0.4773516058921814 phi=0.6017425656318665\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([47])) that is different to the input size (torch.Size([1, 47])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=133.2286513545547 loss_energy=11.542471631091614J gamma=0.9536914825439453 beta=-1.6257336139678955\n", + "loss_energy=0.013456890477387242 loss_energy=0.11600383820110109J gamma=-12.209689140319824 beta=0.7141976952552795\n", + "loss_energy=0.013456890477387799 loss_energy=0.11600383820110349J gamma=-12.209689140319824 beta=0.7141976356506348\n", + "loss_energy=0.013456890477333285 loss_energy=0.11600383820086853J gamma=-12.20969009399414 beta=0.7141988277435303\n", + "loss_energy=0.013456890477332117 loss_energy=0.1160038382008635J gamma=-12.20969009399414 beta=0.714198887348175\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=5000, lr=1, rqps=200000, rtail='50', \n", + " mpred=['energy', 'time'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['sys', 'i', 'itr', 'dvfs', 'rapl', 'read_5th', 'read_10th', 'read_50th',\n", + " 'read_90th', 'read_95th', 'read_99th', 'measure_QPS', 'target_QPS',\n", + " 'time', 'joules', 'rx_desc', 'rx_bytes', 'tx_desc', 'tx_bytes',\n", + " 'instructions', 'cycles', 'ref_cycles', 'llc_miss', 'c1', 'c1e', 'c3',\n", + " 'c6', 'c7', 'num_interrupts', 'QPS'],\n", + " dtype='object')\n", + "[ 200000 400000 600000 1000000 1500000]\n", + "[0]\n", + "645\n" + ] + } + ], + "source": [ + "ddf = pd.read_csv('~/github/energy_trace_experiment_scripts/collected_data/mcd_combined.csv', sep=' ')\n", + "ddf['QPS'] = ddf['target_QPS']\n", + "ddf = ddf[ddf['rapl'] == 135]\n", + "ddf = ddf[ddf['read_99th'] <= 500]\n", + "ddf = ddf[ddf['i'] == 0]\n", + "\n", + "ddf['dvfs'] = ddf['dvfs'].apply(lambda x: dvfs_dict[x])\n", + "ddf = ddf[(ddf['itr']!=1) & (ddf['dvfs']!=65535)] #filter out linux dynamic\n", + "print(df_comb.columns)\n", + "print(ddf['QPS'].unique())\n", + "print(ddf['i'].unique())\n", + "print(ddf.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "loss_time=1.347581989426996 zeta=375.9709777832031 alpha=0.22811484336853027 phi=0.6536439657211304\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_time=0.015020846242065705 zeta=69.32869720458984 alpha=-0.7216720581054688 phi=1.053759217262268\n", + "loss_time=0.015020846242079064 zeta=69.32869720458984 alpha=-0.7216721773147583 phi=1.0537593364715576\n", + "loss_time=0.015020986652044559 zeta=69.32296752929688 alpha=-0.7213442921638489 phi=1.053292155265808\n", + "loss_time=0.01537675617184246 zeta=69.30174255371094 alpha=-0.6992050409317017 phi=1.0310273170471191\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":88: RuntimeWarning: divide by zero encountered in log\n", + " pred_energy = gamma+(np.log(fixed_phi)+np.log(itr))+(beta*np.log(dvfs))\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([90])) that is different to the input size (torch.Size([1, 90])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=inf gamma=-0.9975230693817139 beta=0.7300441265106201\n", + "loss_energy=nan gamma=nan beta=nan\n", + "loss_energy=nan gamma=nan beta=nan\n", + "loss_energy=nan gamma=nan beta=nan\n", + "loss_energy=nan gamma=nan beta=nan\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "run_energy(ddf, n_iter=5000, lr=1, rqps=1000000, rtail='99', \n", + " mpred=['time'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "loss_time=0.43016901033855376 zeta=57.74052047729492 alpha=1.6297779083251953 phi=0.8215769529342651\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_time=0.027117732281898586 zeta=125.97518157958984 alpha=-0.03338911011815071 phi=1.2233915328979492\n", + "loss_time=0.027117753563222227 zeta=126.15747833251953 alpha=-0.03212921321392059 phi=1.2227662801742554\n", + "loss_time=0.02775474995877512 zeta=126.0926513671875 alpha=0.00632043182849884 phi=1.198974847793579\n", + "loss_time=0.02742354888049212 zeta=126.09355926513672 alpha=-0.008886348456144333 phi=1.2021839618682861\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":88: RuntimeWarning: divide by zero encountered in log\n", + " pred_energy = gamma+(np.log(fixed_phi)+np.log(itr))+(beta*np.log(dvfs))\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([70])) that is different to the input size (torch.Size([1, 70])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=inf gamma=-0.9948389530181885 beta=1.2042922973632812\n", + "loss_energy=nan gamma=nan beta=nan\n", + "loss_energy=nan gamma=nan beta=nan\n", + "loss_energy=nan gamma=nan beta=nan\n", + "loss_energy=nan gamma=nan beta=nan\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(ddf, n_iter=5000, lr=1, rqps=1500000, rtail='99', \n", + " mpred=['time'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_q = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "429.2119140625 -0.5882140398025513 -0.9464408159255981 35.02513128982781\n", + "211.00961303710938 3.6269314289093018 0.9249256253242493 0.24131148425789484\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_comb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrqps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtail\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'90'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mrun_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_comb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrqps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m400000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtail\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'90'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mrun_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_comb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrqps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m600000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtail\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'90'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mrun_time\u001b[0;34m(df_comb, n_iter, lr, rqps, rtail, msys)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m#plt.plot(d[:,0], d[:,1], 'p')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mpred_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitr_suppress\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minference_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'mcd'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprint_freq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0mtnum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrqps\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m200000\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36minference_time\u001b[0;34m(d, n_iter, lr, workload, sys, print_freq)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m \u001b[0mp_busy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mp_static_busy\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mp_busy_min\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdvfs\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mbeta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 45\u001b[0m \u001b[0mt_busy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmax_time\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mdvfs\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0mpred_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitr_suppress\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mitr\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mt_busy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "def run_time(df_comb, n_iter=2000, lr=1, rqps=400000, rtail='99', msys=['ebbrt_tuned']): \n", + " df_comb = df_comb[df_comb['QPS'] == rqps]\n", + "\n", + " for sys in msys:\n", + " df = df_comb[(df_comb['sys']==sys)].copy()\n", + " rt = 'read_'+rtail+'th_mean'\n", + " df = df[['joules_mean','itr', 'dvfs', 'QPS', rt]]\n", + " d = df.values\n", + " d = torch.tensor(d)\n", + " #plt.plot(d[:,0], d[:,1], 'p')\n", + "\n", + " pred_time, max_time, alpha, itr_suppress = inference_time(d, n_iter, lr, 'mcd', sys, print_freq=1000)\n", + " tnum = 0\n", + " if rqps == 200000:\n", + " if sys == 'linux_tuned':\n", + " tnum=345\n", + " else:\n", + " tnum=246\n", + " elif rqps == 400000:\n", + " if sys == 'linux_tuned':\n", + " tnum=318\n", + " #df[f'pre_energy lr={lr}'] = pred_energy.view(318, 1).detach().numpy()\n", + " #df[f'pre_time lr={lr}'] = pred_time.view(318, 1).detach().numpy()\n", + " else:\n", + " tnum=245\n", + " #df[f'pre_energy lr={lr}'] = pred_energy.view(245, 1).detach().numpy()\n", + " #df[f'pre_time lr={lr}'] = pred_time.view(245, 1).detach().numpy()\n", + " if rqps == 600000:\n", + " if sys == 'linux_tuned':\n", + " tnum=202\n", + " else:\n", + " tnum=246\n", + " df[f'pre_time lr={lr}'] = pred_time.view(tnum, 1).detach().numpy()\n", + "\n", + "# for pred_name in ['time', 'energy']:\n", + "# if pred_name == 'energy':\n", + "# pred = pred_energy\n", + "# qps = d[:,3]\n", + "# yvalue = d[:,0]/(qps*20)\n", + "# else:\n", + " pred = pred_time\n", + " yvalue = d[:,4]\n", + " fig, ax = plt.subplots()\n", + " plt.title(f'pred:time mcd sys={sys} lr={lr} tail={rtail} \\n QPS={rqps} max_time={round(max_time.item(),2)} \\n alpha={round(alpha.item(),2)} itr_suppress={round(itr_suppress.item(),2)}')\n", + " plt.xlabel(u\"predictions\")\n", + " plt.ylabel('time')\n", + " scatter = ax.scatter(pred.detach().numpy()[0], yvalue, marker = 'o', s = d[:,1], c = d[:,2], alpha=0.3)\n", + " legend1 = ax.legend(*scatter.legend_elements(),loc=\"upper left\", title=\"dvfs\")\n", + " ax.add_artist(legend1)\n", + " handles, labels = scatter.legend_elements(prop=\"sizes\", alpha=0.6)\n", + " legend2 = plt.legend(handles, labels, loc=\"lower right\", title=\"itr\")\n", + " ax.add_artist(legend2)\n", + " \n", + "run_time(df_comb, rqps=200000, rtail='90')\n", + "run_time(df_comb, rqps=400000, rtail='90')\n", + "run_time(df_comb, rqps=600000, rtail='90')" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'float' object has no attribute 'item'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_energy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_comb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrqps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtail\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'90'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mrun_energy\u001b[0;34m(df_comb, n_iter, lr, rqps, rtail, msys)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'pred:energy mcd sys={sys} lr={lr} tail={rtail} \\n QPS={rqps} max_time={round(max_time.item(),2)} \\n alpha={round(alpha.item(),2)} beta={round(beta.item(),2)} \\n p_detect={round(p_detect.item(),2)}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu\"predictions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'energy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'float' object has no attribute 'item'" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/analysis/run_mcd_sigmetrics.ipynb b/analysis/run_mcd_sigmetrics.ipynb new file mode 100644 index 0000000..ad34507 --- /dev/null +++ b/analysis/run_mcd_sigmetrics.ipynb @@ -0,0 +1,1807 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('../bayesopt')\n", + "\n", + "import read_agg_data\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.autograd as auto\n", + "import torch.optim as optim\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as plt\n", + "import pandas as pd\n", + "import math\n", + "\n", + "import pdb\n", + "\n", + "dvfs_dict = {\n", + " \"0xc00\" : 1.2,\n", + " \"0xd00\" : 1.3,\n", + " \"0xe00\" : 1.4,\n", + " \"0xf00\" : 1.5,\n", + " \"0x1000\" : 1.6,\n", + " \"0x1100\" : 1.7,\n", + " \"0x1200\" : 1.8,\n", + " \"0x1300\" : 1.9,\n", + " \"0x1400\" : 2.0,\n", + " \"0x1500\" : 2.1,\n", + " \"0x1600\" : 2.2,\n", + " \"0x1700\" : 2.3,\n", + " \"0x1800\" : 2.4,\n", + " \"0x1900\" : 2.5,\n", + " \"0x1a00\" : 2.6,\n", + " \"0x1b00\" : 2.7,\n", + " \"0x1c00\" : 2.8,\n", + " \"0x1d00\" : 2.9,\n", + " \"0xffff\" : 3.0,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "3073\n", + "[200000 400000 600000 0]\n", + "Index(['sys', 'i', 'itr', 'dvfs', 'rapl', 'read_5th', 'read_10th', 'read_50th',\n", + " 'read_90th', 'read_95th', 'read_99th', 'measure_QPS', 'target_QPS',\n", + " 'time', 'joules', 'rx_desc', 'rx_bytes', 'tx_desc', 'tx_bytes',\n", + " 'instructions', 'cycles', 'ref_cycles', 'llc_miss', 'c1', 'c1e', 'c3',\n", + " 'c6', 'c7', 'num_interrupts', 'QPS'],\n", + " dtype='object')\n", + "[400000 600000 200000]\n" + ] + } + ], + "source": [ + "#df_comb, _, _ = read_agg_data.start_analysis('mcd') #DATA\n", + "#df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: int(x, base=16))\n", + "\n", + "df_comb = pd.read_csv('/home/handong/jupyter/jupyter-notebooks/nic-tuning-experiments/bayesopt/summary_data/mcd_combined.csv', sep=' ')\n", + "print(df_comb.shape[0])\n", + "df_comb['QPS'] = df_comb['target_QPS']\n", + "\n", + "print(df_comb['QPS'].unique())\n", + "df_comb = df_comb[(df_comb['i'] == 1) & (df_comb['rapl'] == 135)]\n", + "#df_comb = df_comb[(df_comb['rapl'] == 135)]\n", + "df_comb = df_comb[df_comb['read_99th'] <= 500]\n", + "\n", + "df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: dvfs_dict[x])\n", + "df_comb = df_comb[(df_comb['itr']!=1) & (df_comb['dvfs']!=65535)] #filter out linux dynamic\n", + "print(df_comb.columns)\n", + "\n", + "# df_comb['dvfs'] = df_comb['dvfs'].astype(float) / df_comb['dvfs'].min()\n", + "# print(df_comb['dvfs'].unique())\n", + "# df_comb['itr'] = df_comb['itr'].astype(float) / df_comb['itr'].min()\n", + "# print(df_comb['itr'].unique())\n", + "#print(10**6)\n", + "print(df_comb['QPS'].unique())" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 50 100 200 300 400 350]\n", + "1675.5\n", + "******* ebbrt_tuned 50 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "174 0.000209 50 1.7 5221529 1.899239e+11 103.8\n", + "262 0.000223 50 1.9 5221089 1.844129e+11 103.4\n", + "351 0.000240 50 2.1 5220371 1.796836e+11 103.3\n", + "440 0.000258 50 2.3 5214101 1.763346e+11 103.9\n", + "526 0.000277 50 2.5 5220459 1.737004e+11 103.8\n", + "616 0.000296 50 2.7 5223052 1.701433e+11 103.0\n", + "706 0.000321 50 2.9 5216163 1.690650e+11 102.8\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 50 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "4 0.000168 50 1.3 6036916 3.025599e+11 104.1\n", + "92 0.000180 50 1.5 6035703 2.803556e+11 104.9\n", + "179 0.000194 50 1.7 6033477 2.668666e+11 105.3\n", + "268 0.000208 50 1.9 6032140 2.624055e+11 105.4\n", + "357 0.000225 50 2.1 6033866 2.551110e+11 105.1\n", + "446 0.000240 50 2.3 6031643 2.471547e+11 105.9\n", + "532 0.000261 50 2.5 6032598 2.480025e+11 106.6\n", + "622 0.000280 50 2.7 6031159 2.420433e+11 105.7\n", + "712 0.000304 50 2.9 6032217 2.407263e+11 106.0\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 50 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "10 0.000171 50 1.3 6206452 3.935791e+11 102.8\n", + "98 0.000185 50 1.5 6205251 3.610531e+11 102.1\n", + "185 0.000199 50 1.7 6204518 3.390249e+11 104.1\n", + "274 0.000215 50 1.9 6204134 3.256803e+11 104.0\n", + "363 0.000232 50 2.1 6203612 3.133541e+11 106.2\n", + "452 0.000250 50 2.3 6202987 3.085547e+11 106.8\n", + "538 0.000270 50 2.5 6202883 3.022569e+11 105.9\n", + "628 0.000292 50 2.7 6202776 2.975200e+11 109.1\n", + "718 0.000316 50 2.9 6201800 2.962684e+11 108.5\n", + "\n", + "2106.63\n", + "******* linux_tuned 50 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1486 0.000213 50 1.3 5218634 4.599466e+11 138.9\n", + "1930 0.000213 50 1.3 5220109 4.599841e+11 138.7\n", + "1548 0.000232 50 1.5 5177475 4.049907e+11 124.2\n", + "2002 0.000232 50 1.5 5176745 4.045315e+11 125.9\n", + "1610 0.000269 50 1.7 5155688 3.624321e+11 117.0\n", + "2075 0.000251 50 1.7 5172205 3.641128e+11 117.0\n", + "2147 0.000291 50 1.9 5153643 3.313907e+11 113.4\n", + "2219 0.000298 50 2.1 5141375 3.035007e+11 108.0\n", + "2291 0.000323 50 2.3 5118452 2.799074e+11 106.0\n", + "2395 0.000353 50 2.5 5094243 2.611035e+11 105.6\n", + "2483 0.000384 50 2.7 5090058 2.452500e+11 105.2\n", + "2571 0.000415 50 2.9 5072154 2.326829e+11 106.1\n", + "\n", + "2400.58\n", + "******* linux_tuned 50 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1934 0.000223 50 1.3 6048093 7.310257e+11 341.0\n", + "2007 0.000225 50 1.5 6039895 6.615798e+11 198.8\n", + "2079 0.000244 50 1.7 6030084 5.970014e+11 151.8\n", + "1675 0.000265 50 1.9 6024476 5.529146e+11 130.2\n", + "2151 0.000264 50 1.9 6024084 5.472257e+11 128.8\n", + "1737 0.000287 50 2.1 6017531 5.003071e+11 118.6\n", + "2223 0.000287 50 2.1 6015240 4.986567e+11 120.1\n", + "2296 0.000310 50 2.3 6012185 4.605418e+11 115.4\n", + "2399 0.000339 50 2.5 6010602 4.298558e+11 111.6\n", + "2487 0.000367 50 2.7 6006830 4.001675e+11 107.7\n", + "2575 0.000400 50 2.9 6005187 3.763980e+11 105.7\n", + "\n", + "2654.84\n", + "******* linux_tuned 50 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2155 0.000283 50 1.9 6190548 7.257716e+11 275.9\n", + "2227 0.000309 50 2.1 6192252 6.796291e+11 194.6\n", + "2316 0.000337 50 2.3 6192616 6.428080e+11 153.5\n", + "2404 0.000365 50 2.5 6191938 5.980526e+11 134.7\n", + "2492 0.000397 50 2.7 6192273 5.626668e+11 124.0\n", + "2580 0.000429 50 2.9 6192620 5.246938e+11 115.5\n", + "\n", + "1675.5\n", + "******* ebbrt_tuned 100 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "15 0.000313 100 1.3 3043321 1.924407e+11 154.5\n", + "104 0.000334 100 1.5 3043262 1.844762e+11 154.3\n", + "191 0.000355 100 1.7 3043290 1.771736e+11 153.6\n", + "280 0.000379 100 1.9 3043897 1.732380e+11 153.4\n", + "369 0.000406 100 2.1 3042843 1.674202e+11 153.3\n", + "457 0.000433 100 2.3 3043840 1.639538e+11 153.5\n", + "544 0.000466 100 2.5 3043557 1.625835e+11 152.7\n", + "634 0.000501 100 2.7 3044127 1.586730e+11 152.8\n", + "724 0.000539 100 2.9 3044273 1.575179e+11 152.2\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 100 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "21 0.000321 100 1.3 3120627 2.911510e+11 154.6\n", + "196 0.000369 100 1.7 3120523 2.644230e+11 154.6\n", + "286 0.000396 100 1.9 3120497 2.553182e+11 154.6\n", + "375 0.000426 100 2.1 3120517 2.490231e+11 154.2\n", + "462 0.000458 100 2.3 3120466 2.429661e+11 154.3\n", + "550 0.000496 100 2.5 3120522 2.435881e+11 153.4\n", + "640 0.000532 100 2.7 3120526 2.366776e+11 154.4\n", + "730 0.000577 100 2.9 3120506 2.346554e+11 154.0\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 100 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "27 0.000336 100 1.3 3124708 3.844774e+11 155.3\n", + "114 0.000362 100 1.5 3124642 3.581078e+11 155.0\n", + "202 0.000391 100 1.7 3124646 3.440160e+11 155.3\n", + "292 0.000419 100 1.9 3124642 3.267733e+11 154.7\n", + "381 0.000451 100 2.1 3124631 3.169717e+11 155.9\n", + "467 0.000485 100 2.3 3124653 3.116417e+11 156.0\n", + "556 0.000525 100 2.5 3124663 3.072970e+11 154.8\n", + "646 0.000566 100 2.7 3124617 3.016410e+11 154.6\n", + "736 0.000615 100 2.9 3124608 3.011797e+11 155.6\n", + "\n", + "2106.63\n", + "******* linux_tuned 100 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1942 0.000362 100 1.3 3016621 4.188141e+11 184.6\n", + "2015 0.000392 100 1.5 3015677 3.737492e+11 174.9\n", + "2087 0.000423 100 1.7 3018319 3.377049e+11 170.9\n", + "2159 0.000457 100 1.9 3017132 3.094803e+11 169.5\n", + "2231 0.000497 100 2.1 3017215 2.865768e+11 168.7\n", + "2335 0.000536 100 2.3 3009006 2.681600e+11 169.4\n", + "2423 0.000583 100 2.5 3007258 2.543001e+11 170.2\n", + "2511 0.000633 100 2.7 2997316 2.437554e+11 171.3\n", + "2599 0.000685 100 2.9 2994394 2.349044e+11 172.5\n", + "\n", + "2400.58\n", + "******* linux_tuned 100 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1946 0.000394 100 1.3 3115897 6.878025e+11 295.9\n", + "2019 0.000427 100 1.5 3116209 6.142148e+11 225.1\n", + "2091 0.000459 100 1.7 3116996 5.384308e+11 188.5\n", + "2163 0.000496 100 1.9 3117158 4.852459e+11 180.0\n", + "2235 0.000539 100 2.1 3117256 4.445204e+11 171.5\n", + "2339 0.000582 100 2.3 3117344 4.099733e+11 169.2\n", + "2427 0.000632 100 2.5 3117357 3.816575e+11 166.7\n", + "2515 0.000686 100 2.7 3117286 3.580680e+11 165.6\n", + "2603 0.000745 100 2.9 3117360 3.378497e+11 164.7\n", + "\n", + "2654.84\n", + "******* linux_tuned 100 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2095 0.000503 100 1.7 3123123 7.270530e+11 392.3\n", + "2167 0.000548 100 1.9 3123803 6.772242e+11 258.6\n", + "2239 0.000595 100 2.1 3124066 6.221400e+11 206.6\n", + "2343 0.000644 100 2.3 3124129 5.734289e+11 188.8\n", + "2431 0.000697 100 2.5 3124197 5.262300e+11 177.7\n", + "2519 0.000751 100 2.7 3124214 4.819835e+11 170.4\n", + "2607 0.000815 100 2.9 3124318 4.537178e+11 167.7\n", + "\n", + "1675.5\n", + "******* ebbrt_tuned 200 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "33 0.000606 200 1.3 1561019 1.734411e+11 249.6\n", + "120 0.000642 200 1.5 1561047 1.644802e+11 250.1\n", + "208 0.000682 200 1.7 1560999 1.602762e+11 249.6\n", + "298 0.000722 200 1.9 1561030 1.547365e+11 249.7\n", + "386 0.000769 200 2.1 1561047 1.485075e+11 249.3\n", + "473 0.000822 200 2.3 1561003 1.465697e+11 249.7\n", + "562 0.000878 200 2.5 1560969 1.443002e+11 249.1\n", + "652 0.000935 200 2.7 1561051 1.411333e+11 249.5\n", + "742 0.000999 200 2.9 1561046 1.390771e+11 249.1\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 200 400000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "39 0.000635 200 1.3 1562466 2.697584e+11 249.7\n", + "126 0.000673 200 1.5 1562481 2.621141e+11 262.6\n", + "214 0.000726 200 1.7 1562444 2.439520e+11 248.8\n", + "304 0.000757 200 1.9 1562470 2.284070e+11 250.3\n", + "392 0.000801 200 2.1 1562464 2.331850e+11 257.3\n", + "479 0.000853 200 2.3 1562467 2.306333e+11 262.2\n", + "568 0.000916 200 2.5 1562460 2.317443e+11 255.8\n", + "658 0.001002 200 2.7 1562459 2.183752e+11 249.9\n", + "748 0.001074 200 2.9 1562468 2.143033e+11 248.7\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 200 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "45 0.000664 200 1.3 1562473 3.654273e+11 274.8\n", + "132 0.000703 200 1.5 1562471 3.502044e+11 270.5\n", + "220 0.000748 200 1.7 1562468 3.326593e+11 265.0\n", + "310 0.000806 200 1.9 1562468 3.219903e+11 258.4\n", + "398 0.000840 200 2.1 1562477 3.065463e+11 265.7\n", + "485 0.000899 200 2.3 1562462 3.100330e+11 270.5\n", + "574 0.000964 200 2.5 1562458 3.063907e+11 264.2\n", + "664 0.001053 200 2.7 1562463 3.064447e+11 258.5\n", + "754 0.001091 200 2.9 1562469 3.003311e+11 268.5\n", + "\n", + "2106.63\n", + "******* linux_tuned 200 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1954 0.000688 200 1.3 1559224 3.796443e+11 296.9\n", + "2027 0.000741 200 1.5 1559379 3.409071e+11 291.8\n", + "2099 0.000798 200 1.7 1559190 3.099416e+11 285.2\n", + "2171 0.000858 200 1.9 1559272 2.868034e+11 276.6\n", + "2243 0.000930 200 2.1 1559222 2.660824e+11 276.5\n", + "2347 0.001003 200 2.3 1558627 2.507451e+11 275.3\n", + "2435 0.001085 200 2.5 1557789 2.370932e+11 276.3\n", + "2523 0.001177 200 2.7 1556529 2.285748e+11 276.4\n", + "2611 0.001270 200 2.9 1557824 2.189206e+11 276.0\n", + "\n", + "2400.58\n", + "******* linux_tuned 200 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1958 0.000764 200 1.3 1562641 6.153520e+11 342.5\n", + "2031 0.000823 200 1.5 1562632 5.384820e+11 317.0\n", + "2103 0.000887 200 1.7 1562706 4.786562e+11 302.6\n", + "2175 0.000960 200 1.9 1562695 4.367089e+11 290.2\n", + "2247 0.001038 200 2.1 1562708 4.033413e+11 293.7\n", + "2351 0.001123 200 2.3 1562699 3.782067e+11 285.2\n", + "2439 0.001218 200 2.5 1562731 3.527546e+11 283.0\n", + "2527 0.001304 200 2.7 1561956 3.340450e+11 288.4\n", + "2615 0.001429 200 2.9 1562750 3.128069e+11 275.0\n", + "\n", + "2654.84\n", + "******* linux_tuned 200 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2107 0.000979 200 1.7 1562786 6.640448e+11 397.4\n", + "2179 0.001064 200 1.9 1562795 6.200477e+11 331.6\n", + "2251 0.001146 200 2.1 1562810 5.502158e+11 310.7\n", + "2355 0.001239 200 2.3 1562805 5.177743e+11 298.8\n", + "2443 0.001347 200 2.5 1562804 4.783754e+11 291.3\n", + "2531 0.001455 200 2.7 1562809 4.431551e+11 282.7\n", + "2619 0.001551 200 2.9 1562781 4.175727e+11 295.9\n", + "\n", + "1675.5\n", + "******* ebbrt_tuned 300 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "51 0.000903 300 1.3 1041618 1.673578e+11 357.1\n", + "138 0.000959 300 1.5 1041598 1.654854e+11 355.4\n", + "226 0.001013 300 1.7 1041607 1.571900e+11 355.5\n", + "316 0.001066 300 1.9 1041613 1.512917e+11 356.8\n", + "404 0.001137 300 2.1 1041609 1.448051e+11 354.9\n", + "491 0.001208 300 2.3 1041611 1.450336e+11 356.1\n", + "580 0.001280 300 2.5 1041616 1.400301e+11 354.9\n", + "670 0.001376 300 2.7 1041523 1.382381e+11 356.1\n", + "760 0.001471 300 2.9 1041607 1.402800e+11 354.3\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 300 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "57 0.000943 300 1.3 1041656 2.507369e+11 366.4\n", + "144 0.000997 300 1.5 1041646 2.352696e+11 362.2\n", + "232 0.001062 300 1.7 1041654 2.267574e+11 360.1\n", + "322 0.001134 300 1.9 1041659 2.217302e+11 350.5\n", + "410 0.001187 300 2.1 1041649 2.224460e+11 356.6\n", + "497 0.001251 300 2.3 1041640 1.928122e+11 360.4\n", + "586 0.001363 300 2.5 1041646 2.028735e+11 350.5\n", + "676 0.001420 300 2.7 1041653 1.985223e+11 356.5\n", + "766 0.001577 300 2.9 1041656 2.066888e+11 351.4\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 300 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "63 0.000984 300 1.3 1041651 3.409537e+11 383.5\n", + "150 0.001049 300 1.5 1041649 3.249638e+11 360.1\n", + "238 0.001106 300 1.7 1041643 3.014450e+11 379.0\n", + "327 0.001172 300 1.9 1041648 2.892195e+11 376.4\n", + "416 0.001264 300 2.1 1041644 2.675962e+11 361.3\n", + "503 0.001333 300 2.3 1041655 2.728385e+11 360.5\n", + "592 0.001418 300 2.5 1041645 2.714869e+11 364.0\n", + "682 0.001550 300 2.7 1041664 2.711051e+11 354.4\n", + "772 0.001618 300 2.9 1041651 2.721008e+11 364.3\n", + "\n", + "2106.63\n", + "******* linux_tuned 300 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1966 0.001021 300 1.3 1041592 3.691559e+11 401.6\n", + "2039 0.001096 300 1.5 1041574 3.273570e+11 396.1\n", + "2111 0.001178 300 1.7 1041542 2.987906e+11 391.8\n", + "2183 0.001265 300 1.9 1041462 2.748956e+11 388.5\n", + "2255 0.001367 300 2.1 1041416 2.579214e+11 388.2\n", + "2359 0.001476 300 2.3 1041232 2.460202e+11 386.5\n", + "2447 0.001586 300 2.5 1040942 2.279152e+11 387.2\n", + "2535 0.001723 300 2.7 1040159 2.231041e+11 385.9\n", + "2623 0.001862 300 2.9 1040332 2.150551e+11 386.0\n", + "\n", + "2400.58\n", + "******* linux_tuned 300 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1970 0.001129 300 1.3 1041887 5.864127e+11 458.6\n", + "2043 0.001210 300 1.5 1041884 5.087747e+11 435.5\n", + "2115 0.001293 300 1.7 1041891 4.531473e+11 429.7\n", + "2187 0.001403 300 1.9 1041876 4.226638e+11 404.3\n", + "2259 0.001530 300 2.1 1041883 3.868485e+11 395.6\n", + "2363 0.001637 300 2.3 1041857 3.606601e+11 394.9\n", + "2451 0.001792 300 2.5 1041901 3.443151e+11 390.5\n", + "2539 0.001937 300 2.7 1041884 3.218515e+11 384.0\n", + "2627 0.002091 300 2.9 1041881 3.097599e+11 391.8\n", + "\n", + "2654.84\n", + "******* linux_tuned 300 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2119 0.001421 300 1.7 1041838 6.089765e+11 480.6\n", + "2191 0.001507 300 1.9 1041851 5.559836e+11 469.4\n", + "2263 0.001621 300 2.1 1041855 5.121567e+11 441.5\n", + "2367 0.001745 300 2.3 1041904 4.860125e+11 433.8\n", + "2455 0.001912 300 2.5 1041789 4.524298e+11 417.5\n", + "2543 0.002068 300 2.7 1041882 4.266260e+11 405.5\n", + "2631 0.002174 300 2.9 1041872 4.091905e+11 405.0\n", + "\n", + "1675.5\n", + "******* ebbrt_tuned 350 200000\n", + "Empty DataFrame\n", + "Columns: [joules_per_interrupt, itr, dvfs, num_interrupts, ref_cycles, read_99th]\n", + "Index: []\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 350 400000\n", + "Empty DataFrame\n", + "Columns: [joules_per_interrupt, itr, dvfs, num_interrupts, ref_cycles, read_99th]\n", + "Index: []\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 350 600000\n", + "Empty DataFrame\n", + "Columns: [joules_per_interrupt, itr, dvfs, num_interrupts, ref_cycles, read_99th]\n", + "Index: []\n", + "\n", + "2106.63\n", + "******* linux_tuned 350 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "1978 0.001184 350 1.3 892879 3.570502e+11 449.6\n", + "2051 0.001275 350 1.5 892890 3.224655e+11 443.8\n", + "2123 0.001368 350 1.7 892913 2.880056e+11 441.6\n", + "2195 0.001468 350 1.9 892689 2.704350e+11 439.8\n", + "2267 0.001585 350 2.1 892793 2.461656e+11 437.7\n", + "2371 0.001702 350 2.3 892838 2.379652e+11 437.0\n", + "2459 0.001831 350 2.5 892558 2.210142e+11 436.8\n", + "2547 0.001982 350 2.7 890542 2.140299e+11 437.2\n", + "2635 0.002131 350 2.9 892014 2.075557e+11 437.6\n", + "\n", + "2400.58\n", + "******* linux_tuned 350 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2055 0.001391 350 1.5 893011 4.871073e+11 494.6\n", + "2127 0.001507 350 1.7 893052 4.437976e+11 478.3\n", + "2199 0.001604 350 1.9 892856 4.091595e+11 477.3\n", + "2271 0.001721 350 2.1 892963 3.782915e+11 474.3\n", + "2375 0.001856 350 2.3 893001 3.538089e+11 453.2\n", + "2463 0.002023 350 2.5 893014 3.372406e+11 453.5\n", + "2551 0.002255 350 2.7 893050 3.162472e+11 433.7\n", + "2639 0.002437 350 2.9 893040 2.952957e+11 433.2\n", + "\n", + "2654.84\n", + "******* linux_tuned 350 600000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2203 0.001780 350 1.9 893015 5.455264e+11 491.1\n", + "2275 0.001926 350 2.1 893030 5.028107e+11 481.3\n", + "2379 0.002068 350 2.3 893027 4.667815e+11 471.4\n", + "2467 0.002237 350 2.5 893023 4.478798e+11 469.7\n", + "2555 0.002428 350 2.7 893039 4.174783e+11 457.4\n", + "2643 0.002644 350 2.9 893024 4.042622e+11 447.9\n", + "\n", + "1675.5\n", + "******* ebbrt_tuned 400 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "69 0.001201 400 1.3 781239 1.599319e+11 443.6\n", + "156 0.001265 400 1.5 781235 1.412006e+11 444.4\n", + "244 0.001342 400 1.7 781243 1.463764e+11 443.6\n", + "333 0.001420 400 1.9 781241 1.442308e+11 444.2\n", + "422 0.001488 400 2.1 781238 1.349614e+11 444.9\n", + "509 0.001570 400 2.3 781237 1.250480e+11 444.1\n", + "598 0.001681 400 2.5 781241 1.333756e+11 443.1\n", + "688 0.001812 400 2.7 781240 1.349334e+11 443.8\n", + "778 0.001923 400 2.9 781245 1.292604e+11 442.8\n", + "\n", + "1836.46\n", + "******* ebbrt_tuned 400 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "75 0.001267 400 1.3 781240 2.735703e+11 444.2\n", + "162 0.001346 400 1.5 781248 2.611021e+11 444.6\n", + "250 0.001425 400 1.7 781247 2.500339e+11 445.5\n", + "339 0.001516 400 1.9 781247 2.397122e+11 441.9\n", + "428 0.001607 400 2.1 781241 2.396535e+11 443.3\n", + "515 0.001677 400 2.3 781238 2.176450e+11 457.4\n", + "604 0.001780 400 2.5 781236 2.123568e+11 445.6\n", + "694 0.001960 400 2.7 781235 2.193248e+11 443.5\n", + "\n", + "1962.71\n", + "******* ebbrt_tuned 400 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "81 0.001323 400 1.3 781240 3.617910e+11 474.5\n", + "168 0.001417 400 1.5 781237 3.475433e+11 471.2\n", + "256 0.001498 400 1.7 781241 3.135289e+11 458.1\n", + "345 0.001624 400 1.9 781248 3.191303e+11 440.6\n", + "434 0.001689 400 2.1 781240 2.783233e+11 469.3\n", + "520 0.001843 400 2.3 781246 3.045776e+11 445.7\n", + "610 0.001945 400 2.5 781247 2.601970e+11 458.4\n", + "700 0.002096 400 2.7 781242 2.692949e+11 452.3\n", + "789 0.002208 400 2.9 781235 2.763436e+11 474.8\n", + "\n", + "2106.63\n", + "******* linux_tuned 400 200000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2063 0.001450 400 1.5 781318 3.150755e+11 493.1\n", + "2135 0.001542 400 1.7 781360 2.784672e+11 491.5\n", + "2207 0.001669 400 1.9 781324 2.666548e+11 488.1\n", + "2279 0.001799 400 2.1 780971 2.503521e+11 487.4\n", + "2383 0.001941 400 2.3 781330 2.320813e+11 485.4\n", + "2471 0.002103 400 2.5 781220 2.191376e+11 485.2\n", + "2559 0.002254 400 2.7 780778 2.117097e+11 485.7\n", + "2647 0.002446 400 2.9 779923 2.031581e+11 484.5\n", + "\n", + "2400.58\n", + "******* linux_tuned 400 400000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2211 0.001886 400 1.9 781397 4.142691e+11 488.5\n", + "2387 0.002144 400 2.3 781376 3.511999e+11 500.0\n", + "2475 0.002367 400 2.5 781409 3.312238e+11 488.0\n", + "2563 0.002517 400 2.7 781419 3.193373e+11 489.3\n", + "2651 0.002699 400 2.9 781391 2.899172e+11 491.6\n", + "\n", + "2654.84\n", + "******* linux_tuned 400 600000\n", + " joules_per_interrupt itr dvfs num_interrupts ref_cycles read_99th\n", + "2391 0.002447 400 2.3 781414 4.945755e+11 478.6\n", + "\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "print(df_comb['itr'].unique())\n", + "for itr in [50, 100, 200, 300, 350, 400]:\n", + " for sys in ['ebbrt_tuned', 'linux_tuned']:\n", + " for qps in [200000, 400000, 600000]:\n", + " df = df_comb[(df_comb['sys']==sys) & (df_comb['QPS'] == qps)].copy()\n", + " #print(df.shape[0])\n", + " print(df['joules'].max())\n", + " df['joules_per_interrupt'] = df['joules']/df['num_interrupts']\n", + " df = df[['joules_per_interrupt','itr', 'dvfs', 'num_interrupts', 'ref_cycles', 'read_99th']]\n", + " #print(df.shape[0])\n", + " #print('')\n", + " \n", + " dfi = df[df['itr']==itr]\n", + " #dfi = dfi.drop_duplicates(subset = [\"itr\", \"dvfs\"])\n", + " #dfi['joules_mean'] = dfi['joules_mean']/dfi['joules_mean'].max()\n", + " #print(dfi.diff())\n", + " print('*******', sys, itr, qps)\n", + " print(dfi.sort_values(by=['dvfs']))\n", + " #print(dfi.sort_values(by=['dvfs']).diff())\n", + " print('')\n", + " plt.plot(dfi['dvfs'], dfi['joules_per_interrupt'])\n", + " #print(dfi)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "def inference(d, n_iter, lr, workload, sys, print_freq=10):\n", + " # p_busy_min = 20\n", + " p_static = {\n", + " 'c1':1.5, \n", + " 'c3':0.5,\n", + " 'c4':0.25,\n", + " 'c7':34, # 34 Watts\n", + " 'busy': 10\n", + " }\n", + " chosen_sleep = 'c7'\n", + "\n", + " p_q = p_static[chosen_sleep]/10**6 # joules/us idle\n", + " # p_detect = p_static[chosen_sleep]\n", + "\n", + " #starts randomly\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " \n", + " #p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " #p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " #p_q = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " #p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " phi = torch.tensor(torch.Tensor(1,1).uniform_(0.98, 1.0), requires_grad=True)\n", + " \n", + " #AA = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " #BB = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " \n", + " \n", + " #df[['joules','itr', 'dvfs', 'QPS', read_99th, 'num_interrupts']]\n", + " qps = d[:,3]\n", + " ninterrupts = d[:,5]\n", + " energy = (d[:,0]/ninterrupts).log() ## joules/num_interrupts\n", + " #energy = (d[:,0]/(qps).log()\n", + " itr = d[:,1]\n", + " dvfs = d[:,2]\n", + " time = d[:,4]\n", + " \n", + " #interarrival_time = (1/qps)*10**6\n", + "\n", + " current_loss_time = -100\n", + " fixed_zeta = -100\n", + " fixed_alpha = -100\n", + " fixed_phi = -100\n", + " \n", + " criterion = nn.MSELoss()\n", + " optimizer_time = optim.Adam([zeta, alpha, phi], lr=lr)\n", + " optimizer_energy = optim.Adam([gamma, beta], lr=lr)\n", + " # optimizer = optim.Adam([max_time, alpha, beta, p_detect, p_q], lr=lr)\n", + "\n", + " for i in range(n_iter): \n", + " t_busy = (zeta / dvfs**(1+alpha)) ## as dvfs increases, max_time should get smaller\n", + " pred_time = (phi*itr) + t_busy ## itr_suppress reflects where pkt is in queue\n", + " \n", + " loss_time = criterion(pred_time/time, torch.ones((1,pred_time.shape[1])).double())\n", + " if i % 1000 == 0:\n", + " print(f'MSE_loss_time={loss_time.item()} loss_time={round(math.sqrt(loss_time.item()), 5)} us'\n", + " +f' zeta={zeta.item()} alpha={alpha.item()} phi={phi.item()}')\n", + " \n", + " optimizer_time.zero_grad()\n", + " loss_time.backward(retain_graph=True)\n", + " optimizer_time.step()\n", + "\n", + " if(current_loss_time == -100):\n", + " current_loss_time = loss_time.item()\n", + " else:\n", + " if(current_loss_time >= loss_time.item()):\n", + " current_loss_time = loss_time.item()\n", + " fixed_zeta = zeta.item()\n", + " fixed_alpha = alpha.item()\n", + " fixed_phi = phi.item()\n", + " \n", + " for i in range(n_iter):\n", + " #p_busy = (p_q*dvfs**(2+beta))\n", + " #t_busy_energy = (max_time / dvfs**(1+beta))\n", + " #t_q_energy = itr#(fixed_itr_suppress*itr)\n", + " #t_q_energy = (interarrival_time - (fixed_itr_suppress*itr) - t_busy_energy)\n", + " \n", + " #pred_energy = (p_q * t_q_energy) + (p_busy * t_busy_energy)\n", + " #pred_energy = AA*(fixed_itr_suppress*itr)**gamma + BB*dvfs**beta\n", + " #loss_energy = criterion(pred_energy/energy, torch.ones((1,pred_energy.shape[1])).double())\n", + " #pred_energy = ((fixed_itr_suppress*itr*p_q)*((20*(10**6))/(fixed_itr_suppress*itr))) + (dvfs*beta)\n", + " \n", + " ## sigmetrics'22 equations\n", + " pred_energy = (gamma*(fixed_itr_suppress*itr))*(dvfs**beta) #+ (AA*(dvfs**beta))\n", + " #pred_energy = gamma+(np.log(fixed_phi)+np.log(itr))+(beta*np.log(dvfs))\n", + " \n", + " \n", + " #pred_energy = (*itr + t_busy_energy)*p_q\n", + " loss_energy = criterion(pred_energy, energy)\n", + "\n", + " if i % 1000 == 0:\n", + " print(f'loss_energy={loss_energy.item()} loss_energy={math.sqrt(loss_energy.item())}J gamma={gamma.item()} beta={beta.item()}')\n", + " #print(pred_energy)\n", + " \n", + " optimizer_energy.zero_grad()\n", + " loss_energy.backward(retain_graph=True)\n", + " optimizer_energy.step()\n", + " \n", + " return pred_energy, pred_time" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "def run_energy(df_comb, n_iter=2000, lr=1, rqps=400000, rtail='99', msys=['ebbrt_tuned'], mpred=['energy', 'time']): \n", + " df_comb = df_comb[df_comb['QPS'] == rqps]\n", + "\n", + " i=1\n", + " \n", + " for sys in msys:\n", + " df = df_comb[(df_comb['sys']==sys)].copy()\n", + " rt = 'read_'+rtail+'th'\n", + " df = df[['joules','itr', 'dvfs', 'QPS', rt, 'num_interrupts']]\n", + " tnum = df.shape[0]\n", + " d = df.values\n", + " d = torch.tensor(d)\n", + " print('SYS', sys)\n", + " #pred_energy, max_time, alpha, beta, p_detect, p_q = inference_energy(d, n_iter, lr, 'mcd', sys, print_freq=1000)\n", + " pred_energy, pred_time = inference(d, n_iter, lr, 'mcd', sys, print_freq=1000)\n", + " \n", + " df[f'pre_energy lr={lr}'] = pred_energy.view(tnum, 1).detach().numpy()\n", + " df[f'pre_time lr={lr}'] = pred_time.view(tnum, 1).detach().numpy()\n", + " \n", + " for pred_name in mpred:\n", + " if pred_name == 'energy':\n", + " pred = pred_energy\n", + " qps = d[:,3]\n", + " yvalue = (d[:,0]/d[:,5]).log()\n", + " #yvalue = (d[:,0]/(qps*20)).log()\n", + " else:\n", + " pred = pred_time\n", + " yvalue = d[:,4]\n", + "\n", + " #fig, ax = plt.subplots()\n", + " ax = plt.subplot(1, len(msys)*len(mpred), i)\n", + " \n", + " if sys == 'ebbrt_tuned':\n", + " plt.title(f'EbbRT @ {int(rqps/1000)}K QPS', fontsize=20)\n", + " else:\n", + " plt.title(f'Linux @ {int(rqps/1000)}K QPS', fontsize=20)\n", + " \n", + " #plt.title(f'pred:{pred_name} mcd sys={sys} lr={lr} tail={rtail} \\n QPS={rqps} max_time={round(max_time,2)} \\n alpha={round(alpha,2)} beta={round(beta.item(),2)} \\n p_detect={round(p_detect.item(),2)}')\n", + " if pred_name == 'energy':\n", + " plt.ylabel('Measured Energy (J)', fontsize=20)\n", + " plt.xlabel('Predicted Energy (J)', fontsize=20)\n", + " else:\n", + " plt.ylabel('Measured 99% Tail (us)', fontsize=20)\n", + " plt.xlabel('Predicted 99% Tail (us)', fontsize=20)\n", + " \n", + " if pred_name == \"time\":\n", + " tmax = yvalue.max().item()\n", + " plt.plot(np.linspace(0, tmax, 10), np.linspace(0, tmax, 10))\n", + " else:\n", + " tmax = yvalue.max().item()\n", + " tmin = yvalue.min().item()\n", + " #print(yvalue.min(), yvalue.max(), tmin, tmax)\n", + " plt.plot(np.linspace(tmin, tmax, 10), np.linspace(tmin, tmax, 10))\n", + " \n", + " print('measurement', yvalue.mean(), yvalue.std())\n", + " \n", + " scatter = ax.scatter(pred.detach().numpy()[0], yvalue, marker = 'o', \n", + " s = d[:,1], c = d[:,2], alpha=0.5)\n", + " \n", + " \n", + " legend1 = ax.legend(*scatter.legend_elements(),loc=\"upper left\", title=\"dvfs\")\n", + " ax.add_artist(legend1)\n", + " handles, labels = scatter.legend_elements(prop=\"sizes\", alpha=0.6)\n", + " legend2 = plt.legend(handles, labels, loc=\"lower right\", title=\"itr\")\n", + " ax.add_artist(legend2)\n", + " \n", + " plt.grid(True)\n", + " plt.tight_layout()\n", + " i += 1\n", + " \n", + " plt.subplots_adjust(wspace=0.3, hspace=0)\n", + " " + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "MSE_loss_time=0.13425779217896575 loss_time=0.36641 us zeta=116.5868148803711 alpha=-1.045999526977539 phi=0.9871797561645508\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " phi = torch.tensor(torch.Tensor(1,1).uniform_(0.98, 1.0), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=9.505161263447323e-05 loss_time=0.00975 us zeta=55.9122428894043 alpha=-0.9653685092926025 phi=0.984230637550354\n", + "MSE_loss_time=0.00015566544681643523 loss_time=0.01248 us zeta=55.56001663208008 alpha=-0.9766871929168701 phi=0.9937692880630493\n", + "MSE_loss_time=9.505173076071447e-05 loss_time=0.00975 us zeta=55.90427017211914 alpha=-0.9655163884162903 phi=0.9842406511306763\n", + "MSE_loss_time=9.505216939477982e-05 loss_time=0.00975 us zeta=55.89499282836914 alpha=-0.9656915068626404 phi=0.9842521548271179\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([43])) that is different to the input size (torch.Size([1, 43])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=263436.71921560145 loss_energy=513.2608685801027J gamma=-1.7898986339569092 beta=0.21935462951660156\n", + "loss_energy=44.99864215353025 loss_energy=6.708102723835574J gamma=-0.271661639213562 beta=-8.063189506530762\n", + "loss_energy=44.84745570318395 loss_energy=6.696824299859147J gamma=-0.26285380125045776 beta=-7.912354469299316\n", + "loss_energy=44.53619207322615 loss_energy=6.673544191299413J gamma=-0.24681691825389862 beta=-7.622581958770752\n", + "loss_energy=43.84220516879002 loss_energy=6.621344664702934J gamma=-0.21872185170650482 beta=-7.059972763061523\n", + "measurement tensor(-7.2562, dtype=torch.float64) tensor(0.6583, dtype=torch.float64)\n", + "measurement tensor(268.5023, dtype=torch.float64) tensor(125.2022, dtype=torch.float64)\n", + "SYS linux_tuned\n", + "MSE_loss_time=0.032600524740982025 loss_time=0.18056 us zeta=32.45799255371094 alpha=-1.308335781097412 phi=0.9925207495689392\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " phi = torch.tensor(torch.Tensor(1,1).uniform_(0.98, 1.0), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.0007445113864997696 loss_time=0.02729 us zeta=86.45873260498047 alpha=-0.47653451561927795 phi=1.0952074527740479\n", + "MSE_loss_time=0.00822561802973988 loss_time=0.0907 us zeta=86.74727630615234 alpha=-0.5331271290779114 phi=1.209136962890625\n", + "MSE_loss_time=0.006746352995719148 loss_time=0.08214 us zeta=86.62887573242188 alpha=-0.352305144071579 phi=1.0106247663497925\n", + "MSE_loss_time=0.0007455941111247975 loss_time=0.02731 us zeta=86.7369613647461 alpha=-0.4701177477836609 phi=1.0940611362457275\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([47])) that is different to the input size (torch.Size([1, 47])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=94186.85635050412 loss_energy=306.8987721554196J gamma=-1.427673578262329 beta=-0.21634268760681152\n", + "loss_energy=43.71175551356884 loss_energy=6.611486634151872J gamma=-0.46601569652557373 beta=-8.911333084106445\n", + "loss_energy=43.234054773989605 loss_energy=6.57526081414187J gamma=-0.41962099075317383 beta=-8.458392143249512\n", + "loss_energy=41.9864803681701 loss_energy=6.479697552214154J gamma=-0.33446675539016724 beta=-7.479580402374268\n", + "loss_energy=35.96470300312656 loss_energy=5.997057862246J gamma=-0.17335185408592224 beta=-4.746431350708008\n", + "measurement tensor(-7.2106, dtype=torch.float64) tensor(0.7405, dtype=torch.float64)\n", + "measurement tensor(274.5043, dtype=torch.float64) tensor(138.1329, dtype=torch.float64)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = 16, 6\n", + "#plt.rc('xtick', labelsize=20) # fontsize of the tick labels \n", + "df_comb = df_comb[df_comb['itr'] != 350]\n", + "\n", + "run_energy(df_comb, n_iter=5000, lr=1, rqps=200000, rtail='99', \n", + " mpred=['energy', 'time'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 82, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "MSE_loss_time=1.0751574963044432 loss_time=1.0369 us zeta=413.80426025390625 alpha=-0.21788573265075684 phi=0.8841455578804016\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.038697982979633 loss_time=0.19672 us zeta=378.96807861328125 alpha=7.47592830657959 phi=1.2770013809204102\n", + "MSE_loss_time=0.03816696878264051 loss_time=0.19536 us zeta=316.9184265136719 alpha=6.3361124992370605 phi=1.268079400062561\n", + "MSE_loss_time=0.00023605369523754943 loss_time=0.01536 us zeta=55.734619140625 alpha=-1.0079970359802246 phi=0.9902999401092529\n", + "MSE_loss_time=0.00023152580411718788 loss_time=0.01522 us zeta=55.73063278198242 alpha=-1.0064697265625 phi=0.9878197312355042\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([43])) that is different to the input size (torch.Size([1, 43])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=202.84454080795783 loss_energy=14.242350255767404J gamma=1.0059535503387451 beta=1.212451696395874\n", + "loss_energy=0.00304647258937297 loss_energy=0.05519486017169506J gamma=-12.850198745727539 beta=0.6686517596244812\n", + "loss_energy=0.0030464725886850982 loss_energy=0.05519486016546376J gamma=-12.850196838378906 beta=0.6686493754386902\n", + "loss_energy=0.0030464725886862336 loss_energy=0.05519486016547404J gamma=-12.850196838378906 beta=0.6686493158340454\n", + "loss_energy=0.003046472588649762 loss_energy=0.055194860165143654J gamma=-12.85019588470459 beta=0.6686481833457947\n", + "tensor(-8.6917, dtype=torch.float64) tensor(-6.2347, dtype=torch.float64) -8.691686489487775 -6.234726301565292\n", + "measurement tensor(-7.2955, dtype=torch.float64) tensor(0.7313, dtype=torch.float64)\n", + "measurement tensor(261.6047, dtype=torch.float64) tensor(125.9583, dtype=torch.float64)\n", + "SYS linux_tuned\n", + "MSE_loss_time=0.32259637871983476 loss_time=0.56798 us zeta=217.72923278808594 alpha=-1.0713615417480469 phi=0.3559999465942383\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.006343832419319423 loss_time=0.07965 us zeta=238.4120330810547 alpha=0.6271592378616333 phi=1.091758370399475\n", + "MSE_loss_time=0.0063402900170654595 loss_time=0.07963 us zeta=242.07852172851562 alpha=0.6487679481506348 phi=1.0916109085083008\n", + "MSE_loss_time=0.006340295237530636 loss_time=0.07963 us zeta=242.26953125 alpha=0.6498933434486389 phi=1.0915906429290771\n", + "MSE_loss_time=0.006380852729538191 loss_time=0.07988 us zeta=242.63905334472656 alpha=0.6554573774337769 phi=1.0824739933013916\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([43])) that is different to the input size (torch.Size([1, 43])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=114.83496568845936 loss_energy=10.716107767676627J gamma=-0.4586305618286133 beta=-1.4429817199707031\n", + "loss_energy=0.0034002285157215353 loss_energy=0.05831147842167557J gamma=-12.767843246459961 beta=0.7585389018058777\n", + "loss_energy=0.0034002285154433585 loss_energy=0.058311478419290304J gamma=-12.767845153808594 beta=0.7585412263870239\n", + "loss_energy=0.0034002285154433585 loss_energy=0.058311478419290304J gamma=-12.767845153808594 beta=0.7585412263870239\n", + "loss_energy=0.0034002285154433585 loss_energy=0.058311478419290304J gamma=-12.767845153808594 beta=0.7585412263870239\n", + "tensor(-8.4089, dtype=torch.float64) tensor(-5.9149, dtype=torch.float64) -8.408920793611529 -5.914861763269418\n", + "measurement tensor(-7.1624, dtype=torch.float64) tensor(0.7628, dtype=torch.float64)\n", + "measurement tensor(283.1814, dtype=torch.float64) tensor(129.9831, dtype=torch.float64)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=5000, lr=1, rqps=400000, rtail='99', \n", + " mpred=['energy', 'time'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 83, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "MSE_loss_time=0.17272639873153672 loss_time=0.4156 us zeta=249.36131286621094 alpha=0.16319942474365234 phi=0.28351062536239624\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.0006018540708879737 loss_time=0.02453 us zeta=52.222633361816406 alpha=-1.0374584197998047 phi=1.0343842506408691\n", + "MSE_loss_time=0.0006010455509550724 loss_time=0.02452 us zeta=51.72865295410156 alpha=-1.0478835105895996 phi=1.0350453853607178\n", + "MSE_loss_time=0.0006349301062736475 loss_time=0.0252 us zeta=51.689640045166016 alpha=-1.0512018203735352 phi=1.04254150390625\n", + "MSE_loss_time=0.0006010455733388009 loss_time=0.02452 us zeta=51.728668212890625 alpha=-1.0478899478912354 phi=1.035050630569458\n", + "MSE_loss_time=0.00569797683705437 loss_time=0.07548 us zeta=51.591453552246094 alpha=-1.0982624292373657 phi=1.1221656799316406\n", + "MSE_loss_time=0.0006012386977086421 loss_time=0.02452 us zeta=51.70746994018555 alpha=-1.0500953197479248 phi=1.035141110420227\n", + "MSE_loss_time=0.0006010455676814547 loss_time=0.02452 us zeta=51.72853469848633 alpha=-1.047888159751892 phi=1.0350511074066162\n", + "MSE_loss_time=0.000602646105768946 loss_time=0.02455 us zeta=51.686798095703125 alpha=-1.0486127138137817 phi=1.0368787050247192\n", + "MSE_loss_time=0.0006011207458396684 loss_time=0.02452 us zeta=51.728939056396484 alpha=-1.0481948852539062 phi=1.035345435142517\n", + "MSE_loss_time=0.0006010618018086115 loss_time=0.02452 us zeta=51.72428512573242 alpha=-1.0482245683670044 phi=1.035162329673767\n", + "MSE_loss_time=0.0019638890396602234 loss_time=0.04432 us zeta=51.47254180908203 alpha=-1.0311903953552246 phi=0.989669919013977\n", + "MSE_loss_time=0.0006010455508674764 loss_time=0.02452 us zeta=51.728599548339844 alpha=-1.0478845834732056 phi=1.035045862197876\n", + "MSE_loss_time=0.0006010473331956158 loss_time=0.02452 us zeta=51.72866439819336 alpha=-1.0479329824447632 phi=1.035091757774353\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([45])) that is different to the input size (torch.Size([1, 45])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=140.40116966362092 loss_energy=11.8490999516259J gamma=-0.07656121253967285 beta=-0.6371498107910156\n", + "loss_energy=0.0022611150704443046 loss_energy=0.04755118369130578J gamma=-12.857898712158203 beta=0.6829619407653809\n", + "loss_energy=0.0022611150704446403 loss_energy=0.047551183691309305J gamma=-12.857898712158203 beta=0.6829620003700256\n", + "loss_energy=0.0022611150704446403 loss_energy=0.047551183691309305J gamma=-12.857898712158203 beta=0.6829620003700256\n", + "loss_energy=0.0022611150709574046 loss_energy=0.04755118369670102J gamma=-12.85789966583252 beta=0.68296217918396\n", + "loss_energy=0.0022611150707249985 loss_energy=0.047551183694257274J gamma=-12.85789966583252 beta=0.6829624176025391\n", + "loss_energy=0.0026922616291300957 loss_energy=0.05188700828849256J gamma=-12.845952033996582 beta=0.695052444934845\n", + "loss_energy=0.0022611171988025057 loss_energy=0.04755120607095582J gamma=-12.85787296295166 beta=0.6829898357391357\n", + "loss_energy=0.0022614020232037345 loss_energy=0.04755420089964434J gamma=-12.857589721679688 beta=0.6832727789878845\n", + "loss_energy=0.002263772976431909 loss_energy=0.04757912332559217J gamma=-12.858841896057129 beta=0.6820195317268372\n", + "loss_energy=0.00226111591549116 loss_energy=0.047551192576960256J gamma=-12.857882499694824 beta=0.6829795241355896\n", + "loss_energy=0.0022744293239424715 loss_energy=0.04769097738506175J gamma=-12.86000919342041 beta=0.6808520555496216\n", + "loss_energy=0.0022641293088105367 loss_energy=0.047582867807757626J gamma=-12.858902931213379 beta=0.6819580793380737\n", + "loss_energy=0.0022611151036184504 loss_energy=0.047551184040131436J gamma=-12.857895851135254 beta=0.682965874671936\n", + "tensor(-8.6718, dtype=torch.float64) tensor(-6.1155, dtype=torch.float64) -8.671807528583088 -6.115486644714664\n", + "measurement tensor(-7.2364, dtype=torch.float64) tensor(0.7498, dtype=torch.float64)\n", + "measurement tensor(270.9156, dtype=torch.float64) tensor(132.8082, dtype=torch.float64)\n", + "SYS linux_tuned\n", + "MSE_loss_time=0.15737716040717736 loss_time=0.39671 us zeta=285.3316955566406 alpha=-0.5010113716125488 phi=0.01248311996459961\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.011878883507503232 loss_time=0.10899 us zeta=323.7709045410156 alpha=0.41723474860191345 phi=1.0539549589157104\n", + "MSE_loss_time=0.009886563153462856 loss_time=0.09943 us zeta=394.76812744140625 alpha=0.6238011717796326 phi=1.0387611389160156\n", + "MSE_loss_time=0.008362449305227391 loss_time=0.09145 us zeta=477.6034851074219 alpha=0.8293798565864563 phi=1.0268357992172241\n", + "MSE_loss_time=0.008671396778044961 loss_time=0.09312 us zeta=561.08837890625 alpha=0.9947963953018188 phi=1.0721476078033447\n", + "MSE_loss_time=0.012014343480092327 loss_time=0.10961 us zeta=635.4136352539062 alpha=1.181275486946106 phi=1.1522527933120728\n", + "MSE_loss_time=0.006772502670620497 loss_time=0.0823 us zeta=692.1300659179688 alpha=1.2539427280426025 phi=1.0140047073364258\n", + "MSE_loss_time=0.010029874649365176 loss_time=0.10015 us zeta=727.7911987304688 alpha=1.3897606134414673 phi=0.9555983543395996\n", + "MSE_loss_time=0.0067057084241711916 loss_time=0.08189 us zeta=746.5087280273438 alpha=1.3454385995864868 phi=1.0130200386047363\n", + "MSE_loss_time=0.0067038519260281875 loss_time=0.08188 us zeta=754.550048828125 alpha=1.3584855794906616 phi=1.012992262840271\n", + "MSE_loss_time=0.006706108036388552 loss_time=0.08189 us zeta=758.0946655273438 alpha=1.3669278621673584 phi=1.0118769407272339\n", + "MSE_loss_time=0.0067036479432578166 loss_time=0.08188 us zeta=759.215576171875 alpha=1.366018533706665 phi=1.0129821300506592\n", + "MSE_loss_time=0.006703655457714383 loss_time=0.08188 us zeta=759.4853515625 alpha=1.3664532899856567 phi=1.0129793882369995\n", + "MSE_loss_time=0.0067618046962619055 loss_time=0.08223 us zeta=759.4154663085938 alpha=1.3752285242080688 phi=1.004847526550293\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([28])) that is different to the input size (torch.Size([1, 28])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=136.87393951645694 loss_energy=11.699313634417061J gamma=0.5920922756195068 beta=-1.064333438873291\n", + "loss_energy=0.0024915203426911212 loss_energy=0.04991513140011875J gamma=-12.735400199890137 beta=0.9075253009796143\n", + "loss_energy=0.0024915203419033543 loss_energy=0.04991513139222769J gamma=-12.735404014587402 beta=0.9075297117233276\n", + "loss_energy=0.0024915203419033543 loss_energy=0.04991513139222769J gamma=-12.735404014587402 beta=0.9075297117233276\n", + "loss_energy=0.0024915203419033543 loss_energy=0.04991513139222769J gamma=-12.735404014587402 beta=0.9075297117233276\n", + "loss_energy=0.002491520343418476 loss_energy=0.04991513140740467J gamma=-12.735404014587402 beta=0.9075311422348022\n", + "loss_energy=0.002491520342392886 loss_energy=0.04991513139713133J gamma=-12.735404968261719 beta=0.9075299501419067\n", + "loss_energy=0.002491520341889518 loss_energy=0.04991513139208909J gamma=-12.735404968261719 beta=0.9075308442115784\n", + "loss_energy=0.0024915203420158537 loss_energy=0.0499151313933546J gamma=-12.735404968261719 beta=0.9075303673744202\n", + "loss_energy=0.002491520341890641 loss_energy=0.04991513139210034J gamma=-12.735404968261719 beta=0.9075307250022888\n", + "loss_energy=0.002491520341890641 loss_energy=0.04991513139210034J gamma=-12.735404968261719 beta=0.9075307250022888\n", + "loss_energy=0.0024915203444111226 loss_energy=0.04991513141734801J gamma=-12.735404014587402 beta=0.9075315594673157\n", + "loss_energy=0.002491520341887533 loss_energy=0.04991513139206921J gamma=-12.735404968261719 beta=0.9075307846069336\n", + "loss_energy=0.002491520341887533 loss_energy=0.04991513139206921J gamma=-12.735404968261719 beta=0.9075307846069336\n", + "tensor(-8.1694, dtype=torch.float64) tensor(-6.0129, dtype=torch.float64) -8.169370425804964 -6.012855916943453\n", + "measurement tensor(-7.0168, dtype=torch.float64) tensor(0.6565, dtype=torch.float64)\n", + "measurement tensor(296.4500, dtype=torch.float64) tensor(119.2272, dtype=torch.float64)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=14000, lr=1, rqps=600000, rtail='99', \n", + " mpred=['energy', 'time'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "MSE_loss_time=3.466123850823403 loss_time=1.86175 us zeta=429.5527648925781 alpha=-0.9666502475738525 phi=0.6700219511985779\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.03406095864768174 loss_time=0.18456 us zeta=405.8588562011719 alpha=7.589137554168701 phi=1.3116512298583984\n", + "MSE_loss_time=0.03397614584678404 loss_time=0.18433 us zeta=390.543212890625 alpha=7.480998992919922 phi=1.3117531538009644\n", + "MSE_loss_time=0.03376778497923761 loss_time=0.18376 us zeta=358.89666748046875 alpha=7.001809120178223 phi=1.3084983825683594\n", + "MSE_loss_time=0.03259997937539418 loss_time=0.18055 us zeta=268.2585754394531 alpha=5.143280506134033 phi=1.2869969606399536\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([45])) that is different to the input size (torch.Size([1, 45])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=167.2809325538804 loss_energy=12.933713022712404J gamma=1.1304125785827637 beta=-0.8106768131256104\n", + "loss_energy=0.002261115071200098 loss_energy=0.04755118369925294J gamma=-12.85789680480957 beta=0.6829594373703003\n", + "loss_energy=0.0022611150704615482 loss_energy=0.047551183691487094J gamma=-12.857898712158203 beta=0.6829618215560913\n", + "loss_energy=0.0022611150704615482 loss_energy=0.047551183691487094J gamma=-12.857898712158203 beta=0.6829618215560913\n", + "loss_energy=0.002261115070408559 loss_energy=0.047551183690929914J gamma=-12.85789966583252 beta=0.6829630136489868\n", + "measurement tensor(270.9156, dtype=torch.float64) tensor(132.8082, dtype=torch.float64)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=5000, lr=1, rqps=600000, rtail='99', \n", + " mpred=['time'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "MSE_loss_time=0.250746345310389 loss_time=0.50075 us zeta=219.5358428955078 alpha=-0.13427114486694336 phi=0.16302889585494995\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.00011815393441662156 loss_time=0.01087 us zeta=40.25274658203125 alpha=-0.861304759979248 phi=0.5298420786857605\n", + "MSE_loss_time=0.00019645266694098368 loss_time=0.01402 us zeta=38.915367126464844 alpha=-0.893722653388977 phi=0.5386803150177002\n", + "MSE_loss_time=0.0004034163373217096 loss_time=0.02009 us zeta=38.81593322753906 alpha=-0.9112141132354736 phi=0.5424319505691528\n", + "MSE_loss_time=0.000111404641078033 loss_time=0.01055 us zeta=38.83777618408203 alpha=-0.8948989510536194 phi=0.5319880247116089\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([43])) that is different to the input size (torch.Size([1, 43])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=125.50113366436524 loss_energy=11.202728849006622J gamma=-1.395688772201538 beta=1.1223609447479248\n", + "loss_energy=0.007790184376058229 loss_energy=0.08826202114192848J gamma=-12.280386924743652 beta=0.6863613724708557\n", + "loss_energy=0.007790184375574674 loss_energy=0.08826202113918916J gamma=-12.280388832092285 beta=0.6863637566566467\n", + "loss_energy=0.007790184375574674 loss_energy=0.08826202113918916J gamma=-12.280388832092285 beta=0.6863637566566467\n", + "loss_energy=0.007790184375574674 loss_energy=0.08826202113918916J gamma=-12.280388832092285 beta=0.6863637566566467\n", + "SYS linux_tuned\n", + "MSE_loss_time=0.17849212517629381 loss_time=0.42248 us zeta=101.657470703125 alpha=0.49157261848449707 phi=0.2722965478897095\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=0.00034110931106035394 loss_time=0.01847 us zeta=63.819984436035156 alpha=-0.47737956047058105 phi=0.6017702221870422\n", + "MSE_loss_time=0.0003411219797596367 loss_time=0.01847 us zeta=63.81975173950195 alpha=-0.47725775837898254 phi=0.6016944646835327\n", + "MSE_loss_time=0.0006163266914316118 loss_time=0.02483 us zeta=63.96173858642578 alpha=-0.5066607594490051 phi=0.6098025441169739\n", + "MSE_loss_time=0.00034111043485742995 loss_time=0.01847 us zeta=63.82070541381836 alpha=-0.4773516058921814 phi=0.6017425656318665\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([47])) that is different to the input size (torch.Size([1, 47])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=133.2286513545547 loss_energy=11.542471631091614J gamma=0.9536914825439453 beta=-1.6257336139678955\n", + "loss_energy=0.013456890477387242 loss_energy=0.11600383820110109J gamma=-12.209689140319824 beta=0.7141976952552795\n", + "loss_energy=0.013456890477387799 loss_energy=0.11600383820110349J gamma=-12.209689140319824 beta=0.7141976356506348\n", + "loss_energy=0.013456890477333285 loss_energy=0.11600383820086853J gamma=-12.20969009399414 beta=0.7141988277435303\n", + "loss_energy=0.013456890477332117 loss_energy=0.1160038382008635J gamma=-12.20969009399414 beta=0.714198887348175\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=5000, lr=1, rqps=200000, rtail='50', \n", + " mpred=['energy', 'time'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['sys', 'i', 'itr', 'dvfs', 'rapl', 'read_5th', 'read_10th', 'read_50th',\n", + " 'read_90th', 'read_95th', 'read_99th', 'measure_QPS', 'target_QPS',\n", + " 'time', 'joules', 'rx_desc', 'rx_bytes', 'tx_desc', 'tx_bytes',\n", + " 'instructions', 'cycles', 'ref_cycles', 'llc_miss', 'c1', 'c1e', 'c3',\n", + " 'c6', 'c7', 'num_interrupts', 'QPS'],\n", + " dtype='object')\n", + "[ 200000 400000 600000 1000000 1500000]\n", + "[0]\n", + "645\n" + ] + } + ], + "source": [ + "ddf = pd.read_csv('~/github/energy_trace_experiment_scripts/collected_data/mcd_combined.csv', sep=' ')\n", + "ddf['QPS'] = ddf['target_QPS']\n", + "ddf = ddf[ddf['rapl'] == 135]\n", + "ddf = ddf[ddf['read_99th'] <= 500]\n", + "ddf = ddf[ddf['i'] == 0]\n", + "\n", + "ddf['dvfs'] = ddf['dvfs'].apply(lambda x: dvfs_dict[x])\n", + "ddf = ddf[(ddf['itr']!=1) & (ddf['dvfs']!=65535)] #filter out linux dynamic\n", + "print(df_comb.columns)\n", + "print(ddf['QPS'].unique())\n", + "print(ddf['i'].unique())\n", + "print(ddf.shape[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "loss_time=1.347581989426996 zeta=375.9709777832031 alpha=0.22811484336853027 phi=0.6536439657211304\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_time=0.015020846242065705 zeta=69.32869720458984 alpha=-0.7216720581054688 phi=1.053759217262268\n", + "loss_time=0.015020846242079064 zeta=69.32869720458984 alpha=-0.7216721773147583 phi=1.0537593364715576\n", + "loss_time=0.015020986652044559 zeta=69.32296752929688 alpha=-0.7213442921638489 phi=1.053292155265808\n", + "loss_time=0.01537675617184246 zeta=69.30174255371094 alpha=-0.6992050409317017 phi=1.0310273170471191\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":88: RuntimeWarning: divide by zero encountered in log\n", + " pred_energy = gamma+(np.log(fixed_phi)+np.log(itr))+(beta*np.log(dvfs))\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([90])) that is different to the input size (torch.Size([1, 90])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=inf gamma=-0.9975230693817139 beta=0.7300441265106201\n", + "loss_energy=nan gamma=nan beta=nan\n", + "loss_energy=nan gamma=nan beta=nan\n", + "loss_energy=nan gamma=nan beta=nan\n", + "loss_energy=nan gamma=nan beta=nan\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "\n", + "\n", + "run_energy(ddf, n_iter=5000, lr=1, rqps=1000000, rtail='99', \n", + " mpred=['time'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "loss_time=0.43016901033855376 zeta=57.74052047729492 alpha=1.6297779083251953 phi=0.8215769529342651\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_time=0.027117732281898586 zeta=125.97518157958984 alpha=-0.03338911011815071 phi=1.2233915328979492\n", + "loss_time=0.027117753563222227 zeta=126.15747833251953 alpha=-0.03212921321392059 phi=1.2227662801742554\n", + "loss_time=0.02775474995877512 zeta=126.0926513671875 alpha=0.00632043182849884 phi=1.198974847793579\n", + "loss_time=0.02742354888049212 zeta=126.09355926513672 alpha=-0.008886348456144333 phi=1.2021839618682861\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":88: RuntimeWarning: divide by zero encountered in log\n", + " pred_energy = gamma+(np.log(fixed_phi)+np.log(itr))+(beta*np.log(dvfs))\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([70])) that is different to the input size (torch.Size([1, 70])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=inf gamma=-0.9948389530181885 beta=1.2042922973632812\n", + "loss_energy=nan gamma=nan beta=nan\n", + "loss_energy=nan gamma=nan beta=nan\n", + "loss_energy=nan gamma=nan beta=nan\n", + "loss_energy=nan gamma=nan beta=nan\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(ddf, n_iter=5000, lr=1, rqps=1500000, rtail='99', \n", + " mpred=['time'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_q = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "429.2119140625 -0.5882140398025513 -0.9464408159255981 35.02513128982781\n", + "211.00961303710938 3.6269314289093018 0.9249256253242493 0.24131148425789484\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_comb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrqps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtail\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'90'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mrun_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_comb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrqps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m400000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtail\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'90'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mrun_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_comb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrqps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m600000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtail\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'90'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mrun_time\u001b[0;34m(df_comb, n_iter, lr, rqps, rtail, msys)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m#plt.plot(d[:,0], d[:,1], 'p')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mpred_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitr_suppress\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minference_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'mcd'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprint_freq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0mtnum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrqps\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m200000\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36minference_time\u001b[0;34m(d, n_iter, lr, workload, sys, print_freq)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m \u001b[0mp_busy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mp_static_busy\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mp_busy_min\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdvfs\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mbeta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 45\u001b[0m \u001b[0mt_busy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmax_time\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mdvfs\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0mpred_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitr_suppress\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mitr\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mt_busy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "def run_time(df_comb, n_iter=2000, lr=1, rqps=400000, rtail='99', msys=['ebbrt_tuned']): \n", + " df_comb = df_comb[df_comb['QPS'] == rqps]\n", + "\n", + " for sys in msys:\n", + " df = df_comb[(df_comb['sys']==sys)].copy()\n", + " rt = 'read_'+rtail+'th_mean'\n", + " df = df[['joules_mean','itr', 'dvfs', 'QPS', rt]]\n", + " d = df.values\n", + " d = torch.tensor(d)\n", + " #plt.plot(d[:,0], d[:,1], 'p')\n", + "\n", + " pred_time, max_time, alpha, itr_suppress = inference_time(d, n_iter, lr, 'mcd', sys, print_freq=1000)\n", + " tnum = 0\n", + " if rqps == 200000:\n", + " if sys == 'linux_tuned':\n", + " tnum=345\n", + " else:\n", + " tnum=246\n", + " elif rqps == 400000:\n", + " if sys == 'linux_tuned':\n", + " tnum=318\n", + " #df[f'pre_energy lr={lr}'] = pred_energy.view(318, 1).detach().numpy()\n", + " #df[f'pre_time lr={lr}'] = pred_time.view(318, 1).detach().numpy()\n", + " else:\n", + " tnum=245\n", + " #df[f'pre_energy lr={lr}'] = pred_energy.view(245, 1).detach().numpy()\n", + " #df[f'pre_time lr={lr}'] = pred_time.view(245, 1).detach().numpy()\n", + " if rqps == 600000:\n", + " if sys == 'linux_tuned':\n", + " tnum=202\n", + " else:\n", + " tnum=246\n", + " df[f'pre_time lr={lr}'] = pred_time.view(tnum, 1).detach().numpy()\n", + "\n", + "# for pred_name in ['time', 'energy']:\n", + "# if pred_name == 'energy':\n", + "# pred = pred_energy\n", + "# qps = d[:,3]\n", + "# yvalue = d[:,0]/(qps*20)\n", + "# else:\n", + " pred = pred_time\n", + " yvalue = d[:,4]\n", + " fig, ax = plt.subplots()\n", + " plt.title(f'pred:time mcd sys={sys} lr={lr} tail={rtail} \\n QPS={rqps} max_time={round(max_time.item(),2)} \\n alpha={round(alpha.item(),2)} itr_suppress={round(itr_suppress.item(),2)}')\n", + " plt.xlabel(u\"predictions\")\n", + " plt.ylabel('time')\n", + " scatter = ax.scatter(pred.detach().numpy()[0], yvalue, marker = 'o', s = d[:,1], c = d[:,2], alpha=0.3)\n", + " legend1 = ax.legend(*scatter.legend_elements(),loc=\"upper left\", title=\"dvfs\")\n", + " ax.add_artist(legend1)\n", + " handles, labels = scatter.legend_elements(prop=\"sizes\", alpha=0.6)\n", + " legend2 = plt.legend(handles, labels, loc=\"lower right\", title=\"itr\")\n", + " ax.add_artist(legend2)\n", + " \n", + "run_time(df_comb, rqps=200000, rtail='90')\n", + "run_time(df_comb, rqps=400000, rtail='90')\n", + "run_time(df_comb, rqps=600000, rtail='90')" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'float' object has no attribute 'item'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_energy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_comb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrqps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtail\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'90'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mrun_energy\u001b[0;34m(df_comb, n_iter, lr, rqps, rtail, msys)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'pred:energy mcd sys={sys} lr={lr} tail={rtail} \\n QPS={rqps} max_time={round(max_time.item(),2)} \\n alpha={round(alpha.item(),2)} beta={round(beta.item(),2)} \\n p_detect={round(p_detect.item(),2)}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu\"predictions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'energy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'float' object has no attribute 'item'" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/analysis/run_mcdsilo.ipynb b/analysis/run_mcdsilo.ipynb new file mode 100644 index 0000000..2cd8023 --- /dev/null +++ b/analysis/run_mcdsilo.ipynb @@ -0,0 +1,1804 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('../bayesopt')\n", + "\n", + "import read_agg_data\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.autograd as auto\n", + "import torch.optim as optim\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as plt\n", + "import pandas as pd\n", + "import math\n", + "import pdb\n", + "\n", + "dvfs_dict = {\n", + " \"0xc00\" : 1.2,\n", + " \"0xd00\" : 1.3,\n", + " \"0xe00\" : 1.4,\n", + " \"0xf00\" : 1.5,\n", + " \"0x1000\" : 1.6,\n", + " \"0x1100\" : 1.7,\n", + " \"0x1200\" : 1.8,\n", + " \"0x1300\" : 1.9,\n", + " \"0x1400\" : 2.0,\n", + " \"0x1500\" : 2.1,\n", + " \"0x1600\" : 2.2,\n", + " \"0x1700\" : 2.3,\n", + " \"0x1800\" : 2.4,\n", + " \"0x1900\" : 2.5,\n", + " \"0x1a00\" : 2.6,\n", + " \"0x1b00\" : 2.7,\n", + " \"0x1c00\" : 2.8,\n", + " \"0x1d00\" : 2.9,\n", + " \"0xffff\" : 3.0,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Index(['sys', 'i', 'itr', 'dvfs', 'rapl', 'read_5th', 'read_10th', 'read_50th',\n", + " 'read_90th', 'read_95th', 'read_99th', 'measure_QPS', 'target_QPS',\n", + " 'time', 'joules', 'rx_desc', 'rx_bytes', 'tx_desc', 'tx_bytes',\n", + " 'instructions', 'cycles', 'ref_cycles', 'llc_miss', 'c1', 'c1e', 'c3',\n", + " 'c6', 'c7', 'num_interrupts', 'QPS'],\n", + " dtype='object') [ 50000 100000 200000 300000] 772 [135]\n", + "[666 2 4 10 20 30 40 50 100 200 8 300]\n", + "[1.2 1.3 1.5 1.7 1.9 2.1 2.3 2.5 2.7 2.9 2. 2.4 2.6 2.8]\n" + ] + } + ], + "source": [ + "#df_comb, _, _ = read_agg_data.start_analysis('mcd') #DATA\n", + "#df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: int(x, base=16))\n", + "\n", + "df_comb = pd.read_csv('/home/handong/jupyter/jupyter-notebooks/nic-tuning-experiments/bayesopt/summary_data/mcdsilo_combined.csv', sep=' ')\n", + "df_comb = df_comb[(df_comb['rapl'] == 135)]\n", + "df_comb = df_comb[(df_comb['read_99th'] >40) & (df_comb['read_99th'] <= 500)]\n", + "\n", + "df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: dvfs_dict[x])\n", + "df_comb = df_comb[(df_comb['itr']!=1) & (df_comb['dvfs']!=65535)] #filter out linux dynamic\n", + "df_comb['QPS'] = df_comb['target_QPS']\n", + "\n", + "print(df_comb.columns, df_comb['QPS'].unique(), df_comb.shape[0], df_comb['rapl'].unique())\n", + "print(df_comb['itr'].unique())\n", + "print(df_comb['dvfs'].unique())\n", + "\n", + "# df_comb['dvfs'] = df_comb['dvfs'].astype(float) / df_comb['dvfs'].min()\n", + "# print(df_comb['dvfs'].unique())\n", + "# df_comb['itr'] = df_comb['itr'].astype(float) / df_comb['itr'].min()\n", + "# print(df_comb['itr'].unique())\n", + "#print(10**6)" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1884.97\n", + "******* ebbrt_tuned 50 50000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "308 0.000760 50 2.4 207.6 2286601\n", + "309 0.000763 50 2.4 209.1 2281185\n", + "169 0.000777 50 2.5 216.8 2283760\n", + "347 0.000768 50 2.5 204.9 2288942\n", + "348 0.000773 50 2.5 206.7 2286974\n", + "188 0.000793 50 2.6 214.5 2279336\n", + "725 0.000781 50 2.6 200.8 2281031\n", + "391 0.000802 50 2.7 213.0 2278641\n", + "392 0.000798 50 2.7 209.3 2285971\n", + "785 0.000802 50 2.7 215.2 2282442\n", + "485 0.000816 50 2.8 213.2 2279115\n", + "486 0.000818 50 2.8 213.7 2276347\n", + "256 0.000821 50 2.9 201.6 2294758\n", + "605 0.000818 50 2.9 202.5 2292782\n", + "606 0.000821 50 2.9 201.7 2296562\n", + "\n", + "2057.1\n", + "******* ebbrt_tuned 50 100000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "319 0.000560 50 2.4 231.4 3379238\n", + "320 0.000561 50 2.4 233.9 3373842\n", + "173 0.000572 50 2.5 232.1 3374071\n", + "355 0.000568 50 2.5 225.4 3382096\n", + "356 0.000568 50 2.5 226.4 3380300\n", + "192 0.000582 50 2.6 234.9 3373166\n", + "729 0.000576 50 2.6 220.1 3385658\n", + "399 0.000590 50 2.7 225.6 3378658\n", + "400 0.000590 50 2.7 225.3 3377941\n", + "789 0.000590 50 2.7 226.0 3377435\n", + "493 0.000601 50 2.8 225.2 3379146\n", + "494 0.000602 50 2.8 225.1 3376897\n", + "260 0.000608 50 2.9 217.1 3385263\n", + "613 0.000608 50 2.9 218.3 3376464\n", + "614 0.000608 50 2.9 218.3 3380814\n", + "\n", + "2361.02\n", + "******* ebbrt_tuned 50 200000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "130 0.000342 50 1.9 470.9 4543952\n", + "129 0.000341 50 1.9 470.1 4536209\n", + "96 0.000356 50 2.0 438.2 4554590\n", + "97 0.000357 50 2.0 447.8 4552256\n", + "98 0.000356 50 2.0 437.9 4555408\n", + "106 0.000367 50 2.1 390.9 4583525\n", + "107 0.000367 50 2.1 400.6 4581577\n", + "108 0.000368 50 2.1 404.7 4575906\n", + "118 0.000387 50 2.3 325.7 4648134\n", + "119 0.000391 50 2.3 325.7 4635509\n", + "120 0.000392 50 2.3 338.1 4637670\n", + "331 0.000459 50 2.4 292.1 4683721\n", + "333 0.000459 50 2.4 289.8 4682948\n", + "332 0.000460 50 2.4 285.1 4676293\n", + "364 0.000466 50 2.5 283.9 4685977\n", + "177 0.000469 50 2.5 287.5 4680726\n", + "733 0.000472 50 2.6 269.9 4702530\n", + "196 0.000476 50 2.6 279.6 4694979\n", + "407 0.000483 50 2.7 272.0 4689886\n", + "408 0.000483 50 2.7 274.7 4694106\n", + "793 0.000479 50 2.7 258.1 4710093\n", + "501 0.000490 50 2.8 260.7 4709970\n", + "502 0.000490 50 2.8 261.1 4711296\n", + "91 0.000490 50 2.9 255.4 4704643\n", + "90 0.000504 50 2.9 280.6 4684985\n", + "621 0.000499 50 2.9 260.2 4711846\n", + "622 0.000499 50 2.9 258.3 4708421\n", + "264 0.000491 50 2.9 243.5 4726507\n", + "89 0.000504 50 2.9 276.4 4681500\n", + "\n", + "2038.49\n", + "******* linux_tuned 50 50000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "1023 0.000769 50 2.4 225.2 2150969\n", + "1024 0.000770 50 2.4 224.2 2139774\n", + "1025 0.000771 50 2.4 224.2 2137031\n", + "1203 0.000811 50 2.5 216.4 2125659\n", + "1201 0.000811 50 2.5 218.3 2130965\n", + "1202 0.000809 50 2.5 216.5 2135844\n", + "1379 0.000850 50 2.6 206.5 2115891\n", + "1380 0.000850 50 2.6 211.1 2120551\n", + "1381 0.000849 50 2.6 208.6 2121038\n", + "1560 0.000889 50 2.7 205.6 2114199\n", + "1559 0.000889 50 2.7 204.6 2117923\n", + "1558 0.000891 50 2.7 206.6 2114941\n", + "1736 0.000937 50 2.8 202.9 2104355\n", + "1738 0.000937 50 2.8 201.5 2103556\n", + "1737 0.000935 50 2.8 199.5 2105342\n", + "847 0.000977 50 2.9 195.1 2085514\n", + "846 0.000951 50 2.9 193.4 2122632\n", + "845 0.000979 50 2.9 193.8 2080311\n", + "\n", + "2270.59\n", + "******* linux_tuned 50 100000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "1035 0.000558 50 2.4 267.5 3272344\n", + "1036 0.000553 50 2.4 261.8 3287364\n", + "1037 0.000554 50 2.4 261.9 3281601\n", + "1215 0.000582 50 2.5 252.6 3269426\n", + "1213 0.000583 50 2.5 257.8 3273264\n", + "1214 0.000580 50 2.5 249.9 3277485\n", + "1391 0.000608 50 2.6 240.1 3258411\n", + "1392 0.000608 50 2.6 246.5 3256867\n", + "1393 0.000610 50 2.6 242.4 3253675\n", + "1572 0.000636 50 2.7 235.1 3254811\n", + "1571 0.000640 50 2.7 230.4 3230417\n", + "1570 0.000636 50 2.7 234.7 3265340\n", + "1747 0.000668 50 2.8 228.0 3252413\n", + "1749 0.000668 50 2.8 221.0 3241213\n", + "1748 0.000666 50 2.8 227.1 3247747\n", + "859 0.000692 50 2.9 216.8 3255121\n", + "858 0.000696 50 2.9 215.8 3233093\n", + "857 0.000695 50 2.9 213.4 3238275\n", + "\n", + "2690.66\n", + "******* linux_tuned 50 200000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "1048 0.000438 50 2.4 461.5 4824316\n", + "1049 0.000441 50 2.4 490.7 4793155\n", + "1225 0.000460 50 2.5 466.5 4826889\n", + "1226 0.000458 50 2.5 437.6 4825461\n", + "1227 0.000460 50 2.5 485.8 4803310\n", + "1403 0.000478 50 2.6 369.7 4795754\n", + "1404 0.000479 50 2.6 395.1 4796708\n", + "1405 0.000481 50 2.6 433.7 4785246\n", + "1584 0.000504 50 2.7 384.3 4766427\n", + "1583 0.000500 50 2.7 361.3 4807674\n", + "1582 0.000503 50 2.7 370.2 4794533\n", + "1759 0.000528 50 2.8 365.5 4771471\n", + "1761 0.000523 50 2.8 333.6 4805133\n", + "1760 0.000525 50 2.8 364.0 4772534\n", + "871 0.000551 50 2.9 332.3 4744091\n", + "870 0.000548 50 2.9 325.0 4764936\n", + "869 0.000548 50 2.9 316.4 4760085\n", + "\n", + "1884.97\n", + "******* ebbrt_tuned 100 50000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "343 0.000939 100 2.4 257.5 1726178\n", + "344 0.000883 100 2.4 260.8 1753270\n", + "345 0.000934 100 2.4 261.6 1873086\n", + "181 0.000950 100 2.5 255.3 1869108\n", + "371 0.000948 100 2.5 253.0 1872631\n", + "372 0.000950 100 2.5 254.1 1732189\n", + "200 0.000965 100 2.6 253.9 1867540\n", + "737 0.000962 100 2.6 250.3 1866446\n", + "415 0.000975 100 2.7 248.0 1732356\n", + "416 0.000975 100 2.7 248.4 1871114\n", + "797 0.000978 100 2.7 246.4 1869595\n", + "509 0.000999 100 2.8 247.8 1864195\n", + "510 0.000996 100 2.8 246.9 1867656\n", + "268 0.001010 100 2.9 241.6 1865213\n", + "629 0.001013 100 2.9 243.4 1859982\n", + "630 0.001009 100 2.9 246.9 1868043\n", + "\n", + "2057.1\n", + "******* ebbrt_tuned 100 100000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "46 0.000693 100 2.5 292.5 2455258\n", + "47 0.000694 100 2.5 293.1 2449326\n", + "48 0.000694 100 2.5 296.7 2449420\n", + "49 0.000694 100 2.5 293.7 2451602\n", + "185 0.000784 100 2.5 273.2 2461104\n", + "379 0.000781 100 2.5 271.5 2463812\n", + "380 0.000781 100 2.5 274.6 2462290\n", + "204 0.000795 100 2.6 272.5 2459251\n", + "741 0.000792 100 2.6 266.6 2463194\n", + "423 0.000803 100 2.7 267.1 2465148\n", + "424 0.000804 100 2.7 263.8 2462863\n", + "801 0.000804 100 2.7 260.9 2462053\n", + "517 0.000823 100 2.8 263.2 2461436\n", + "272 0.000834 100 2.9 256.2 2464578\n", + "637 0.000835 100 2.9 261.3 2461645\n", + "638 0.000836 100 2.9 261.2 2460177\n", + "\n", + "2361.02\n", + "******* ebbrt_tuned 100 200000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "15 0.000581 100 2.0 442.4 2753481\n", + "16 0.000581 100 2.0 435.6 2752314\n", + "17 0.000582 100 2.0 445.3 2751714\n", + "18 0.000581 100 2.0 440.6 2752499\n", + "102 0.000573 100 2.0 408.8 2767691\n", + "103 0.000577 100 2.0 432.5 2760321\n", + "33 0.000593 100 2.1 393.8 2780692\n", + "34 0.000600 100 2.1 401.7 2774015\n", + "35 0.000602 100 2.1 400.7 2768591\n", + "112 0.000610 100 2.1 436.9 2754212\n", + "113 0.000609 100 2.1 434.7 2552469\n", + "114 0.000610 100 2.1 431.6 2754595\n", + "125 0.000651 100 2.3 369.3 2784483\n", + "123 0.000650 100 2.3 373.4 2787213\n", + "124 0.000650 100 2.3 364.2 2787032\n", + "387 0.000774 100 2.5 322.5 2819073\n", + "388 0.000776 100 2.5 326.0 2818402\n", + "745 0.000809 100 2.6 307.8 2633359\n", + "431 0.000799 100 2.7 301.6 2825563\n", + "432 0.000800 100 2.7 304.2 2825926\n", + "805 0.000799 100 2.7 301.9 2826323\n", + "525 0.000816 100 2.8 299.2 2827013\n", + "526 0.000816 100 2.8 300.8 2826290\n", + "276 0.000828 100 2.9 293.1 2830874\n", + "645 0.000830 100 2.9 297.0 2827925\n", + "646 0.000813 100 2.9 273.6 2841015\n", + "\n", + "2038.49\n", + "******* linux_tuned 100 50000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "1059 0.001009 100 2.4 274.6 1618081\n", + "1060 0.000999 100 2.4 272.7 1628162\n", + "1061 0.001007 100 2.4 274.9 1613815\n", + "1238 0.001048 100 2.5 268.1 1611133\n", + "1236 0.001049 100 2.5 265.6 1612262\n", + "1237 0.001016 100 2.5 265.2 1655704\n", + "1415 0.001092 100 2.6 262.4 1621211\n", + "1416 0.001106 100 2.6 261.6 1601842\n", + "1417 0.001097 100 2.6 259.8 1611967\n", + "1596 0.001138 100 2.7 255.1 1624070\n", + "1595 0.001143 100 2.7 256.7 1616765\n", + "1594 0.001142 100 2.7 257.9 1620255\n", + "1771 0.001205 100 2.8 248.8 1605777\n", + "1773 0.001195 100 2.8 249.5 1612822\n", + "1772 0.001197 100 2.8 250.4 1612224\n", + "883 0.001248 100 2.9 245.2 1608497\n", + "882 0.001236 100 2.9 244.8 1624762\n", + "881 0.001255 100 2.9 245.8 1604580\n", + "\n", + "2270.59\n", + "******* linux_tuned 100 100000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "1071 0.000771 100 2.4 308.3 2351972\n", + "1072 0.000769 100 2.4 305.3 2347149\n", + "1073 0.000769 100 2.4 301.7 2346579\n", + "1250 0.000801 100 2.5 298.1 2345703\n", + "1248 0.000806 100 2.5 292.5 2328742\n", + "1249 0.000803 100 2.5 298.0 2339908\n", + "1427 0.000843 100 2.6 288.7 2331254\n", + "1428 0.000841 100 2.6 290.8 2341655\n", + "1429 0.000842 100 2.6 291.5 2341670\n", + "1608 0.000877 100 2.7 283.2 2339562\n", + "1607 0.000879 100 2.7 283.7 2332536\n", + "1606 0.000877 100 2.7 282.5 2346177\n", + "1783 0.000918 100 2.8 272.8 2338086\n", + "1785 0.000917 100 2.8 274.4 2329280\n", + "1784 0.000909 100 2.8 275.5 2349988\n", + "894 0.000955 100 2.9 270.5 2339976\n", + "893 0.000954 100 2.9 269.7 2337561\n", + "892 0.000962 100 2.9 265.2 2329445\n", + "\n", + "2690.66\n", + "******* linux_tuned 100 200000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "1084 0.000741 100 2.4 490.3 2811733\n", + "1260 0.000771 100 2.5 429.1 2818060\n", + "1261 0.000771 100 2.5 443.4 2822512\n", + "1262 0.000771 100 2.5 452.4 2821474\n", + "1439 0.000804 100 2.6 422.2 2820648\n", + "1440 0.000805 100 2.6 412.1 2827700\n", + "1441 0.000809 100 2.6 424.9 2818202\n", + "1618 0.000849 100 2.7 389.4 2802841\n", + "1619 0.000847 100 2.7 402.5 2805369\n", + "1620 0.000844 100 2.7 399.2 2815929\n", + "1795 0.000882 100 2.8 389.1 2811247\n", + "1796 0.000880 100 2.8 404.6 2806014\n", + "1797 0.000879 100 2.8 374.1 2821687\n", + "904 0.000921 100 2.9 371.4 2809968\n", + "905 0.000917 100 2.9 358.4 2818355\n", + "906 0.000918 100 2.9 368.4 2820659\n", + "\n", + "1884.97\n", + "******* ebbrt_tuned 200 50000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "749 0.001391 200 2.6 340.3 1283558\n", + "439 0.001420 200 2.7 341.6 1281279\n", + "440 0.001417 200 2.7 339.9 1282744\n", + "809 0.001418 200 2.7 338.4 1282004\n", + "533 0.001445 200 2.8 336.9 1187835\n", + "534 0.001442 200 2.8 338.7 1281692\n", + "280 0.001465 200 2.9 334.1 1190696\n", + "653 0.001458 200 2.9 334.7 1282780\n", + "654 0.001460 200 2.9 330.8 1281706\n", + "\n", + "2057.1\n", + "******* ebbrt_tuned 200 100000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "753 0.001356 200 2.6 355.7 1430949\n", + "447 0.001384 200 2.7 352.4 1430027\n", + "448 0.001383 200 2.7 355.4 1430425\n", + "813 0.001379 200 2.7 353.1 1430745\n", + "541 0.001396 200 2.8 348.2 1430814\n", + "542 0.001397 200 2.8 343.7 1430663\n", + "284 0.001432 200 2.9 345.0 1430676\n", + "661 0.001426 200 2.9 351.6 1430081\n", + "662 0.001426 200 2.9 346.4 1430838\n", + "\n", + "2361.02\n", + "******* ebbrt_tuned 200 200000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "757 0.001513 200 2.6 387.3 1463507\n", + "455 0.001519 200 2.7 365.3 1463542\n", + "456 0.001526 200 2.7 370.0 1463565\n", + "817 0.001538 200 2.7 379.5 1463504\n", + "549 0.001561 200 2.8 367.9 1463541\n", + "550 0.001563 200 2.8 380.7 1463480\n", + "288 0.001595 200 2.9 367.3 1463528\n", + "669 0.001596 200 2.9 375.5 1463555\n", + "670 0.001594 200 2.9 378.3 1463553\n", + "\n", + "2038.49\n", + "******* linux_tuned 200 50000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "1094 0.001365 200 2.4 359.8 1182000\n", + "1095 0.001368 200 2.4 364.1 1175140\n", + "1096 0.001365 200 2.4 364.9 1177873\n", + "1274 0.001429 200 2.5 357.1 1174553\n", + "1272 0.001435 200 2.5 358.7 1168575\n", + "1273 0.001547 200 2.5 357.9 1174894\n", + "1451 0.001496 200 2.6 354.1 1171144\n", + "1452 0.001490 200 2.6 351.9 1175825\n", + "1453 0.001487 200 2.6 350.9 1177507\n", + "1631 0.001631 200 2.7 345.6 1173025\n", + "1630 0.001564 200 2.7 349.3 1167497\n", + "1629 0.001561 200 2.7 346.1 1169352\n", + "1807 0.001638 200 2.8 347.7 1169906\n", + "1809 0.001624 200 2.8 344.4 1173897\n", + "1808 0.001637 200 2.8 345.9 1167149\n", + "918 0.001702 200 2.9 334.8 1168153\n", + "917 0.001692 200 2.9 338.5 1173259\n", + "916 0.001700 200 2.9 338.7 1172109\n", + "\n", + "2270.59\n", + "******* linux_tuned 200 100000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "1106 0.001273 200 2.4 391.9 1397346\n", + "1107 0.001268 200 2.4 390.5 1401269\n", + "1108 0.001265 200 2.4 392.6 1405964\n", + "1286 0.001390 200 2.5 381.5 1403902\n", + "1284 0.001322 200 2.5 389.0 1405156\n", + "1285 0.001323 200 2.5 384.5 1400478\n", + "1463 0.001380 200 2.6 378.8 1405735\n", + "1464 0.001378 200 2.6 373.9 1403504\n", + "1465 0.001379 200 2.6 377.3 1403808\n", + "1643 0.001443 200 2.7 367.4 1399826\n", + "1642 0.001436 200 2.7 363.6 1404110\n", + "1641 0.001442 200 2.7 364.4 1400175\n", + "1819 0.001512 200 2.8 366.3 1400410\n", + "1821 0.001507 200 2.8 362.3 1402222\n", + "1820 0.001507 200 2.8 365.1 1404415\n", + "930 0.001569 200 2.9 357.8 1402985\n", + "929 0.001564 200 2.9 356.3 1408580\n", + "928 0.001570 200 2.9 362.3 1403548\n", + "\n", + "2690.66\n", + "******* linux_tuned 200 200000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "1475 0.001529 200 2.6 495.0 1461265\n", + "1476 0.001528 200 2.6 486.0 1461235\n", + "1477 0.001530 200 2.6 484.6 1460475\n", + "1653 0.001587 200 2.7 460.3 1462077\n", + "1654 0.001590 200 2.7 464.0 1462238\n", + "1655 0.001594 200 2.7 481.7 1460510\n", + "1831 0.001673 200 2.8 457.3 1461852\n", + "1832 0.001671 200 2.8 467.7 1461003\n", + "1833 0.001674 200 2.8 462.5 1462213\n", + "939 0.001740 200 2.9 433.7 1461358\n", + "940 0.001840 200 2.9 442.8 1461932\n", + "941 0.001735 200 2.9 440.6 1462093\n", + "\n", + "1884.97\n", + "******* ebbrt_tuned 300 50000\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "209 0.001899 300 2.6 421.8 933140\n", + "761 0.001910 300 2.6 432.0 932694\n", + "463 0.001942 300 2.7 433.5 932836\n", + "464 0.001939 300 2.7 433.1 932540\n", + "821 0.001945 300 2.7 434.3 932811\n", + "557 0.001973 300 2.8 432.9 863800\n", + "558 0.001967 300 2.8 432.7 933739\n", + "292 0.002007 300 2.9 429.2 933005\n", + "677 0.001997 300 2.9 429.9 932778\n", + "678 0.001997 300 2.9 430.3 933287\n", + "\n", + "2057.1\n", + "******* ebbrt_tuned 300 100000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "149 0.001922 300 2.4 441.5 972673\n", + "213 0.001980 300 2.6 436.6 972445\n", + "765 0.001988 300 2.6 445.1 901785\n", + "236 0.001997 300 2.7 436.1 911921\n", + "471 0.002021 300 2.7 444.8 972635\n", + "472 0.002018 300 2.7 438.5 972587\n", + "825 0.002026 300 2.7 442.5 972572\n", + "565 0.002059 300 2.8 437.7 972559\n", + "566 0.002060 300 2.8 440.7 972584\n", + "296 0.002098 300 2.9 437.5 972763\n", + "685 0.002097 300 2.9 440.1 972482\n", + "686 0.002095 300 2.9 440.0 972672\n", + "\n", + "2361.02\n", + "******* ebbrt_tuned 300 200000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "153 0.002164 300 2.4 482.2 976510\n", + "62 0.002072 300 2.6 490.1 976522\n", + "63 0.002078 300 2.6 484.5 976522\n", + "64 0.002077 300 2.6 489.7 976514\n", + "65 0.002080 300 2.6 491.2 976513\n", + "217 0.002247 300 2.6 470.4 905818\n", + "769 0.002234 300 2.6 463.2 976522\n", + "240 0.002286 300 2.7 462.3 976517\n", + "479 0.002295 300 2.7 469.1 976518\n", + "480 0.002296 300 2.7 475.7 976511\n", + "829 0.002304 300 2.7 474.1 976505\n", + "573 0.002344 300 2.8 467.8 976521\n", + "574 0.002347 300 2.8 471.1 976520\n", + "300 0.002399 300 2.9 461.6 976519\n", + "693 0.002367 300 2.9 450.3 976520\n", + "694 0.002365 300 2.9 457.8 976511\n", + "\n", + "2038.49\n", + "******* linux_tuned 300 50000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "1130 0.001794 300 2.4 461.2 893150\n", + "1131 0.001793 300 2.4 460.2 890880\n", + "1132 0.001796 300 2.4 456.6 888533\n", + "1310 0.001874 300 2.5 448.7 889371\n", + "1308 0.001868 300 2.5 452.4 894991\n", + "1309 0.001870 300 2.5 454.7 891698\n", + "1486 0.001955 300 2.6 444.2 894048\n", + "1487 0.001960 300 2.6 451.6 891107\n", + "1488 0.001958 300 2.6 445.8 890833\n", + "1667 0.002039 300 2.7 439.7 893422\n", + "1666 0.002036 300 2.7 442.6 895890\n", + "1665 0.002038 300 2.7 443.0 894675\n", + "1843 0.002133 300 2.8 440.4 892283\n", + "1845 0.002109 300 2.8 438.5 895327\n", + "1844 0.002125 300 2.8 438.8 891069\n", + "953 0.002217 300 2.9 435.8 891277\n", + "952 0.002217 300 2.9 435.4 889465\n", + "951 0.002205 300 2.9 434.8 891584\n", + "\n", + "2270.59\n", + "******* linux_tuned 300 100000\n", + " joules_per_interrupt itr dvfs read_99th num_interrupts\n", + "1142 0.001822 300 2.4 487.7 967731\n", + "1143 0.001817 300 2.4 482.9 968015\n", + "1144 0.001820 300 2.4 478.8 968141\n", + "1322 0.001903 300 2.5 476.4 967530\n", + "1320 0.001904 300 2.5 479.2 967615\n", + "1321 0.001902 300 2.5 476.3 967365\n", + "1498 0.001983 300 2.6 469.4 969106\n", + "1499 0.001991 300 2.6 469.7 966475\n", + "1500 0.001990 300 2.6 473.6 967886\n", + "1679 0.002178 300 2.7 462.6 966471\n", + "1678 0.002087 300 2.7 462.5 963477\n", + "1677 0.002076 300 2.7 461.7 967134\n", + "1855 0.002179 300 2.8 464.7 966686\n", + "1857 0.002158 300 2.8 457.6 967136\n", + "1856 0.002156 300 2.8 458.4 967648\n", + "965 0.002256 300 2.9 448.7 967052\n", + "964 0.002254 300 2.9 450.3 967404\n", + "963 0.002345 300 2.9 444.5 968078\n", + "\n", + "2690.66\n", + "******* linux_tuned 300 200000\n", + "Empty DataFrame\n", + "Columns: [joules_per_interrupt, itr, dvfs, read_99th, num_interrupts]\n", + "Index: []\n", + "\n" + ] + } + ], + "source": [ + "for itr in [50, 100, 200, 300]:\n", + " for sys in ['ebbrt_tuned', 'linux_tuned']:\n", + " for qps in [50000, 100000, 200000]:\n", + " df = df_comb[(df_comb['sys']==sys) & (df_comb['QPS'] == qps)].copy()\n", + " #print(df.shape[0])\n", + " print(df['joules'].max())\n", + " df['joules_per_interrupt'] = df['joules']/df['num_interrupts']\n", + " df = df[['joules_per_interrupt','itr', 'dvfs', 'read_99th', 'num_interrupts']]\n", + " #print(df.shape[0])\n", + " #print('')\n", + " \n", + " dfi = df[df['itr']==itr]\n", + " #dfi = dfi.drop_duplicates(subset = [\"itr\", \"dvfs\"])\n", + " #dfi['joules_mean'] = dfi['joules_mean']/dfi['joules_mean'].max()\n", + " #print(dfi.diff())\n", + " print('*******', sys, itr, qps)\n", + " print(dfi.sort_values(by=['dvfs']))\n", + " #print(dfi.sort_values(by=['dvfs']).diff())\n", + " print('')\n", + " #plt.plot(dfi['dvfs'], dfi['joules_per_interrupt'])\n", + " #print(dfi)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [], + "source": [ + "def inference(d, n_iter, lr, workload, sys, print_freq=10):\n", + " # p_busy_min = 20\n", + " p_static = {\n", + " 'c1':1.5, \n", + " 'c3':0.5,\n", + " 'c4':0.25,\n", + " 'c7':34, # 34 Watts\n", + " 'busy': 10\n", + " }\n", + " chosen_sleep = 'c7'\n", + "\n", + " p_q = p_static[chosen_sleep]/10**6 # joules/us idle\n", + " # p_detect = p_static[chosen_sleep]\n", + "\n", + " #starts randomly\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " \n", + " #p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " #p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " #p_q = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " #p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " phi = torch.rand(1, requires_grad=True)\n", + " \n", + " #AA = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " #BB = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " \n", + " \n", + " #df[['joules','itr', 'dvfs', 'QPS', read_99th, 'num_interrupts']]\n", + " qps = d[:,3]\n", + " ninterrupts = d[:,5]\n", + " energy = (d[:,0]/ninterrupts).log() ## joules/num_interrupts\n", + " #energy = (d[:,0]/(qps).log()\n", + " itr = d[:,1]\n", + " dvfs = d[:,2]\n", + " time = d[:,4]\n", + " \n", + " #interarrival_time = (1/qps)*10**6\n", + "\n", + " current_loss_time = -100\n", + " fixed_zeta = -100\n", + " fixed_alpha = -100\n", + " fixed_phi = -100\n", + " \n", + " criterion = nn.MSELoss()\n", + " optimizer_time = optim.Adam([zeta, alpha, phi], lr=lr)\n", + " optimizer_energy = optim.Adam([gamma, beta], lr=lr)\n", + " # optimizer = optim.Adam([max_time, alpha, beta, p_detect, p_q], lr=lr)\n", + "\n", + " for i in range(n_iter): \n", + " t_busy = (zeta / dvfs**(1+alpha)) ## as dvfs increases, max_time should get smaller\n", + " pred_time = (phi*itr) + t_busy ## itr_suppress reflects where pkt is in queue\n", + " \n", + " loss_time = criterion(pred_time/time, torch.ones((1,pred_time.shape[1])).double())\n", + " if i % 1000 == 0:\n", + " print(f'mse_loss_time={loss_time.item()} loss_time={round(math.sqrt(loss_time.item()), 5)} us zeta={zeta.item()} alpha={alpha.item()}'\n", + " +f' phi={phi.item()}')\n", + " \n", + " optimizer_time.zero_grad()\n", + " loss_time.backward(retain_graph=True)\n", + " optimizer_time.step()\n", + "\n", + " if(current_loss_time == -100):\n", + " current_loss_time = loss_time.item()\n", + " else:\n", + " if(current_loss_time >= loss_time.item()):\n", + " current_loss_time = loss_time.item()\n", + " fixed_zeta = zeta.item()\n", + " fixed_alpha = alpha.item()\n", + " fixed_phi = phi.item()\n", + " \n", + " for i in range(n_iter):\n", + " #p_busy = (p_q*dvfs**(2+beta))\n", + " #t_busy_energy = (max_time / dvfs**(1+beta))\n", + " #t_q_energy = itr#(fixed_itr_suppress*itr)\n", + " #t_q_energy = (interarrival_time - (fixed_itr_suppress*itr) - t_busy_energy)\n", + " \n", + " #pred_energy = (p_q * t_q_energy) + (p_busy * t_busy_energy)\n", + " #pred_energy = AA*(fixed_itr_suppress*itr)**gamma + BB*dvfs**beta\n", + " #loss_energy = criterion(pred_energy/energy, torch.ones((1,pred_energy.shape[1])).double())\n", + " #pred_energy = ((fixed_itr_suppress*itr*p_q)*((20*(10**6))/(fixed_itr_suppress*itr))) + (dvfs*beta)\n", + " \n", + " #pred_energy = (gamma*(fixed_itr_suppress*itr))*(dvfs**beta) #+ (AA*(dvfs**beta))\n", + " \n", + " pred_energy = gamma+(np.log(fixed_phi)+np.log(itr))+(beta*np.log(dvfs))\n", + " \n", + " #pred_energy = (*itr + t_busy_energy)*p_q\n", + " loss_energy = criterion(pred_energy, energy)\n", + "\n", + " if i % 1000 == 0:\n", + " print(f'loss_energy={loss_energy.item()} loss_energy={math.sqrt(loss_energy.item())}J gamma={gamma.item()} beta={beta.item()}')\n", + " #print(pred_energy)\n", + " \n", + " optimizer_energy.zero_grad()\n", + " loss_energy.backward(retain_graph=True)\n", + " optimizer_energy.step()\n", + " \n", + " return pred_energy, pred_time" + ] + }, + { + "cell_type": "code", + "execution_count": 87, + "metadata": {}, + "outputs": [], + "source": [ + "def run_energy(df_comb, n_iter=2000, lr=1, rqps=400000, rtail='99', msys=['ebbrt_tuned'], mpred=['energy', 'time']): \n", + " df_comb = df_comb[df_comb['QPS'] == rqps]\n", + "\n", + " i=1\n", + " \n", + " for sys in msys:\n", + " df = df_comb[(df_comb['sys']==sys)].copy()\n", + " rt = 'read_'+rtail+'th'\n", + " df = df[['joules','itr', 'dvfs', 'QPS', rt, 'num_interrupts']]\n", + " tnum = df.shape[0]\n", + " d = df.values\n", + " d = torch.tensor(d)\n", + " print('SYS', sys)\n", + " #pred_energy, max_time, alpha, beta, p_detect, p_q = inference_energy(d, n_iter, lr, 'mcd', sys, print_freq=1000)\n", + " pred_energy, pred_time = inference(d, n_iter, lr, 'mcd', sys, print_freq=1000)\n", + " \n", + " df[f'pre_energy lr={lr}'] = pred_energy.view(tnum, 1).detach().numpy()\n", + " df[f'pre_time lr={lr}'] = pred_time.view(tnum, 1).detach().numpy()\n", + " \n", + " for pred_name in mpred:\n", + " if pred_name == 'energy':\n", + " pred = pred_energy\n", + " qps = d[:,3]\n", + " yvalue = (d[:,0]/d[:,5]).log()\n", + " #yvalue = (d[:,0]/(qps*20)).log()\n", + " else:\n", + " pred = pred_time\n", + " yvalue = d[:,4]\n", + "\n", + " #fig, ax = plt.subplots()\n", + " ax = plt.subplot(1, len(msys)*len(mpred), i)\n", + " \n", + " if sys == 'ebbrt_tuned':\n", + " plt.title(f'EbbRT Memcached-silo @ {int(rqps/1000)}K QPS')\n", + " else:\n", + " plt.title(f'Linux Memcached-silo @ {int(rqps/1000)}K QPS')\n", + " \n", + " #plt.title(f'pred:{pred_name} mcd sys={sys} lr={lr} tail={rtail} \\n QPS={rqps} max_time={round(max_time,2)} \\n alpha={round(alpha,2)} beta={round(beta.item(),2)} \\n p_detect={round(p_detect.item(),2)}')\n", + " if pred_name == 'energy':\n", + " plt.ylabel('Measured Energy (J)', fontsize=16)\n", + " plt.xlabel('Predicted Energy (J)', fontsize=16)\n", + " else:\n", + " plt.ylabel('Measured 99% Tail (us)', fontsize=16)\n", + " plt.xlabel('Predicted 99% Tail (us)', fontsize=16)\n", + " \n", + " if pred_name == \"time\":\n", + " tmax = yvalue.max().item()\n", + " plt.plot(np.linspace(0, tmax, 10), np.linspace(0, tmax, 10))\n", + " else:\n", + " tmax = yvalue.max().item()\n", + " tmin = yvalue.min().item()\n", + " #print(yvalue.min(), yvalue.max(), tmin, tmax)\n", + " plt.plot(np.linspace(tmin, tmax, 10), np.linspace(tmin, tmax, 10))\n", + " \n", + " print('measurement', yvalue.mean(), yvalue.std()) \n", + " scatter = ax.scatter(pred.detach().numpy()[0], yvalue, marker = 'o', \n", + " s = d[:,1], c = d[:,2], alpha=0.5)\n", + " \n", + " legend1 = ax.legend(*scatter.legend_elements(),loc=\"upper left\", title=\"dvfs\")\n", + " ax.add_artist(legend1)\n", + " handles, labels = scatter.legend_elements(prop=\"sizes\", alpha=0.6)\n", + " legend2 = plt.legend(handles, labels, loc=\"lower right\", title=\"itr\")\n", + " ax.add_artist(legend2)\n", + " \n", + " plt.grid(True)\n", + " plt.tight_layout()\n", + " i += 1\n", + " \n", + " plt.subplots_adjust(wspace=0.3, hspace=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 88, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "mse_loss_time=0.044235873081799416 loss_time=0.21032 us zeta=380.1231994628906 alpha=0.03659391403198242 phi=0.7398692965507507\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mse_loss_time=0.0015300347123205933 loss_time=0.03912 us zeta=427.68243408203125 alpha=-0.1661704182624817 phi=0.8333784937858582\n", + "mse_loss_time=0.0015287127363827486 loss_time=0.0391 us zeta=429.7547912597656 alpha=-0.1601545214653015 phi=0.8329697251319885\n", + "mse_loss_time=0.0015287922106866107 loss_time=0.0391 us zeta=429.8895568847656 alpha=-0.15998131036758423 phi=0.8333905935287476\n", + "mse_loss_time=0.0015577923604585496 loss_time=0.03947 us zeta=430.0694274902344 alpha=-0.16780132055282593 phi=0.8342252373695374\n", + "mse_loss_time=0.011999699369438682 loss_time=0.10954 us zeta=429.8278503417969 alpha=-0.04028362035751343 phi=0.70084148645401\n", + "mse_loss_time=0.004397477436694978 loss_time=0.06631 us zeta=429.9031982421875 alpha=-0.18488870561122894 phi=0.9574081301689148\n", + "mse_loss_time=0.00255630105720437 loss_time=0.05056 us zeta=429.7351989746094 alpha=-0.1962783932685852 phi=0.8725701570510864\n", + "mse_loss_time=0.0037378442799273037 loss_time=0.06114 us zeta=429.8645324707031 alpha=-0.20900660753250122 phi=0.8975691199302673\n", + "mse_loss_time=0.002640447895143197 loss_time=0.05139 us zeta=429.7777099609375 alpha=-0.11820291727781296 phi=0.7978096008300781\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([136])) that is different to the input size (torch.Size([1, 136])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=163.16593311943146 loss_energy=12.773642124289825J gamma=1.2727737426757812 beta=0.7652895450592041\n", + "loss_energy=1.5743924309381194 loss_energy=1.254747955144028J gamma=-11.119006156921387 beta=0.328808456659317\n", + "loss_energy=1.5743924309378778 loss_energy=1.2547479551439316J gamma=-11.119004249572754 beta=0.3288061022758484\n", + "loss_energy=1.5743924309378778 loss_energy=1.2547479551439316J gamma=-11.119004249572754 beta=0.3288061022758484\n", + "loss_energy=1.5743924309378778 loss_energy=1.2547479551439316J gamma=-11.119004249572754 beta=0.3288061022758484\n", + "loss_energy=1.5743924309378783 loss_energy=1.2547479551439318J gamma=-11.119004249572754 beta=0.3288061320781708\n", + "loss_energy=1.5743924312705941 loss_energy=1.2547479552765146J gamma=-11.11899471282959 beta=0.3288176953792572\n", + "loss_energy=1.5744524022563913 loss_energy=1.2547718526713896J gamma=-11.123424530029297 beta=0.32436561584472656\n", + "loss_energy=1.5743927390588086 loss_energy=1.2547480779259272J gamma=-11.119321823120117 beta=0.3284887671470642\n", + "loss_energy=1.5745956867747533 loss_energy=1.25482894721741J gamma=-11.127156257629395 beta=0.32065001130104065\n", + "measurement tensor(-7.2225, dtype=torch.float64) tensor(0.3974, dtype=torch.float64)\n", + "measurement tensor(320.8581, dtype=torch.float64) tensor(86.3402, dtype=torch.float64)\n", + "SYS linux_tuned\n", + "mse_loss_time=0.044959508359825505 loss_time=0.21204 us zeta=136.27224731445312 alpha=-1.4470112323760986 phi=0.7350638508796692\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mse_loss_time=0.0014046586209432415 loss_time=0.03748 us zeta=396.38330078125 alpha=-0.16469012200832367 phi=0.930742084980011\n", + "mse_loss_time=0.0009588526045312358 loss_time=0.03097 us zeta=439.8896789550781 alpha=-0.04854448884725571 phi=0.9106070399284363\n", + "mse_loss_time=0.0009576663715681676 loss_time=0.03095 us zeta=442.3106384277344 alpha=-0.042426496744155884 phi=0.9097850322723389\n", + "mse_loss_time=0.0009615119912138804 loss_time=0.03101 us zeta=442.3870849609375 alpha=-0.044897835701704025 phi=0.9109970927238464\n", + "mse_loss_time=0.0009673123619033184 loss_time=0.0311 us zeta=442.3908996582031 alpha=-0.037859611213207245 phi=0.9215520620346069\n", + "mse_loss_time=0.0012637686429205926 loss_time=0.03555 us zeta=442.4069519042969 alpha=-0.07935082912445068 phi=0.8546128869056702\n", + "mse_loss_time=0.00157992194414897 loss_time=0.03975 us zeta=442.3743591308594 alpha=-0.06929401308298111 phi=0.9385915994644165\n", + "mse_loss_time=0.0009576663435715482 loss_time=0.03095 us zeta=442.3520202636719 alpha=-0.04232311621308327 phi=0.9097727537155151\n", + "mse_loss_time=0.0009577713770835857 loss_time=0.03095 us zeta=442.381103515625 alpha=-0.04160509258508682 phi=0.9101172089576721\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([131])) that is different to the input size (torch.Size([1, 131])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=145.76224176097105 loss_energy=12.073203458940425J gamma=1.6804687976837158 beta=-0.9685630798339844\n", + "loss_energy=0.537395430194817 loss_energy=0.7330725954466016J gamma=-12.187847137451172 beta=1.3059039115905762\n", + "loss_energy=0.537395430194817 loss_energy=0.7330725954466016J gamma=-12.187847137451172 beta=1.3059039115905762\n", + "loss_energy=0.537395430194818 loss_energy=0.7330725954466024J gamma=-12.187847137451172 beta=1.3059037923812866\n", + "loss_energy=0.5373954301955461 loss_energy=0.733072595447099J gamma=-12.187846183776855 beta=1.3059037923812866\n", + "loss_energy=0.5373954301948788 loss_energy=0.7330725954466438J gamma=-12.187847137451172 beta=1.3059035539627075\n", + "loss_energy=0.5373954302161584 loss_energy=0.7330725954611579J gamma=-12.187849044799805 beta=1.3059005737304688\n", + "loss_energy=0.537395724512775 loss_energy=0.7330727961892836J gamma=-12.18814468383789 beta=1.3056039810180664\n", + "loss_energy=0.5374047983639523 loss_energy=0.733078985078656J gamma=-12.189530372619629 beta=1.3042173385620117\n", + "loss_energy=0.5373954313035246 loss_energy=0.7330725962028076J gamma=-12.187828063964844 beta=1.3059213161468506\n", + "measurement tensor(-7.0147, dtype=torch.float64) tensor(0.3768, dtype=torch.float64)\n", + "measurement tensor(307.6763, dtype=torch.float64) tensor(96.7625, dtype=torch.float64)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = 16, 6\n", + "run_energy(df_comb, n_iter=10000, lr=1, rqps=50000, rtail='99', \n", + " mpred=['energy', 'time'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 89, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "mse_loss_time=0.6201902526874258 loss_time=0.78752 us zeta=124.9257583618164 alpha=1.2964670658111572 phi=0.5917387008666992\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mse_loss_time=0.034591218549027754 loss_time=0.18599 us zeta=199.0268096923828 alpha=-1.1532318592071533 phi=0.8372260928153992\n", + "mse_loss_time=0.020588839470863356 loss_time=0.14349 us zeta=266.0412292480469 alpha=-0.8396839499473572 phi=0.8120812177658081\n", + "mse_loss_time=0.01256565234944531 loss_time=0.1121 us zeta=326.6261901855469 alpha=-0.610337495803833 phi=0.8015867471694946\n", + "mse_loss_time=0.007712427535763936 loss_time=0.08782 us zeta=382.781982421875 alpha=-0.42791682481765747 phi=0.7984515428543091\n", + "mse_loss_time=0.004825124643985077 loss_time=0.06946 us zeta=434.6590881347656 alpha=-0.27553239464759827 phi=0.7987238168716431\n", + "mse_loss_time=0.003227437049480078 loss_time=0.05681 us zeta=481.1071472167969 alpha=-0.1560964733362198 phi=0.8028275370597839\n", + "mse_loss_time=0.002475866302898427 loss_time=0.04976 us zeta=519.9154052734375 alpha=-0.061184562742710114 phi=0.8069775104522705\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([120])) that is different to the input size (torch.Size([1, 120])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=194.5207101704154 loss_energy=13.947068156799672J gamma=1.4988617897033691 beta=1.8283369541168213\n", + "loss_energy=1.443746659071825 loss_energy=1.201560093824618J gamma=-8.380693435668945 beta=-3.144900321960449\n", + "loss_energy=1.2779554992333062 loss_energy=1.1304669385848072J gamma=-9.89204216003418 beta=-1.2982559204101562\n", + "loss_energy=1.2750287032792191 loss_energy=1.1291716890177592J gamma=-10.115682601928711 beta=-1.024998664855957\n", + "loss_energy=1.2750268994251934 loss_energy=1.1291708902664792J gamma=-10.121347427368164 beta=-1.0180764198303223\n", + "loss_energy=1.2750268993983294 loss_energy=1.1291708902545838J gamma=-10.121358871459961 beta=-1.0180630683898926\n", + "loss_energy=1.2750268993884193 loss_energy=1.1291708902501956J gamma=-10.121366500854492 beta=-1.0180541276931763\n", + "loss_energy=1.2750268993858493 loss_energy=1.1291708902490576J gamma=-10.121370315551758 beta=-1.0180494785308838\n", + "measurement tensor(-7.3890, dtype=torch.float64) tensor(0.5563, dtype=torch.float64)\n", + "measurement tensor(331.8983, dtype=torch.float64) tensor(81.8955, dtype=torch.float64)\n", + "SYS linux_tuned\n", + "mse_loss_time=0.5849095090343447 loss_time=0.76479 us zeta=227.89625549316406 alpha=1.0648009777069092 phi=0.4109758138656616\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mse_loss_time=0.01384327388869347 loss_time=0.11766 us zeta=274.3163757324219 alpha=-0.7436586618423462 phi=0.928888201713562\n", + "mse_loss_time=0.009505535946927872 loss_time=0.0975 us zeta=335.20050048828125 alpha=-0.5382591485977173 phi=0.9052091240882874\n", + "mse_loss_time=0.006449914246384198 loss_time=0.08031 us zeta=396.58953857421875 alpha=-0.3636426031589508 phi=0.8872371315956116\n", + "mse_loss_time=0.004373626046026039 loss_time=0.06613 us zeta=456.42462158203125 alpha=-0.21594147384166718 phi=0.8738256096839905\n", + "mse_loss_time=0.0030029694510895436 loss_time=0.0548 us zeta=513.86376953125 alpha=-0.08995487540960312 phi=0.8638300895690918\n", + "mse_loss_time=0.0021531552695119994 loss_time=0.0464 us zeta=567.2172241210938 alpha=0.016170112416148186 phi=0.8564781546592712\n", + "mse_loss_time=0.0016735284866313401 loss_time=0.04091 us zeta=614.605712890625 alpha=0.102992482483387 phi=0.8513789772987366\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([104])) that is different to the input size (torch.Size([1, 104])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=130.85362438512513 loss_energy=11.439126906592353J gamma=0.37222933769226074 beta=-0.1968533992767334\n", + "loss_energy=0.7808529686107424 loss_energy=0.8836588530709928J gamma=-8.760951042175293 beta=-2.746077299118042\n", + "loss_energy=0.40526530231313884 loss_energy=0.636604510126294J gamma=-11.651817321777344 beta=0.5054211020469666\n", + "loss_energy=0.3847397217902826 loss_energy=0.6202739086809009J gamma=-12.447798728942871 beta=1.4007035493850708\n", + "loss_energy=0.38464312232655234 loss_energy=0.6201960354005436J gamma=-12.505709648132324 beta=1.4658387899398804\n", + "loss_energy=0.3846431130696183 loss_energy=0.6201960279376338J gamma=-12.50625991821289 beta=1.4664568901062012\n", + "loss_energy=0.38464311305830423 loss_energy=0.6201960279285125J gamma=-12.506271362304688 beta=1.4664701223373413\n", + "loss_energy=0.3846431130552454 loss_energy=0.6201960279260464J gamma=-12.506278038024902 beta=1.4664775133132935\n", + "measurement tensor(-7.2035, dtype=torch.float64) tensor(0.5070, dtype=torch.float64)\n", + "measurement tensor(328.7692, dtype=torch.float64) tensor(91.4714, dtype=torch.float64)\n" + ] + }, + { + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAABHgAAAGoCAYAAAA99FLLAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOy9eZwdVZn//36q7tZ79j1kIxEIhkDYhQCyqIgou8iqo+KCjL8R56syjozb6IyOjs64K4iAgoM6OCLIlrAnEBIgELKns6eX9HJv37Wqnt8fp5rcdHpLr7eT8+Z1SXedqnOeqlv16XOeOs9zRFWxWCwWi8VisVgsFovFYrGMXJzhNsBisVgsFovFYrFYLBaLxdI/rIPHYrFYLBaLxWKxWCwWi2WEYx08FovFYrFYLBaLxWKxWCwjHOvgsVgsFovFYrFYLBaLxWIZ4VgHj8VisVgsFovFYrFYLBbLCMc6eCwWi8VisVgsFovFYrFYRjjWwWOxWCwWi8VisVgsFovFMsKxDp5eICI3isgz3ZQvEZGPDqVNIxkRuVNEvj5AdXX73XRz3Jkisrbo9y0ict5A2GSx9BerOQOL1RyLpWc63qOWg2MgdVlEbheRu/tw3DUi8rei31VEjhwImyyWgcZqTv+wmmPpCuvgCQk72xkRSRV9/msA6p0Z3uztdW4RkS+EZa8XbfdFJFv0+5c6qev2sK5bOmz/bLj99v7ae7igqk+r6tsGqj4RGS0iXxeR1SKyV0Q2icjPRGR2D8cdKyKPiEiDiGgn5WNE5I8i0iYitSLyoQ7l54rImyKSFpEnRWRGUdl+g1oRmS8iu0Tkc13YIiLyeRFZHz4LW0XkmyIS61BnPrxH94rIoyJyVFg2SkR+JSK7RSQpIutE5P/1/ioeXljNObywmtOpLVZzhgHpwrk40PfoQdhzZ6gnF3fY/v1w+41DbdNIRVXvUdULBqo+EZkqIv8ZPltNIrJWRP5DRCb2cNw5oT60iMiWTspnhuXpUE/O61D+oVB/2kTkTyIypqhsv0GtiJwd2vbBLmyJi8i/hvqSCfXmVhGRDnW2/z1sEJE/iMjksGyaiDwQbm8RkdfsPXlwWM05dLGa06ktw6451sGzP+9T1cqiz80DWPcoVa0ELge+LCLnq+r89raAp4Gbi9r+Zhf1rANu6LDt+nC7ZRgIBxvLgQhwGTAeWAQ8D/xNRLoTvgJwP/B3XZT/N5AHJgLXAD8Wkflhu+OAPwBfBsYALwH3dWHjQuBJ4Buq+t0u2voB8HHM/VQFvAc4D/hdh/3+LbxnpwF1wJ3h9u8BlcDRQA1wMbCxi7YsBqs5loPGao7VnEOQ/XRGRCLAFdjvc9gQkXcAzwB7gAuAscBZwFbgufAZ74o24FfA57so/y2wMqzzNuB/RGR82O584KfAdRgdSgM/6sLGC4A/AR9R1Y660c7vgXOBCzE6cx1wE9BRl24OdWYeMAqjLwC/AbYBM0J7r8dcE8vIxmpOiWE1ZwA1R1XtRxVgC3BeF2U3As8CPwRagDeBc4vKlwD/iulwtwD/C4wJy2YCCkSK9l8OfL5DG0uAj/Zg4+3A3cAaYH64bX74+93A7UX7XgSsApqB54AFHc7188CrmAfil5gb+q9AEngMGF20/xlhHc3hDXdjuP29mIelNdx+ewd7uzruTswg4i9he8uAOUXHHQU8CuwF1gJXFpWNBR4M21wOfA14pptrdiHwRtjODuDWcPvZwPbOvn8gDnwf2Bl+vg/Eu6g/BrwOnN9F+QzMH5FRPXy3RwLaYVsFZqA1r2jbb4BvhT9/HHiuw/4Z4Kii6/x14GSgobv7C5gL+MDJHbZPB3LAWcV1FpW/F0iFP68GPjDcz/JI+WA1x2qO1RyrOcPwoQvt6eIevRXz3LZgnHmJsOzGjs8BRneODO/RVcBnwu0uRs/+uQt77gS+A+wm1AGMnvwV09m/sWjfj2D0pwl4BJjRof1PAevD5+9rwByM47MV49iMFe3//tDOVsyg7t3h9g+HbSSBTcBNHezt6rglYZvPhsf+DRhXdNyp7NOnV4Czi8pmAUvD4x4F/gu4u5vv8MbQtiSwGbims++l/TsJf64B7gLqgVrgnwCni/rHhvUu6KL8eOA1iv7OdLHfecCWDtvmYZ7xqqJtTwOfCH/+JnBvUdkcjC5VFV3nj4b3SDNwYTftnwtkgekdtp+C0Z/ZxXUWlX8aWB3+nAIWDvdzO5I/WM3pSTus5ljNGVDNsTN4es8pmBt7HPAV4A/F07cw3rWPAFMAD/Nm8gBE5FTgWGBDP2z5TdgeGO/zXR3aOAHjxbwJ88D8FHhQROJFu10GnI+56d+HEbUvYc7PAW4J6zoiLPsh5i3xQozIgBmoXY/xOr4X+KSIfKAXxwFcDfwLMBpzLb4RHleBEZp7gQnhfj9qf4OMGaRlgcmY6/2RHq7VLzFCWYW57k/0sD8Yz+6poc3HYQYr/9TFvldjhO1REXm7iLwoItvEhLY8p6q1wK+Ba3vRbkfmAb6qFs+UeAUzwCb895X2AlVtwwj//KL9TwYeBv4/Vf1FN22di/kju7x4o6puA17AeNL3Q0QqMW/4V4abXgC+ISIfFpG5PZ+epQes5ljN6QyrOVZzhpIrgXdjBgMLMJ35blHVPOb++6qIHA18ATPg+kY3h2UxjtT2Ke/Xc6DOfACjGZdinvGnMW9li3k3ZjbbqcA/Aj/D3DPTMc/j1WFdJ4f1fx6jJ4sxg0sws8QuAqoxA6/vhRrX03EAHwqPmYAZdN4aHjcV42D+Omb2263AA+1vkDH6swKjh1/jwFmTxdehAqP37wl15nT217qu+CFmwDUb81b8+tDWzrgZ+JmqvhqGI7wuJgz0cyLyN1VdiXn+3t2LdjsyH9ikqsmibd3pzEZCx3PR/u/DvGS4XFUf6qat84Floa68haouA7ZjdGg/wpmKl7G/zvy3iHww/DtjGVys5ljNsZrTT82xDp79+ZOINBd9PlZUVgd8X1ULqnof5i3ve4vKf6Oqq8MO75eBK0XELSpvEJEMxqv7I8z0rr5yN3C1iEQxwtQxKdbHgJ+q6jJV9VX11xjP5alF+/xQVfeo6g6MYC1T1ZWqmgP+iPGUghGpx1T1t+G5N6rqKgBVXaKqr6lqoKqvYkTvrJ6OC/mDqi5XVQ+4BzOwASNwW1T1DlX1VPVl4AHg8vB6XobxyLep6mrMQKY7CsAxIlKtqk1hfT1xDfBVVa1T1XrMoPC6LvY9n33hBL8Afo75o7QTM/AGI4JH9aLdjlRi3mAU04KZ7tebcjDfeQtm4Nsd44BdXZTtwvxha+dWEWnGDJIr2ffH9zOY7/Jm4A0R2SAi7+mh3cMdqzlWc9ptt5qzD6s5pcMPVHWnqu4F/sy+56Zbwmfl65hn+1bgOlX1ezjsLuB6EanBPNcdNesm4F9VdU34HH8TWChFeaCAb6tqq6q+jpnh9TdV3aSq7fdku878HfArVX001JMdqvpmaPtfVHWjGpZi3oqf2dNxIXeo6jpVzWDe3rdfr2uBh1T1ofC4RzEhjheGHfiTgC+rak5Vn8Jc6+4IgGNFpExVd4Xn2yWhll0FfFFVk6q6BRMu0K3OiIhgBoK3YgY7ozGDSBhenTkHM1Px2R7aOhid+UGoM6+EZf8Qbr8C8zfry8BmEVklIif10K6l71jNsZpjNaefmmMdPPvzAVUdVfT5eVHZDlUzbyqkln2daTDhAMVlUcyX3M44zA10K2ZKYrSvRqrqVkxH95vAeu3gJcRM0f9c8cAR40kutrc4li/Tye+V4c/T6SIeVUROEZOwql5EWoBPsO+cuzwuZHfRz+mi9mYAp3Sw/RpgEuahiHDgtW6350uyL2HsT8LNl2FCJmpFZKmInNaNTe1MKa6XA7/rYiZgwjAA3o6Z3uix/wB4etE+B0MK480vphozPbI35WBmH7wIPCoio7tpqwEzQ6EzJmOmV7bznfD5mKSqF6vxdKOqGVX9pqouwsziuB/4fYdZJ5b9sZqz73erOfu3YTVnH1Zzho+unpve8GtMyOhDqrq+p51V9RnMM/dPwP+FA5ZiZgD/WfSc7gUEmFq0z0DozHtE5AUxSb2bMc/zQOjMFR105gzMvT4FaFLjrG+nWGd+UqQzXwr3uwqjf7tE5C8SJh7vhnGYQVJHnZna+e5v6cx4TEjEX0Odubdon+HUmS9jXiL8SfafKdqRg9GZW0Kdmaqq14TOdkJH/RdUdT4mtHhV2K50Uqel/1jNsZpjNaefmmMdPL1naocLewTmjWk70zuUFTBf8luoebP9Xcy0wE/10567gM/RYTphyDZMYsvigWO5qnacVtgbtmFiETvjXsz0xumqWgP8BCN8PR3XU3tLO9heqaqfxDwUHgdeawDCjn57wthPhNteVNX3Y4TjT5gBQE/sxAhjcRs7u9i3+EF+Dbg29FpfCyAiizBvme/t/PBuWQdEZP/Qg+Mw+TcI/z2uvUDMFMo5ReVg4j2vwSQoe0REOgpYO08A08VMBX0LEZmOeSO/9GAMV9VWjDOgAjO7wHLwWM05EKs5VnM6xWrOkNMGlLf/IiKTOtnnR8D/Ae8SkTN6We/ddK8zN3V4VstU9bmDtL29rgP0Iuy4P4DJzzFRVUcBDzEwOvObDrZXqOq3MG9uR4fPUzvFOvMJ7ZAMX1UfUdXzMVrwJmYmX3c0YP5GdNSZrgZL7TpTD3jhADSCCQdBRM7FzCjtLlShK14HZotI8dvx7nRmNiZPWXHoaBtmEFyDSZba1QuMxzAO/GINbw97OQJ46mAMV9UGzL0xBRP2Yhk6rOYcfHtWcwyHneZYB0/vmQDcIiJREbkCs2pH8U12rYgcIyLlwFeB/9GupwZ+C/hHEUn0w577MDkKOhs8/Bz4RPi2W0SkQkTe2+HG7i33AOeJyJUiEhGRsbIvi3kVsFdVs+GN+6FeHtcd/wfME5HrwmsdFZGTROTo8Hr+AbhdRMpF5Bi6jxmNicg1IlKjqgVMcrKepmuCCfv4JxEZLyYu8p85MCSlnScwqxSBScD1MYyH+kiMGHwNM020trODw+8nQTj9UEQS7Z7h0GP+B0xMcYWY7PLvx+RDATMN9VgRuSys45+BV3X/qZuE534FRjwf6iDo7fuswwyW7xGRU0XEFZOD5AFMgrbHurle7efy5fC7ioX2/D0mGdnano61dIrVHKs5nWE1Z9+5WM3pO9Hwu2//RA7y+FeA+SKyMLz2txcXish1mLwUN2Lya/1aTA6lnvgBZqp+Z53gnwBflH2rutWE2tgXfgl8WETOFRFHzNK8R2GeizhFgwz2zwfV1XE9cTfwPhF5V3ivJ8TkmZgWPqsvAf8S3stnYPI9dIqITBSRi8PnKod5+9ytzoRadj8mZ1WVmBCTf6AHnVFVxThsv4uZxZnDDDY/gZmB2jGsod1GJ7wvouZXSYhILLRlHeaN9FfC7Zdgcq08EB5+T3itzgzP8auYENvit+mEv78bM/C5V/YPUW7f5zHgcUzukfnhtT81bOMuVe1RK0Tk2yJybPh3pQr4JLBBVRt7OtayH1ZzrOZYzRkizbEOnv35s+ybkpYSkT8WlS3DrPrRgEnadXmHC/0bTFb23UCCMGFoF/wFk439Y93s0y1qpqY/pgdOJ0RVXwrr/q+wnQ30IklZF+1sxXgsP4eZmriKfV7OT2EGAklMR//+Xh7XXXtJjLB9EPMGezfwbYz4gcm1UBluvxO4o4cqrwO2iEgrRhx6k3j06xjhexXzhvzlcFtn3A2cLyJnqckNcpKqTlPVf1Qzte5i7T4HxwzMFM52L3KG/QcnnwLKMPlYfgt8UsO4VzVT+S7D3I9NmKS8H6QT1CSguxQzk+PPIlLWyW43Y3J63I2Z5rkaM3D8gKoG3ZzDW81gvo8GzHd3PvBeVU314tjDFas5B9ZlNcdqjtWcwechzHff/rn9YA4OO8xfxTji1mNWngHeSnj+feB6VU2p6r2Y+/t7ndXVod69qvp42MnvWPZHzLP5u/D5Wg30KeeSmuTeHw5tasHMGJsR6sEtGG1pwjiRH+zpuF60tw3jLP0SZiC3DZM0tb0f/iHM87QXk1S/s9kE7TgYndsZ7n8WvZuh+RmME3gT5vu6F5McvzN+CNwsxtH9pKoeo6ozVfVrqjoduFrDUMkuWIy5rx7CvLXOYPKKtPNB4ETMNf4W5u9be3jC6xjtvAejQ1VdnZ+qNrMvef9dItLZuOYy4ElM8vcsJi/dw5hVAXtDOca53Yy5djOAi3t5rGUfVnOs5ljN6R391hzp5H62WCwHgYi8HbNM9c8w4rADEyLwRSBQ1d4+0CWFiHwV+ACwOBQ0i8VSAljNsVgsg42InINxoH4LM7OvAbPazNeB5ara3QpFJYuI/BqTB+TC0BFtsVhKAKs5A9imdfBYLP1HRCZilmV8LyYh1nZMSMt3df8kZiMKEbkZMy3w4eG2xWKx7MNqjsViGWzE5KL4EnAeZjWbjZiZjD9SkwB1xCEmd8Y/YHKvvTDc9lgsln1YzRmgNq2Dx2KxWCwWi8VisVgsFotlZGNz8FgsFovFYrFYLBaLxWKxjHAONoP5Ice4ceN05syZw23GfrS1tVFRccCiI8OKtan3lKJdpWDTihUrGlR1/LAaUQL0VnNK4TvrilK2DUrbvlK2DQ4t+6zmGIazn1Pq91NvOBTOAQ6N8yj1c7Cac2j0ccDa119K2b5Stg0GqJ+jqof1Z9GiRVpqPPnkk8NtwgFYm3pPKdpVCjYBL2kJPPPD/emt5pTCd9YVpWybamnbV8q2qR5a9lnNGf5+TqnfT73hUDgH1UPjPEr9HKzmHBp9HFVrX38pZftK2TbVgenn2BAti8VisVgsFovFYrFYLJYRjnXwWCwWi8VisVgsFovFYrGMcKyDx2KxWCwWi8VisVgsFotlhHPYJ1nujEKhwPbt28lms8PSfk1NDWvWrBmw+hKJBNOmTSMajQ5YnRaLZeDoTHMGWgcGkp5ss5pjsZQ2Q9XPGSods5pjsZQuI62PA7afYxnZWAdPJ2zfvp2qqipmzpyJiAx5+8lkkqqqqgGpS1VpbGxk+/btzJo1a0DqtFgsA0tnmjOQOjDQdGeb1RyLpfQZqn7OUOiY1RyLpbQZaX0csP0cy8jGOng6IZvNDptzZ6AREcaOHUt9ff1wm2IpYTRoRr3toEmQKEgVEpmFSGy4TTsssJpjsXRPMpdjW2sL6UKBlbt2kohGmVEzinL79rRPWM2xWLon8BrAWwvaBMTAnYREj7L9oj5wKOkNWM2xDDyqyu5UioZ0mnShwCu7d1EVTzCjpgbXOfiMOtbB0wWHigjBoXUuloFDNUALGyBzL+SegqAFCAABcVFnClp2CSTeixMZP9zmHvIcSs/poXQuluFDVdnW2sLz27by8u5doMqCbJ4nV7+CILjicPLUaZwybRqTK6vsfXeQHErX61A6F8vwoeqjhVWQuhMKL4JmgX33ljrj0bKLoeyDOJFxw2ZnfxCRLUAS8AFPVU8UkTHAfcBMYAtwpao2hft/Efi7cP9bVPWRPrbbb9tLiUPtfCzDQ973WdtQz9LaLWxpakYJWJgr8NiqlbjiUJNIcNbMmSycNJmqeLzX9VoHj8VyGKKaRVv+BXJPgOaBKEg5+/Ku++DvgNR/QvoOgorPIOWX2T9oFotlSCj4Pn968w2W7dhOzHWZUF6B6zhE801MraoBwAt8lu3YznPbajln1mzefeQ8HKtRFoulDwR+Cpo/C4UV4ZYESA20a4oGEDRB2y8hfQ9B5d8j5VeN1H7ROaraUPT7F4DHVfVbIvKF8Pf/JyLHAB8E5gNTgMdEZJ6q+kNvssVyaNGYTnPHqpfZ3tpKtlCgvq2NrO9xdFkla5OtuCJMqKxgz+ttPLJxA9cuOI6jxvXuhbtdRWsIuP322/nOd77TZfnTTz/N/PnzWbhwIZlMZggtsxyOqObQvZ+B7F9BHdOBccpBXNOREQGJgFMJUg1BGpLfQtt+gaoOt/mWXmA1xzKS8YKAe157hWU7tjOlqpoJFZWdTlGOOC6TKiuZWFnF45s38sCa161GDRNWcywjmcBPwt7roPASUAFODTjxfc4dAHHAqTD9Ig0g+W009Yths3mAeT/w6/DnXwMfKNr+O1XNqepmYANw8jDYdwBWcywjDS/wyfp5vMCnMZ3mv5a/wKt7drOhsZFtrS0gUBWP44hQFY8Rj0TYnUyxcW8jG/fu5ccvLuf1uj29asvO4CkB7rnnHm699VY+/OEPAyaxl8UyGKgq2vxlKCwHqsDpQQJEQCogyEDqp6g7FSm7cEhstQweHTXHYikl/rp+Ha/u2c306ppevR2POA5Tq2p4fttWJpRXcNZMm/Sy1LCaYylVVBWaPwP+ZuO8kR7efYuAlEHgQNtPCNwpOOXvHRpjBwYF/iYiCvxUVX8GTFTVXQCquktEJoT7TgVeKDp2e7htP0Tk48DHASZOnMiSJUv2K6+pqTlgbOP7fr/GO7lcjmg02mUdd955JzfffDPXXnstnucddFu9sS+bzR5wrkNFKpUatrZ7QynbN5S2BShZv0DKy+IHZuKbAjnPZ6oPU1RwyyvNdlUUpVqE85wIOIITNWFZgSqSyfHyC8vYWVlJtIe8PNbBM0h84xvf4K677mL69OmMHz+eY445hpNPPpnly5cDsGXLFi6++GJuueUW7r//fh555BEee+wxvvOd73D55ZfT1taG53n8+Mc/5swzzxzms7EcKmhhLeQex7yhOojH3ymDIAXJ76OJdyM9dYBGKMMVmz4Q9EdzrrrqKlpbW63mWIad1lyWZ7bWMrWq+qBCHxwRJlVW8bdNGzh12nTiEdu9GWx6ozkXXXQRn/3sZ63mWEoWzT8LhVeAqp6dO8U4cQgKkPoBWnbhSArVeoeq7gydOI+KyJvd7NvZSR0wTTJ0Ev0M4MQTT9Szzz57v/I1a9YcsCJVX1bR6kxzzj333E77OX/84x954okneOaZZ/qkOb2xL5FIcPzxxx/UOQwUS5YsoeN1LiVK2b6hsM0LfJ7c8zovNKwj7efJeDnyvo9HQCafp7a5lQCIRCI4uTIyLTH8IADgI2PG8au9DSimb1MTT1AVj1PwfXwNuHz6ND58/Andtm97QIPAihUr+N3vfsfKlSvxPI8TTjiBRYsWkc/n2bRpE7Nnz+a+++7jyiuv5KMf/SjPPPMMF110EZdffjnf/e53Offcc/nqV7+K7/uk0+nhPh3LoUTbHUAATh9WgZAKCOrR3DNIYvGAm1ZCjLjY9P5qzrve9S5uu+02qzmWYeflXbtQtE+rRsRcl5znsaa+joWTpwyCdZZ2eqs5l1xyidUcS2mTutP867h9OLgcgjo0/zwSP30grRo0VHVn+G+diPwRE3K1R0Qmh7N3JgN14e7bgelFh08Ddg6pwSG2n2MZKeT8Av+z9QVW7N1MWyFL0s/iIETFRQR2t6XIU8CJQNorgJuBqgTR9ChEHUSEWPiSSlVpzmZpzmaZUFFBECiPb9rIZcccQ3U80aUNh+Zr+GHm6aef5pJLLqG8vJzq6mouvvhiAK688kruv/9+AO677z6uuuqqA4496aSTuPvuu7n99tt57bXXDtq7bbF0ReCnIL8U6FoQukUEEGi7cwCtGhGUfGx6fzXnjjvusJpjGXb8IGBp7WZGJ8r6XEd1PM6TtZsH0CpLZ/RWcy699NIDjrWaYykVAm8PeCuB8r5V0O6ITt0xYDYNJiJSISJV7T8DFwCrgQeBG8LdbgD+N/z5QeCDIhIXkVnAXGD50FptsP0cy0jA14D/3fYiz9avZVemiVxQICERvMCnpZCmLpMk5WdAAjw/AFGIBBDP4pe3oB0myBlnj0vEddjTlkKB3akUy3fs6NYOO4NnkOhsquZVV13FFVdcwaWXXoqIMHfu3AP2Wbx4MQ8//DBLly7luuuu4/Of/zzXX3/9UJhsGaF4fkBTusD4qh6Wz8u/BJozSZX7TAK8N/pxfMkz4LHpMPjx6dlslnw+/9b++XyeXC7HRRddxA033MAFF1yAqjJp0iSSySSFQoFMJkMymeT444/noYce4pFHHuGaa67hlltu4UMf+lC37dnY9L5TyrbB8NiX8z2yfoFCEDA9lyXiuETEJRKGS6hCUw7GJCDqeUxpbOqyrilAvqW0r/GhQm/6OUceeeQB+yxevJinnnqKv/zlL7afY+mUutYs4yrjOM4ghz3lnzcJk51oPyoZUf2iicAfw2c3Atyrqg+LyIvA/SLyd8BW4AoAVX1dRO4H3gA84NPDuYJWf8ZWVnMsQ8GG5G6W1L1OUz5NVFxaCmkyXh5EcBDyvo+Pb4IfI4AKfiaBE8sTxDOQ7/wlvCNCzHVpzKQpj0ZZvn07582e06Ud1sEzCCxevJgbb7yRL3zhC3iex5///Gduuukm5syZg+u6fO1rX+vUwwxQW1vL+PHj+djHPkZbWxsvv/yyFSFLt6ze2coH/vtZfnH9iZx3zMSud9QGQDqPqO41LpBFVUdSvPnBMOCx6TD48ekXXHABN954I1/5ylfwPI9HHnmEm266ieOOO45oNMr3vvc9rr766rfqi0ajlJWVUVVVRW1tLbNnz+Yzn/kMvu93aktHbGx63yll22Do7Mt4BV5sqOXpPRtIFrLEHJeCH7Bqdz3xSIQAJeFEOSI+ltb6Ch57OcMVZ1axKJJk59jR3da9M9nKJaePjHCJkUp/+zlTp061/RxLl9xwx4tMHZXgFzecNLgNBS0DUIkL5AagnsFHVTcBx3WyvRE4t4tjvgF8Y5BN6xGrOZaRwMM7V1GfTeKKw55cC64IUSfy1oJ8Wc8DFcxwQVCUljfn4kQLjJq/Fi1r67JuEcERIVfw2Nzc9YsusA6eQeGEE07gqquuYuHChcyYMWO/RF5XXXUVn//859m8ufMp5EuWLOHb3/428XicyspK7rrrrqEy2zJCWVFrHvK3T+tpZk67oPQHo1CHqoNnpMam91dz/v3f/51oNGo1xzIkNOXS3LnhBXZnWhkbq2Bq+SgAMoUCMYlS4ZocYYXA443kbta/NomxNREmjnZhb8/1K2ZlLcvgYTXHMljUJbOs2dXKRQsmD0FrA6ETZqBmGakNkEEAACAASURBVFys5lhKmUB9trZtY03L60QkQ7IQEHPinS5KUzyGyuychNdWQfXRawFFoznwuh6rRR2XVD5HplDo1h7r4BkkbrvtNm677bYDtt96663ceuut+22788473/r5hhtu4NJLL7XxoZZes6J2L1NHlTGxuofcOs54868q9Nk54wExnENw8BTGozuqmiyKTf8q+2LTv8WBsen3ish/YCJDhi02HfqnOTfccAMWy1CQLGT5xbpnSRVyTCsfRSHw2ZFupi6TJOt71HutpIhQ6SYod+M01VZRyAuTFzTha2WP9ed8j0QkQsztS8JUy8HQG81pD+W0mmPpLc+sN2scnDVv/OA35o41//arX+TT59yGloPC9nMspUbWS7Ih9QxvtjxGfXYHM8rbCMoVcEh5lezKTKEpPxZlX8h5u0s4yEdpq51ObFQz8TFN4AQgoH7QdYNi/pfzvW7tsg4ei2UEo6qsqG3ilFlje945diJIIszD09fOSBYi3S/NN4IZ0bHpFkupo6r8fvNKWgtZqqNlrG3Zw5bUXtoKWfKBTwDk8EjlM7Q4aSRdQf32MUyYkkUq2lif2cUMKrptozGd5tzZcw7JGYYWy+HAU+vqGVsR45jJ1YPfWOwskDho3vzbJ7IQO2dAzbJYLKWNasCqvf/L6pa/4Ad5Ik6CvCbIBAUEM0OnIpJiXvWbFPwYG1NH0lwYi4i8Nd8vteUINHConLMl9C+LSbosXc/gUVUQqIh1vxqydfBYLCOYHc0Z9rTmOHFm9zkpABynnCB+DmT/SndvmwL1UPVRAgRBxMWRKAQBoFDx4YE7gRJiJMemWywjgT2ZJOta91AdSfBs3QYas2kCNToTdVyTczAitOZzFIKA1OYJSMTHnbqDuFSzJ99C0M1qN4EqirJocqe5zi0WS4kTBMrT6xs4c+64wU+wDDhuBUHsbMg9DPTBwRP4gEDlRwbYMovFUqqoBjxb90vWp5ZS5tRQFq1GNSDQFA4eThiW5WkMVIk4PkdVv8n65FwyhTEgkG+pJFs3nvJpO4iUZcOKBcTv1sETqOKKMKWy+0ifkoqzEJHPiMhaEXldRP6tk/LpIvKkiKwJ9/n7orLbRWSHiKwKPxcOrfUWy9DTnn/nhCN6dvAAUP5hwIEgv99mVcULMmT9BrJ+A7lgL/mgmVzQRNZvJOs34NNM4IyD2GkDfBYWi+Vw4MWGWrwg4Lm6TexJJwEl5rjEXBdHBBEh6rpEHZdCw2j8ZAVl03eTcdrYkW8i0ACvm0lydekU88dPZFx5H5c8tlgsw8obu1ppbMtz5twhCM8K8cuvwQcKQZJ8kCYfZPCCHKrdhEmAibWgDSJzcaIHrtxksVgOTVY2PsD61FLKnTGIuKS9ZpoLO1FaKYvkSbhZEm6GMjdDRHx8dfHV5ciq9YyJt+DgkNw4Cyeeo2J698udF6OqFHyfiliMI2pGdbtvyczgEZFzgPcDC1Q1V7QUcTEe8DlVfVlEqoAVIvKoqravT/g9Vf3OUNlssQw3K2qbKI+5HDWpdzmbnNhcgvi7IPcQBA44EQL1yAVNqHqY4E5B2lfbUlAU0Tyewuqcx+zca4wtO2Cii8VisXRJPvBZ3rCFLalGGnJtxF0Xt5PkgwBlToLGrZNwK9OUTWhGcUj5WSI45PE6TfLekG6jOpbg0qPnD8XpWCyWQeCp9fUAnDlv3KC31eY1sjW1gtq2FczwxzIrsoMCDorz1uzliMSISAxHOgyXVEHbTFhX9e2DbqvFYikN2gqNvNbyEGVODbkgSdZPggguEYQIvgY44RhKUGJODhByQQxFmFu1mTfXnYOfrqD6qLWI24kjuZMJPKpK3veJuRGmV1Uztbr7ENaScfAAnwS+pao5MKvYdNxBVXcBu8KfkyKyBpiKyYFhsRx2rKht4vgjRhFxD2IyXs2/QHMz5J/DD6LkNWliOnEOzDEoiqvG5bPWG8Uu36Nu9z9w4oR/ZWLFyQN5KhaL5RDEV58dmZ281PgKu/OryDvNTKx0CTROrlCJF8RQFTw/IJcv4AdK29bJqBeh7IjN+BrgOg6OCq1+hsAJ8AmIYJIo532f+nQb48vL+bvjT6Q63tc8GhaLZbh5al09R0+uZkLV4CYtrs9sZFnjb9ib20rGb2ILFRCvZFY0iUeAbzKdEqhHQXLEpZyIE2pL4ANtIDGo+TZO7JhBtdVisZQOb7Y8TqAeWd/M+HMl9tbYyRHjHG5HTTYeBIg7OfJBFPGE5JapxEe3EB/bZF6iA/tWOhY6rsrnBwFeEJBwXSZUVjKuooITJk/p1s5ScvDMA84UkW8AWeBWVX2xq51FZCZwPLCsaPPNInI98BJmpk+ni8SLyMeBjwNMnDiRJUuW7FdeU1Pz1soPw4Hv+wPefjabPeA8D4ZUKtWv4weDUrQJhs6urKe8sTPN++ZEe2zvQJuuQP13oEEzUiQvxbRvUSCrETxcQPEIeGH9K1RFm3Gk+yRfFovl8CTQgNdb3mBV86u0FlppKaRoC/YSdQtm5o7miEfayHsR9ibLSOfMcqJ+uoxs/VgS4/cSTWTxvQDfURwHAhQfJZXLoSqkC3likQhnz5jFWTNnUdlD0kGLxVK6tOU8VtQ28ZEzZg1qOw3ZTSzZ89805jchCC5xHMfh+cKRpHUn82L1xFECVTwJUPXJqY9SIEr4tt2ZDDXfxInb2cwWy+GAqhLkVxPL/IzFsRYc8ShojMagnM3eOLJEiYiLSLgoX7uvBjOOUhxiToGnVp6F+sKoubW4EQcvCPZFSxBA4IIKfhAQqNGhiONQFYtRk0gwd8xYJldVM6WH1baH1MEjIo8Bkzopui20ZTRwKnASZtWa2WqmFnSspxJ4APisqraGm38MfA1zHb8GfBfoNOuZqv4M+BnAiSeeqGefffZ+5WvWrBnWZcqTyeSAt59IJDj++OP7fPySJUvoeJ2Gm1K0CYbOrmc3NKAs49LFCzn7bZ1FNHZv0+rGH7Kr5W5mRHwmu21EZP9pgmmNss2vpi6oQnCJhttVlYK2Ull2FidP/uYAnpHFYjkU8AKPJ+uWsrrldTJ+lpSXJOvlCDRNzN3nPA7UIVCHcdVZWjPVtKYrSW2dgkR8KqbtwXEdIjj4foCIoK5xHFXGYoxOlHPC5CkcM34C8UgpvauyWCx94fmNjRR85axBzL+T9Vt5as9PaMhtJCIxXCdaVOrwijeN171JzIk0MC/aQDmF8CWYRyHIEMROIl71KYieiOOUVBpTi8UyCARBANmHIXMvQWEtRzpJNOzECBmm0cL86B52+lW8lh9P2nfwusjftbNuMus2HcVx81eyt9xHcBBH8IIgnL/jgBcBFSKOg+s4RMUh4jqMLS/n6HHjacykeeesWT2uFDqkvSJVPa+rMhH5JPCH0KGzXEQCYBxQ32G/KMa5c4+q/qGo7j1F+/wc+L8BNt9iKSlW1DYhAsf3NsFyEYF6bEs+hEcZG4NyNgc+FVIgQkCA4KlDGzE6ThMEEBFcTVCXfZ68lyQWGT5nqMViKS0CDVha/zQvNa2gKd+Mrx75oIAfBDhOsN/0ZQePWEQI1GOU00Lrzgl4beVUzdqOE9nXQXJd4+SJiguu8MmTTqEiakOxLJZDiafX11MWdVnUi1VB+8r6lqXsya7txLmzD48Ia71JrPUmECMgiocXQEpzzKl6O+fFbXi6xXI4EAQFSH4NMn+lfY5NXhwo6skIiiPKjEgLU9wkS3QCb+YrzaqeGo6iBIJAeHr5WVSWJ1n09hd5vvFkkoUCrgiO6xCgqCc4xIiIS3U8gSPCtGqTbyfhuuxMJTl75myOnTCxR9tLyf38J+CdACIyD4gBDcU7iHFX/RJYo6r/0aFsctGvlwCrB9XaIeAjH/kIEyZM4Nhjj+20PJvNcvLJJ3Pccccxf/58vvKVrwyxhZbhZEVtE/MmVFFT1nknpTt2pJ4kH7TiiIlzD3BJaoImLadFy2gjTmfOnXYcieFrni2tD/TVfEsJ0lvNOf30063mWDplY3ITLzQuoz7bQMZPkw8KRHBxnQjghOGggqqggCNKxAlwfKWpdjqxqhTxsc0H1Os6DjnfwwEyXmGIz8oyWHSnOWvXrmXhwoVvfaqrq/n+978/DFZahoKn1jdw6uwxxCPuoNTvq8drLX8FoUvnzv445InQRoKckwDi1KZfos3rNPuDZQRwMHozdepUqzeHMUEQQPIrkPkzEAGpAA7UDRNY5VDAISLKOyv2MDeaMiuDEoZnKby+9lgam8Zx+olPE4/kQSERcfEJAKFcEkxOjOH4UbOpjMVZPGMGZ8+cybyxY/ECn53JFO+cNYf3zp3X4+wdKK0cPL8CfiUiq4E8cIOqqohMAX6hqhcC7wCuA14TkVXhcV9S1YeAfxORhZhruQW4aagM3/RqLc/8YRl7ttYz8YjxnHHpKcxeMKPf9d54443cfPPNXH/99Z2Wx+NxnnjiCSorKykUCpxxxhm85z3v4dRTT+1325bSJgiUl7c2cdGC7pNsdUVd+gXAJATrCyICgbAn8wLzuLFPdVj6znBrjqqSSCSs5hymJJtSLPvLCkSEU993IhXVZllyVeWZxueozzbg4REhghN2RERNqkENl4fQMJlg+2/b1h2F70WY9rbXSEsns3Pae0qYqcuWoWU4NOdtb3sbq1aZrp7v+0ydOpVLLrmk321aSo9te9Nsbmjj+tP6f091RV1mHS35nUQ605de4EqcnJ9iXcuTHD/20gG2zlJMKejNlClTrN4czqT/BzIPA2XghPn9DswaU4TgAa4GnFuxh4ZkGc2+WVWrLZ1g+SsnM23yVmZO24igpNI5YkQZnShHHAfx4hQ8KHeqUHzShQJZzyNQZeao0Vw5fxZHjxvfK+cOlNAMHlXNq+q1qnqsqp6gqk+E23eGzh1U9RlVFVVdoKoLw89DYdl1qvr2sOzicMWtQWfTq7X8/rsPkmxqY/y0cSSb2vj9dx9k06u1/a578eLFjBkzpstyEaGyshKAQqFAoVDo9RdvGdmsr0uRzHqcOKNvU5lzfhNvBZH2EUdc8kFrzztaBhSrOZbh5qGfP8bKx1fz8qOv8bc7n3xre11uF9uTL1Hu1DPGTVLhtJmkgWDeZkmRn6aIVNNo9myZyeRZmxg7bg8aeASB7rejT0BEHUShImLDs4aS4dScdh5//HHmzJnDjBmD5wCwDB9L15lsDIvnDV7+ndrUSyh+OJvw4HEcQRA2p5b1vLOlz5SK3syaNcvqzWFKEASQvgNw9jl3AJGeZhcKvjhEJeC4+F4kEMQXlq08Hd+PcPqip3AdJVuIEitEiBcixHdHmJkfw4KJE7l21jtYMG4qUcdlzpixnD1zFp87/R186qSTOWb8hIPqb5fSDJ4RyTN/WEblqEqqRlcAvPXvM39YNiDe5p7wfZ9FixaxYcMGPv3pT3PKKacMepuW4WdFrZkivKiPDh4jEt15onuHUzo+4sOGUtCcd7zjHWzatMlqzmFKc10L1WOrCPyApj0tZLw6tqX+xhtNv2dmvA7grY6IpzHqChNp8CYSERcPn+K1E1Rh02sLiMZzTHvbm7iuR9TJ0JYrA8BxBXGFWNixiuISdQcnhMPSOcOtOQC/+93vuPrqq4ekLcvQ89S6eqaOKmP2uIpBayPp1fe8Uw8ILml/7wBYY+mKUtGbyy+/fEjaspQgheUQ7AYq99ssCA4uAT5dp7EwIVvzokmW+mPZWT+FdZuP4vijVzC6ohknEDLJcmLbXcQHf1TAnrpWFseO4YrjFwGwZG8TZx+3sF+nYEdn/WTP1noqasr321ZRU86erf3/Q9IbXNdl1apVbN++neXLl7N69YhPPWTpBS/V7mVsRYwZY8t73rkTytzx3aXY6RWqPjF38JIhWjqnFDTn2WeftZpzGHPO1WfQ1poml8ly9DUNPL3jE2xuuY9c0EZOoxRIUNA4BY0h4jMlto23l6/gyEQdqopb1PXYs2UGbc2jmXHMaiIRD9dVyuI5s9Soo3gaEGQDYgUXBcrFLoU+1Ay35uTzeR588EGuuOKKIWnPMrQU/IDnNzayeN64QZ0RGtD5yjYHi3axQo5lYCgVvbHhWYcxbXcDAk74MknB83y8ggfqtG/qEg8hLj5zJM0zL51JZXmS449+CcdR8gWHVCpBLvDxRYmtiTD55Ro2PruHlub0gJ2CdfD0k4lHjKetZf8vpK0lzcQjBm+aaWeMGjWKs88+m4cffnhI27UMDy/XNrFoxug+d4amVp6HAEHg99ECRUWZVnlBH4+39BWrOZbhZt6iOXz6Bx/hnH/J0lL+NyJOBQl3AoVAiYpHVPJEJI8rHqoOBY3hq8ussu0cXb4DJUBUKGRjbH3zGKrH1jN26o4wAbMSjeXxRXEDh7JChLgXIVnIUZ6NUhmzDp6hZrg1569//SsnnHACEyf2vHKIZeSxalszyZzH4kFcHh2gIjKq33UoAQm7cuigUip6M2HChCFpz1KC+BtpD3LyCh7J5hRtLWkyyRxeLkA9QTXo2skTpsB4ZePbaWwZx+nHPEeCAo4P2boqMqsnMfrVCqqeSKBvKK17smzftpfVr24bsFOwDp5+csalp5BqTpFsaiMIlGRTG6nmFGdcOvhhC/X19TQ3m9VGMpkMjz32GEcdddSgt2sZXhpSObY0pvscngUwrmwRCXciPplOyxUlF+Ro9VpoLjTRVNhLc6GJVq+FfJDHC7JEnQqmVb6rzzZY+obVHMtw4hU8Nr9Wy7qGe9iVfYyYOxZVj7S3iygpYk6BqJhPTPIknAwxySKi5DXKzEQDMxJ1ILB1zTEEXoRZx75mOiMKqOAGipsSpA2CguJFfCK+w5gNZZTFrYNnqBlOzQH47W9/a8OzDmGeWleP6winHzluUNuZkHgbgkPQxxk4qsbBMyl+9ABbZinG6o1l2NEcIBTy3lvORjfi4EZcHI2aWTy+06mTx6yapdSnK7l/5dlMH7+VI6dvwHGgdd04tLGc/KoJJJqjxCIRIhGXZDJDsiXNs0vfHLBTsA6efjJ7wQyu+NzFVI2uoH57A1WjK7jicxcPSJzo1VdfzWmnncbatWuZNm0av/zlLwG48MIL2blzJ7t27eKcc85hwYIFnHTSSZx//vlcdNFF/W7XUtr0N/8OmPwYs2ouQ/E6zOJRMn6a5kITbV4KL/AICFCUgAAv8Eh5rWT8VnI6D0cOfol2S/8oBc057bTTrOYcpiy571nu//7veWPHb3C1inzQSMavI1APxCVQh4D9P44occkRkwIeUeaW15FqrKFu2wwmz9pIeVUSaI8aVbQQwUEIIkpefIIMTNhSyYRClUl+aBlShlNz0uk0jz76KJdealctOlR5al09C6ePoqZscPsTsypPIeFW4Qf5Ph3vB3lciTF/9IUDbJmlGKs3lmFHIihKJpXBcR3E2RctIYGLIIi64DmoKvv+MyjwH89eRN6PsnjRUggcmt+cSJCP0bZ9FH5m34sqxxGi0QipZI4d2/biewPTx7FJlgeA2QtmDErir9/+9redbn/ooYcAmDJlCitXrhzwdi2lzcu1TcRch2On1vSrntnVl7M9+QithY1EtQpESHlJCkEBhM7DvwRc9ckEMZY2ZtlR+CmfPPJjuI5NejqUDLfmJJNJqqrsNPXDkbbmNJVHb0GdAvmgGZU8jsQQBFcieBwY9qkIiuCKKfMDYevqY4klMkybuzZcMB0QRREyXgQ/orgFIZ6PULExhtssjDu6iky2MKTnazEMl+YANDY2Dni7ltJgb1ueV3e08Pfnzh30tqJOnHlV7+SV5j/hqo/T44o4+1BVPPJMShzNmPj0QbTSAqWhN8lkcsDbt4wQnMn4+TpUXRz3wLGQFKJoNI+oi+YVN+4Q4L3l4lm1awZ/evNkLjjqeWRHnIbm0UTK8wSeS+MrUw9szhHEgfq61v0WoejXKQxILRaLZchYUdvEsVOrSUT751RxnTinTfoeFdHpFIIkbV4z+SCPiFkKtCOCTwSPAjHezBxPxKnm1ZZX+fmmXw2YIFksltLmnA+dwRHn11E+KoZK4S3nDkBEoiDSZVx6gIMrPi++eQLNLWOZ87bXiYkiAaDGyRN4DoWWcir3xhi7rpzxKysY21bBuNGVNOxNUhigt1sWi2X4eXZDA6qDuzx6O3m/QEthDmkvQkshyd58K835NjJevts+jKpSCHJEnTJOHHPloNtpsViGmbLL0cBHpAtdUAe8GITlgkvUKSPqlIGW869LLmFSZRNj03Xkm8uJVmcJCi67nz4SP9t5mLnrOuRy3gCsb2ywDh6LZQSR83xe3dHCiTPHDEh9iehYFk/5OY47Cy8oEJUABz98397+MY4dAVJ+Fa+1nUBOK3DFJeEkeLl5JS80LhsQeywWS2kTq84RGdWGuIojEYrXk4hK1KyQ1c1gqSVTxaMvn8OcyZuYNHEHLg4xP0IkEBwPgvpyqpePYcxr5YxNVTB5dA0Tx1dRXhajNZWzIVoWyyHEU+vqqSmLcty0/idA7orWfJofvfkwVz79Pf5tzZMsb5xOzhcCLVDQHK1emvpcK0kvQ1CkXaqKFxTwghyuE+GE0ZdxROWiQbPTYrGUBlL2LgqFCiIRr+t9AgcpxEDFJLEIc3vd9+rJrGucyrXHPI5bXSBWk6Vt2xh2PjmPQjLRZX1BEBBxB84tY0O0LJYRxOodreS9gBOOGLjlyWNuFcuSc8h4cWbE9zAu0oAj+8IsAnWo98awqzCddFBN8frqEcfFCRwe3f0Yp407dcBsslgspUner2cULYyLZigT4wRWhLRGaAgqKDhR0kGwL+yqAw8vP5eCF+V9pz3EmuxkskEEXMVBoBCnetcRjKkZhzNacDqEiaoqvm8dPBbLoYCq8tT6es44chyuMzjLo29v28utL9/FrmwzMXGpjCTIBlNYl4wyt2odEfFwJACEjJch7+eojJThihi/tQhRt4zjR1/KwjE2L4vFcjggEiOVPo+ayj+iXoBqF46XwEELUeLlZQR47GmL8cPn38np09eSTWZoeO1I0ttH4+e6zy+maj6VNV07gA4W6+CxWEYQLw9AguWObEhupD7XQJkzii25MdTmfGKSx8XDx6WgMYJupCIhcXZkd7KtbRvTK2xsusVyKKKaR7N/I55+kLdF9hIXP5y9Y5Y2H40w1skw1Y2wpRBhp+ea1STY5+jZvHs6Kzccx1kLnmHSqDre2DMRnMAsKVqIEmmpIt44DuniLZYjgjOAb7iGAxH5DHAz4AF/UdV/7FA+HbgLmAQEwM9U9T/DstuBjwH14e5fUtWHsFhGIOv2pNjTmuPMuYOzelZ9tpW/f+lX7M2nqHITOEVOpL358bzaXM7ksp2Mi9cTEQ9QPFVaChlGxyqIR8qZWr6A+aPew5SyYwbFRovFUpqMmvU5dq96jolT6/C8eKdOnsAPiMUjuE4Elwj/+cz5ZL0o76nZysMPnEY+7xOL9exqKRQ84okok6eMJhIZmD6OdfBYLCOIl2r3MmNsOeOr4gNW55N1S1EUR9qDLVxyWtbr49uPe7JuKdfPunbA7LJYLKWBagZN/gKyf8YNGilzPFQdgrdm2AgOSoXkKdc8NTGHComzoZCgfY6PFwgPPvceRlU0c/ZxTwEQeBEkiKC+g9tWQXTDLDPluRPyBZ94PDJob/qHAhE5B3g/sEBVcyIyoZPdPOBzqvqyiFQBK0TkUVV9Iyz/nqp+Z6hstlgGi6fXGz/lYOXfuW3Vb2nMp6iKJDpdNCLjV7ApNZctbbMYH6+jIpIiQoG0Dw258dy+4ItURgd36XaLxVKaJCpGk3G+ya6tX2TyEfWoOvheFMUBNSFVOEK8PA5a4KUdE3hgzUI+eVo9VbmriMVXEo26tLXlcV0Ht5OXU74f4PsB5RVxXMfh5NOP7HyBmz5gHTwWywhBVVlR28ziAX7b1ZhvxOl3Oi6hMW9XOrFYDjVUC2jqF5C9D4IMIgkCTYeBWfsIEMzye0pCfN4WzaI4bPISqArL3jiJPU0Tufbc31MWzZP3Y1CIo+k4Ea+M6MYZRJKdr8zm+wG5fIFpk0d12kkaQXwS+Jaq5gBUta7jDqq6C9gV/pwUkTXAVOCNjvtaLCOZpevqOXJCJVNG9f6FUm9Z07yD9cldVLidO3eKCTTCnuyUfb8HSsrPsiWV5tiBmyxtsVhGGEefdipvLv//2bvz+Kqu8+D3v7X3mUfNEwLEPJgZgcE4gO14CLbjKRMZHL92Q5obt+mb5mZo3t42923aNNdpkzZu38Rx2jRp7CSNXTsJHrFlMDbGDLbBYGYhkITm4cxnD+v+cQRGIECgc46OYH394XPEOXtYW0aP9n7WWs/6HnveeogJkw7hC8YAiZQC3aXjdOlABNNy8Vcv30V1yObBW9bS2xHjzTca6Wjrx+V2EoumSKcz9XwyY54zXC4HoXAm/lVUhlly9ZSstV0leBRljDjWnaAzmmJRFqdnAaTtkS87LBAkrXQWWqMoSiGRqa0Q/zXIJIgAmUXPHUgMhi60I7DQcGo2M50J+mwvjdEQL+5YxYzaQ8yp24+ORsoIErLK6W8OYTQHcRM4a9kHKSVpwyJtmEybVIFA4HWffy57gZsOfEAI8W0gCXxFSvnmuTYWQtQBC4HTq9g/KIS4F9hGZqRPzzn2XQesA6isrKShoWHQ5+FwOC/LAFuWlbflhpPJ5FnXmQ3RaDQnx823QrqOtCXZcijOdeMdF9Wm4V5DS6KHteaMs+p4DZctJW9v2Uan9/Al7a8oyuVh5tJFmMZ/cOTdtzh09FnKSl7F5Urh0CRWUqcvWcy/v303ezvC/PPaBfjdbnzjXMxdMIHd7xyjsz1CMORF1wWmaWPbEk0TOBwalpWZyF5WEWbugvFU1WSv2LxK8CjKGLHtaDcA9XXZTfB4dQ9yhAvzSWnjd/qz1CJFUQqBlBJiPwEZBRECIcDOLAkK5sAonqEeoDJJHpdmRvDqaAAAIABJREFUMc2Z5Edb78GydT567cu4NB2Bi1LHdIoPLWRauIimSDcnOiLYtsxMwRIg7UxUCvjdzJhSgcftwrZtXK7CHsEjhHiRTP2cM32TzD1XMbAMWAL8WggxWQ6xRrMQIgD8FvgzKWX/wNv/CvxvMqm1/w18D7h/qHZIKX8M/Bigvr5erl69etDne/fuJRgcesRUNkUikbycB8Dj8bBw4cKsH7ehoYEzv39jUSFdxyv7OzDsray9fiGrZww1U3Fow7mGtGVwW8Pfo7kELv3SHnPStoUlLZ5e8VW8zuxNiVcUZeyJpU1eaEpzoruecOc1hHygazaW7aQvqfH0/n6q/dB1ooXEzAq8bie3311PPJbG43Vhpk3aTvQhhEDXM/dM0pZUVRfhdOnU1JZw+52LszY9C9Qy6QXt/vvvp6Kigjlz5pxzm7q6OubOncuCBQuor6/PY+uUfNt+tIeg28G0iuzeLNf5Jo48wSOgzjsxSy1SRstwY86yZctUzLkCSOMwmHsADwiBlJJoX4x0PLNEOpxvRXSBDRxqqebNgzO4acFOqsIRnEKnwnsVi6rvosgfIp5IM7WugqsX1jFjcgU1lWEqy0JMGFfCwjnjWTC7lmDAS3dvjOuvmZGnK790UsoPSinnDPHnKeA48ITM2EqmiPJZc26FEE4yyZ3/lFI+cdqx26SUlpTSBh4BlubnqnJnODHnH//xH7nqqquYM2cOa9euJZlM5rGFSi5s3N+By6GxbFJp1o/dmYqQtk2cmn7Jx3AKDcO2aEv1X3hjZcy4mHhz9dVXq3ijEE+m+Y8XttHVH6OmLIzf68WSXtKWH0u6aDiaxLThthkhGtt6ePzlnRimhd/vZu29K5g3fwL+gIeJkyuYMq2SqdMrmTK9komTK/AH3MxbOJFPfOYafP7sJpLVCJ4sOLyvlc0b9tDe0ktFTRErbpjN5BnVIz7ufffdx4MPPsi999573u1efvllyspUIbjL3fajPSyYUJT1IqM3Vt3AS+0NmLaF4xJuiAzbxCEcXF+5KqvtUs5ttGPOH/7wB+rq6kZ8PqXAJX8HMp0ZvXMaaeoIQBcuLJlGysw0zTMH8yQtJ99uuIvKYA8fXPAaUpqUe5fi0L1Uhxaz9g4Hv3jiDTp7opSE/VSWDz4PQDyRpqcvzg0rZnLVjBpeOXEghxecc/8NXA80CCGmAy6g8/QNRKYL71Fgr5TyH874rHqgRg/AXcDu3Dc54/DeZl57dhftzT1UjCvmmlvmMnnWuBEf90Ixp7m5mX/6p39iz549eL1ePvaxj/H4449z3333jfjcyujZdKCDpXUleF2XnoQ5l4iRBMSIesNP7ttvxLPUKuViFEK8MU2TBx54QMWby4CUknh/HITAF7y4ml+bdh2msy9GdenZ9yfH+w12t6dZXuuh1OdAegMcPtHFjgPNXD1rAl6fiw/fU09fb5x33znGoYNtpFOZFbMmT63kqrm1hIt82brMQVSCZ4QO72vltz97lUDIS1lVmGh/gt/+7FXu+ey1I37gWrlyJY2NjdlpqDKm9ScN9rVF+NCckT/En6nIVcS0wFT2RffjYOhAY0lJ1ExhSgspJUJkVs/y624MmWJeaC4BZyDrbVPOpmKOkjfGTjKFkzMPO0IIAmE/ErCIY2OgCw+2TGWmc51M9Az4xc6VHOqp4m/X/Ayf00W5dzkgKXbPw60X4S6B+z62nJc27+O9QydAgtvtQAiBYViYlk1RyMs9axYye1p1Vocvj5KfAj8VQuwG0sBnpZRSCFED/ERKuQZYAXwG2CWEeGtgv5PLoX9XCLGAzBStRuDz+Wj04b3NPPHjBgJhL2XVRUT74jzx4wbuXrd6xA9dw4k5pmmSSCRwOp3E43FqamrOu71S2Fr7Euxvi/KRxbU5OX7A6YYRjkqGTCwLOLJfAFo5v0KJN0IIFW8uA8f2NfPy45tpa8ys2jduWhXl9eFh7ZtMm7y57zhl4dNKUEhJPGWQMk2eOZAi6BIsH5+JE0IIioM+Nr97hPoZtehaZqJUuMjHNStncM3K/I1CVgmeEdq8YQ+BkJdAKPM/9+Tr5g17stKjfiFCCG666SaEEHz+859n3bp1OT+nkn9vNfUiJSzOcoHlk26vuZUDBw6SstO4tfeXKU7ZFlEzSdo2BxdUlYC0SFkpdKHhYjK2baNpatZnrhVCzLnzzjvRdV3FnMudHeHMmdxCy6RwhPRhyAgSG134kMLClgZSWgCciIT5l603cv3k3dw4vZN2x0dI2h14HZVMDN566nhFIR93f2ghfZEEe/a30tbZj2naBAMeZk6pZHxNCdoYXhr9dFLKNPDpId5vAdYMfP0qQ5SuHvjsMzlt4Dm89uwuAmEvgXCmA+Dk62vP7spKr/r5jBs3jq985StMmDABr9fLTTfdxE033ZTTcyq5tWl/ZtBarpZHL3MFcQqdtGVecg0ew7LQhaDKe3avvZJbhRJvPB4PN998s4o3Y1jzwVYe//v/xhvwUjEhM9Olq6UHvdWm/VgnFePPP/tl//F20paJ06Fj2zadfTGaOnqJxlOcSLvpSvuZ5omw50ic8RVFlAR9+NwuWrv6aWrvZVJVST4uc0jqaWyE2lt68QU8g97zBTy0t/Tm5fybN29mx44dPPPMMzz88MNs3LgxL+dV8mv70R40AQsmZK/C+ummhabysdp7sKRJys6shhUz03Sno6QtE4FAEwKN9//oQqIhaO0r41/37uDPt/4Ww7Jy0j7lfYUQczZt2qRizpVAnPvhSAiBUwTQ0JFYCDQcwoND8+PQfDz06t3Ytsb/WvU7NFzErRb8zvFMK/oMuuY563jhoJfliydz580L+Miti7h51Wwm1pZeNsmdsay9ueesYe2+oJf25iEX8Mqqnp4ennrqKY4cOUJLSwuxWIxf/OIXOT+vkjuvHOigIuhmRmVuim+7HS4Wl0wiaZuXfIykbbCgeBI+x9mxSsmtQok3+/fvV/FmjNv02zfweN2ESgIIkZm2GS4LIYRgy++3X3D/jt4YDl0nbZjsPNjMO4db6eiN0h5J0pTy4pUJtGQ/J7ojvHWwmd1HWjEtG4DeaCLXl3deKsEzQhU1RcSjgwtwxaNJKrK41Nn5nBw6WFFRwV133cXWrVvzcl4lv3Y09TCzKkTAnbtBd9dVruaT49cCkl4jQsSMIQBNDF4nR2AjsJAIemLV2HYFAYebhraDfHnrb7FtO2dtVFTMUfJIryZTB3hoQmg4RAAHmZtxGwuJxatHp/DsgTl8fmkDteFuTIqYFLqb6UWfwamp1fbGmopxxcQjg29W45EEFeNyM6L0dC+++CKTJk2ivLwcp9PJ3XffzWuvvZbz8yq5YdmSVw908oFp5TmdcvnJSR8AJNYl3I/Yto2Ukk/Wrch+w5QLUvFGyQYjbdD03nFCZWcnknWHzsEdhy94jLRlYds2bx1qobWrn95YgljSoFMUYQtBjdaPEBBNpuiJJjja3su7ja1YMrMc+mhSCZ4RWnHDbKL9CaL9CWxbnvp6xQ2zc37uWCxGJBI59fXzzz9/3srwythk2ZKdTb05m551upWV13LvhM/TEfOC1NCwEcJ6/w82EkEsXUR73xQSRmb4oUPTCDrcbG4/xL8f3JLzdl7JVMxR8sb9wcyrPF+SR6BrblxaCKcIYJk+vt1wGxOLuvjc4i1oeCgv+jJl3kVowpmnhivZdM0tc4n2JYj2xTMxpy9OtC/BNbfMzfm5J0yYwJYtW4jH40gp2bBhA7Nmzcr5eZXc2NXcR1/CYOX03C4MMrdoAnX+cmJW+jwr/Z1NSohaacb7y1hYMil3DVTOScUbJRs0TUPXtFOJFtMwMc3MqD4pJbrzwh3mQa+b4519tHT1kzRMXA4d0+GhHx+lIoZby5SmcDkdOB06iZTB0bYeevrjuF2jWwVHJXhGaPKMau757LUEQl46T/QRCHmzUuwUYO3atSxfvpx9+/ZRW1vLo48+CsCaNWtoaWmhra2Na6+9lvnz57N06VJuvfVWbrnllhGfVyks+05EiKbMvCR4AJ5uOkhzfyVtfTPoi1cRTZYQTxURTZbQG6/iRO8M+uK1WHLwkn4OTcOp6Tx+ZJsaxZNDhRBzrrnmGhVzrgSeG0EUgRzeMrGacPBvOz/AkZ4y/mr1BrwOAylqwbk4xw1VcmnyrHHcvW41gbCPztZeAmFfVgqewoVjztVXX81HPvIRFi1axNy5c7FtW9X9GsM27u9ACPjAtNzU3zlJCMHfzP8EIaeHqJkYVpJHSoiaCYJOD387/xOXQ1H3MalQ4s2yZctUvBnDdIfOrOXTOb6/mV2v7uWN9Tt44w87ePf1fRgpg3mrLpy4m1hRzPGOXpKpNNKwiEeStJhBHNKknMigbYUQuJyZJM/xzn4mlOdnVP25qCLLWTB5RnVOips+9thjQ76/fv36U1+//fbbWT+vUli2N2XmHecjwZMwDV5uPYBXdyLRiaUvrpfNqznpTMbZ2HaQ1dXTc9RKZbRjTiQSIRjMTf0EpXBomgfbexvE/xOkARcYgdPcF+LhN5Zz/aS9zK94h1jS4pdvTeWd9qe4ddFMVl41mZBX1bQYiybPGpeTAqfDiTnf+ta3+Na3vpX1cyv5t3F/B3NqwpT4XRfe+BJIKTnW38f6A/vZeLQR2yoj6TtG3OjHpTkIOj04dO2sfZKWgSEtil1+/n7BpxgfyO0II+X8CiHeqPucsW/Kgjp++/0/IG2bQHEApKT9aCfp5CQmL5t4wf07e/uJRZOYhoUORN1FpDU3JbEWYmYUX9A7aCTQyTo/hmnSHYkT8o/e/Y5K8ChKgdve2E150E1tce6X63yq6W1SlknYdWlBSdMEGvCfh95UCR5FuRz4PwOpTWAfz3Rx4zy1bPrpbFvylxtWAZIvL/sdGiYHOip55t3JJK0u/vmZ1/j5xp2s++DVXD93iuodV5QrTH/SYOexXv541eScHP9wdzf/9Mbr7DjRSk8iiX4yxPQUoRVFsHwpUlYETdfwaA50oWEjkVIScHq4ruwqHph6PZXe0e15VxQlO95u2MOspdOIRxJ0Hu8CIZgyvw6Pz83uV99j4uzx593/D3/YhkxZaA6BFDp9nlI8ZpyAHc+Uq+hP4A/70B06wMBqwgIQHGnrpm4UV9FSCR5FKXDbm3qon1iclweiplgPkpEVBtM1jZZ4X5ZapCjKaNL0cuyiv4fePwerDYQNUuP0RI9tS37/3jgaGmfy4JJnqQr20NRbwndfuoVo2oUtbUDS0R/jod+9Qtoy+dDCmaN6XYqi5NdrB7uwbMnKHEzP2tp8nK8+/xzdiQS6EITcrtPumdwQ82PGTNLOfvAlwCmoC4cp94VYVTGLm6rnEXL5st4uRVFGh2mYHH33GBUTykgn0/jDPhAQKg3icOns23yQW9fdSMo0ea+zgzeOH6c7kUAIqPAHmB8uY/eeJlwuDV0Iml1lSDRKkh0IQGgC25akYim8IS+WLbFtm4qiANFEio6e2Khev0rwKEoBa+9Pcqw7wWeX1+XlfAnTGPExNCFIjWB5UkVRCovmnI5d/EPo+yswjwxM10oNJHqgO27z0Gu3UBdu5yOztrLnRC0/fPUmelMBBjq2kFJi2jaxpMEP17/OjJpyJleWjuJVKYqSTxsPdBBwO1iU5enme9rb+Z/PPkMknSI4KLEzmAMHDqME2SuJpFK0d/v57l13MSGsRuwoyuVGCAECjuw+Suvh9kHvr5pej9AFrx9r4g8H9pMyTfwuF25dR0o43NPNxrffo6nSRsQ03NJP3BUilOpG2GksQAASQcqw0NMmbpeDkqIQHpeDaCKNGOUqx6rIsqIUsO1H81d/B8DnGPkqN1JK3LrKHSvK5URzTESUPAJF3wX3ChBhwI0pnfyfN1dwvL+UNVN28tArt/K3G+6kNxkYtL8QAqeuo2uCnliC/3r9ndG5EEVR8k5Kycb9HSyfUopTz96jh2nbfOWFZ4mkUoRc507unE4IQdDtpiMe46vPP4e8mGW2FEUZE3SHTqDIz5F3mvCFfATCfgJhP96Ah2Q8RecEL795912CLhfjQiGKPB68Tidep5NSnw9/QuLRNPqKJMfdJThtk1qzn6DuwCk0dKFlFpcBygJeqkszyR0pJUJAbWl4VK9fJXgUpYBtP9qD26FxVU1+AsWkwMh71E3bZrxf9YgpyuVGCCea+2pE0Xeh5D+g5GF+uXsd/75zNcXOCC++N4/9HTXY8ty3Fk5dR9cFG3YdIpJI5bH1iqKMliOdMY73JFg5LbvFizc3NXKsr4+g2zVkbbBzEULgdzrZ09HBno72C++gKMqYIqUkFU/jDXpJRpJYpoVlWiQiSXDpvEOUmlAQt2PoDmlN1whaAssoIi3clFtd6AKcQhDQdYIDf7wI3E7HqeRyPGlQHPAxqXp0RyirBI+iFLDtTT3Mry3C5cjPj+rtE+bhdbhIWuefqiXJJHIMyyZt2Ri2jZSZWhw28OnJV+elvYqi5J8QAs1RieaczT+/GkACRXp82Pu7dJ1IIs17zerBSlGuBJsOdAKwcnp26+/8dOeOTD2MS6hR6NB1JJJHd+zIapsURRl9RtrESBksumEelXXlGGkT07ConloFQRda3MChnfvZqqSyCCyNpFmKrsWw3ZGztjlZVNnpzsx+MC2blGkxf0o1lcWBs7bPJzWPQlEKVNKw2N3cxwPX5mbFiaG4dQcfrJrB747vxqOfPV3LlpKUZZEyTWwpEQgkmVcAhKTM4+eaikl5a7OiKKPjhT1tdCUdhLUenJo97P10TcOWNi3d/TlsnaIohWLj/g4mlvqYWOrP2jF7EwneaWvDO4Kp5R7dwaamRkzLwqHrWWuboiijy+HU8QY8CE0wed5EJs/LLIverZlYAkIe93n3D5eHaNUrwNII6x3EnTbpmI3r5AhlKTHTJqHSIEITpA2LZNpgXEmINUtnjfpKoWoEj6IUqF3NfRiWzFv9nZPun34NLk0nfkbB5aRp0ZdMkTAMBAKHpqFr77+CJC0twlaI/963F9Me/gOfooxlUkq6E3HaY9Er5t99Im3x10+/i1szCYqze7YURVEA0qbN64e7sr56VlNfL5Zt43RcemLG7dBJGiZ9yWQWW6YoymjTNI36m+fT3dozqM5Wm0gjbcm4qdXn3b+1x6LdClAteimRBk4hSDlsUrZN2rJIpAwcXheOgJtIPIUAakpD3LZsNtNrs79S4MVSCZ4Cdv/991NRUcGcOXPO+mzfvn0sWLDg1J9QKMT3v//9UWilkiv5LrB80sRgCX85/0OY0j6V5EmYJnEjjTaQ0DkzMW3ZNgYWkz3lLC+ZyuZjTfxy19tXzMPu5WK4MWfFihUq5gywpeS/9r7L3726kYdee5V/efMNIqnLv7bMvzQcpLk3wexSE+sif85taSOAieWqVteV7nwx56Qf/OAHzJkzh6uuukrFnDFo29Fu4mmLD2S5/k4knR75QQZW2unPxrGUgncx8Wbp0qUq3oxx9TcvYMbSKbQd7aD9WCftxzrp6O7D4dSpnDA4CWNYFnEjTdwwSJsWL70dJ+gV3LKqmqmVpXgticuSeNIWDltSWh6iekIZJSEf48vDTKwq4c4Vc7hx8fRRH70DBTZFSwjxJ8CDgAn8QUr51SG2aQQigAWYUsr6gfdLgF8BdUAj8DEpZU8+2n2wsZ1XthygraOfyvIQq5ZNY2pdxYiPe9999/Hggw9y7733nvXZjBkzeOuttwCwLItx48Zx1113jficSuHY1tjD5DI/JX5X3s9964Q5mNLib995ju5UHNuWODUHZ4Ys27YxsJFIpngqWRmeia5p1AZDvN12gmKPl9tnzMx7+y93ox1zent7mTlzpoo5wDsnWnnt2FHGh4oQwPH+PtYf3MfHr5o32k3LmSOdMX70ymHuXFDDggqLf32hdWDliOHd1CTSJiVBP5Mq1DLpY8Xhg2282rCPthO9VFYVce3qGUyeWjni454v5gDs3r2bRx55hK1bt+Jyubjlllu49dZbmTZt2ojPreTHpgOdODTB8inZ/XkPut0gxEXFnjOd7Nkvcp9/uoaSX4UQb1KpFB/96EdVvBnDHE4Hd3zxQ7QcOkHj7mMIXaOmRCK7WxFaJnb0JJMc6+ulPZapIyiAri43nf1+rl/oIhj2MHPJFLydxVxbVI0dtXmvswfTysQOn9vJ1bMmMn9KDcUB7yhe7WAFM4JHCHEdcAcwT0p5FfDQeTa/Tkq54GRyZ8DXgQ1SymnAhoG/59zBxnYee2obkWiS8tIgkWiSx57axsHGkRePXLlyJSUlJRfcbsOGDUyZMoWJEyeO+JxKYZBSsqOph0V5Hr1zujsmzufRFZ+iQg8DgpRtkLQNUrZJyjZJ2gaGsCl2+FldNJvVxbPRB+awCyGoCQTZ1NRIbzIxatdwOSqEmNPQ0KBizoATsShu3YEmBEIIijwejvddvrVlpJT81dPv4nZo/MWts7hpwTSKfB7iw+wBt2wby7ZZObuOsN+T49Yq2XD4YBu/+eUWIpEE5RUhIpEEv/nlFg4fbBvxsS8Uc/bu3cuyZcvw+Xw4HA5WrVrFk08+OeLzKvmzcX8HiyYWE/Rceq2coUwMhdGFGNFI4bRl4XE4CHtULCoUKt4o2SSEYNzUalbcuZRrbq/nqim1SJkZsfPWiVa2NTfTEY+jCQCJYUDbCS+BgEGr0crezg4s20Z3OVgydwofvWUJ/+tTN/KNtTfwzU/ewFc/fh2r508pqOQOFFCCB/gC8B0pZQpASnmxTyt3AD8b+PpnwJ1ZbNs5vbLlAEG/m2DAg6YJggEPQb+bV7YcyMfpAXj88cdZu3Zt3s6n5N6RzhjdsXTep2edySndzHNP5pOV17AwUMd4VykVziA1rmJm+mq4s3QJd1csZaqv6qweNF3TkMCOlpbRafxlqhBizm9/+1sVcwZU+QOkLQtbSqSU9KWS1IZDo92snHnu3RNs3N/B/7xxOhVBD9XFIT44bxoOTSeRPv/qe5ZtE0+lqS0Nc8/Vc/PUYmWkXm3YRyDoIRgciDlBD4Ggh1cb9uX83HPmzGHjxo10dXURj8dZv349x44dy/l5lezoiKR4t6WfVVlePQsg7PWysKqahGFe8jGSpsl1kyajnWc1HSW/VLxRcmlmWTlCwPbWVtqiMQzLpCMWoy0apSMe52izG9sWeEu6sKXN0d4+dra2Uun3Mz4UBjJJI4/Lgeu05dELTSFN0ZoOfEAI8W0gCXxFSvnmENtJ4HkhhAR+JKX88cD7lVLKVgApZasQ4pzzFYQQ64B1AJWVlTQ0NAz6PBwOE4kMr2jk8ZYuykoDpE/rvXQ6BcdbuoZ9jDNZlnVq32g0im3b5zxWOp3mqaee4pvf/OZ5z5dMJs+6zosRjUZHtH8uFGKbIDvt2nQ886Bktx+koeHwqLWpMx5nkWXhMGJMphhEMafmaVlAxADOPROySkra9uzh5Sb1CzJb2jr6KS8NDnrP73PT1pGfUSPpdJr169fz0EPnG2R55ZhXVc01PT280XwMAYwPh1kzdcZoNysn4mmT//d3e5hZFeTe5ZnRW0IIPrNqEe29UXYcaSaWSqNrGi6HjiYyq+xZlk3atJBAbWkRH18xn8mVanrWWNF2opfyisFJS7/fTduJ3pyfe9asWXzta1/jxhtvJBAIMH/+fByOQrp1Vc7n1YMdAFmvv3PS/QsXs62leWC54otL0piWhSYE/2PBopy0Tbk0hRJvvF6vijeXIbfDgUNoHO/rw7QznXMOPXO/kkq4SEb8BEsiON0W3Yk0Lk0nZqS5dXph1NYZrrz+qxVCvAhUDfHRNwfaUgwsA5YAvxZCTJanl77OWCGlbBlI4LwghHhPSrnxYtoxkBT6MUB9fb1cvXr1oM/37t1LMBgcYs+z1daUEokmCQben78biSaprSkd9jHOFIlETu0bCATQNO2cx3rqqadYvHgxU6ZMOe8xPR4PCxcuvKT2QGZKxpnfp9FWiG2C7LTruSfeIeRpZe2a69C0kQeUS2mTlJKvb3ieCl8IfQS9W63Rfr6ydMkl768MVlkeGog57w8pj8VTVJbnZ9TIM888w/z586msHPl8+MuBJgT3zJrN9ZMmYdmSYq8Xx2XYGyyl5DvP7qGlL8nXb59KJJ2i2JsZklxVFORPb13BTza8SVNHDyd6IkRTaeyB2hgOTVBVHKIyHOC6OZO5Y+nsrMQ1JT8qq4qIRBIEg6fFnFiKyqr8FMl+4IEHeOCBBwD4i7/4C2pra/NyXmXkNu3vpMTvYk5NOCfHv2b8eCaGwxzt6yfodg37AcyWkphhMLeiklnlo7/ijfK+Qok3kUiEv/u7v1Px5jKTtiwM28awLUzLxut0ggApobc9jO4wCZZE0YTArTuIGWn8TidNvX0jqveVb3lN8EgpP3iuz4QQXwCeGEjobBVC2EAZ0HHGMVoGXtuFEE8CS4GNQJsQonpg9E41MPKCFMOwatk0HntqG5DpRY/FU0RiKW77YH6Gnz/22GNqqsRlaFtjpv7OaD4EmbaNJe0RJXcyBGnTykqblMKIOR/96Efzcq6xQghBidc32s3IiaRpsKutjSd2HeRXr8eoqxZsbt3Lq617mVZSygcm1jG9tIzxZUV8+fYPsONwM5vfa6QnmsCSNprQcOoaM8aVs2JmHdOqylRyZ4y5dvUMfvPLLUCmJz0WSxGNJPnQ7Qvycv729nYqKipoamriiSee4PXXX8/LeZWRsW3JxgOdXDs1dz/zmqbxDzev4X88/SSRZAq/24V2gQcwW0piqTTlPj/fu/lDOWmXcukKJd4cO3ZMxZvL0L7ODtKWSanPRzSVJmEa6EIj2R/ESDkpqe5GExJjYOp9sceLy6Gzq6ON5kg/taHcJKuzrZC6Gf8buB5ACDEdcAGdp28ghPALIYInvwZuAnYPfPw08NmBrz8LPJWHNjO1roK1d9QTDHjyC9BrAAAgAElEQVTo6IoQDHhYe0d9Vla0Wbt2LcuXL2ffvn3U1tby6KOPArBmzRpaWlqIx+O88MIL3H333SM+l1I4+uIGB9qj1I9y/R1d0xAIzh5Ed/EudMOlDF8hxJzbb799xOdSCl9XPM4/v/E6j+9+h1d2JXA6BDctCFEdDFEVCNLU38cjO7bx2K63SZkmYZ+H6+ZM4Wt3ruZPb13BF25azv91yzL+7ztXcf/1S5hRU66SO2PQ5KmVfPSTywgGvXS09xMMevnoJ5dlZVWbC8UcgHvuuYfZs2dz++238/DDD1NcPLq/G5Xh2Xuin85oipU5qL9zumllZfzLmtso8XqJpFIkDAPrjMLLUkpM2yZhGERSKaqDAX5yx51UX+JIeyV3CiXefPzjH1fx5jK0v6sL05YEXC6qg0GqA0EcOOnrCuLyJtA9MdKWhc/pZFwwRIXfT8Dloi0ao/USS6+MhkKaWPhT4KdCiN1AGvislFIKIWqAn0gp1wCVwJMDw6McwC+llM8O7P8dMtO6HgCagLx1MU+tq8jKw9WZHnvssSHfX79+/amvu7q6sn5eZXTtOJapaTOaK2hBJikTcLlIDawycSnkQOFZvyu7q2dc6UY75lxqfTFl7OhPJfnR9jeJG2niER8tXTGum+fF5870C2lCUOr1UeKR7DzRiiUln5o7H13TcDp0Jparm+LLyeSplVl5wDrTcGLOpk2bsn5eJfc27s/00eaq/s7p5lVV8/N7PsL/eXMrLzceIZpOI+DUdIqTHVUht4e7Zk7h8/VLKPcHct4u5dIUQrw5vVyGcvk43NNNmPc7nn0uJy3NAZCCqRNNPJ5MWQr9tM4oh9AwbIumvj6WjBsbU/YKJsEjpUwDnx7i/RZgzcDXh4H559i/C7ghl21UlHzY3tiDrgkWjM/PfOPzWTF+As8fPkhN4NLqu/QkE0wtKSXkVkuQKspY8szB/fSlEpS6gzy5u4+KsM7cSe6zthNCMC4Y4q0TrcyvrGJ+VfUotFZRlEKz6UAHM6uCVIby8/u/NhTmb264kd5kgqf3vcfLR44QSSURCMIeLzdNmcyHpk0nqO5HFOWKZViDS0ZEIhqdXU5qqtKEAgLQh9xPDLFvISuYBI+iKBnbj/YwuzqEz3VxP55S2mB3gUyCcIBWhBDeEbVlUXUNzx06gC3lJU2zSpgGKyfWjagNiqLkVySVYmdrK+W+AK/tSRBLSm5b6jtnDBBCEPZ4eLnxCPMqq8ZMEUJFUXIjnjbZ1tjDfSvq8n7uIo+Xe+cv5N75l76wiKIol6cKfwC7IzP7RUo40uTG5bIZV50+5z4nK1VU+P35aGJWqASPohQQ07J561gvH18yftj7SDuOTL8DqfVgNoJMAzpoQaTnBoR7xSW3p8TrY05FFXs726nyX9xQ1b5UkiKPl6klajlkRRlLdrW3YdmS3qhk56EUcya6qC45/+1CyOWmub+PlmiEccH8rOimKEph2nK4i7Rls3KaWqFKUZTRE4sl2fLqAVpbenC7XLjH6SQFpE2L7k4P8YTOtCkJ9KEH7gCZzupij5fKwNiZsqcSPIpSQPa2RkgY1rDr70jrBLL/u2C8C3YvYIEQcLIusrkHmXgC5NpLXt7vrpmzOL61j65EnNJhrhQUTadJGAZfXLLsslwyWlEuZ8f7+3DpGi/viON2CFbMfn8koC3loNoWJwkhEELQnUioBI+iXOE27u/E49Sor1O1uBRFyT/btvnFf23iV7u2EBkXw3JJSIBjl87ayll09iZpbgkRDpmUFJ176pVlZ1bUmlhSxJSSsRPPVIJHUQrI9qPdAMNaQUtaHcjevwBjVyapI5yANzNRVADSBjsN9n6wTiATf0D4brvoNoXcHtYtXsJPdmyjNRKh3O8/Z9LGlpKueBwbyecW1TM+PDaWE1QU5X2GZXP0hKS5y+SGBT6kZnKgq5/mSD+GbSMBv8PJhKIiKv0BXKd1fdlnrF6jKMqVZ+OBDq6eVIrHeZ5u8SyyrTZIPAWpl8COZaap61PAvxbhXIAQ+WnHWCEy35BtQLOU8jYhRAnwK6AOaAQ+JqXsGdj2G8ADgAX8qZTyuVFptKIMk2lb/Nlvf8aO1GHkLImwNIQFOMDwmuC0aOvTsGyYUJvkXH3fpmUTMwyqAgFumjIVj2PsLBijEjyKUkC2N/VSHfZQU3T+2jlSSmT/Q2C8DcINwnX2RkID4QHpAiyI/gDbNRfNMfGi21Xu8/PFJct44fAB3mxuxpKSkMudebATmSDYl06ChNnlFdw8ZZpaflRRxiin5mDbPpPKIh3N18erTf1oArwOJ249c9tg2Dbvdbazr6uTWaXl1ASDSMB9iSvuKYpyeTjeE+dwR4xPXX3x9xoXy7aT0P+3mcSOjJEpkDrwtGY2QnojUp+IDP0VmmtOztszhnwJ2AucHG75dWCDlPI7QoivD/z9a0KI2cAngKuAGuBFIcR0KeXYqTarXFFM2+Jb23/DdusQwhJouobttbE1QIKwBcf7dVLd5fhqmkl6DOxUELfuQB/ovDZsC8OycGg6U0tKKPF6WTEh9/Esm9SdmKIUkO2N3cOaniWtY5B+BXAMndw5ndAADexOiD0G4a9fUttCbjf3zJrDLVOms6v9BFtbmomkUti2xO90snTcVBbX1FAyzGlciqIUlq7eGMmUwbNv9JBMS2ZPj9EciRF0uTClTdRIY9o2AnDqOn6nCylhd0cbKdMg4HYzMTz6q/8pijJ6Ti6Pvmp6bpdHt+0U9HwRjB2AC0Rw4H5ngAQwwDwEvX+MHf4+mrs+p20aC4QQtcCtwLeBLw+8fQeweuDrnwENwNcG3n9cSpkCjgghDgJLgdfz2GRFGbZfNm5ic+t+bEsiQoCUCJvMCB5AInn+qBfNlcJXeYx0wMVUrYqeqEXSNBAIAk4X40ozuU+X7uBzi+sp8Y5s0Zp8UwkeRSkQLb0JWvqSfG449XfivwGZAjHcKVACcELqeWz7T9C0S68E73e5WFY7gWW1Ey75GIVMDV1WriTdqRgbjx3gV7t30BGNsooaXjqQwhfs52hfFxXBICdiURKGgeRU3zgAHSJTXDnocrOro51Pz52P1zl2hjAripJ9G/d3UBP2MKU8kNsT9f81GNuBAGhDPM4IACfgADsKfX+OXfJfaI4rvvDz94GvAqcPs66UUrYCSClbhRAVA++PA7actt3xgfcGEUKsA9YBVFZW0tDQMOjzcDhMJBIZ9J5lWWe9V0iG075kMnnWteZLNBodtXMPx2i0z0ZiRTv4uD0V6ZcgRSYOnJb33XbCxYtxnTunJZnpnokwBA5h43F7EG4vkswsCRIpfE4nIV1wcMdODubxOrLxvVMJngJ2//338/vf/56Kigp279495DY/+MEPeOSRR5BS8rnPfY4/+7M/y3MrlWzZ0dQDwOLhJHhSGwF98NPWhQhPphCzuR9cavnQ87hihy4PN+b86Ec/QgihYs4YZto2Tx59i98f3cW+9jYMLIRb8Ow+F8JhodUdIy4tjkST+IVvyMSNBPpTKSKpFD6ni7Sqv6NchGPHjnHvvfdy4sQJNE1j3bp1fOlLXzpru2effZYvfelLWJbFH/3RH/H1r1/aKFQl90zLZvOhTtbMqb6kRR2GyzbbIfUi4Bs6uXM6IYAA2H0Q+zmEv3z+7S9jQojbgHYp5XYhxOrh7DLEe/KsN6T8MfBjgPr6erl69eBD7927l+AZ0/YjkchZ7+XSxcYbwzBYt27deeONx+Nh4cLRuZ9uaGjgzO9zIRmN9j3X8ja/eu91+vriyLREaIP/+VqGk47jC5gUNtgSfIfX0za+kIsij5/P1d5GMm2jaYIyn585FRUUeUZn1E42vncqwZMF+1o62PDOQVp6+qkpDnHDvKnMqBl5D8F9993Hgw8+yL333jvk57t37+aRRx5h69atuFwubrnlFm699VamTZs24nMr+betsQePU2NW9TBWoJG9XPSPr9BASrBaAZXgGcpYGbo82jHn5ZdfprS0VMWcMcqSNo8eeI0nGt+iKxbFEBa6ppHsLKIzquOta8JypTO9WEUWiT7wcvZoQQFoQpC2LbwOB/u7OuhNJkbtpkjJnYON7byy5QBtnf1UloVYtWwaU+sqLrzjeTgcDr73ve+xaNEiIpEIixcv5sYbb2T27NmntrEsiy9+8Yu88MIL1NbWsmTJEj784Q8P2kYpHG8f7yWSNFk5PcejZGK/AGmAGOZoZCEy9QhTTw+MYr5iRxquAD4shFgDeICQEOIXQJsQonpg9E410D6w/XFg/Gn71wItuW5kIcSbcDjM9ddfr+LNGPJq+17SlolT00ljnvV5pHkC0ta4cWKM35EZ4ePQHMStNMVhneXls0ah1bmhEjwjtK+lg581bCfkcVNVFKQ/nuRnDdv57OrFI37gWrlyJY2Njef8fO/evSxbtgyfL1PzZNWqVTz55JN89atfHdF5ldGxo6mH+bVFOPVhLCsuR9IzplaTOI+sD12G7A5fPniii1++tougx0Wxz0VnXz8/eWELn7xmLlOrSod/pUNYuHAhR48exbbtIc+9Y8cOFi9ejNvtJpFIsGzZMh5//PEhR/GooctDK4S29RtJRKKfu2QZtihDOCFpwiPH/EwM2HysohjEabV0KkBYOtoQnblCCByahpQSPZFmy6ub8eSw0HIhfP+uNAcb23ns6W0E/W7KS4NEYkkee3obaz9cP6KHrurqaqqrqwEIBoPMmjWL5ubmQQ9TW7duZerUqUyePBmAT3ziEzz11FPqgatAvbK/E03AtVNzV3/HlhZGcj0CsGUCIQWa0NGEA3G+Yc3CmxnFY7wD7sU5a18hk1J+A/gGwMAInq9IKT8thPj/gM8C3xl4fWpgl6eBXwoh/oHMSOVpwNZctrFQ4k0kElHxZow5kewFJD6vm3TSzHRqD4wkTEeDJLoq8Fcdp8QbRKZBd2h4nE4iZoLuVHR0G59lKsEzQhveOUjI4ybk8wCcet3wzsGs9Kifz5w5c/jmN79JV1cXXq+X9evXU1+vCsiNRfG0ybst/fzxqsnD20GrBOsgmQ6YYbJNQAN9bFWCz5dcDV2G7A5ffn3zbkqDgVOxxuv24HImef1QKwun1Q2j2ecXCATQNG3Icy9ZsoS/+Zu/obe3l4qKCjZs2EB9ff2Q26qhy0Mb7bYZtsWnX/l3jtCJU+gkDQNN14g0VZM0A1w3Kc6PrMOkpYUmBHLgP0/aS7FZPFBkWeDSdYJuN25dB0tg2hZ9qRTfWbqUxTVD5jmzYrS/f1eiV7YcIOh3EwxkYs7J11e2HBhxr/pJjY2N7Ny5k6uvvnrQ+83NzYwf//4AgtraWt54442snFPJvo37O5g/voiwL/sjZGxpkbbjbGz7V5bYnWjCRpKpDYaUmWQzbhyae+gDCJH5DW2fyHrbLgPfAX4thHgAaAI+CiClfFcI8WtgD2ACX8z1NHQVb5RLlTANhBC4HDpOl46RtsjcxQj6miahOVMEqpqBmSDA73efWiI9ZiVHte3ZphI8I9TS009V0eCHm4DXTUtPf87PPWvWLL72ta9x4403EggEmD9/Pg61RO2Y9PaxPixbDq/+DoDvNoj8w6Ds9IUlQatCOKdecjsvc2Ni6HIhxJw777yTUCikYk6Bsyybns4IgaAXjy+z2t7b3cc5Gu3GpTsQtkACRsxDsr0Eb0U3lX4XtvF+nlIM3B4ZrhQlbi/Oc9w2ODQdy7bpjMfycWlKHrV19lNeOjjm+H1u2jqzE3Oi0Sj33HMP3//+9wmFBk9RlvLsnHkua7sol643nuad4708eH32p+za0mJXz+9JWgm8woXQdDLR6/1xhRKJIZPYtoVT855jNI9AjWLOkFI2kJlyjpSyC7jhHNt9m8y09bxQ8Ua5VCVuPy2JbhAQDvno649jGBax9krMhJ+iSe8hBpbTCvg9eNxObDvz/7zcPYzyGGPIMOaCKOdTUxwimkgNei+aSFFTnJ9/KA888AA7duxg48aNlJSUqFoYY9TJAsuLJgwzweO5PbMkqExkkjwXJDN/fB9BCPVAPhQp5TeklLVSyjoyxZNfklJ+mswQ5c8ObHbm0OVPCCHcQohJ5GHoMhRGzNm0aZOKOQXOsmz++xev8bN/foGf/uA53ms5xM6et/j10edwOfpxCdA1gZQQPVqDcJj4xrUBQwxNk2ALSYz4Oc8nZWakj8/lyt1FXQaEEH8ihNgnhHhXCPHdc2zTKITYJYR4Swix7bT3S4QQLwghDgy8DvMXxshUloWIxQfHnFg8RWXZyGOOYRjcc889fOpTn+Luu+8+6/Pa2lqOHTt26u/Hjx+npqZmxOdVsm/zwS5smZvl0ff1vcSJ5HvowolTc5MmiGBwUXdBZqqWJQ0Me4jeeNsayO9MyXr7lOxR8Ua5VHOKJmQG6dk2mi4Ih334PEGirRNwh3rxl/Xi9bnQHRo+nwsEpKVJ0OFlgj9300pHg0rwjNAN86bSn0zRH09iS0l/PEl/MsUN8/IzSqK9PTOYoKmpiSeeeIK1a9fm5bxKdm0/2sPUigBFvuE9HGl6EXg+nvmLTJ6V5JFSYksT205j20nAzkzN8t6Z5ZZfEb4D3CiEOADcOPB3pJTvAieHLj9LHoYug4o5yvD0dEY4fKAVMTPG3pmv8XDTv/BY069pM96mJtROZdFBinytyL4wZsxHYHwbmiPzwKQN9FhKOTBIcKAvPIlxzvMlTJMit5ew+yKmjV5hhBDXkSnOPk9KeRXw0Hk2v05KuUBKefq865Mr+k0DNgz8PedWLZtGJJYiEk1i25JINEkklmLVspEld6WUPPDAA8yaNYsvf3nolY2WLFnCgQMHOHLkCOl0mscff5wPf/jDIzqvkhsb93cQ9DiYX1t04Y0vQszs4Vh8JyHH+9NzOpk/kOA5u4Mrk+RJY5/16zgO2ng1irnAqXijXKpFJZMIOb0k7cy9iqYJehprwdaYMLeNkuIAwaAnM/FBZBJBpm0z3ldKpSe7cWu0qQTPCM2oKeezqxcT8nk40Rsh5PNkpcAywNq1a1m+fDn79u2jtraWRx99FIA1a9bQ0pKZCXLPPfcwe/Zsbr/9dh5++GGKi/PSoadkkW1Lth/tYfFwR+8MEKHPgftmMj/GcZApbDtN2u4labeRtjpI252k7W4sKTnMAtLDGu2jSCkbpJS3DXzdJaW8QUo5beC1+7Ttvi2lnCKlnCGlfCYfbSuEmLNkyRIVcwpcIpni2Izd7Kl5k0QgiltzZYqSIjPj+aTAIeJEj1XjDvXhLO45ta+GOFV/BySalrlVkAy9DLotJaZtURkIEHSfo/6FAvAF4DsDK+8hpWy/wPZnuoPMSn4MvOYlYz+1roK1H64n6PfQ0RUh6PeMuOApwObNm/n5z3/OSy+9xIIFC1iwYAHr168H3o85DoeDH/7wh9x8883MmjWLj33sY1x11VXZuCwli6SUbDzQwbVTy3AMZ6GIi9AS351JMov3j9sl5mLhRgyxUk6GwJKnJaRtC7DA90k15abAFUq8qa+vV/FmDLBti8bYNt7qfhLTepOFxUncmiRhGUS7ffS0FFM2qQtv0OC0EIJl2yRsg1J3gBuq5hJwXl6dU2quRhbMqCnPSUHlxx57bMj3TwYkgE2bNmX9vEp+He6M0pcwhl9/Z4AQLij6S2RsHCSew7COYtoxBDJTN0MKLHQi0kFcarwbeZb3ohtZVPH/UONfmaOrUfJhtGPOuQpAK4UhnTb44dafEK3pQJM6rrBGypFAWAJN2OjCRgjJkb3zMU0HsxftoM/0EYl7M/nigTyOAIQmEEIiJTiGuGWwbJuokWZCKMz4UJgJobOXU1dOmQ58QAjxbSBJZgWbN4fYTgLPCyEk8KOBIu1w7hX9cm5qXUXWCpyedO211w5Z8wIGx5w1a9awZs2arJ5bya5DHVFa+5L8yfXZ/710PL4Trz64d11qTlqsFdTSAJjIM2KTEBqmTOPEM5DciYI+XY1iHiMKId6o+5zCljRjvNX9BHv7nyNh9ZP5tSkocZtcXyVoiRXzhy234/CkqZj0fl+KZdtIKUnaBjXeYur85VxdfvmN6lMJHkUZZduPZnrOF9dd/EgIIVyIwB/TaHjojP0TFboHpwZSasRsJ60yQEy6kThwamEsO862tr9kadXfUeW7JtuXoihKATgUO0RXWUtm7QiXjdvhPtVr7dHcpO0E0e5i2o5OpGbKIQKhCD4ZRzIZzRL43E40S2AjsZCY0kICPuk5VaDHtG0SZqaHfGZpGULA6rpJ6NqVPTBYCPEiUDXER98kc89VDCwDlpBZtWayPPupY4WUsmUggfOCEOI9KeXGi2zHOmAdQGVl5VlLy4fDYSKRyMUc8pJYlpWX8wAkk8mzrjMbotFoTo6bb7m8jucaM7HA1X2QhobDWT12xKhEH6gdaCZ0ut4OANDFag5Rj5OThd0Hj8yRUp6abgpucEwAXs9q2xRFyb9IupOnjn+DfqMVTThxCT+alvlZt21JjBidTeNIRgJctXg3SZFCnBzsJwQOoXN16VR0ofOhcQuo9ZWO3sXkiErwKMoo29bYQ5HPyeQy/yXtHzNa2dX7byDDdOCFc1SBEQgcmh/DjrGj/a+5afzvcOhqOoWiXG5e63kdRwCc0jEouQPg0Z1EjCSH3pmH051i/Ix9CE2iSwO/J46IC9xOBw5bpz+dQpcghYZXuJEpjYhMgQSXQ2daSSlVgSB9qSTlPj+LVTFKpJQfPNdnQogvAE8MJHS2CiFsoAzoOOMYLQOv7UKIJ4GlwEbOvaLfUO34MfBjgPr6ennm0vJ79+7NS+90PnvBPR4PCxcuzPpxGxoaOPP7Nxbl8jr+7adbmVwe5yMfyu7xbWnxYuv3CDoqEELQ9XaA0vnR9zeQkiK7iUq24qULDWugH18gpY2uV6F5bgL/F9D0y2uVHEW5EiXN2KnkjksETk0hP0nTBDJZypa3rmZCdRM3zN5I6v9n787j4zrrQ/9/nnPOrJoZ7av3fY33xFkcx3biECcsSZxygQChpGw/4HIv7Y9CNyi9tJSS21DK0kBDIRQKpUkokDgLjuPEsZN4jx0vshxLtmTty+wzZ3nuHyM7XmR7ZI00I+l589LL0syZc55jha+/51m+j3MnaacKTRPo6HhPgdfQuaN2MdeWj82i66qDR1HybFdTpv7O1a4LP9b7Mxxp4tayKxBmaH5MJ8zJ6G+YVnzfVV2zUAkhaoA6wAd0Am9JKdP5bZWijJyIGeFg35sYmo5LuC6KK7oQdDbNINZXwqzluzBcNsjM2Lff3c2ZjbJ0TVDkchFOJ9EQLAtNYU5RHZbjIIRAF4KUbdMRj1EXDPLg0uV4DdfI3/Do8iSwDtgihJgNuMnEqbOEEEWAJqWM9H9/O/DV/rfP7Oj3dc7f0U9R8iZp2rz6Vhfvu3Zyzs+tCR2vXowlk7iED4CE3Udn8jgRqxMpLYQwCBoLmOIJUSVacRPDQiMsyphf/jU0fWwtG1V5jjKe7e76JX3maTwDdO6c8cLrSzFtgw037sHQweElTO5DInDrBiGXi8/NWU2x2z/CrR85qoNHUfKoO5bmeEeM+5ZPvKrP27bFqegzaGQ/E0cgEFKnoe8/x0QHjxBiBfBHwB3ApAveTgshXgd+Dvy7lDI80u1TlJHUle4m5aQxhD5gp3Ey6eKtg7MpqeikYkJz/x40AoHErWe2pnWkxHQcbOlQ7PYyxVdJkVNESySSWfYlAAlFbjd3zZzN9ZMm43epzp0sPAo8KoQ4AKSBB6SUUghRB/xQSnknUA080f+7M4CfSSk39X/+62SWdT0INAF/MOJ3oCgX2Hmih6TpcMvs3NffAZgaWMHhvs1IBCkHToZfAEDD6J+pk6IrdYquNBQZZUwpuoWUE2Vm8OYx07mj8hxFyex6dTjyHDruS3buNLVWsq9+BjctPkB1aRTHCWKIGBvrZlPjm4tHN9javnVMd+6A6uBRlLzafab+ziB30DojaXdgyRguMbipx7rwkrBar+qahaI/4fkmsBp4A/gNsIfMcocEUAZMA1aSeTD6uhDiG8BDUspkXhqtKMNESknMOkVL9FlqXU24hZs0XiJ2MaZ8uwP44P7pWJbOkhXHkLqOLTMFBx1ACBtHSlK2hVc3CLqKWF4xif85fy1uYXCsp5t4Oo2uaYQ8HmaUluHS9fzd9CjTP8r+wQFebwHu7P/+OLD4Ep/vAm4dzjYqymBtre/ArWusnF42LOev9s7lQO/T1IdfICTXYwgPmrjw4c6FIx1iVjdH+16k1j+fWt/8YWnPSFJ5jqK87a3YDpJ2BLcYuKSF4wiefmUFoaIYNy85AJDpCLLhcN/vmBZcNJLNzSvVwaMoebSrqQdDEyyamN3yqgulnTAgrmJ5l4a8VLGe0eNF4AfAp6SUhy53oBDCS2aL4S+Q2Sfob4a/eYoyMvpSxzgZ3UTSbidpxijSExiaSUBGKDc6iDtFdJg1nG6vovGtWmbPbaKsOIkt/SRtk6RjIaWJg4ZL05kRrGBCUQm31s3lxqrpBFyZDqLF1QPVDlYUZTzberSDFVNL8buH55HCrfkIp9tIOTEE2gCdOxma0DCkl7SM0pduwSXGxAi9ynOUcUtKSWtLL6eaOvF5WqByE7OMPjTNJCrd9Dh+HN6OBzsPzaKtu4w/uHUrbtfbzzgCnV7zVD5uIW9UB4+i5NGuxh4WTCjG5766UXCPXgJIHMe55HTFgdkIRv3I+wwpZVbTkPpHsn4B/EIIUT28zVKUkdOZ2M2J8BO4tBA+vRbhTuLIBmIpC0NzYRgaXi3BBNdbbNp9Iz5fkrnzG4FMPZ4iw40PFzFTUu6uocYX4kuLb2JWqBK3rlIERVEurS2c5HBrhC9umDts12hJHCRh9RI0qgCJLS00zl+CmpmFaCNxCOiVpJwYzfF9TA4sH7Z2jRCV5yjjkmnaPPXfuwl3bmfW1APowUrOvXsAACAASURBVC78sdMU+yNoWgRT6sSkmxa7mGa7mHDczwu7FjN9Qgtzp5686HyOtAa4ytilsjdFyRPTdth3spf7V0656nN49UrcWgmWE0Mj+9EqmyRB9/Srvm4hyDbpGeBzbblui6LkQ1+qgRPhJ/AYlejCDYAhBH5chEUSxxZg6JjSzb7Dc+ntDXHjTfsyhZXPJSW6pnFj5TKKet0sKK3Nw90oijLabD2a2QBu9azhqb8D8GbvM2iajs8oRhMGhvBgyiQ4meLwEkCAS3jwGiEM4SFh93Gw75lR38Gj8hxlvHpt+1Fk4inWXL+HIl8XXm8EhIXUTIQwAUFaJijVMl//5/V7MC2dDTfu5MJFDRIH7zjbRW8wQ/7KCDp58iRr165l3rx5LFiwgG9961sDHvfRj36UqqoqFi5cOMItVIbqYEuYlOWwfMrV1d+BzNrSyYF34mBm/RkpHSQOM4rff9XXLTRCiAohxOQLXvuEEOLbQoh35qtdo8lgYs706dNVzMkzKSUtsecxtAA6LlJ2D93JN+hIbqfGH6faG6XK34Nfi5BMuNi291qm1jaxYMqb558HSdJJUe4uZVlp7reYVpRLUXnO6PdSfScVAQ9za4LDcn4pJaeTB/FqmYczgUbQVUmxq5aAqxy/UUbAVU6xq4agqwqX5kUIgVcL0p48gu1knxsVOpXnDM1g483KlStHuIXKGbbl0N70FNcvfZny0rfweKJYlgfLLMJEIy01LASGcCjV4oTbi9hbP5MbrjlEeXHkvHNJKQGH2aG1+bmZPFEdPDlwuKOdh3e8whee28TDO17hcEf7kM9pGAYPPfQQhw4dYseOHXznO9/hzTffvOi4j3zkI2zatGmAMyiFbld/geUVU6++gwdgRsn70IUXy4lndbwlY3j0Mib4bxvSdQvMo8AXz/wghPhL4HvAB4BfCyH+R74aNhzyHXMef/zxIV9PGZqE1UrMbEYXPrqS++lK7CFhteM4Jh5N4NN1NCHxiDjbdy/DtjXWrNxOiasXrb/+li0dEnaSEncJ80LzqPOpmTvKwI6e6uD7v93Ol3/yDN//7XaOnuoY8jlVnjO6OY7k5WOdrJ5VgaYNtg5gdiyZxpE2mjh/SbkuDNyaH49ehFvzo4vzd/HTNB0pJeks86JRYtzkOSrejG+pZB9L5j1PKNBGMqETj2mkkham6YBtkJm3J3DQSDguvvHie6gJ9HLX0tcvOpflpHBpPuaF1o/4feST6uAZosMd7fxgz076UklqAkH6Ukl+sGfnkB+4amtrWbZsGQDBYJB58+bR3Nx80XGrV6+mrGx4di5Qhtfuxh4mlPioDnmHdB6vUcrSyr9AIrGcWH9v9RkSS5o40iFhx0nYPQgMVlb9PfrYqq+xAvj9OT9/EvhbKWU58B3g83lp1TAohJhTWjq0Tkll6HpTRwBJT+oACbsN04mSsrtJWr1YdgKXAJ/m0NxWx76Ghdx0zQ5qi5uR0kKnj4SdxJYWE30TmFY0lTVVt1yyeKkyvh091cFjz+8iHE9SXRokHE/y2PO7hvzQpfKc0e1ASx/dsTSrh2l7dACtv1bg+XnNlUkpQXB26eoYMS7yHBVvFI/YT1V5M4mkhu1oCE0gNIHjOJiJzLOLzCzO5Jdv3MDhzgl8afWTLPSdv6LRdmxs0iwI3Ymhuy66zlimsrkh2tRwjJDHS7HHiyYExR4vIY+XTQ3HcnaNEydOsGfPHjVdcAyRUrKzsXtIy7PONSGwlhXVX0XTPJgyjOlESdgx+sxewlYvEhvTiRC1JNv6pvHjk7+nPlyfk2sXiDKgDUAIsRCoAX7c/96TwJw8tSvnVMxRANJOH0m7g6TVQtrqIW2niMcdEnFJIu5gJgS25eXX2++iNNDL+qUvETBSeDWbkMvH7OAsFpVcQ62vhturb2Oif0K+b0kpUJv3HiPo9xDyZ2JOyO8l6Pewea+KOePZmfo7q2ZVDNs1dM3Ab5RjysHNxEk7CXx6CJc2tAG0AjMu8hwVbxSR+k8EEtvWz9bTcZDYAkxHYKU8SCnpiPn55+3v4IZJR7l1xhtMNHoxsJFSYtopTBljWtEN3FD1QH5vKA/G1BB+PrREwtQEzl97HHR7aImEc3L+aDTKxo0befjhhwmFxleBqLGsuTdBWzg15OVZ56oruoVK7woaeh9nb/ejSBlBCNAROFKjITGXTrsa03E4FD7M0chR3l37Lu6ouz1nbcijLmBi//frgBYp5ZkeLBdjqDNbxZzxTco00jxEKP0cU5ydOJoFmkZ7UqNdeAmj40iwHcm2gyto66nkQ7f+Fx5DogkHlybRXbU4Isic4BzmF8+jzK1mZCmXdro7THXp+TEn4PNwulvFnPFsa30nC+pCVAQ8w3qdeaFb2dn9CzwEsv6MKePMD9593k5bY8C4yHNUvFFs8w1sR0fXNRzbwRaZDh4B6JrAsQwwdB7e9k6Slos/XfMkthB4MdGcXlLSjUvzsiD0Hm6qehAxDmcnqw6eIaoLhuhLJSn2vD1KEEmnqAsOPWiYpsnGjRu5//77uffee4d8PqVwnKm/s2xybh+sDM3P77uj7Oudi19z49IEEo0lsohOuw4Al6bjwkXKSfNky6/xG35WV63KaTvy4HngK0KICuCPyYxmnTEXaMxLq4aBijnjl7TeQsZ+AnY7busYUsQRQmBLqHNDuZ6ixzY4EvPRGwvxwhurmF13jPl1b2GnDQy3g0uzWFC6jBmhtXj1MTW6rQyT2rIQ4XiSkP/t/16iiRS1ZSrmjFeRpMnuxh4+tnr4d+OcGVzN3p4nSdsJfFkcbzoJNGEwZ+wVVR0XeY6KN4qQSWxHx+XSMTWB7djoaGj9HbYSyd6W6fzm8Ao+unQrU4u7EegIJDXuchYE72Ne8TvwGkV5vpP8GXSXlhCiTAgxQQgxvF32o8QdM2YSTiXpSyVxpKQvlSScSnLHjJlDOq+UkgcffJB58+bx+c+PiWW1yjl2Nfbgd+s533nijd4D7O97A6/mQ9Pc2Lhw0Ac81qO50YTOr049TspK5bQdefAF4CTwd0AD8NfnvHc/8HI+GjUcVMwZn6TVgIw8DOn9YDchSJDZD0/gOBK3cAi5LOpcKRYGojy3bx22o3P70udIWxaOI0ilQUqbKk+V6ty5DJXnnG/dkplE4inC8UzMCceTROIp1i1RMWe82t7QheXIYd0e/QyfEeK68vtJyxgS57LHmk6SpB1lRfn78LvG3MzEcZHnqHgzdvX2xDh+rI1oJHnZ4zSh43Jlau7Y0kETAtE/i8dyHNKOxte23kFNoI+PLd+OY/rxiBI8mp91dV9kafm947pzB7Lo4BFC1AghviiE2CKEiAMdQBMQF0KcEEL8RAixQYyxeZDZmltZxceWrqDY46U1GqHY4+VjS1cwt7JqSOfdtm0bjz32GJs3b2bJkiUsWbKEp556CoA777yTlpYWAN7//vdzww03cOTIESZOnMi//uu/DvmelOG3q7GHpZNLMPTcTht8pvU5BAJDG7hT50JezUPSTvJy17actmOkSSnbpJTrpZRBKeU6KWXnOW/fBnwuX23LtUKIObfddpuKOSNIOlFk5HtgNYCQIFxoxCgSEr9mUaTb6EJiSYmuOTS1T+GNpgWsmvcKZYEeHCRpy8RxbNKmTty6uLDkeKbynMubPbGSD922nJDfS1tPhJDfy4duW87siUN7uFd5zui1tb6DIreeszqCA0nZJvu7G/nOkWf4ZVMXrYm5WI5FT7qdtJM8W3hZSonlpIhZ3aSdBCvK3zcmd8wZL3lOocSb+vp6FW9y6HRLDz96ZAuP/+I1/u0HW+jtiV36YK0Sjyez1NyyHSzbwez/UwJPHLmW+u4aPnfd0xgi01lk2SkQbtBqRuaGCtwll2gJISYCf0Nm+70IsB34JpnEJ0Gm2Nc0YCXwW6BRCPGXUsp/v9rGCCE+C3wGsIDfSSm/cMH7c4BfnPPSdOCvpJQPCyG+Anysv30AfyalfOpq2zIYcyurhvxwdaFVq1ZdcteAMwEJ4Oc//3lOr6sMv1jK4tDpMJ9ZO7TRiAuF02GOx4/jGeSgsyY0trS/xK3V63LankIhpczNwu0Cku+YE4lECAZzO/tMuTSZ3gXWm4ABTidIEw0L65zpym5N4gYSlsE3t97DhFAnt83fhgkIQNMdUqaBbUPSNPN4N4UjH3nOaDV7YuWQH7AupPKc0eul+k5umFGO28h9bQspJds76vlV03ZOxDqIWsnMfjlSUml5OBr2Uu3tIORy4dZdICWG5mFq0Urml7yDSu/wLxsrNGMtzymEeKPynNw6fLAFx3aoqg5x+nQvjSc6KSm9xCwbz7uQ0X9GCB0hBE7/700I6Er4+f7OdVxXd4y10w7jOBIpwecywViGZuQ2Nx6tLleD5wjwLHA38KyU0r7Ugf1J0v3AN4QQdVLKfxhsQ4QQa4H3AIuklCkhxEW/ISnlEWBJ//E60Aw8cc4h/yil/OZgr60oI2nvyV4cCctyPPJ1OnkaR0p0PbvZO2e4hIteszenbRlpQohHr3CIlFI+OCKNUZQcktKB+JMgTZC9gAbCk/kZE4GG7P8fwM/2reZEbzX/dNcPKSuKUp/0oetgWS5iCR9+XxLhjLmlC1drRPMcRRkLGrtiNHbF+ehN03J+biklTzXv4ZFjvydqJtCEwCV0NC3TkeRInb29U9BJMcEneO+Ua7mmdDol7on4jZKct6eQqDxHGc3KKwKYaZtIOIF0JKHQZSpq+d9NvOMRXEaatKMhhEDKTJbz3Z23k7IN/vf1v8NxHDQBhm7hSAP8Hxix+yl0l+vguUlKuTebk0gpTwF/L4R4GJh6lW35FPB1KWWq/5ztVzj+VqBBSjkmioop48euxh6EgKU5LrCcdq5uVF4gsC/9XDNarAMuHJopA4JAb/+Xoow60ukF+yg4URA6iMw/2xqucw7SkDi0Rov53mvrWTftDdZNP0inaXA0UUQ84cU0PbgMk1jcA3Z5nu6m4Ix0nqMoo96Z7dFXz859/Z3d3cf5fv1zxK0UPsN9tqjqGUKAT3djOwZvxdL8sOEY31x2w5jv3Omn8hxl1FqwaBKJhEnTWx2sWjuPqdMvHT9S6RL2H1nB4rmvYugmtiOwpMYbbVN4umEZH7rmRaaWdKKJzLJ0x4He5HR8nltG8I4K2yU7eLJNei74TIrMiNjVmA3cLIT4GpAE/kRK+fpljn8fcOG83c8IIT4M7AT+WErZM9AHhRAfBz4OUF1dzZYtW857v7i4mEgkclU3kQu2bef8+slk8qL7HIxoNDqkzw+HQmwTXLldz+1JMqFIsOfV3Na9MR2Tm5IrEWhcWCnCb/pZ1rp4wM9JKRFCFOTfZbaklFMHel0IsRr4PpmRd0UZfZxukGFAnu3cAdCECyF1JJkChJYj+IeX7gYEf7zq1yDBLwThaBBD1xHCQdNsesJVGNTl7XYKSR7yHEUZ9V482smkMh9Ty/05Pa+Ukkfqf0/UShIwvBflMefSNQ0fbjpSYX58/EX+atF9OW1LIVJ5jjKa6brGyhtnsvLGK5en0A2N1/bdTjAYp6ZqP7aUaBo89OpdVBf18kdLN+PSHSxbw7Q1WnpKOdT3IB+c5R6BOxkdRnSbdCHE88BA1Y/+vL8tpcD1wLXAL4UQ0+UACyaFEG7g3cCXznn5e2TW0sv+Px8CPjpQO6SUjwCPAKxYsUKuWbPmvPcPHTqU13WXw7Hu0+v1snTp0qv+/JYtW7jw7ynfCrFNcPl2OY7ks1ue5Z2LJrFmzTU5va7t2Hxh/5+RsBN4tfPr8CxrXczumn0Dfi5qx1hesow1Mwdu82gmpdwqhPhH4NvAqN8LXhmHpANOAsT505mF0NCFF1smQDi80jSb5xsW8emVTzMh1IsEDOGgaxqasDEMk0jUT9OpuZSuHt+7SyiKcnXSlsP2hk7uXjqBXNccb4i0cizahldzXbZz5wxd0/A4Bjs6j9KTilHqGZ9xTeU5yljjdhvMmTuJJzbfSelUg3dcc5hNby2ivruOv7nl5xiaQ9rSSZoG+0/N5D933sg1M1TnzrkuV2T5J1meQwIp4ATwuJTy8CUPlPK2y1zvU/2fl8BrQggHqODtosnn2gDsllK2nXPus98LIX5ApiCiohSU+vYokaTFimHYeULXdG4qv4FNbc8i8ZBN6mVLB4FgQ+3tOW9PATkOXH3vpqLkkzAyO2cNwBA+JBZJC76+9R4mF3fwwNIt5x3jdqdxbEFvJEhPXzVVRcsoLcrtyPtoNRx5jqKMZXuaeoilbW4ehu3Rn2rZg+3YuFzZP6i5dRcRK8HLHYd418QVOW/TKKLyHGVMuXntPA4eO82vdy7h6f3LOO6ewKziZsq0MLubZtDUU8uBlsWYTpCEGSfgH9wGM2Pd5WbwrObitZ6X4gPKga8IId4lpXzmKtryJJn1pVuEELMBN9B5iWPfzwXLs4QQtVLK0/0/3gMcuIo2KMqw2tWYWTU4XFuL3lq9lq2dL5F0Eng132U7eRzpkLATzAxMZ3LR5GFpT74JIQzgI8CpPDdFUa6O8AIBkMkBZ/G4CPC93Sto6qvku+/6AW7dRgiJjqDXctHVW4pL14lGS6k/vpS/uHd5fu6jMI10nqMoo9rW+g50TXDjzNzX8Toeac/MChrExCAhAAlvRQcaCx4fVJ6jjEXFJX7e+4EbOPBoL3vDLixHx5/08/gb9yO0t4NEIm3icRvUlAby2NrCc7kaPFMHcyIhRCnwA+BrwNUkPo8CjwohDgBp4AEppRRC1AE/lFLe2X8dP7Ae+MQFn/+GEGIJmWTtxADvK0re7WzsprzIzZQcr10/o9hdzCemf4zvNvwLCSeBR3jQRWb3CdOxidopTNvqr9shMUSANeXvGZa2jCQhxOYBXnaTqe1VDnxyZFukKLkhNC/SmATWUZB2ptDyOZrDpXz/tVu4Y9abrJl+grSlg7Sx0OhKFuFYIY42TiEcnswDt1zH4qmq/s4ZechzFGVU23q0k2WTSwh5XVc+eJCsq97sQWJLJ6dtKUQqz1HGm8m1pVROrCVyJEmtK0HQp51dGmrbDomUicdlUFMZYuG02jy3trDkrAaPlLJHCPF94L+v8vNp4IMDvN4C3HnOz3EygezC4z50NddVlJG0u7GH5VNKc752/Vzziufyudmf4dHj/0ZXuhsJWI5NVzoMQKa7RxBL+zjZW86nWv+LGcFKvnndvUwOjNrtkzUuHomPAI8D/yGl3DLiLVKUXBDFYEwDux2cPsB9XifPV7esQ9ckf7FmK169GEMkMC2LnoSHN04t48SJNVwzuZY775rLzJryYY09Y91Q8xxFGc26oikOtPTx+dtmD8v5KzwhZNYT6jIyVToFpe5xMXqv8hxlXJESdraD34DbZ4do6wrTG02AAJeuM622jIDXg8dtMKNO7Q56Lu1SbwghrqYr7FXgwatvjnLGyZMnWbt2LfPmzWPBggV861vfuuiYZDLJddddx+LFi1mwYAFf/vKX89BSJVud0RQnuuLDtjzrXDMDM/jqwi/zvokfpidu4EiBdHSkNIini2kPT6M3Np2gK0jAcNMQaefDL/2Yt8KXWhVZ2KSUa6SUay/42iCl/KRKerIzmJhz4403qpgzQoTQwXMb6FNBK8nM4pEpkBa/b5jO8w2z+OzKV6gLdINMYwg3Ps8kasqv5Z6bPs+3H7ybz921ilm1Fapz5wIqz8mvbGJONscoI+PlY51ICTcPw/boAKur5gJgO9nPxrEdG13TubFi1rC0qZCoPGdoBhtvrrvuOhVv8uynOxpp6kmy8ZoyHNtm9sRKbr5mOjctmMbKuZPxuTMzCT+wbikuXb/C2caXy83gOSaE+CHwvSsVFBRC+MjUvfkC8Ksctm9UONLbyrMth2lJ9FHnK+b2urnMKRlos7DsGYbBQw89xLJly4hEIixfvpz169czf/78s8d4PB42b95MIBDANE1WrVrFhg0buP7664d6S8owGO76OxfS0PjHA3tojtVgCS9t4bn975z/kKdrGiG3j0g6yae2/4Lf3vZJDBUoC1q+Y46UEq/Xq2LOCBHupcjUc6CFwKoHGSdp2nz1hXXMLOvgD5e+ArhA8/TP+JmMpk+lyD9ddepcnspzsnSktYPn3jxGS2+YupIQ6+fPZE7N0B70s4k52RyjjIytRzsp8bu4ZkLxsJz/pqq5lLqLCJtJ/MJ9xZ20pISEYzI9UMWskFqeMZYUQrxpaWlhzZo1Kt7kSUckxUPPHuXmWRV8+b4V7G1o4aU3jtPRFz2b1yyaXsfNC6dRUTw+d9C7nCsVWf4GcFAIsR94CdhHZlerFJktzacD15Epjuz0H/9/h7PBheZIbyuP1u8g5PJQ4w0RTid4tH4HH511/ZAeuGpra6mtzfyDFQwGmTdvHs3NzecFGSEEgUBmWqppmpimqZL5Ara7sQe3rrFwmJKjCz3fcoRTsV5ChhdsuFzlQgEEXB7ak2F+d+oA75myeETaOBRCiHuklE8M8jO1wBQp5Y5hatawK4SYE4lEVMwZQUILIX3vg/hPwL0cnD6+v72Uk+FS/v0P/hu3uxy0IGhTMsu3hAvhf7/63VyZynOycKS1gx9t20XI66GmOEg4keRH23bxhzctH9JDVzYxJ5tjlOEnpeSl+g5WzaxA14Ynrnh0F3dPvI6fHN9K2jZx65feLl1KSNomhtC5f+qqMRvrxmOeo+KNAvD1pw+TtGy+8u4FGLrOitmTWDZzIj3ROLYjCXjd+L1qa/RLueQSLSnlLinlrcC1wA7gnWSKCz4JPA38DPgLoJjMiNYkKeXfSimTw97qAvJsy2FCLg8htw9NCEJuHyGXh2dbcreL6okTJ9izZw8rV6686D3btlmyZAlVVVWsX79+wGOUwrCrsYeFE0J4XSMzO+axhlfRhEDLMhnThEATgn9veH2YW5Yz3xFC7BNCfFIIUXa5A4UQNwshHgGOAYtGpnnDoxBizk033aRizgjT3AvB/xHApLE3yPdfW8y75vdyw6xp4L4Z9NkgbNBDiMCnELpaj34lKs/JznNvHiPk9RDyeTMxx+cl5PXw3JvHcnaNy8WcwRyjDI/DrRHaIylWD9PyrDPeN/VGbqichY0kaZuYjt1fZ6efhLRtkbDTCAHvnriCW2oWDGub8mzc5TmFEm8aGxtVvMmT109081+7T/FHN09nRuXb9bU0TVAeKqKqJKA6d67gikWWpZS7gU8BCCGqgDrAC3QBJ6SU5rC2sMC1JPqo8YbOey3g8tKS6MvJ+aPRKBs3buThhx8mFApd9L6u6+zdu5fe3l7uueceDhw4wMKFC3Ny7UIj+/+VH40jNSnLZn9zHw/cMGVEruc4Dof72vDrg9vpokh30xDtJGVbePSc1WAfLjOBPwG+CnxbCHGIgUffV5B5QNsKrJdSvpKf5uZGIcScbdu2Ydv2mI85hUZzz8fRv8BXfvUKbj3Fn609CjINSDCmIDy3gDELIXK/w81YpvKcy2vpDVNTHDzvtYDXQ0tvOCfnv1LMyfYYZfi8VJ/ZhvzmWRXDeh2P7uLPFt7Dtw9vYnvnUeJ2CtOx0frzPgeJFFDs8nNn7RI+POOWszuFjlHjLs8plHjzoQ99SMWbPLBsh7988gB1xV4+u25mvpszag3qCU5K2Q60D1NbRqU6XzHhdIKQ23f2taiZpM439GU4pmmyceNG7r//fu69997LHltSUsKaNWvYtGnTmHvYklLyyskmnmmox5ION0+ayu0zRtf/6Q80h0lbDsunXHYAJmeiVhoHB10bXOKjaxpY0J2KUesfmaVkV6t/R72vCiH+DrgXeAdwPec/nB0GvgX84ko1NkYLFXPGt+cOx3ix3uIv7lpAzYR1gAXCQ6ZEjDJUKs+5WF1JiHAiScjnPftaNJmirmToDz7ZxJzBxCVleGw92sns6gC1xbmPM6ZtU9/dxevNzfSlkmgIpgWmc8PsuewJN7C76y1S0kQAnqTBeyffwPqaRUwqGvu7Ao7HPKdQ4s173/teFW/y4Kc7GjncGuG79y/D7y74geaCpf7mhuj2urk8Wp9Z5hpweYmaScJmivumLh3SeaWUPPjgg8ybN4/Pf/7zAx7T0dGBy+WipKSERCLB888/z5/+6Z8O6bqF6M2Odv7r8EGq/QF0TfDc8WP4XaNrhHp3f4HlZVNKRuR6Pt0FCKQjEYNYLy8lSAE+Y/RMfewfXf9F/9eYVwgxR9f1MR1zClUibfPV37zJnOogD9w4DTHIDlxFuRrr58/kR9t2AZmR9GgyRTiZYuPyoXXsZhNzsjlGGV6JtM1rJ7r58PW5nYEspWRnSzO/PnSIhp5u2mJR0o6DALyGi4nBIPOrqvnS3PsoL/IhgDd27GLtzLU5bcdoMJ7ynEKJN5/5zGeGdD1l8M4trLxh4dA2DhnvVHY4RHNKavjorOsJuX20JsOE3L4hFzsF2LZtG4899hibN29myZIlLFmyhKeeegqAO++8k5aWFk6fPs3atWtZtGgR1157LevXr+ed73xnLm6roNR3d+HVXXgMA0PTKfH6eLNzdA2w7mrsYXKZn6qg98oH54BL1wkaHpKONajPJR0Tv+4iZHiGqWXKUBVCzLnhhhvGdMwpVP/8Qj3NvQn+5u6FuHT1z7cyMubUVPKHNy0n5PPS2hch5PMOueApZBdzLneMMjJ2vNVF2nJyWn9HSslzDcf4260v8szxYxzu6qQvmSJtWSQti654nD1trTxx6E2+8fI2emNpKrwhxGU2i1DGhkKJNzfddJOKN8PsYHsbX3lhM+/+2U+586c/4V3/8gxx0+JLG+aM+dl5w03N4MmBOSU1Q364utCqVavO1py50JlgU1dXx549e3J63UIUdHtI2/bZnxNWmmJPGUQTeWxV9qSU7GzsYfUwr12/0J0TF/AfJ3bjJ/vZTmnb5l2TF6CpmQEFLd8xJxKJEAwGBzxWmv1+nQAAIABJREFUGR7HO6I8svU49y6dwHXTRmapp6KcMaemcsgPWBfKNuZc6hhlZGw92oHH0HIad/aePs0/7tjO6UgYt2FQ5HJx0ZZZUpK0LHafPs3/eXEL33zHhpxdXylshRBvVJ4zfKSU/GjPbh7bvw9NQNDjwYp7aO3UKS6L8vVXn+f/3r6BiiK1/fnVUk9xSsG7fuIkaoIBToX7aI6E8Rou1o+iGjwnuxN0RlMsm1I6otd9YNYNGGik7Oxm8aRtGyEEfzjz+mFumaIogyGl5C9/fQBDFyyYAU8cOkhTb2++m6UoyjjwUn0n100ry9kOoI6U/NNr22mOhPG5XLh1/eLOHQAh8Pa/v7etlScOv5mT6yuKkl/PNtTz4317KfV5qSwqwqMbnGr24XY7zJ4MJ/vCfOn3z+E4Tr6bOmqpGTxKwStyu/nMtSs51t2NIyVTS0oJeUbPEqJdTd0ALB/hDp5qX5CPzrqBR46+jOTyI6Bp2yZup/ng9OuYElSzAxSlkHztud1sO9ZFWVWYnx5shv6FCouqqvnTVbdQq0YZFUUZBi29CY61R3nftZNyds5jXV3sb2vDaxhZbQTh1nXSts1/vXmQL04cmZ1IFUUZHlJKfrJvH0VnOneB1nYX8YTOrOkJdAOq/H7qu7rYdfo0106YkOcWj05ZzeARQnxNCKGiqpI3XsPFwqpqFlXXjKrOHYCdJ3oIegxmV4/8Q9gn5q7iwzOux0HSZyZJO5mlbo6UxE2T9kSM1liErmScMruYuZ4JxNLpEW+noigD+9ne/fxoazNer8X0CVDq9VHm9VHu87G/o43PPv0b2qLRfDdz1FN5jqJcbOvRzPbouay/899HDpO2bdyDWAruMwxaI1ES1uDqCiqKUliOdnXSEgkTdLtJmCanemI0nXLh8iWIiC56k0kcQNcET9UfyXdzR61so+v/BBqEEE8JId4thFBLuxQlS7sae1gyuQR9ELtZ5YoQgv+1cC1TispYUT6ZtGPTmYzRFo/Sm05gOw5ux01VugIR9fEP27fxgcd/yZOH31R1DxQlD0zT5sCRFpqau3ntzUb+/tlD2JZOWXUfTeFeToXDnAz3cSoSxqMbdMbjfG/na/lu9lig8hxFucDW+g5qQl5mVQVyds49p0+jCTHwsqxL0DUNh8zAlKIoo1dXPIFA0J1M0BKJ0NkWQEpBWXUYTQi64nGaw2E0BO2xeL6bO2plu0SrBvgg8HHgSaBZCPFD4IdSyubhapyijHaRpMmRtgh35Hm7P7/h5vs3vp9vvrqVX9Xvo0gHx5FYSQ1dGiSABEn8Lhdx0+ThHa/Qm0zwwOJlqpK9ooygQ8daefSX22iM9NFaDH2xWjyBCLYrhlt7uwaGIyWd8Rhe3eDlphP0JhKU+Hx5bPmop/IcRTmH7Ui2Hevi9vnVOc0DEpZ51XthOWrgSVFGDSklfbEkAMVFXoQQBD0ekrZFOmUhTD/JaBHB0ggutw0IPJqBadt0JeKUjLIVG4UkqxEqKWVMSvkvUsrlwErgWeD/B94SQjwhhLhjOBupKKPVnqZepIQVU/Jf1+b54w389vBR3KYXK+aCpBuf5sGt67h1HZeuk7BMepOZmT3/tncP20425bvZlySEcIQQdpZfal63MioksHg93sFxJ0prshiEg+VrxbLOLzaoCYFHN0jaFtF0mqNdXXlq8dig8hxFOd++U730JcycLs+CzI45g54h3H+4Ps4GnFSeo4xWlu3wyFM7ePjxrTz8+Fb+5Xc7ON0dZm5FBbbjICX0tpegGxbB8vOXmRuaRtq2uaY6v4Pjo9mgiyxLKV8HXhdCfBH4T+A9wLuFEI3AQ8D3pJSq7LWikFmepQlYPKk4303hJ/v2kDAz//579It3wxCAq3+GQNK2EI7gsX27uWnS5EKdxfNVuEL1aEUZRWzb4W//ewu2JsEoRtpFiGArQrPpS9i4dB1DP39cxqXpxMw0Zn99LWXoVJ6jKJn6O0LAqpkVOT3v8to6dp9uQUqZdW5hOTaG0PC5XDltyyig8hxl1OmNJugMx+hOQnVp8OxrP352J/fdtphJwRAHGy3MtIuy2m407fz/xBOWhd/txlALpa/aoDt4hBAzgE8AHwFKgCfIJEDvAh4GFpOZ4qwo497uph7m1IQIevOblCQsi6PdnTjSwWtcuS0e3SBpmuxva6Ohu5uZ5eUj0MrBkVJ+Jd9tUJSh6osnSaYtyoN+XjlygtaeCEVeLx3JCjQ9Cb5ehBA4jiSWSlPs9150DiklIY87D60fm4YzzxFCfBb4DGABv5NSfuGC9+cAvzjnpenAX0kpHxZCfAX4GNDR/96fSSmfupp2KMqVbD3awaKJJZQW5Ta2bJy/gMf27yNtWXiy6bCRmRxmYiiEf5x18Kg8RxlNmjv7ePq1w+yqP8W1VZA2PZnlmEJQEvBxujvMgcbT1BaVsLvPwfAmEJ4I0tFBCCzHIWXb+AyD6ydMJG6pgaurlVUHjxBCB+4hk/CsBdqA7wH/IqVs6T/sP4QQLwF/j+rgGbKTJ0/y4Q9/mNbWVjRN4+Mf/zif+9znLjpu6tSpBINBdF3HMAx27tyZh9YqA7EdyZ6mXu5Zmv8t/qLpFAnTJODOfj2ryzCIm2m2N58syA4eJbcGE3OKiopwuVwq5gxByrR48rWDHDjZClIQ9Lkz47QCuq0gNhrFvi6iGkgkQjDgLJ2EZVLhLyLkUfV3hmIk8hwhxFoys4EWSSlTQoiqC4+RUh4BlpzTpmYyHUxn/KOU8puDvXYhyibmJJNJVq9eTSqVwrIs7rvvPv76r/86Ty0eP/oSJntP9vLptTNzfu4JoRDXT5zIS42NaHZmZuIlSUiaJrqmcf81i6G7J+ftUcaHwcabdDrNe9/7XhVvstQXS/Jvz76Orml4XAZgcbCxFUOvoyzkpzsSp7G1h9ZwhF1RA6SLmxd6ORnz0Z2II8msLlhQUcn8qirCqTRlXpXXXK1sZ/A0A5XAVuD9wBNSyoHWeu4BRn4v6Dxrip1kV89uOtNdVLjLWV66jMlFk4Z0TsMweOihh1i2bBmRSITly5ezfv165s+ff9GxL7zwAhUVuZ1CqwzdkdYI0ZTF8iml+W4KKctGkqnbkS1dCBwpOdFTmAmVEOKvyBRAben//nKklPJvRqJdIyHfMed3v/sdU6dOHdL1xrvNB46xr/E0daUhNCGIJlMcaunAlC56rSKKjThFmk3SBksHKUE/p2yelJKkbeHSNOaUV1DsVcUIh2gk8pxPAV+XUqYApJTtVzj+VqBBStl4ldfLmcMd7WxqOEZLJExdMMQdM2Yyt/Ki/qlBySbmeDweNm/eTCAQwDRNVq1axYYNG7j++uuHekvKZbxyrBNH5nZ79HP9yY2raOjupi0WxZESQ9PQhcbZ6ssysyzLsh2kgBW1ddwzbz6vbts2LO0pVOM1zymEeNPd3c2GDRtUvMnSm41tpE2b2vIiYsk0CBuPy6CpvYeeWIKT7b2kTYsSbwWdvRrloTgTAiUsmjAby3FwpMSl62hCIKWkJ5lkaW1tvm9r1Mq2g+c/ge9KKQ9d7iAp5atkv/X6mNAUO8nTrc9QpPspd5URs2I83foMG2reMaQHrtraWmr7/8MOBoPMmzeP5ubmAR+2lMK0qynTMVIIHTxyCEu4Tbtg6/Z9BdgEtPR/fzkSGBOJj4o5o4/jSLqiMTwuAwk0tHbx+/3HmFAWOtvpGvB6qAj4iQkNTTpUuCJIW8fjGAjHwjRAGIKYmQYy/0EXuz1cU13N8roJlKiRrqEaiTxnNnCzEOJrQBL4k/56P5fyPuDnF7z2GSHEh4GdwB9LKQfsgRdCfJz+WUbV1dVs2bLlvPeLi4uJRCJZNfpodxc/PrCfkNtNmdtNZyTMd1/bwQMLFzG77PKzO23bvuR1AoEAs2bNOvv+rFmzqK+vZ9Kki+NYJBIhHo+TSqWIx+MDnjOZTF50n7kQjUaH5bwjbTD38R8HUvgM6Du+jy0nclODz5GSWCJNyrKQEh4IlhH2FWFLeTZHOad/p/97gd/lYkIwxKvbto2Z38UgfIVxlucc7mjnB3t2EvJ4qQkE6Usl+cGenXxs6YohdfJkk+MIIQgEAgCYpolpmoVag7LgxFNpNC3zd1VVEkBLxklbNo7j0BtLogtBkc/DgV6DIrdDZXmMA01Jbl4w7bxZfFJKWiIR5ldWUhccd3NGciarDh4p5WeHuyGj1a6e3RTpfoqMIoCzf+7q2T3kEfUzTpw4wZ49e1i5cuVF7wkhuP322xFC8IlPfIKPf1ytjisUu050UxX0MLE0/w9ehqZddZm+cn9RbhuTI1JKbaDvx7pCiDl33303uq6rmJMFx5H88pX9HDjZipSShT6T5ze/zuGWDtp6IsyaUIlAEPJ5aI5qpKSXMr2LWDKJJkBzQHg0FpSUUVtdTDidRNc0JgSLCbjdRM00t0+fle/bHPVylecIIZ4ns+X6hf6cTM5VClwPXAv8UggxXQ6wpZAQwg28G/jSOS9/j8wD3JkHuYeAjw7UDinlI8AjACtWrJBr1qw57/1Dhw4RzDJ5fungG5QHAhR7MvWfvB4vrlSSl063sHzK1Mt+NhKJZHWdEydO8MYbb7B27dqLjrdtm+XLl3Ps2DE+/elPs27dugHP4fV6Wbp0aVb3NBhbtmzhwr+/0Sjb+5BS8uc7XmD1nHJuW7diyNcNx5L8++bd/H5PPX3RJLZ0kA4IAZpbw1PjJhWQpHAyy1HJ/DszraSEe+ct4B0zZuLtr70zVn4X2RqPec6mhmOEPN6z8ebMn5sajg15Fs8Zl8txLow3Ax2jXGxmXQUv7mvAdhxcho7P48bQEqQti7TlUFceIm6E6DiZ5u65AQJFBq92NHOss4tJZSXoQhBNp0lYFjPLynj/wkWqc20Isq3Bs/oybztAH3BYSmnmpFWjSGe6i3LX+Vtg+3U/nencbFkbjUbZuHEjDz/8MKFQ6KL3t23bRl1dHe3t7axfv565c+eyevXlfl3KSNnV1MPyKaUFEaACLje6lilgZmjZ5QimY2NoOtdNyH8NIeVthRBzgsEgiURCxZwstPdFeeNkK3UlQd482UZU2DS0RUibNkf6OmkLxyj2e0lakqORUhZPLOHr91zLz7bu4a2OHop9HiZOK6HTTmLogtpQCEc6JEwT03H4+LIVTBjg96QMTq7yHCnlbZe5xqeAx/s7dF4TQjhABW8XTT7XBmC3lLLtnHOf/V4I8QPgt5drS660RMLUBM7vdAm6PbREwjk5/5Vijq7r7N27l97eXu655x4OHDjAwoULc3Jt5WLHO2M09yb4/9bOGPK5OnqjfPFfn6KhpROBQNdEZrS+f9mpnbaJNiZwGYK7Vs1lxfxJaJpGbSDInIqKQS0rV8aGQok3Z2r2qHiTnclVJdx8zXRePvAWAKGAw23LZuHzuDl8sp1goIhHdvUxtcRgTrkLIdyYfptZ1ZV0WglMx2FqSSk3TZ7MzLLyrJ9VlIFlu0RrC1ce/48LIf5JSvnnQ2vS6FLhLidmxc6OogPE7TgV7qEXpTVNk40bN3L//fdz7733DnhMXV0dAFVVVdxzzz289tpr6mGrALSHk5zsTvDADVPz3RQAvC4XtYEQp6PhrAstJy2LycXFzCxT9Z0KSSHEnEgkomJOljwuo7/GTprTvWEWBfy4dA29f8eIRNok6HfTYvpI2XDfjSU0Nncwy1XMJ9+7kgk1JQC0x6LsPt1CcySMW9dZWFnN/MoqPMagN8NUBraF4c9zngTWAVuEELMBN9B5iWPfzwXLs4QQtVLK0/0/3gMcuMp2DEpdMERfKnl2JB0gkk5RFxx6x2I2MeeMkpIS1qxZw6ZNm9QD1zDaejTT37h61tDq7yRSaf78R09T39yB29BxG8bba7D6GYaG25Ek0ibPbT3Mkupa7rhu7pCuq4xuKt6MTkII1i+fzZKZdbT1RGk5dpDb1l3LoaY2DjW18sKJOKYjua5KI5m28LgMfEJn48KFlAbyv9JhrMm2e+w9wEkyo0UfITOy9BHgKeAU8IfAT4AvCCH+JOetLGDLS5cRs+PErBhSSmJWjJgdZ3npsiGdV0rJgw8+yLx58/j85z8/4DGxWOzsOvRYLMazzz6rglCB2NVYOPV3AFyaxpqp0/C73MTNgQegHekQT5tEkim643HcQufWqTOpLCrMJVoXEkJ8XAixRwgRF0LYF37lu325omLO6FJS5GVSeQnbjzbSHU2gCYEAEqaJz2UQrDKIV0u6Yj6KKyI8076Lhw+/wHORQ/zm5TfOnqeqKMAdM2fz4NIVfGjRUpbW1qnOndwaiTznUWC6EOIA8B/AA1JKKYSoE0Kc3e5cCOEH1gOPX/D5bwgh3hBC7Cez09f/vsp2DModM2YSTiXpSyVxpKQvlSScSnLHjKHtsJRNzOno6KC3txeARCLB888/z9y5qgNgOG092sG0iiImlfmHdJ5ndh7lUFM7HpeB23Vx584ZQhP4PW4s2+F7v30FU22NfEnjIc9R8WZ0qywOsHBqDW5DR9MEsydWErYNDrSnqTYSNJ1u45WDJ9j+5gmumV6rOneGSbYdPHcDm6SU75FSPialfLb/z3eRKf61Vkr5aTLrwx8crsYWoslFk9hQ8w6KjCK6zG6KjKIhFzuFzDKIxx57jM2bN7NkyRKWLFnCU09l8r8777yTlpYW2traWLVqFYsXL+a6667jrrvu4o477sjFbSlDtKuxB4+hsaCuON9NOetds+eyuLqGYH/djpRlIQHTtumJxmkPx+hNJImk0qRTNpGeBE9tOcQjz71KIl3Yqy/7i45+G3gd8AI/An4KhIEG4Kv5a11uFULMufHGG1XMyVJHOEZTZw+3XjOTWTWZ2XCxlInlSLRiSbwiycnGYnTdwetvp7srSSJpc1L2cTB0mnA6mec7GDeGPc+RUqallB+UUi6UUi6TUm7uf71FSnnnOcfFpZTlUsq+Cz7/ISnlNVLKRVLKd58zm2dYza2s4mNLV1Ds8dIajVDs8Q654ClkF3NOnz7N2rVrWbRoEddeey3r16/nne98Zy5uSxlAyrLZcbyb1bOGNnNXSskvX9yHJsBlXGYb9DME+DwuusNxtu4/PqRrj1XjJc8plHizZs0aFW9yIGlavNiUxqM5THQnEGRm7nk9LuKpdL6bN2ZlO/x3D/A/LvHer4Bf9H+/if5dG8aTyUWTclbc9IxVq1YxQN1FgLMBCWDfvn05va6SG7uaelg8sQS3UThrSOdXVXHf/IU8eeQQcTNNQ3c3HbEYybR1dscKA4FhC3QEQdtNLJ7ih79/jS0HG/j2R++mPDS0Eb1h9L+AvyNTePSPyOyGs1sIUUpm6UVuCtQUiHzHnGwLpyqZQqJn1JWFMLQYc+oqKfK7OOxrwR+pIRp1UVzRiuGSGLpGeUmAtGUT19O8cPoI75myOH83MH6oPOcy5lZW5azA6RnZxJy6ujr27NmT0+sql7bzRA8J0x7y9ugNLV00d/bhcWU/y1DTNISAx1/ez63LVOH4AYybPKcQ4o3Kc4buaPg0//D0AbqTktWzJbWUkE46lAR8VBT7qW/uoqM3SmVJIN9NHXOyffrUgUtVW5vZ/z5Aqv9LUcattC050NzHsgJZnnWuddOm84GFi6gJBJnoD+JEbNxpQZGl47cNvJaOW+qUpDwEbDdFHjfFPi/HWrv5n4/+mlThzuSZBWwlUwzVIVPbgv4thL8GfC5/TVPGs4pgEesWzuT/sffm8XFUV97391b1rn23ZNmW8SrvljcwtlkNDoQJYEICzCQEEpgskxDmebKQPEmevDMJk3mYwDvDTLY3PAkzgTBZCZjV2NiYxWAb413yItuyLVm7eu+uqvP+0S3jRbLbai0tqb58+LS66lbdUy330bnn3vs7J7uC+NwuKgpzuXP5PD6yfAoTS4ppOJqN0x3C4evAUkJbMMhJf4BQLEZbW5TNLYcJGfYs1yBgxzk2o54Ndc04dcWll6Sn6XbgRAumZeFIZfXOaTh0naPNnRduODqx4xybYcHhYAtNkU5+uecN1u2IkJ0T5KhzD+86dnMw1siuI428W9tAPG7Q3BkcanNHJKkmeNYAP1BKrVZK6QBKKV0pdRsJp/J8st1MEssEbWxGLfVdFnFTMkZ/53SUUlw2bjzfXnYlxw924DQ1PMqJQ3Tcpk5ezE1J2IvXdJ5xTa7HRV1jC79984MhtP68hAEtWZ2mEbjktHMBoGJIrLIZ9SiluHr2JP7HLZfx8WsngGZQXAJ6NnQ0FmGaGjljGzEL4kSzo4SyI0R9UUJE6QiGMUyLlkhgqB9jNGDHOTajng21LSyYUECWOz19r5hh0pcCWEqBaVlp9T2CseMcm4znWKiNJw++jgJ2fJCFaSpyShpwiQOP5iI8phOtyCBumBxsbMNzEav8bFIn1U/174A/Av8NGEqpdqAgef0byfOQKCP6g/420sZmOLG/PaFzl4kJnm427z9KsCNOmTcLZV44CtM0Daem8/t3dvA3V9RkROn3ZFnjrSISAHaQmGV/FdgIPKSUOgQYwPeAvUNlp83oJmJGeLt1M/sDBxCxKIjn85cTz7P9iMm+AxWUjAsQzfbjxIEmCkSwlBD3xIjHTC5c2Mmmn7DjHJtRzUl/hD0nuvjaqmlp36swx4cIiGWhLqLcsWkKWR5X2v2PFOw4x2Y4ISK8ePx9EGFvm8GuAw5yxzdijWmlU2k4DSfesJeOvHaK/GUYMQtNG/rxxEgkpQSPiLQAy5VS1wFLgHLgBPC2iLxyWrtfDYiVNjbDiLoOi0uKsyjMytwg5bdvvI+muKhEjdfloLE9wPb6E8ybmBETReuAy4DNwM/4cDbrf5EIgN5IvveTEFC1sRlUomaUNSdepC3WToEzH01pKA0KHcVsftfC6Y5RMekYDZ0OYhIHDRQKzVJggngsWqJBij32/vSBxo5zbEY7G2tbgPTLowPMnliOz+0kZpi4XakneAzLYsn08Wn3P4Lo1zhHKeUhsc3LTWIM+DsR+a5SqpCEzlgVUA/cntz6hVLqmySE5U3gyyLyUn88mM3wpaUzyDt7DuN2Obh85kS87sSq/+ZoFwf8TexsP0rj/uk4PDGKq04SMx3ELZOYI0Y8O4476CHsDjM5v5xQNGOlH4Y1F0zwKKVcJL70PxaRl4GXB9wqG5thioiwv93k+jmZu3oHoKG1C4d+cQLQ3QKIdSdaMiXBcyo7JSK/Pe3n/UqpmSSCIh/wZnLwZmMzqOzs3EVLtJUSdzFNoU5q204yN1rJMxv8nGytoGruXhy+E0g4H2UqLAssLCwdNIfCrbtAgVu3lzAPJHacY2MDG+uaKcpyMaM8N+175fo8XFo9gdfe358QWk5hMikeN3HoOh9fYYvKn0Z/xzlR4GoRCSilnMAbSqkXgFuBtSLysFLqG8A3gK8rpWYAnySxNbUCeFUpNVVERkRJdpuLJxY3+NXL7xKMxIibFk3tAe66pgaAjliQ2q7jHK/PoSWkM37eMZxOMETDo2tYlmAoEzMnxgRvDt5OJ/nZniF+opHJBUd4IhIDrk2lrY3NaKe+NYQ/DgszeHsWgCnddbMuHsPK/L/rIhIUkVdF5NlUkztKKY9SarNSartSapdS6n8njxcqpV5RStUlXwtOu+abSqn9Sql9SqnrB+p5bIYfhmWws3MXWbqPLS37eO3wXk4E2wjELfbtLSG7oANvSSNxy6TAY6IrHaeu49YdODQdpRSiC06l0RjuGurHGdHYcY7NaMeyhI11LSyfUtxvWybuuqaGLLczMUPfS/WibkzTJBKPs2h6JeNK8/ul/5FOX+IcSdAt6uZM/i/Ax4Du1Ym/4sPVQB8DnhaRqIgcAvYDi/vtIWyGHcFIDH84Skl+NqX52Rxuaj91rjMW4qQ/TNvBsUzMN8gr68Lp0FEkVic7dA237sTSLFoMP5UleZQXpp9QtjmXVKcFNwGXkijDNyAopX4LdG/8zQc6RGReD+1WAY+RqGjxCxF5OHm81+WFNjaDxXv1bUBm6+8AZHtctAfCF3WNiCBAUXbWwBjVNzSlVEqDMhG5kHKjPbNlkzamFSVu+emIdxGxIjRFWjjUFsDj0BBl8Wq9B9PUGVe9H1NMPOh43DE8RhZx00xUntE08lwecl1eTkYCxExjqB9rNDDgcY6NTaay+0QXrcFY2uXRT2dqZQn3rFrML17cTDAcw+124NDPrKplWRYxwyRuWEwqL+J/3HZlRmj8ZRj9GeeQFJHfQkLP53EReUcpVSYiJ5L3OKGU6q5RPhZ4+7TLG5LHzr7nfcB9AGVlZaxfv/6M83l5efj9/jOOmaZ5zrFMIhX7IpHIOc86WAQCgSHre1a+QSyWyClOyHedsqMrHiavdiYNpsbHq+K4ghMSF0hyYlm63wpup5PxnjCvv/76oNs/lJ9dKvSHfakmeP4e+JNSKgD8icS+9DPS8ak4lfMhIp/o/lkp9QgJIcMzSDqlx4GVJJzMu0qpZ0VkN4lB1zmDsHRssrG5WLYeacfngEklma2Zcd3cyfzslXcRkZSDqZhp4nE4WDa9amCNuzg2pdhOuIC/S1am6G1m68rk8V+RGAB+ndNmtoBDSqnuma23UjffZqQQNTtoCr1JS3gLgknEjKKb9VjxfCKGD9EM/J251DW5KZ1wCOXrABRxyyTf6cHry8UQC1B4dAdOTccSoTkawLCrygwGAx7n2NhkKq/XNgOwvB/0d7pRSnHbFXPRHRrPrNtOc2eAaMz4MOZIjve8biezq4r5+ievpqwgp9/6H0H0W5wDkJyEmqeUygf+qJSadZ7mPQWI5yzHEpGfkdAIYuHChXLllVeecX7Pnj3k5Jz5u/X7/eccyyRSsc/j8TB//vxBsuhM1q9fz9mf82ARjRvsP9aC06EzueLDVX+Pv7eZHSebKZzYhCvbx7PZh09dIwKGaWFYJnFMbh63kGuqrxqn0UCyAAAgAElEQVQS+4fys0uF/rAv1QTPjuTrY8n/zyYlp5IKKuH5bweu7uH0YmC/iBxMtn2axCBrN70PwoYlR48e5VOf+hSNjY1omsZ9993HV77ylVPn9+3bxyc+cSonxsGDB/n+97/PAw88MBTm2iTZcridyQV6xqvCf2LpXJ5Yt5W4aeJyXPirKyJE4gbXz52CL7MqXPySRLK3XxiIma3kfTN+dquhoYH777+fpqYmNE3j7rvv5gtf+MKp83V1ddx9991A4t/D4cOHeeihh/jiF7/Y4/1G08yWYBAxWhAElVyIKgh5xmRysZhsaoRNF//f7lzyXMLdY/JwhmsQFBqCppwonOfeVwQhh0NbdnBM0885PxCMhpmtXhi0OMcmwYXinNMxTZOFCxcyduxYnnvuuUG2dOSzsa6ZGeW5lOS4+/W+uqaxetkcFkwZx8YdB9jwwSGC0RiWaeF06IwrLeCjS6qZM6mCHG//9j2C6Nc4pxsR6VBKrQdWAU1KqfJkjFMOnEw2awDGnXZZJXC8v20ZDC7W3yxbtoxx48bZ/qYH3E4HM6vGEDY6OR7+gJiE0cXHf7/eidMTp2ziSSRahchZ8lua4NA0vJqTSl/hkNk/Gkg1WPk+g1erdTnQJCJ1PZwbCxw97X0DiWoXAL0Nws6hL4Ot8+GP76cpuoGo1YRbK6PMvYIc5+SUrz8b0zSJRCJ8//vfZ968efj9flasWMHSpUuZPn06ABUVFWzcuPFU+2nTprFy5cpe7U53sJWJQX+m2RSMC7VNIT46QTLKLuj5s/rSgko6QuGEeHIP11giWFbia9+t2FORbWXas/1cRDb3180GYmYred9+nd3qiNZyPLCWsNmIVx9DRfY15LunnsfUC5Ofn8+jjz5KTU0Nfr+fBQsWcNNNNzFjxgwAampq+OCDDxL9d3Qwffp07rjjjl5tHC0zWyIWu9oeJ2K2YVgBgvFjCBamZRCINNMe1zHiDtbvvJzG4EJunRbglZz3iAMxS+FymphGJcWusjPua1gmITPO+OxCPjJnIRW+vEF5ntEws9ULgxnnDDv2tDfx0tFajgU7GZuVx/XjplJdUHbhC8+Dw+HgkUceOcPnrFy58pTPOZ3HHnuM6upqurpsPar+Jhg12HK4nXuWTUz7XpYlHKlvoaUl8XsqLs5lfFUxE8cUMnFMITcvnU1HMIxhWnhcDkrzcy664MMopN/iHKVUCRBPJne8JLTH/gl4Fvg08HDy9c/JS54FfqOU+hcSW9GnkKjoNaBkgr+ZOnUq4fDFSRmMFgwryp7OVzgR3o0gaGis3V5AfXM5l152AM3nRo8p4mJ++FdVBK/DRbbDS9iMcfWY2UP6DCOdVMukf68/OlNKvQqM6eHUt0Sk25ncATzV2y16OHbRAVlfBlu90RGt5Vjo9zgdueRolcStAMfiv2dK1qf6PODy+/1MmTKFKVOmAJCTk8PMmTPp6Ojo0a6XX36ZyZMnM3PmzF7vme5gKxOD/kyzad2+k8C7zCz1ZpRd0PNnZZgmX/nls7x7oAGvy4lLTwi7RqJx/KFIsqLPh1+6/E7wxHWKC7O45/alXLu8erAfY9DI5JmtjmgtdR2/xqnl4tHLiFl+6jp+zZT8vvscgPLycsrLy4GEz6murubYsWM9Bj/r169n0qRJTJgwoc/9jRT88XqCsQZC5nFMK4YiUW3OEkEkToEzRnM4n/21s8kuaGFGkcZBASxwuwyCcReReAyPFsOlaYhAzDLRlGJqbinFnmzGeG0RwoGmv+Kckcie9iZ+vmczeS4P5b5cOmMRfr5nM5+rXpzWoCtVn9PQ0MDzzz/Pt771Lf7lX/4lrWexOZe3DrQSN4Ur0tieFYnEeOm57Tz/7DaaTnSc0lRWCsZU5HPTzTVc+5E55GZ5yM2yq+YMIeXAr5KrlTXgGRF5Tin1FvCMUupe4AjwcQAR2aWUeobETgkD+OJA6wxmir/56le/yk9+8pO0nmUkYkqcbW1/oC12BIdy0Rk/QXsA/vTWVGaOD7FqZgubW4NoqpJxviJiVkJD0JGUkWqNBrhx7HwK3ZktZTHcuejlxkqpbKAIOC4iF1W8XkSuvcC9HSQETRf00uR8A6reBmEDyvHAWpxaLi49EYB3vx4PrE17Rr2b+vp6tm3bxpIlS3o8//TTT3PHHXf0S182fWfr4XZ0TXFJ3vCYjXLoOo/d81c8/Md1vPLBfroiUUzDIhpL/O1WKpHccQiURl1kuRxYDqG9I8Q///QVWjtCfOKm3r6qw4/hMrOVCT7n97//ve1zkrRHduM3DiNWnLgEsCQRzBiWgUeLY4hi0/blGKaDubO3osyFKGWS5bEImG7ilkaeO065Nxd/PIKmNCZ4sylx59AcDXBV+VQ0W3R0UEknzhmJvHS0ljyXhzxXYmDe/frS0dq0Z9W7OZ/PeeCBB/jRj36U0YKsw5kNdc14nToLqvpWHKK5qZP/9bXfcryhHU3X8PlcaFoiDrIsi6YTnfzs39by/J+38f/88ycoLrET1kOFiHwAnDPbKyKtwDW9XPOPwD8OsGmnyBR/09TU1C99jTSawrU0R+tojRwhaLYBwnNvriBmKC5ftAaX5qGm0ActBq1RPy7NgVIQME1QcF35XO6ZNDTaO6OJlEeiSqmPKqW2khA/PgjMTh7/hVLqzn6y51pgr4j0ttf0XWCKUmqiUspFooLNs8lz3YMwOHMQNqCEzUac2plZSKeWTdhs7Jf7BwIBVq9ezaOPPkpu7rl/FGOxGM8++ywf//jH+6U/m77zXn07M8pzcTuGz2DMoet8+7Zr+fPXPs310ydhhkx0SagLe01FRcRFVdhLlpXIBWuawud14XToPPHbTWx6b/9Qmv8roLkf71cOrFNKfUDC17wiIs+RSOysVErVkRB4fxgSM1tA98zWiwzCzBZkhs9Zs2aN7XOShI0TxM1OolYHALpyoys3SjmwUBxtGsf2/XO5fNbbVJS249CF4lyTfB+U+uI4NIUhiktyillcUsWConHkOr20RAMsLZ3IwmJ7ldRgMUhxzrDjWLCTHOeZ+ig5TjfHgufUwugT5/M5zz33HKWlpSxYMHImEzKNDbXNXDapCLfj4nW+ujrDfOOrT3H8WAfZOR6ystynkjsAmqaRleUmK8fDsYZ2vvHAb/B32dteLoL+jnMyHtvfZDYH/G/QGN5L0GzBqVw0nhzPzv1TuWz2XkrzQ3QaJ3CoFordbm6fcBkTs0sZ5yvmuoo5/Ljm03xh2nXog6QpOJpJKcGjlLqZRMKkhYRw8ekj2EN8mFhJl09y1vYspVSFUmoNgIgYwJeAl4A9JJYW7ko27XEQNtB49THErcAZx+JWAK/e0060iyMej7N69Wruuusubr311h7bvPDCC9TU1FBW1j9ZbZu+YZgW7x/tyPjy6L2Rn+3l+K5WSjt1JkV8TAz7qIwmEjuqh52Rblci4fN///vtc84NFiLyGRE51I/3+0BE5ovIHBGZJSLfTx5vFZFrRGRK8rXttGv+UUQmicg0EXmhv2w5H5ngc+bOnWv7nCQxs4uY6Qd0LFFIctewpnRMS+PZt24kP6uDa+aux6kJioTwKIBDN6nKCzI1z0fYjHM83MmJcBfFnmw+PflSbh4/1169M0gMYpwz7BiblYc/Hj3jmD8eZWxW+rpQF/I5mzZt4tlnn6WqqopPfvKTvPbaa/z1X/912v3aJDjSGqK+NcTyKcV9uv6n//oKJ5s6yc52n7cip6YU2dnuxGqex9f21dxRR3/HOcOBTPE3n/nMZ2x/cxaWmBwP7yJiBHEqHyI6L7y5kNysIMvn7UBTOi7lJWR2YEqET1Zdzg/n38mPav6a+6esZHx23/yMzcWT6gqe7wJPiMh1wKNnndsJnE+INGVE5G4R+clZx46LyA2nvV8jIlOTg6p/PO14r4OwgaQi+xriVhcxswsRi5jZRdzqoiK7x5WOKSMi3HvvvVRXV/Pggw/22u6pp56yt0pkAHsb/YTj5rBN8NQdOsmRY634POdW8ukNr8fJ4YZW6g4Nym5ImySZ4HPs1TsfYmFhiElXNIY/GiUQixI1DeKmxTt7FtPUXsYNS17E5YoRNS0EMCXx6lYuXBrcNfEKHpp7Pd+as4rvzLuBv522jJkF5ecdMNn0O4MS5wxHrh83lc5YhM5YBEvk1M/Xj0tvS2gqPueHP/whDQ0N1NfX8/TTT3P11Vfzn//5n2n1a/MhG+oSi0NWTL14/Z1QKMY7b9bh8ThT8lVKKTxeB2+/UUskMup3Ptr0Qqb4myeeeML2N2chInTGj6OrxLar9/ZMoamtkOsu3YLL2S3voNDQMKzYEFs7ukk1wVMN/Db589mixu0k9qqPSvLdU5mS/ylcWg4RswmXlpO22CkksshPPvkkr732GvPmzWPevHmsWbMGgBtuuIHjx48TCoV45ZVXep1ptxk83qtP5BOHa4Ln5dd3I8IZS6svhJYUhH359d0DaJnN2WSCz7npppv641GGPSJxWoOdRAwNpcWJYxA0YnRGwxzvcLJ265VMGVvHzKo9mJaOUyW+M17di1fzEMegyF3MwsIadKWR7XTjdaQ2WLLpd+w4pxeqC8r4XPVi8lweToS6yHN50hY8hdR8js3AsqG2mbH5Xi4pzrroa196/n1iUROXK3U5T5fLSTRi8Mqa7Rfdn83owPY3mYuFiSUmmtIJhDys2zKXS8aeoLrq6BntlNIQrCGy0gZSF1nuAnpbV1XFKNsfejb57qn9Jm7azbJlyxDpuUBYt0MCaG1t7dd+bfrGliMdlOd5qMj3UjvUxvSBlra+i1emc61N3xhqnzOaxU5FBMyDSPQNiO9hvNpBmTvICcNNm4Ko0rBQvLTlSgxT58YlLxK1HIjoRK1EAjWWnNkqdBbwt5M/h0t3DeUj2SSw45zzUF1Q1m8Cp92k6nO6ufLKKzOuQuVwJm5avHmglZvmVvQpqbx/byN9KGSLIOyvtQVsbXonE/zN8uXLueGGG3poPboQK4jEdoJ1CGUJ5Y44Jw149d15xA2dVZe9y+nuI/EZCyp1mV+bASDVBM8rwDeVUi8A3ZG9KKXcJDRxBkV7wsYmU9l6uH3Yrt4B0PW+O2K9D8KMNjbDERFBoq9B5CVQXtDG0BA9So7WxDhnjGJTsTPiY3/jOHYcmsVlM99E6SZm3ENInARibkBR4CxgYWENV5Qup9ht70nPEOw4x2ZU8f7RDgJRgxV91N8Jh6N9SgwpIBSKXrCdjY3N0JGId96A0JNgHgeJoQHLPW28fnQcH9RNYumcnWRltxOMCw6l4dQUljLQcODUPEP9CKOaVBM83yJR/ncfsIZEyv4bwBwgD7h5QKyzsRkGnOgMc6wjzGeXTxxqU/pMRVl+368tTV/4rr9RShUAl5KIJd8eLE0um5GNxPdC5AXQKkA5CMTDCD4Mcmk3wmRpYaY4Izz6zkfIzepkcfU7aEroDBXQGpxOa9NELK+Pb1R/nTxX9oU7tBlM7DjHZlSxobYZXVMsndy3BE9evg+rlxUR50OAvHxvn/q0+RA7zrEZSCT6BgR+BJYFmg+0PMLxOJ3RMP++4QZKsztYOuM1/DHHqYoEmtIpcheBiuPRM29sMJpIadpeROqBGuA5EhWqTGAF8DawRETsjYs2o5Yth9uB4au/A/DRa+eg6wrDSL3Kt2GY6Lrio9fOGUDLLh6l1BXAAeBJEpoaB5RS6SkQ24x6RASir4HKA6sTom/giT3LfN9WJro7KdBjtMe9vLhzKa1dRSyb9zptsSyOdxbQEclhX0sVPmcuLs1BLG5r7GQadpxjM9rYUNvMvHH55HlTL65wOksun4oCLElda8MSCwUsWTqlT33aJLDjHJuBRCQGgX8HS0DLB+UiblqcCAb4w46l1LVW8MDlf2F5TiuBsItwzEsk5sMfcdMS6aQqexEuzU7iDiUp78sQkQYRuVdEKkXEJSLlyfJ9Ry98tY3NyGXL4Xa8Tp3q8tyhNqXPlBbnMGtqBeGLqGwRjsSZNbWC0uKcAbSsT/wYeFBEioEC4CnOrYpjY3NxSCeYR8FogOgLYB5CQzBFcGAw1hWl2FI8vfVKZlXWUVjaiI4TwyymqesaZpVMYnH5WJRS+Jx9G1DZDCx2nGMzWmgLxvjgWCcrplx89axuFl82ifyCLKJhI+VrImGDwqJsFiy+pM/92gB2nGMzgEh0M1gnES2XsGkQisfpikVoD3p54r0lzK84zPzKOsqdYcrcYZQyUFoMsKhrHEuRsov/DDW2ApKNTZpsOdzO3HF5ONPQsckEPv83V+DxOAmGLlzaMBiK4fE4+fzfXDEIlvWMUupflVI9ZZeqgKcBRMQA/gBMGETTbEYiEgWrGYytIE4ilpuOmEnMgpilEzLdPLbpo1ii8ZF5m9nVMZk3jy9gd9OlTMyvIt/jpTEYwOd0kuWyRZVtbGyGjk37WxCB5VP7rgGmaRp/tXohccPENC+8+tc0TUzD5GO3Lbyoip2jGTvOsRkS4h9gWMLRYCfHQ500hrtoDAX46eZLiRgOPrvoNTrD2YRiHsIdBew9NpFdR+bw+q4VbD4wjUPto7cQR6aQcm3D5HLAO4DxwNnKSSIi9tJAm1FHKGaw63gXf3tFz7NRlhWF8B8h9DRYTYAGjmrI+gzKvTSjyiFPqirhuw/cyPcfXYM/EMHtduBynukiYnGDaNTA63HxnQduYFJV32f/+oFLgFql1IMi8tRpx98BfqyU+lcgG3goeczGps8IPojvB9GwlINwLAKASu4+33x0Ei/vn8sXl7xEUVaUUNtkTA2qcvPoikZoDwvzy8vJb+0YysewOQ92nGMzWthQ20ye18ncyr7r7wHc+onF1O05zlub6vB4nTidPQ8r4nGDSDjO0hVT+dhti9Lqc5Rhxzk2g44pcTrjUSzx4NYT3+kdJ8p5df8MPjl3CxV57UQNENFo7izgQNM0XA4dBCyJYFh2ifShJqUEj1LqfuA/gFagDjhb/j5zRqk2NoPIBw2dmJb0qL9jmW3Qdg+Y9YAOuAET4luhYwvi/giS9w8ZNZO1YM4Efvy92/jlb9/k/V0NdAXDpwawIoLb5WTxvCru+eRSLhk/pMkdRORGpdQtJIKczwKfF5Fa4G+B3wA7k003A3cOkZk2I4YgECHxXQbDsjDFwlIWpunk/2z8KOPy2vhszRuctCaxJT6GhcXjWVUxk7ZImDy3m0Kvj/Xr1w/lQ9j0gh3n2IwWRISNdS0sm1yMrqX3z1rTNL723Zv5j8de5rWXdxGJRHA6NHRHIq4xDYtY3MLp1Fl103zu/7trMyrmyXTsOMdmKDgZK8UhFo6kfzAsxeObrqTY5+eWWW/i1DRiCILQECzAldzBEDHiuHWdyYVFdDS3DOUjjHpSXcHz9yQcyT0icuH9GzY2o4RugeWa8T0ILLd/IZncyYUzAhoPWAZE10BgDOR+eTBMTZlLxpfwD//zY7R1BFj7xj5ONHcBUF6SyzXLplGYnznVf0Tkj0qpF4HvAu8ppf5f4B9E5HKlVDagRMReK2qTPmYr4AMVAbFQSqFEoVA89cFSDncU8R9/9SS6Q8NhxNA0xaWlE8nzeMjz2OVChwF2nGMzKqhtCtDYFWFFGtuzTkfXNb704Cpuv+synv39Fjau200wmPgKZed4uOKaGdx0ywJKyuyqOn3BjnNsBptWq4Z8y4WuRTDx8uedM9nfWsz3rnuBHI8Flka+K8aJUA61XWMQ4gjg1DRmlZVRnpODvVZ5aEk1wTMWeMIOegaPo0eP8qlPfYrGxkY0TeO+++7jK1/5yjntHnvsMX7+858jInzuc5/jgQceGAJrRy9bDrczuTSbfN+ZmhpW9D0w93FucieJ5gDLDeFnsLI+j6ZnnuhqYX42H//ogqE244KISBj4hlLq/wL/BuxWSn1ZRJ4bWsuGFxfjc37605+ilBpdPkfLBeUAVYqymnFpJoZSHOvK54ktV3DNpF0sHn+IoOmmy3ByY+VMJmQXDrXVNqljxzmDTKo+p6qqipycHHRdx+Fw8N577w2BtSOHDbXNACxPQ2C5J0pKc/nsF67ms1+4GstKlE/X0lwhZJPAjnPS52L9jVIKl8s1avzN3pZmnq/dR5bLRYHXw7Hmmawet5WTQSe/2LyYhZVHuXziAfJcHgqcJi0hN2ubP8KkwhJC8TiFXi95bg81FRWUZWezZ6gfaJSTaoJnC4l9oGsH0JZhixXbC9GXwToOWgW4r0NzTU/rng6Hg0ceeYSamhr8fj8LFixg5cqVzJgx41SbnTt38vOf/5zNmzfjcrlYtWoVN954I1Om2OUnBwPLErYeaWfVzDHnngw9A0LPyZ1ulBekA2JrwbtqwOwcySilNGAKCb2MfSJyrVLqTuCnSql3gb8biRVwhtrnrFu3jqKiolHlc5Q+FtErwGrDUGMJSiO6BPnxpo+ggPsu20hMlZDj8uPNvZ38/JH/mYww7DjnPNR1nWBd0y4aIx2M8eRzVdlMpuSWp3XPVHxON+vWraO4uH9WnIx2NtQ1M7k0m4r89MsYi9mIxDZD7D2QKKLlgetylKsGpWVchc1hyWiMczLB37jdbnJyRse/4WAsxq/f34bX4WR380nq2lopzlmC12Hw3t5LiBoOHlz2Ell6iHynC10rxpn7KfxqDFX5YZRSWCLMLCnl9lmzh/pxbEi9itaXgQeUUisG0pjhiBXbC6FfgtUFakziNfTLxPE0KC8vp6amBoCcnByqq6s5duzYGW327NnDpZdeis/nw+FwcMUVV/DHP/4xrX5tUudgS4COUJyaHvR3sBq5oGSDAtDAbBoA60Y+Sqk5wF5gD7ANaFBK3SIivwGmA4eBHUqpryulUhaUz3RsnzM0KKVheG7BkgDtsQARyWLj0SWsPzSDTy/cQmmOgS4BfI4ScnM/OtTm2lw8dpzTC3VdJ/iv+jfwx8OUuvPwx8P8V/0b1HWdSOu+qfgcm/4lEjfZfKgtrfLo3Vix7Yj/MYi+BaIAb6LaYGQNEngUMRvTN3iUMxrjHNvfDD4RwyBuWTRHA9R1tdARixAR4cm9S1m7fzYLJx6krLCY/KzFOLI+hyp4hKKC2/mfl6/gi4sv5e55NXzt8uXcPb8GnzPzdiSMRlJ1Bn8BcoF1SqkQ0H7WeRGR0VmeL/oyqNzE8n1I/Gwlj6c5o95NfX0927ZtY8mSJWccnzVrFt/61rdobW3F6/WyZs0aFi5c2C992lyYbv2dngSWUTkklvD0TDhWRn3732FaPjipAx8MjJFJCgoK+OCDge2jG13XueSSS/B6058dvAA/IxHwLAdCJDQ0fq2UKknuR/+KUuqXwOPAp4CZA23QoJAhPkfX9VHjc/xxP5vb3qU+2MJcRyWVajcOK4tHNyxjfH47d87fTJYeJGw56fR8jQLNN9Qmn0M4HObgwYMXLGc8mL6iL/RkXz/5HDvO6YV1TbvIcXjIcSY+3+7XdU270p5V76Y3nwOglOK6665DKcX999/Pfffd1y99jkbeOdRG1LDS1t8RowFCvwGJgHmchCa5AhHQckBKkeAvIedBlLI1yNJg1MU5meJvRITPf/7zo8LfFHq9zCgt4de7tuJUOoVeL9kON4eO+3A6TSaMn8HeyEdo1bOYlVuGLykroYAJ+elV4rMZGFJN8KzlfKPV0Yx1PDGLfjoqO3G8HwgEAqxevZpHH32U3NzcM85VV1fz9a9/nZUrV5Kdnc3cuXNxOEZEAn9YsOVwOwU+J5cUZ5170nsjxDaBZfW4Tau+/e8oLplISUkBSvMOeLl00zTRdX1A+wCwLIvm5mYOHjzIzJkDHmfMAL4lIk0ASqlHge+QKHFcCyAi24FlSqnPDLQxg0YG+Jybb76Z3NzcUeFzwmaYZ48/R3O0hYgR4U/GZAot4URtJce68nnspqdwaRYHYzWs75zPPaXnLvfOBA4ePEhxcTElJSXnrWIzWL6ir5xtXz/6HDvO6YXGSAel7jMFcrMcHhoj/SOjeT6fA7Bp0yYqKio4efIkK1euZPr06axYYS+06gsbaptxOTSWTCxK6z4S3QhGA0gXKF9yUuvUSTD2g/iR2G6UuyZNq0c1oy7OyRR/c/DgQW655ZZR4W+UUiwaP5YtnUco9+VypL2L7ftihEMOcsc00x4r4I0jh4lbFmsPHuQLixbbxSMynJQicxG5e4DtGL5oFcmtEqc5CQkkjqdJPB5n9erV3HXXXdx66609trn33nu59957AXjooYeorKxMu1+b1HjvcDsLJhT0nJxxXQfaj8FqBcmBs9qYVlYyueMY8OTOYKJpGiUlJTQ1Dcq2s3dJiA52kKhf/SUSJY4Pnt1QRJ4YDIMGhQzwObfffjs5OTmjwufs7aplv/8AEStKRzRCTCx2dY7h/feXUlZ+gj8H5nG4YxqdpoMcl5sidw8J3wzANM0LJneGI/3lc+w4p3fGePLxx8OnZtIBgkaEMZ70Z25T8TkVFQnfVlpayi233MLmzZtH/IBroNhY18ziqkK8rr4ncUViEH0drA7Q8s6MbxSAG3CAeQxi68FO8KTDqItzMsXflJSUjCp/k+/y4nE68bmcjMstYN3JIEWFFrlFJtOLik7FDse7/Lx+uJ6/mtY/K8ZtBoaRFekNBe7rEjMYVheIlXztShxPAxHh3nvvpbq6mgcffLDXdidPngTgyJEj/OEPf+COO+5Iq1+b1GgLxjjYHOxZfwfQdAfkP5aY2ZJOsKKJ1TyWBVYQSGh6wMjbqzqIA8h7SUSS7wI7gKuB20TEGCwDhgTb5ww4/niEdSdqeaPpAFvathEyQ8RMiImJU2kc2TkLpYTyGbtoiCuORgxMsbitan5GJ2xHWnKnm5H6XJnCVWUz8RsR/PEwlgj+eBi/EeGqsvRWaabic4LBIH6//9TPL7/8MrNmzUqr39FKW8SitimQ/vYsKwJmI2jecyavTqF0EC2xkscmHUZdnB6jy9UAACAASURBVGP7m8HFsCzebjzM5pNHyHI4ORro4PVtYQwDyicEmJiXf8bf2Hyvh9rWliG02CYVeo2KlFJ/UEpNPuvYg0qpkrOOzVZKZe6G/QFGc00H3z0JPQxpTLz67km7os2mTZt48sknee2115g3bx7z5s1jzZo1ANxwww0cP57YjrF69WpmzJjBTTfdxOOPP05BQc8JB5v+ZWtSf2fhhN7LIGuuGVD0X4nVPApQ/sT/Wm4iKFLujB4MZjoiUi8iK4BsoFBEponI60Nt10CTCT5n0aJFI9bnmGLx89pN/PHw+zxzaAs7Og4RMuK0xFqwCHP8eBatTSVMmF6HLyvC9KIibho3m6/OvJrKrJH1WYx07DgnNabklnNX1TJynF5ORjvJcXq5q2pZ2noYqficpqYmli1bxty5c1m8eDE33ngjq1bZVSf7ws6WhP7WiqnpCiwLEAS5wASVcoDlT7Ov0c1ojHMyxd9cddVVI97fiAhP73+fZw5s573mBjqiYcJdLuoOWyya4eaOWdUUes7UFAzGYpRlZQ+RxTapcr4tWjcDD3e/UUrpwD8D64Hm09r5GAGiXumguab3m7hpN8uWLUOkZzmAbocEsHHjxn7t1yY1thxpx6Ep5lTmnbed5pgAhT9CJIwYzYAT5SiBxt0DltyZP38+27ZtY9++faxfv577779/QPrJFEQkREJ8cNQw1D7H7/eP2PKhgViULS1HCMRjmJaJx9tFljuIJYpY3EHt9pl4c/xUXFKHQ9P5q/HzWTFm8oVvPIIZxj7HjnNSZEpueb8JnHaTqs/Zvn17v/Y7WtnZYlKa42ZaWXq+WykdUUUgYeB8W1LjoJWl1ZdNgtEW52SCvxnJcU43LZEQ77ccZ1x2PkopDJePJzf6GZPn4YlPXIlgUd/ayXG/n3yPh1A8hmFZXDVx4lCbbnMBLnZds73cwMaGhMDyzLF5eJyp7WNXyovmHA96KS2R7QiCJWavf2zSYdu2bQAcOHCAp59+usc28Xi83/sdKpRSq5RSP1VKbVJK7VFK7VJKvaqU+q5SavxQ22czvBAlBIwYlliEjBhRQ0NHx6FpNNRNIhLyMXHWdlCCQmN2/rihNnnIGWE+x45zbEYcpiXsajVZPqUk/ckl5QXHBMAA6eV7LWFAB9e89PqyAew4x2ZgsMQ6Y5fljv1xOrsUX71+Mj6XgyyXiy8uXszSceNxaBpTC4v50uJLqcw9/+S2zdAzssuf2NgMADHDYvvRDu5aknrFXBFhf+dTHOj4DVGrnSr5NyyJAQpNdJTqP7Fln89HKBTim9/8JgcPHmT69OnccccdFBYWsmbNGqLRKKFQiLfffrtf+hsqlFLZwO+BlclDQmJwZpCY6foq8HWl1FdF5KdDY6XNcMOnu5hdUEF7JMTJcBfoHpwI/oCbhrrJlFQ2kF/cjlvzcElOJQ7dLrzUk8+58847KSgoGFE+J12UUr8FpiXf5gMdInLOCFgptQp4DNCBX4jIw8njhcBvgSqgHrhdRM4u525jcw47jnUSjJO2/g4kV/B4VoHZAVZTomqW8pCYMzaS793gGIdyX552f6MZO86xGUiKPVlMyCngsL8dp+nmze1Rqsqd3Dbvw5xhvsfLzdOrh9BKm75gKxP2wkCsrBgqRtKzZAK7T3QRNSwWVqWut/F+84/Y3fY4MSuA81T1o0RCx8LAkli//55++MMfsnDhQvbu3cu3v/1tALZu3crTTz89UgZaPwQWAB8HcgAvcCWJ0qEbgWLgQeDflFI3DJGNKTOSvqfD9VksiRMyDrB6nMH8wjaWlhVR7C5Elzxqt89A0ywWzj/GlLyxLCqaSamnCLfuHmqzM4bTfc53vvMdYMT5nLQQkU+IyLxkUuf3wB/ObpPcJvY48BESJZLvUErNSJ7+BrBWRKaQKOv+jcGx3Ga4s6G2GQUsn5Ku/k4C5VoEzkvAORX0CYAJRBK6O/pU0MeBa2HynE0ajKg4xyaz0DWNe6YvYumYKrbvFixL8YObZ+E3wsM2jrNJcKEVPD39dkf8b9zj8dDa2kpRUdGwF8EVEVpbW/F4PENtyohhS1JgeUEvFbTOpim4mSOBv+DAh6b1JEqoECwEEzXAi+qWL19OaWnpgPYxiNwGPCQivz/t2Aal1KeBN4H/LSI/UUrNBL4JrOnpJpmA7XOGnrjpp7bzScLxRpRyMK/ABBqZUzCJn25x0dJUyOWL2llWWY1bc9NldFHhqSDbYYsNno9h4HMGPc5RiS/57SQq4pzNYmC/iBxMtn0a+BiwO/l6ZbLdr0hoBX19IG21GRlsqG2mKlejMMvVL/dTWjZkfw4J/hfQANpUUE6QGGCBaxHKe0uyWqhNGoyYOMcmszgZDhAzDSqy8hinKth18DCLZsParrd4tRMqfYUsKZ6Cz+GmxJ1LtnN4xXSjnQuNJv+ilIqddWyNUur0Tbf989cig6isrKShoYHm5uYLNx4AIpFIvw6OPB4PlZWV/Xa/0c6Ww22MzfdSlpva72h/568BeknufIiIkZj9GkCyss4niDjsyAcO9XD8IIn68+OATuAl4O7BM+vi6cnn9Lcf6E8uZNtw9DmH/c8Rih1DsIgaJ9GUA4+jFKe1nwM7FlNaGOeKuXHilkXICJHnymNZydKhNjvjGQY+ZyjinOVAk4jU9XBuLHD0tPcNwJLkz2UicgJARE4opXrNnCml7gPuAygrK2P9+vVnnM/LyztVDnggMU1zUPqBhF86+zn7g0AgMCD3HSxCcWHrkRArK2UAnmM2MC2huSNWIoZRXhLDizf7ua/h/7voAyMmzrHJHDadqOdP9TuBCDML8vjPFzSyfMKi2Yp8Tz6dsRBrG3fycuMHVOdWoinFoqJJXDtmNg4tNe1Rm6HlfKPJXw2aFRmG0+lk4hAqhK9fv5758+cPWf82vSMibDnczqWXFKV8TXt0DzoX2sahEAQR6bcVHLm5uQQCgX65V4ayB/gk8MpZx+8ksT/9SPJ9BLAG0a6Lpiefk8l+IJNt6wsxs4uWyPuE4g2IGCjlwhKTkHGSP719Ka1+xc/vmUt+QSsRK0KFp5zxWeNxXiBpO9oYhj6n3+McpdSrwJgeTn1LRP6c/PkO4KnebtHDsYteUSQiPwN+BrBw4UK58sorzzi/Z8+eQakQM5iVaDwez4D4pfXr13P25zeceHFnI5ZsoabcO6yfA4b/76IPjJg4xyZzeLlhF6VZdehaAy9sL2V/0yyuXLaHbPc0ImaMHZ1HcGo6YoGGotidw1vNdehKY2X5nKE23yYFek3wiMhnBtMQG5vhwLGOME1d0ZS3ZwGImGgM/kBw0aJFOBwOpk2bxp133klhYeGg2zDA/AB4RilVBbwIxIBLSSxp/oWIdCXbzQL2DYWBNsODuOknFD+CoOHQc+iKRWiLhWjpyOPF9ydz1Yw2Vk6dCNilQc/H6T7nrrvuoqAgdT85FAxEnCMi157vvFLKAdxKQlejJxpIzMp3UwkcT/7cpJQqT67eKQdOpmuvzchnQ10z2W4Hk/Lt7VLDEDvOselXRIRC714Cxn7agjG27ryc8jHHKK/YxvFwPXFrCXHLxLBM2mNBQmaUUncu47KKeae1jstLpuFz2NqDmY5dRcvG5iLo1t+pGZ/6wMXlyCdqtKeQ5OmflTuhUAgAt9vNW2+9BSSWyev6yFpWKSK/U0rdBXwf+Kfk4Wbgu8A/n9Z0J/A/Btk8m2GEKCFuBXFpRVhi0RYLoKPz/NuX43QY3HF5TztpbLrpyefY9Mq1wF4Raejl/LvAFKXUROAYidn7O5PnngU+DTycfP1zj3ewsUkiImyobeaySUU4tPRW18WtMIf873IsvB1T4uS7KpmacwU5ztJhrx2Xqdhxjk1/EzRaGZvdxv5AIzt2LMM0HVyxeCM5ri6iZozGaD2dMScWFrrSyHV4iVsmezobKPPk0x4L2gmeYYCdzrexuQi2HG4ny6UzfUzqS84nZH8MIY4l51s9Kyg0O0i6SETkqWRFmRKgUETKROQHIhI/rc2rIrJh6Ky0yXQ0dDx6MaYE6S4csePQRA6eGMvKmjcpy809/w1sbFLnk5y1PUspVaGUWgMgIgbwJRKaGnuAZ0RkV7Lpw8BKpVQdibLJDw+a1QPA0aNHueqqq6iurmbmzJk89thjPbbr6OjgtttuY/r06VRXV9tJxIvgUEuQhvYwK6amVz3rSOB9fnf479nQ/Dh1Xa9zwL+JrW3/zR+Ofo23W36FJWY/WWxzNnac0z9crL9ZsGDBsPY3hmX0WAmrK95Ie7yeky1l1B6czrwZuyjMjyCi49FDKJqIWgYAbt2BR3fh0h14NBcN4VZ0Wzh9WGCv4LGxuQi2HG5n3vh8HHrqDm5S3sc57P8jEbMNB9lo5zhHARTaAAssj2REpHWobbAZXliWBcZekABufQLZrgmE4o1EzTa84mLN5kWUFzVz47wgxd55Q22uzQhBRO7u4dhx4IbT3q+hh2o4ST93zUDa1xtHgkfZ0r6Vllgrxa4iFhTUMD5r3IUvPA8Oh4NHHnmEmpoa/H4/CxYsYOXKlcyYMeOMdl/5yldYtWoVv/vd74jFYqdWjNlcmI11LQCsmFLMoR09afVemKbwPtY2PkLU9KMpB0ppaCgsLCKGnw86/oIlFpeX3tOfptucxWiKczLB3zzxxBO43e5h529EhPfatvB+5wdk6T6uH3MdRe4PJRqaI40E4iFef+d6sn1+Fs/ejIaJU9OxAKcWxhILn+6l0J1N97yzgYlbc2JattTTcMBOw9nYpEggarDnRBcLLmJ7FoBTz2Zp+eN4HaUY4idudW+ZFrqTO7py2uVE+4hSqlop9U2l1H8ppV5QSj2vlPqFUurTSqmML+FjM/hYse3Q/jfQ8WXo/Daq/VNM1lrx6PkUe2t4Y8f1+EPZfHllO2W+sRR7e5NLsbEZ+RwJHuWFxpcIGkGKnIUEjSAvNL7EkeDRC198HsrLy6mpqQEgJyeH6upqjh07dkabrq4uNmzYwL333guAy+UiPz8/rX5HExtqm5lQ5GNCUd//FL7Z/EvCZicOzYNDc6ErB5rScSgnTs2DWBa7O1+kK97Uj5bbnM5oinNsf5MebbE2tna8j1u5CRoh3m5959S5zliIVxvr2LpnGq3tRVy+cCMOZxSFga4iOBDGZ41lRl4lHt1J1IwTMeME4hF0pTE+qwRNs3caDAfsJQM2Nimy/WgHlsCCqosXK85xjePaymc4FlxLfdcfwU9yS5aOQre3ZvWBpFjpvwP3cGay2gA6ksd/pJS6R0SeHwITbTIQyzgKnQ+BFUmU9dUMsJzksJ2pjjm8drKQ57aWcc2sk8wZ56Qq9zO49cwWC7axGUi2tG8lS/eR5UiMI7tft7RvTXtWvZv6+nq2bdvGkiVLzjh+8OBBSkpK+MxnPsP27dtZsGABjz32GFlZI2pMOyDEDIu3Drayuqayz/foiB6nKVKLA1cPq49BKXBqXqKWn93tL3Jp6afTMdnmLEZjnJMp/mbbtm0sWrRo2PkbwzLZ799PwAiglEJTGs2RLnZ2HGW//zD1HS288/5ixpcfZsqEg4BCqYQKqKbpzM6ZQ8i0mOAr5mS0k5hlUuDyke3woGs6pZ68oX5EmxSwlwzY2KTIlsPtKAXzxvUtm69pDsblXM/ysT9BKR1dcyeXO9vJnT7yHeAu4OvAbGAqcDeJyjL/RKJU8RPAH5RSlw6RjTaZRuh3YLUCIVAxEBMIgsTINnfzmw2LyfE4+P5NK5lR+EWynGOH2mIbmyGlJdaKT/edccyn+2iJ9c+OkUAgwOrVq3n00UfJPUvvyjAMtm7dyuc//3m2bdtGVlYWDz88rKWHBo33DrcRiplp6e+cjNRhiYGuuXptoxQgiuPhXb22sekzoy7OyRR/88YbbwxLf9MR7yBsRtCVDqI4Fj7OEwfXsa7pfd5q28jW7WUYpoNli98koZyl0JUbh8oix1HAhOwSqrJLCVkxxmeVMCNvLD6Hm5AZ48aKGluDZ5jQ6woepdT4i7mRiBxJxxCl1G+Bacm3+UCHiMw7q8044NckHJoF/ExEHkue+x7wORLq8gAPJfex29j0C+8dbmdqaQ553v4peS4iCB8KoCmUney5OD4NfEdEHjnt2H6l1FHgOeA/ROQbSqlK4HvAqiGw0SbTiL0FYoDK4tTmcnTA4M97xvNufYAf3DKbsXl9n/W2GR4MdpwzXCl2FRE0gqdm0gFCZohiV1Ha947H46xevZq77rqLW2+99ZzzlZWVVFZWnpppv+2224bdgGuo2FDbgkNTXHrJxa867sYgCpzmKntBKYUhsT73Y9Mroy7OyRR/4/f7h6W/cWpOPA4PWZqPiBXBoZxETQOPI0q0o5Bjh8cyb+Yu8nIjgAOn5iHXkY1HzyLfVY5CuLNqGe+1HmBz6366YmEuySplWel0xmcVD/Xj2aTI+bZo1QPnym/3Tlo1mEXkE90/K6UeATp7aGYAfy8iW5VSOcAWpdQrIrI7ef7HIvJ/0rHDxqYnLEvYdridm+ZVpH8vsRAEQ4xkkiexNFIphZb8z070pEQZ8H4Px7cCXmAisAv4A/DLQbTLJpORxIDl7BFLV8TDDzZcz5wKjU8s6p9l4DYZTz2DGOcMVxYU1PBC40tAYiY9ZIYImiFWlCxP674iwr333kt1dTUPPvhgj23GjBnDuHHj2LdvH9OmTWPt2rXniKLa9MzGumZqJhSQ4+n7pFSxqwrQsCwTTev9n79gUeyu6nM/Nr0y6uKcTPE3FRUVw9LfVGVN4NLCxWxrf58cZw63jP0Yfzm2g6PBJj7YOolsX5zFc2oRivHoQq7Dx4TsiXj1XPzxJvJdFXh0J8tKp7OsdPpQP45NHzlfguce/n/2zjw+qur8/+9zZyaZbCQBkpCEJSAoIciOoBAWWxVpbRUEi2i12mr7q235WttvXVurorVV3Lq4fPuttVrbCnX5CgiiAVzQQlC2hLAnJCwJJJB1krn3+f0xk5BAlkkyk5kk5/16zYuZc8/c89ww88xzn3PO5zkT+IQD9wGngX8Cx/CsolkIxAAP+csg5bmzXQhcevYxETkCHPE+L1dK5QCpwK6z+2o0/mTP8QrKXe52CyyfjYiw+sgakmVAk7Lp4j1miYVN2bBpXR5fOABcAaw7q30OnhV+R72vS9F6Y5p67MPBLADLAuPMUuOnPr2EE1XRvHTjUGyGQkT0d7DnE5Q4p7sxOGoQVw64oklVmxkJmZ3Ww/j444955ZVXuPDCCxk3zrNge+nSpcydO5e5c+fy0ksvkZKSwrPPPsvixYupra1l2LBh/O///q8/LqtHU1zuYmfRaX52xQVtd26Ffs6h9LEnUu4+TphENusTTasWQ9kYGXtFp8bSNEuvi3NCxd/U1NQwfPjwbudvDGUwf+A1fC3lSmzY+E/pZqIcJRw9GEf5qSjmXbqNeGcNogTTiqWfM5UIow9V7pNE2vvR3zks2Jeg8QMtOgMR+Uv9c6XUU3iyxdeIiDRq/zXwJuDP9GYmcExE9rTWSSmVBowHPmvUfIdS6tvAZjwrfUpbeO9twG0ASUlJZGVldd5qP1JRUaFt8oGutOnD/DoA3EfzyMra22rf1uxyWS6Ka46TkjoAQ5rfxyoimMpEcSaQKi4u5t///je5ubmMHDmSa665hoQE3/fViwimaTZpq6qq4pJLLqG2thbTNLnqqqv43e88C+BWrFjBz372MyzL4oYbbuChh9p3byMiXfF/8wzwnFKqH7AaqAWmAncAbzYqKXoBsD/Qxmi6Cc65UPclWGVg2QAbOcV9+euXk7l2zAF2W/Gs27GOk65KvpoyktnJ5wfb4qBQXFzMihUr2LVrF6NGjWLevHnt8jnNUVVVxdSpU3G5XA0+Z9myZQAsX76cu+66C9M0ueGGG1i6dKk/LqNVghjndDsGRw3ym8BpPdOnT6fRn7oJK1ee2WE/btw4Nm/e7Nexezof7fWoFcwY0bnvrE05mNhvIRuLn6fOqsGmwrB5V/KICCa1mOJmYOQ4BkT0Tl8ZYHplnBMK/qa8vJyYmBi/2tBVKKWIsEWwv2I/X5aux6o22bZ9NIOSC0lMzcOUOCwpwaaKsZFCufs4fcMHkxF3JTblHxkKTXDxNdu7CLhZzvpmiIgopf4E/AX4r7ZOopR6H8+M2NncKyJvNRrr722cJxpYDiwRkfqa03/EM8Mm3n+fwDM7dw4i8gLwAsCkSZNk1qxZbZnepWRlZaFtapuutOntf35Bv6hiFs6d3easfmt2PbzjUfZU7WUMF2Ipq9k+4HHOYV5Rw+LiYn7wgx9w8uRJoqKi+OKLL1izZg0vvPCCzzdcpmliszVdXh0VFcVHH31EbGwsLpeLyZMns379embMmMGdd97JmjVrGDp0KGPHjuXaa69tKC/pC0qpgP/fiMgflVJxwN3Ad7zNFp5lyj9t1PUkvXj2XdMUFT4ZcWWCeRDMo1hSywMfziPOWcfEMYrXD2wnNjySsfGprCnMYVzfgcSHR7Z53p5EcXExt912WxOfs3r16nb5nOZwOp1s3Lixic/54IMPmDFjBkuWLOmUz/EDfolzNJpQYENeCX2jwshI6dN25zY4P3YWp+qOkHtqHTXWaUxLQBQowa6cJEWOYuaA/4eheuUOxoCi4xxNRxERck6txbL2seY/k6lzG3x16mdE28MYGDkYp+18RCpIcA4nPfYyouy6WmhPwtcETzTQUlSXCPhUP05EvtracW85wHnAxFb6OPAkd14VkRWNzn2sUZ8X8YiPaTR+IftQKROHxHdqy4aIcKD6oM9961mxYgUnT54kNdVTzScuLo7CwkJWrFjB7bff3mF7DMMgNtZT7rC2tha3241SivXr15OWlkZ6ejoA8+fP54033ujqmy2fEJFHlVJPAqMBE9gjIpVn9flnUIzThCTK6AMx30eqV4D7ICu2J7OlKInHvhHG5EELOGR9SVHVKSrctdgMA0cruhM9lV7qc/wS52g0wcayhI17Spg+vD+G0fltpjblYHL/6xkQkc6e0xsodx9DRHAYEQyLnsLQmEv0zWEA0XGOpiNUuk9QYxZy9Fgi2/cM5PKxm0mJO4kYfUhyJqJQ1FlOqs1T+vvbA/E1wZMFLFVK5YjIf+oblVIXAY94j/uDrwK5InK4uYNefZ7/AXJE5MmzjiV7NXoArgF2+MkmTS+nuNzFwRNVLLqoXQVXzsE0TUwxMWhfwLVr1y6iopreW0RFRbFrV+elp9xuN6NHjyY/P5+bb76Z2bNn85e//IWUlDNi0oMGDWLTpk2dHitQiIgL2BJsOzTdB2VLgqjvc6qqmN9kbWXC4CgWTp2GicWkfoPZogro43AyO/l8oh3hwTa3y+mlPieLrolzNJqAknP0NCUVrk6VRz8bm7KTFj2ZwVETqHKXIliE22IIMyL8NoamZXSco2kvVe6TDDcO8/zn00iKLuOBi98kwmFyyEykxnSjbA5McRFtTwy2qZoA4Gsx+zsAF7BJKXVQKfWZUuog8ClQ4z3uD77FWduzlFIpSqn6zZHTgBuBS5VSX3gfc73HHldKbVdKbQNmo5dSa/xEdr5HymnikM5luM9skWpfgmfUqFFUVjaZrKGystIvyv52u53c3Fzy8/PZsmULmzdvbnaPshab1fQ0lFI8sfY4pVVuHrr6QgxDsavsCCsLd3Ks+jRTE4dyQWxSsM0MCr3U53RVnKPRBJQNeSUAzBjh/5LGhrIR7ehPjCNRJ3c0mhBlf8UBckr+xifbw8grGcBdMz7E4bBRh53h9uMk8iWWuKmxKhkS1eKmGU03xqcVPCJyQCk1ErgZj7hXMp4VMp8CL4tInT+MEZGbm2krAuZ6n39EC3fHInKjP2zQaM4m+1ApYTaD0amxnTqPUooII4Iqq6pd75s3bx6rV6+msLCQqKgoKisr6du3L/PmzeuUPY3p378/mZmZvPPOO8yYMYOioqKGYwUFBU1m1zWansD2w6f422eHuOniNDJSPN/tGIeTMMPzsxhj730rd+rpjT6nq+IcjSbQbMgrZuSAGBL7OINtikaj6QIsMTnhOoApbiCC9468wEXWFn7/6W1cMng3Xx/+ESgwReHGST/ZwgFzEBfEzKJ/+HnBNl8TAHwuqecNbl70PjSaXsPmQ6WMTu2D09F5LY7M/tN47/jaNvvZGokVJiQk8MILL/i9ok1RURFhYWH079+fyspKsrKyuOuuu5gxYwYHDhwgNzeXtLQ0li9fzmuvvdapsTSaUMKyhPve2kG/qHD+67IzlV+GxfTnx6NmAZAc2bmEbnemt/ocHedoujuVLjebD53kO9OGdvgcIkJRdSnbyvIpc1UQaQ9ndNxg0qITsClfF/5rNJquYvfpDzlU8TkInKw7grIqefnT6bjcDu6duQKUHYXCYQhhuFEKZvRdRLjDv5XKNKGDzwkeAKXUGGAG0A94XkSOKqWG4ylrXh4IAzWaYOJym2w/fIqbLhnil/PNS72azaXZrfZRqCYJHvDccHVG3LQ5CgoKuPnmmzFNExHh6quv5lvf+hYAy5YtY86cOZimyeLFi5k4US/h1PQc/rG5gC8Lynhy4VhiI5qWBO3NiZ3G9Fafo+McTXfmswMnqDOlw+XRq921vHFoE1tLD3DUVYbbtDAMRcKR7Zwfm8L1adPoF949S0drND0REeFoxSr6yCFEaolUpZw8lcpjuRP5/qS1DO9bjFIRnFFlcaFw4TD6BtNsTYDxKcGjlAoH/oanwpXCU4r8HeAo8DiQB/wiQDZqNEFjR+Fpak2LiUP84widDie/yriP/Tn7mz3uybA7ukR/YsqUKeTk5DR7bMGCBSxYsCDgNmg0XU1pZS2PrcohfaCDUedVYomFoWelu4RQ9jk6ztH0BDbkleB0GExKa79moNsyeXl/FquLvqDCXQOAgUKA4urTHKg8TqmrgjtHfZ1ou97+pdGEAlW1V3i2+wAAIABJREFUOURxgGrLIooqkowSfr3hJpKjS/nB5PcxEKAOIRwQFIIQhTJ03NOT8fV/9xE8Fa5uBJJoqoOzCrjCz3ZpNCHBlkMnAZgwJM5v5+wT1gdDGTgMB4YyGh4Ow9FlyZ2egFJqcHsewbZXExo8tnoX5TV1ZE49SlbJBvLK9wTbJE1ooOOcLqagoIDZs2eTnp5ORkYGTz/9dJPju3fvZty4cQ2PPn368NRTTwXJ2u7Bhrxipgzt16Et5Xmnj/Bu4VbK62qIsIUTaQ/HaQ8jwh5GpD0cl1XHR8W5bDzW+Wp6Gt/QcY7/aK+/SU1N7Rb+ptq9l75hw0gIS2aQvZJ/78hkb0kyP818m3BHLQAKN4o6FHVYOBDbCKDzshOa0MXXLVqLgPtE5DWl1NmfiANAml+t0mhChC2HShncN5LEGP/PVtUndjQd5iCeWXZf0b9mvZyt+aX8c3Mhk0dXMTKpD6V1pZTWlQXbLE1ooOOcViiu2c/e8o1UuI8TbU9keEwmCc5hnTqn3W7niSeeYMKECZSXlzNx4kQuu+yyhmptF1xwAV988QUApmmSmprKNddc0+lr6akUnKxif0kli6d2bEv58oJNlLtriLE7OXueSSmIMMKocLv4V/5nXJEyTscvXcNBemGcEwr+JiUlpVv4GxtOYqx9RBpuyirhxU2XM3XwPmadX4hbbJgoHFgIYdQShagYoiNmoVRYsE3XBBBfEzz9gObXVXtWAfXeciOaHouIsOVQWUBKjTY3loU0/IwbSumVPG1zC2cCn3DgPuA08E/gGDAAWAjEAA8Fw0BN6GBawn1v7iAu0uDqi8M4UXeCcMPJBTEjgm2aJjTQcU4LFNfsJ/vkPwk3oom29cdllpN98p9M6LuwUzddycnJJCcnAxATE0N6ejqFhYUNN1yNWbduHeeddx5DhvhHD68nsnGPpzz6zPM7FrNsPXkQuzLOSe40oCDcZie/soRqdy1RDr1NqwvodXFOqPiboUOHdgt/08dwU6VqqbXgtx/Npdrt4JezVxFmS8SwLCzrFG4Vhot+hBkxhIelo5xzg222JsD4muA5AFwMfNDMsYuA3X6zSKMJEfJPVlFS4WLCkPbvZfcVEcEUwRQTq9EcjVJgU4Y32NKJnuYQkb/UP1dKPQVkA9eIiDRq/zXwJnDuL7imV/Ha5/nsLDrN3FnFVInFwkELiLA7CTP0LJYG0HFOi+wt30i4EY3T5hHXrf93b/nGTs+q13Pw4EG2bt3KlClTmj3++uuvs2jRIr+M1VPZkFdMSqyT8xKiO/T+KtOF/ZzFa02xKxvVUkuV2XqCp7q6mv3792OaZodsaYn4+Hi2bdvm13N2BJvNxrBhw4iIiAjoOL0xzgkVf3Pttdf6ZaxAo+q2E+WcxvZ8G2/njOa2yRtIiz+G20rARTxhhg2HfQhhYekYYZNRYVNQhv9kJ0KF9vicUPEjLdGcfe31Ob4meP4K3KOUOgis8LaJUmo28F/Ar3w8j0bTbdhyqBSAiQFM8JhiUWdZeCZoziRyRMAtJpYSwgybTvK0zSLg5sZBD4CIiFLqT8Bf8PgqTS+kpMLFb1fnMmVYPPPGRtA3PJ4+jhj9vdI0Rsc5LVDhPk60remqkHAjigr3cf+cv6KC+fPn89RTT9GnT59zjtfW1vL222/z6KOP+mW8nojbtPh4XwlfuzC5w34tyhbO6bpqWtvlU2e5sRs2Im2tJ8b3799P//79SUhIwPCjmKtpmthswd2FZFkWxcXF7N+/n4yMjK4culfEOaHib+69916/jBdwVBhudzUPrRlBcp86fjgNDBWJgQuHbRBhEd/DHnkVSrWrcHa3oz0+JxT8SGucbV9HfI6vXvdx4F3gFeCkt+0j4H1gtYg867PVGk03YcuhUmLC7ZyfFJiSoJaIN7njqZ6loMkDFJZYuMUKyPg9jGigpbqwiUBUF9qiCTF+syqXqlqTR66+ELvNzubSLXx+cnOwzdKEFjrOaYFoeyIuq7JJm8uqJNqe2Olz19XVMX/+fBYvXsy8efOa7bNq1SomTJhAUlJSp8frqXxRUEZ5jZsZ53esPDrAxL7DqBMLS5qXfBGBWstNWmQCkfbWdyyapun35E6oYBgGCQkJfl+d5AO9Is4JFX+TmNj58QKJ4OZk1VqqSeTVLRHkHA/nvstOEBMRg8M5i4j+bxDV7684oq7p8ckd0D7nnPf40klETBH5FjATeAJ4CXgGuFREFnfEWI0m1NlyqJRxg+OwGYGZ5XdbJp6Shc1Tn+QxLQtpIeDSNJAFLFVKTW7cqJS6CE91nKwg2KQJATYfPMm/thzm1syhDEuIYk/FXiJsEeSV5wXbNE0IoeOclhkek4nLqqDGLEfEosYsx2VVMDwms1PnFRFuvfVW0tPTufPOO1vs9/e//11vz2qDDXtKMBRMO6/jmoHXDplKtC0cl+nGbZnUhx0intXGLrMOh2Fn4eCLfVol1BNvtOoJ0rVl0QviHO1v2sZtllFnHqe0ag27TnzJkxvPI3NoGXOG7wUVjoq6BcPWv9etUtY+5wxtpvSUR2Z7E/ALEVkDbOyYaRpN9+F0TR27j5UzZ/SAgI3hmSVr3fkqwBKLD7OyeOvNNykqKiIlJYWrr76amTNn9mhn1k7uwDPTvkkpVYBHfDAJGIRHW+OOINqmCRJu0+L+t3aSHOvkx5eOwFAGF/ebws5Tu5gQPz7Y5oUslmWxfv163uwlPkfHOa2T4BzGhL4Lm1S1yYib22k9jI8//phXXnmFCy+8kHHjxgGwdOlS5s6dy9y5c3nppZeIi4tj7dq1PP/88/64lB7Lhrxixg6KIzbS0eFzjIhJZsGQqbxRsAm3ZVFr1aEwEO9ElMOwcXH/87k0ebT/DNe0h14R52h/0za15nEQIdyeyksfxVNdZ/Cra+ZixF4FKhqlK9z1etpM8IhIrVJqKODuAns0mpDgi/wyRGDSkL5BtcMS4bePPc77a9YQERFBdHQ0OTk5ZGdnM2fOHO6+++5O3XCVlJRwww03sHv3bpRSvPjii3zlK19h+fLl3HXXXZimyQ033MDSpUv9eFX+R0QOKKVGAjcDU4FkYAfwKfCyiNQF0TxNkHhl0yFyjpzmD4snEBXu+bkbHZvB6Ngu1U3oVogIjz32GKtXr+41PkfHOW2T4BzmN4HTeqZPn97i6tSVK1c2PD9x4oRfx+1plFXVsu1wGT+6tHMVAZVSfHvYTPo7Y3izYDPFrtNY4knuxDoi+MqAMSxKuwS7EXztivHjx7N161Z2795NVlYWt99+e7BNCji9Kc4JBX9TXl7u1/H9SZh9AKDIPljKeztGcMt0B+clBk4vVNP9fI6vm/LWApfTfHUJjabHseVQKYaCsYNiAzeIwrP2uZVVPB9t3Mj7a9aQmpracFPldDqxLIvVq1czffp0Zs6c2WETbr/9dq644gpWr15NTU0NFRUVuN1ulixZwpo1axg6dChjx47l2muvZcKECR0epyvwBjcveh+aXs7x8hqeXJNH5oj+XBnAlXg9jQ0bNrB69ere6HN0nKPplny0twRL6JT+Tj1KKb6WOpHLk8eSe7qQMlcVkfZw0mNT29Td6Uq2bt0KwL59+3j99debvdmqq6vD4ej4iqZQRMc5GgCbcmIQxh/WnU9yTBV3Tg9DxES1UQVP03G6m8/xNcHzLPA35VFpehM4gqfsTwMist/Ptmk0QWPLoVIuGNCHGGfgvqg2ZeCW1gWz3n7zTSIiIs6ZMTcMg4iICFasWNHhm63S0lI2bdrEv/71L8BzE+d0Olm3bh1paWmkp6cDMH/+fN54442QT/AAKKXGADOAfsDzInJUKTUcOCYioTsdo/E7j67MxeW2ePAbGb1uH3pneLP3+hwd52i6JRvyiolx2hk70H8TUg7DzoVxQ/x2Pn8TGRlJVVUVd999N/v372fkyJFcf/31xMfHs3LlSlwuF1VVVWzatCnYpvqdzsQ5SqlBeCoGDgAs4AUReVop1Rf4B5AGHAQWikip9z13A7cCJvBjEXkvIBemaRURC3F9ALXZIC4+LBhJbnEcv59XRCS7kdoBqPDmy75rOk938zm+rrNej2eP553e53nAnrMeGk2PwLSErfmlTApgeXQAuzJQKIQWKlYAR4qOEBXVfGGEqKgoioqKOjx+bm4u/fr1Y+HChaSnp3Pddddx+vRpCgoKSElJaeg3aNAgCgsLOzxOV6CUCldK/QvYikcY9QGg/iIeB9qsd6mUGqSU+lAplaOU2qmU+om3va9Saq1Sao/33/hG77lbKbVXKbVbKXWF/69M0xF2nzT599ZCvjtjCAP7mlqkvB0UFRURHR3d7LEe7nN0nKPpdogIG/eUMH14f+y23qe78eijjzJp0iRyc3N54IEHAMjOzub1118PmRstf+GPOAfPNtSfikg6nm1eP1RKjQJ+AawTkRHAOu9rvMe+BWQAc4A/KL1MJDi4d0PNe4BQXLqLFfucZA6rYs7IalCRYOYH28JeQXfxOb7+Gnyn0eOWFh4aTY9g99FyKmtNJgY4waOUwmHYGpI8Ao0enhvSlJQUKisrm31/ZWVlk5ui9uJ2u9m1axc//OEPycnJISoqigceeKDZm+FusALiEeCrwI14RAcbG7wK8CX5ogOfHkCdafHXXS5S4sKZPWYV20qe4MDpNxCxgm1atyAlJYWKiopmj/Vwn6PjHE23Y+/xCo6cqunw9ixLhMLTp9lz4gQFp05hWt3fT2ZmZoZ8iesO0uk4R0SOiEi293k5kAOkAt8EXvZ2exm42vv8m8DrIuISkQPAXuCizl+Kpt1INaBARfD4xkuoNRW/vLwEhYDUgNH5LZqajhGKPsenLVoi8nLbvTSansGWQycBAp7gAbAZBkqB27KwkIYNAUoZ2JTBvGuu4YEHHsCyrCZbJizLorq6mnnz5nV47LS0NJKSkpg9ezYA1113HY8++ihXXXVVk1n6s2fXQ5RFwH0i8lozSZYDeJYdt4qIHMGzLQMRKVdKNQ58Znm7vYynFOl/0yjwAQ4opeoDn087ezGajvPyJwcprBAeWyAo4zQR9mROunaQbM4iwq4DoLa4+uqr+eUvf9nrfI6OczTdkfV5xQBkjmhfeXQR4aP8Q/zli63sLimm1jSx22wMj+/Lt8eO4yvDzusOEzvN0tKq5x5Ap+Ocxiil0oDxwGdAkjcGQkSOKKXq71ZT8VQYrOewt+3sc90G3AaQlJREVlZWk+OxsbHniBabphnSQsa+2FdTU3POtQYOC6yJ7CkVlu+M4fKBJof39eHwPkClgiF4wtPQoKKiogv/NhAfH49pti57UY+I+Ny3MaZpYnmT4PXvFxEiIyM7dL722iciPv9NfdXg0Wh6DVsOlZIYE87A+IguGc9QBmE2AxHPOh7TEkyxqLNMpkybxmWXX86a994jMjKSqKgoKisrqa6uZs6cOWRmZnZ43EGDBpGcnMy2bdsYM2YMa9asYeTIkcyYMYMDBw6Qm5tLWloay5cv57XXXvPjFQeEfnhmoprDANqlDunPwMd7vlaDn+bo6h/H9hCqtpXWWPxuYzUZ8ULfMhfHSy8AFIp4PrNvx/dFq4EjWH87X4OfzMzMBhHkxj6nqqqKK6+8kmnTpnU4kElJSWHAgAFs3bqVMWPG8N5773HBBRcwbdo0Dhw4wK5duxp8zt/+9rcWA5zOBj4aTU9hw54ShiVEMTA+sl3ve3X7lzz16SfUWRYOw8BmGNSaJl8eO8rP31/Dd8dP5AeTLwr5JE+fPn1aXHHYA/FbnKOUigaWA0tE5HQr/8/NHThnyaWIvAC8ADBp0iSZNWtWk+M5OTnExMQ0aSsvLz+nLZTwxT6n08n48eO7yCKoc9fwm+c+JiVWmD/SYObMcYACWzIe+bjQISsri7M/B4Fk27Zt2Gy+LaI3TdPnvo2x2WzExcVRUVHR8H6lFEqpDp2vvfYppXz+m/r0aVBK/bmNLiIit/o0okYTIogImIWIey8AyjECjBS25JcycUh8UAKbWtNqsl1BKcVd//3fXDxtGm+9+SbHjhwhPT2defPmkZmZ2alyxQDPPvss119/PbW1tQwZMoTXXnsNh8PBsmXLmDNnDqZpsnjxYiZOnNjZSws0B4CLab4CzkXAbl9P5O/AB9oOfpqjq38c20Oo2vajv2/FUi4WZbiIzdhEf+c4HLY+9HeOJ9KRHGzzgOD97doT/Nxzzz1kZmayYsUKioqK/OpznnvuOW688cYmPsfpdLJs2TLmzp3b4HMmT57c7Pv9Efg0h45zNN2NmjqTz/afYNFFg9v1vh3HjvLUp59iiRATFgbe3zgH4LTZqHa7eTF7M+OTk7l4UPvO3dVMnjwZu93OBRdcwOLFi4mP79Glov0S5yilHHhinFdFZIW3+ZhSKtk7iZUMHPe2H8ajTVbPQKDjQmyaTvHqZ0fIOVrDHxdPIPzEbpQ9tL+fPZHu4nN8Tfddyrk3Ln2BGKDM+9Boug0ibqTq71CzBqxiQBAjkeN1V1BwMp6bLk7rcpvqrObFYJVSTM/MZHpmJkpBuM3ut+TTxRdfzI4dO85pX7BgAQsWLPDLGF3EX4F7lFIHgfqARZRSs4H/An7ly0l04NN9+XhvCe98WcSSr44g0cjBUDbiwtPpFzE22KZ1OwzDYObMmZ0qh94SIexzdJyj6Vb85+BJXG6Lme3U3/mfrdm4TDex4eENyZ0GlCLC4eBUTQ0vbdkcsgmeqqoqAMLDw/n0016zK7rTcY7yBI//A+SIyJONDr0N3AQ85v33rUbtrymlnsQj6DwC+LzTV6JpN8XlLp5Ym0fmiP7MGT2A9et9nrfU+IHu5nN8mooTkTQRGXrWIxaPLsVRYH4gjdRo/I3UfAgVf4a6XDBLwSyDuly27N0AdI3+ThN7xLM1q+1+HlFEzTk8DrwLvAKc9LZ9BLwPrBaRZ9s6gQ+BD5wb+HzLW9liKDrwCRq1bosH3trB4L6RfH/meYQZsYzt/3P6OscE2zRNN0HHOV1PQUEBs2fPJj09nYyMDJ5++ulm+y1btoyMjAxGjx7NokWLqKmp6WJLQ5MNecWE2QymDOvbrvd9WlBAuM12bnKnEZF2O1uPHtUVCEOLTsc5wDQ8Is2XKqW+8D7m4knsXKaU2gNc5n2NiOwE/gnsAlYDPxQR/4mNdCHt9TdTpkwJKX/z2KpcaupMHvxGRshvndQEn05t2BORDUqpZcCzwHT/mKTRBBYRgcqXQE6BigblzXOKRXZRf8JsJhkpsV1qk9mOCj+mZWHr5DaJnoY34PiWUur3eCpJJAIn8AQ96308TX3gs10p9YW37R48gc4/lVK3AvnAAu+YO5VS9YGPm24c+HR3/uejA+wrruTPN0/C6bABwvHqz7DETWrUV7AZ7ZJg0mga0HGOh0pXDqXV7+FyFxJuTyU+4gqiwtM7dU673c4TTzzBhAkTKC8vZ+LEiVx22WWMGjWqoU9hYSHPPPMMu3btIiIigoULF/L6669z8803d/KKuj8b8kqYPDSeyLD2hfI17joiHI5W+9gNgyp3LbWmSbg9tLQ9eiv+iHNE5COa314O8JUW3vMIngpeXUYo+Bu3282tt94aEv5m88GTLM8+zP+bdR7DEqKDaoume+APr70fjxipRtMtEDHB3A8q4kxyB0AZZBcNYUzSYeyGRVcKsrZnkkzPpzVFKRWGR+z4FyKyBtjYkfN0l8BH05SismqeWbeHy0YlcenIJABMcVFUuR5EiLAlkRAZ8hpSmtCmV8c5la4cjpS/iN2IJcyWjNs6xZHyF0nme5266UpOTiY52aONFRMTQ3p6OoWFhU1uuADcbjfV1dU4HA6qqqq6Q1XHgHPsdA27j5VzzYSR7X6v0+6gzmx9oshtCXbDwKEnk0ICf8U53YFQ8TdKqZDwN27T4v63dpIS6+SOS4cH1RZN96FTnlt5JLtvxqNFodF0D8QEsUCa3svX1NnZcTyZiSn5XW5Se5Zb6pWZTRGRWmAonlU0ml7Gw+/uQhAe+PqZIE1hw6YisBlOnPZ+QbRO093RcQ6UVr+H3YjFbsSilNHwvLT6Pb+NcfDgQbZu3cqUKVOatKempnLXXXcxePBgkpOTiY2N5fLLL/fbuN2VDd7y6DNGtE9/B+DiQYNwme5Wt19Vu92MHzCg06LqGv/Qm+KcUPE3I0aMCAl/87dNh8g5cpr7vz6q3av1NL0Xnzy3UuqDZh4f4REUvR74XUCt1Gj8iDLCwEgCaposndl+LJE6y8bE1HKU8l+5O1+wtSNrY1M64GqGtYCO+nsZG/KKWbn9KD+cmUhq+JtYNWsRqcVQDkb3+xEZfX9MTFhasM3UdAO6Is5RSv2jke7FwUZbQRv3GaSU+lAplaOU2qmU+kmjY79SShWepZ0RcFzuQmyqablgm4rB5S70y/krKiqYP38+Tz31FH369GlyrLS0lLfeeosDBw5QVFREZWUlf/vb3/wybndmw54SEmLCSU9uf5np706YSJjNRk3duUkeEaGmrg6bobh1wiR/mavxD70izgkVf5OXlxd0f3O2sLJG4yu+3ikaeLYuNH6U41Fx/4qIvBgY8zQa/6OUgshFeHYoVoO4QFxkFyUCMH7YJV0uYKaU8klXx9d+vZBngUVKqd8ppaYrpc5TSg1r/Ai2gRr/4nKb/PLtnaT1i+S749+FuhyoeQ9xfQxAuC0ep7194qOaXk3A4xwRuU5ExonIODzV+lY0080N/FRE0oGpwA+VUo33ECyrP4eIrOysTb4Qbk/FlPImbaaUE25P7fS56+rqmD9/PosXL2bevHnnHH///fcZOnQoCQkJOBwO5s2bxyeffNLpcbszpiV8tKeYzBH9OxSrZCQm8ZOpU1EKKmprqXG7qXObuNxuKmprsUS4dfwELgnRClq9mF4R52h/c4Z6YeVfaWFlTTvxaa2XiMwKsB0aTZeioq5FzP3g+hSoBmDLkfMY2reG/onnOv2uwGEYiMg5VbJEhK1bt7LuvfcoKSkhMTGROXPmMGHCBL18+gz1AoN34ikX2hxduyxLE1Be3LCfAyWVvHzzhYTbVoKRAlIM1ikgLtjmdWssyyI7O5vVq1dTXFxMQkJCj/c5XRnneCv2LcRTmv1sO44AR7zPy5VSOUAqHjH3oBAfcQVHyj35LZuKwZRy3NYpEqIWduq8IsKtt95Keno6d955Z7N9Bg8ezKZNm6iqqiIiIoJ169YxaVLvXlmyo/AUpVV1ZA6PRepyAQHbIJThu/jqjWPGMyy+L3/OzianpJg608RuszGmfwI3jhnLZecN1zeUoUeviHNCxd+ISFD9TWNh5fO0sLKmnXRqM59Sqp+InPCXMRpNV6FUBPS5G6ndBbWfIgLZR9K4NH2A51hQbFKE2WyYIrgtCxHh1KlT/PK++8jJycFus+F0OtmxYwdr165l1KhRPProo8TF6ZtZ4DvBNkDTdRScrOK5D/dy5ej+ZCY+BrX7wFYIjjGo8GnAzmCb2G0pKyvjvvvuY9euXdi8Pmf79u291ucEKM7JBI6JyJ42xk7DI+78WaPmO5RS3wY241npU9rCe28DbgNISkoiKyuryfHY2FjKy8ubeWdzDKSPbTHlte9TY+XjMJLpE/Z1rNqBlNe2fg7TNFsc59NPP+WVV14hIyODMWPGAPDAAw9wxRVXMH/+fJ577jlGjRrFVVddxbhx47Db7YwZM4ZFixY1e86amppzrtMfVFRUBOS8HeXtfbUA2I5msf5kLZ7SCw4w+oHR8nezuev4dlxf6vrEYYmFobyiyocLWX+4c9th4uPjMU3/F5UUkYCctyOISFd/LnpFnBMVnk4y32tSRSshamGnq2h9/PHHvPLKK1x44YWMGzcOgKVLlzJ37lzmzp3LSy+9xJQpU7j22msbJjMmTpzIbbfd5o/LahdaWFnTWXxK8CilvgfEichvva8vBFYByUqprcDXReRo4MzUaPyPUmGo8HEQPo4DJZWcrMpi4pDgCrIqpbArhd0wsCyLB++/n925uaSmpDSoK8fhCSxycnK45557eO655zo8q/7www/z8ssvIyLcdNNN3H///QAsX76cu+66C9M0ueGGG1i6dKm/LjEgiMjLwbZB03X8+v92YSjFvXNiwcwFFQ7GEFTMz7pcP6snYVkW9957L7m5uaSkpDSZwe/pPsdfcY5S6n2gObGEe0XkLe/zRcDf2zhPNJ5tXEtE5LS3+Y/AQ3ju6B8CngBuae79IvIC8ALApEmTZNasWU2O5+TkEBPju35LDBNJpP3V6MrLy1sc5/LLL29R6HfNmjUNzx977DEee+yxNsdyOp2MH+//YmdZWVmc/fcLJr/f9R6jE4/w9Ql/BBUGKM9Wc2WDmAcxIq9s9n1deR3btm3DZvO/LzZNs13nXbhwIe+//z79+vVjz54z+VR/+BulVJd+LnpTnBMVnt7phM7ZTJ8+vUV/s3Llmd2uDz74IA8++GCrvivQ1Asr/3HxBC2s3M0IpM9pD75GaD+ifh+LhyeBMmAJEAv82s92aTRdyuaDJwGYNCQ+yJacYcuWLezctYsBAwbgCeAAqdeFViQnJ7Nz5062bt3aofNv3ryZl19+mezsbHJycli1ahU7duzA7XazZMkSVq5cSV5eHsuXLyc7O9uPV6bRdJwPco+xdtcxfvyVEaT2Ox8ivweOCRB5tU7udJJ6X5CcnHzO9gylerzP8UucIyJfFZHRzTzegoaqXPOAf7R0DqWUA09y51URadDpEZFjImKKiAW8CFzUvkvUdHfKa+rYeriWzCF5oGI8yW0VBkaMp0Jo5R9arY4VymzatInnn3+eTZs2+e2ct9xyC++8806TthDxNxpNs2hh5a6jJ/scX9OCg4FcAKVULDATuFpEViqlTgCPBsg+jaZLyM4vpY/THlL7XFetWo3dZkO1WDVLYbPZWLVqFRMntn9mdfv27UyYMKFhhmLatGn84x/quZy9AAAgAElEQVT/YNasWaSlpZGe7pk9mT9/Pm+88QYTJkzo6KUEHKXUn9voIiJya5cYowkYNXUmv3p7F8MTo7ll2lCUMlBRi4JtVo9h9erV2Gy2FrU3lOrRPqer4pyvArki0mzZda8+z/8AOSLy5FnHkr0aPQDXADv8ZJOmm/DJvhO4LYMZQwo8MuCNUVFgHUascpTNUx3IsoStew/z1qe7GBpeyf8+/hoZgwfw9YszSB+cGDI6O5s2beLnP/85SileffVVHn/8caZOndrp886ZM4fdu3c3aVu/fn0o+Jt2o+Oc3oEWVu4aerrP8XUFjw2wvM+n41lLkOV9XQAk+tcsjaZr2XzwBCNTXGSX/JrtJc9Q6gqaniXg2Q5RXHyc8PDwVvuFh4dTXFzcoTHGjRvHZ599xrFjxygvL2ft2rUUFBRQUFBASkpKQ79BgwZRWOif8pQB5FJg9lmP+cDNwNXe15puzp/W7yP/ZBW//mYGYfaeKfYbTIqLi9v0OU6ns6f6nK6Kc77FWduzlFIpSqn6PQLTgBuBS5sph/64Umq7UmobHp/WktCqT3TXlR7N0ZOupTU27ikmyuFifHIz+UEREEX9x9hV6+bXr6zhnj+vYuP2/dS5TfKPn2L15t389E9v88e3P8ayQuPv9uWXX6KUIjU1FaUUX375ZcDGChF/0xG6dZzT076jgbieemHl72YOC6kJ555IT/c5vq7g2QN8DfgAT3DyiYhUeY+lACc7a4hS6h/ABd6XcUCZt5To2f0O4ildagJuEZnkbe+LZ8lzGnAQWNiS+KBG05j8shz2HK/iG2lbOFm9HcFNYeX7DIn5OiPjvxeUDLppWfRPSGDnztbFYl0uF/379+/QGOPHj2fJkiXMnj2byMhIMjIysNvtzf5ohfosgoikNdeulJoB/AlY3KUGafzOoROV/CFrH98Ym8Il53XsM69pnYSEBLZv395qn5qaGhISEjp0/hD3OQGPcwBE5OZm2oqAud7nH3Hu2oz6fjf6wwbwJOpOnDhBv379Qt6/t4WIcOLECZxOZ7BNCTgb8kqYOug4YbZKkJgGbT7P3u0qMAagjFgAnvr3BrK27cNmGDjsnpV5zjA7pmXhcrv518btxMdEsehS/+sWtZexY8fy6quvUlhYiIgwduzYgI0VIv6m3XTnOKcn+RsIjM9pLKz8Iy2sHHB6us/xNcHzO+AVpdRNQDywoNGx2cC2zhoiItfVP1dKPQGcaqX7bBEpOavtF8A6EXlMKfUL7+v/7qxdmp6NZVm8tfNV4GJGpboIt/f1tteRX/4OCRGT6B/R9ct2LREuu/wK1r3/PiJWs9u0RCzcbjdXzJnT4XGWLFnCkiVLALjjjjsYNGgQgwcPpqioqKHP2Znn7oSIbFBKLQOexTMrr+mGiAi/ensnYTaDe7/mX+FFzRnmzJnD2rVrEZFmg4/6CjZXXtm8iKsvhLDPCXicE0oMHDiQw4cPd3g1lq/U1NR0SeLF6XQycODAgI8TTA6WVJJ/sopbpo4A7CBVIHY8+cA6wIDoO1BKUVxWzpotedgMRZjDk9xRylvIwWbDZhhUu+p49YNs5mVeSLgjuEKuU6dO5fHHH+fLL79k7Nixftkq0RIh4m/8RneIc5rzN13lGzpKW/b52+doYeWupaf7HJ8+QSLymlIqH5gC/EdENjQ6fAx4218GefefL8SzFLE9fBOY5X3+Mp6l1TrBo2mVstoccorCMJTFsKQzC74Mw4GybBScXhmUBA8C48aPJ33UKHIbRE/PJHlELI4cOcKoURmMG9fx2bfCwkJSU1PZs2cP7777Lp9//jlxcXEcOHCA3Nxc0tLSWL58Oa+99po/ripY7MdTaljTTVm76xgf7i7mvq+lk9QndAPC7s6ECRNIT08nNzf3HKFlEeHIkSNkZGR0qlJRqPqcroxzQgGHw8HQoUMDPk5WVlZAKlv1Rjbs8dwczxo1FSIfgsrnwCrxrN6x9YeI72JEenbzrfrPbly1bmIiw5tN1iqlcIY7OFVZzae7DjJrbPBXDEydOjWgN1n1zJgxI+j+JgCEdJzTnL8Jdd/QlfZpYeXg0JN9js8pQu+y4Y+aaf+lXy2CTOCYiOxp4bgAa5RSAjzvLQUKkFQvPigiR5RSLe6XV0rdBtwGkJSURFZWlt+M9wcVFRXaJh/wh01uq4I9+0aTGm1Su+cSahsdEzEpUg5OOdo3hi92xcfHY5pmi8cNBQ6bwSMPP8z999/Pzp07sdvthIeH43K5cLvdZGRk8NBDD2EoWj2X51qk2T7f+MY3KCsrw26389RTT9G3r2cF0xNPPMGcOXMwTZPrr7+ecePGtTnG2eOFwufFW7HmZqBZQVNN6FNda/LgO7u4ICmGmy5JC7Y5PRrDMHjkkUcafI7NZsPpdFJTU4NpmmRkZLB06dIOl0gH+OY3v0lpaSl2u52nn366YbvXsmXLGnzO4sWLOyTi3Fm6MM7RaNrNhrwSBvWNYEi/SJS6Ast5Kbj3ACbYz8cwzuhn5RUcB1rfBmAoT3XOvYUlIZHgCQRXXXUVmzZtorS0lKSkJO6++26WLFkSEv7GX+g4p/ujhZV7DqHic9q9BsybODlnClVE8n147/tAc6nJe+tLiAKLOEuA8CymiUiR1461Sqncs2ba2sSbFHoBYNKkSTJr1qz2vD3gZGVloW1qG3/YVFadz6EPspmRfoiYUU21J2rcx0mNvpzR/done+CLXdu2bcNma7mks4hQU+cmqk8sTz71NF988QVr31tNcUkJCf37c9kVcxg7bhyGt6pNWz8Ipmk2O96WLVua7X/ddddx3XXXNXvMF5RSXfp5UUp90ExzGHA+0A/4fpcZo/Erv/9wL4Vl1fzz9otx2LSwcqCJi4vjueeeY+vWraxatYri4mISEhK48sorGT9+fKeSO+Apld4cCxYsYMGCBc0e62o6E+doNIGg1m3x6b4Srh6f2vB7bxgOCBvVbH/DZvNMh7aBALZOfqdDmbPLFdcTSv7GV3Sc0zOpF1b+wazztLByDyBUfI5PCR7l2RvyMHA7HgHk5mj5btWLiHy1jXHswDygxbSWV4wQETmulPo3cBGwAThWX0JUKZUMHG/LHo3m8MlYat0OhiQdQCwL5Q103GYFCjtpMVcFxS6lFHbDwG1ZoBTjx49vslS0Pm6z+5Dc6SUYnBvOlgMrgNdFJKvLLdJ0mv3FFbywYT/zxqdy0dC+wTan12AYBhMnTuzWs9rtxV9xjkYTCLLzS6msNZlxvm8C5xOHp7Jua16LelrgKaFuKMX4Ean+NFUTOHSc08PQwsqaQOHrCp4lwA+B3+AJgB7BU4dxsfffx/xkz1eBXBFpdpmhUioKMESk3Pv8cuDX3sNvAzd5bbkJeKu5c2g0jdlyyKO7M35IDDXWPpSlAMGuIhmbcBfRYUOCZpvdu1rBtKxzftEVeIUSdXIHQERmBdsGjX8REX759k7C7QZ3z9XCypqA01VxjkbTbjbkFWM3FJec18+n/rPGncfzKz+lsrqWKGf4OXXZRISqGhfJfWPJSNOaH90BHef0PLSwsiZQ+Lou8zt4Eim/8b7+t3dPejpQCAz2kz3f4qztWUqpFKXUSu/LJOAjpdSXwOfAuyKy2nvsMeAypdQe4DJ0MKbxgc2HSkmOdfK14b9mStLvSO/7fcb0+xmzBr5MUuQlQbXNU+3CIMxhx24Y2JTCphQOm0GY3Y6hFCLNl9/TeFBK+RYNa0KO1TuOsnFPCT+9/HwSYsLbfoNG0zm6Ks7RaFpErAqkbqfnYVU2tG/YU8yEwfHEOB0+nSc2KoIbvjIJh91GRXUNbrfpWfshUFtnUuFN/Nx+1cU4Wtkurgl9dJzTPdHCyppA4mu6cBiwWURMpZQbiAAQkTql1FN4SvP9qrPGiMjNzbQVAXO9z/cDzRaqF5ETwFc6a4Omd5F9qJQJQ+JRShHvvIB45wXBNqkJSikUYNg9AZiIYFkeweTGaR1DKQyb4RFN7IUopb4HxInIb72vLwRWAclKqa3A10XkaDBt1PhOpcvNr/9vF+nJfbhhavBW0Wl6FV0S52g0LWG5tkDNchDL06BsSMQCSmvT2VF4mp9edn67zrdgxhhqautYuyWPY6XlmCJU1rhw2GwMTYrn2hljmDXmvABciSYQ6DinZ6GFlTWBxNcVPKc4IzhYBDS+C7YDWhxB0+04cqqawrJqJg2JD7YpPiEiuE0L06vLoxo9RATTbWJZvXY1z4+A6kavnwTK8Gy7iOXMVk5NN+DZD/Zy5FQND189umGrokYTYHScowkaYh6F6n+Bigdbiueh4qDqH2zM2w/gs/5OPQ67jZsun8RDN8/h25dNIiYinFljh/O9r03l4Vuu5KqLMzD0Nu/uhI5zegj1wsrfzRymhZU1AcHXFTxbgVHAe97Hg0qpasCNZ596dmDM02gCR73+zsRukOApLS1l06bPqKisICoyismTJxMX38hub7lT07RQyuiNswGDgVwApVQsMBO4WkRWKqVOAI8G0ziN7+w9Xs5LG/ezcNLAbvHd7KmUlpby+eefU1lZSVRUFBdddBHx8T36/0PHOZqgIXVfgjJANdqOqjzaORt2HyQ+0sGghCrK6+qItvfDowneNjbDYHhqf4an9icrK4vr588KzAVougId5/QAtLCypivwNcHzFJ7lywC/BCYAr3pfHwLu8LNdmgAh1klw7wcjEWXv3ZICWw6VEuGwkZ7cp0m7iHCk+ihHa47RxxHDsOihGD4GU/7m1KlTPPPMM3z44YeYpmeFjmF4SqPPmjWLH/7wR/SJ9dqvPLa3VjWjB2PDI4QKMB2P2kCW93UBkBgEmzTtRER44K2dRIXb+e85I4NtTq/k1KlT/P73v2/wOfX+xGazMXv2bH784x8TGxsbbDMDgY5zNMHDKgc5NyS3TIMP97gYmHyUf+b/CUMZxDuGkJl0GwMiQmtLuSbg6DinB6CFlTVdgU+fLBFZ2+j5UaXURcB5QCSQIyJ1AbJP40fEqkQq/gDWaUBB9A9Q9rRgmxU0sg+VMnZQLI5GW0DK68p5Yf+f2V+xD1MslFL0C+vLLUNvZlj00C6179SpU9xxxx3k5+eTlJSEzW5vKIRRV+dm3bp15O3Zw9NPPXMmyQP0Us3lPcDXgA/wiLV/IiJV3mMpwMlgGabxnXe2HeGTfSd4+OrR9IvWwspdzalTp/jJT37S4HPs9jMhgtvt9Tl5eTz33HM9Lsmj4xxNULGfD7Wfe37A6ydoRHgnv5LSKgcXJ+/Hhh1EKHbt4f8OP8C8QY/T16k1ynoROs7p5mhhZU1X0aFlCeJhr4hs00FPN8I6CVYl2AYCgphFwbYoaFTXmuwsOt1kC4iI8NyeP5BXvhsDGxE2Jw4clLhO8Oye31NWW9alNj7zzDMUFBSQmpra5EYLwOGwk5qaSkF+Pn/4w3NNjrUnv7Nw4UL69u3LiBEjGtr27dvHlClTGDZsGMOHD+fhhx9uOLZ8+XKGDh3K4MGDueeeezp0XQHid8ASpVQJcD0eQdR6ZgPbgmKVxmcqXG4e/r9djBkYy6KLevfqwmDxzDPPkJ+f36zPsdvtpKSkkJ+fz7PPPtvCGdqmu/gcHedouhLlGAn2oWAd9sRpViWmu4D/2+fR5zh/0Alshh2b4SDciKLWqmJTySsA1NTU8fmmvTz+8Nv84r9e41d3/4t338ym9GRFMC+p3ZSVlbF7927KyvwTa4WiX+kkOs7p5mhh5dCiJ/scnxM8SqlUpdSTSqnNSqkDSqnR3vYlSqkpgTNR4zdsSWAbAGYRqAiUfVjb7+mhfHm4DLclTRI8+yr3c7AqH6cRgcOwo1DYDRsRtggqzSrWHfuwy+wrLS3lww8/JDGx9RW3iYlJfPjhh5SVlja0tec345ZbbuGdd95p0ma323nyySfZv38/mzdv5qWXXiI7Oxu3282SJUtYuXIleXl5LF++nOzs0JClEJHX8OxHfxSYLSIrGh0+RtNASBOCPLU2j+IKFw99czQ2LfzZ5dT7nKSkpFb7JSUl8cEHH1DayOe0h876nK1bt3ZoXF/QcY4mWCgVhor6Dji/BioMjHBKjAnsPTyIhPhS+kRVN+qrsKkwiqp3cKKkgqX3r2DZo+/y2cd7yNtVxLath3j5pfXcc+ff+SL7YPAuqh2sX7+ehQsX8oMf/ICFCxeyYcOGTp+zO8YyraHjnO6NFlYOLXq6z/EpwaOUygC2AzfiqS4xGAjzHh4C/CQg1mn8ilJhqOjvo6JvQ8XcibL13uWB9QLLEwafSfDknt4NgO0svR0FGCh2l+d1mX2ff/45pmk2mUVXnLs6x+Gw43ab/Oc//2loa0+p9Dlz5tC/f/8mbUOGDGHatGkAxMXFMXz4cPLz81m/fj1paWmkp6fjdDqZP38+b7zxRruvLVCIyEci8oSIbDir/ZcisjJYdmnaZvfRcv73k4N8a/Jgxg6KC7Y5vZLmfE5z2O12TNPk888/79A4nfU5y5cv79C4baHjHE2wUcqJ4ZyF0eenGDF3UqPGc/hYMuelHjm3r3hWHf/24bfY/mU+dXVuHA4bzogwwsMdiAhHj5Sx7NF3OXSwOAhX4ztlZWU88sgjREREMGDAACIiInj44Yc7PaveXWOZ1tBxTvdECyuHFr3B5/i6gucJIAcYCswDGt9BfgJM9bNdmgChVDjKPgxl9Cz9hPay5VApwxOjiYsMa2gLN8JpbYNTuK3rNEEqKyuRdojpVFV5+tsM/1bQ2r17Nzt37mTmzJkUFBSQkpLScGzQoEEUFhb6bSx/oZRKVEoNPvsRbLs0zSMi3P/WDvo47fz8Ci0aGiza63MqKysDYkcQfY6OczRBoc48wcnKNRw9/Qql1Vm4rdMA7C+KxbRsDEk51KS/CLipJaxyELm7inCE2QgPd2CzGSgFhqEIC7PjdIZRWlrJ63/9OBiX5TPHjh3D7XYTFRUFQFRUFHV1dRw7dsxvY3THWKY1dJzTvagXVr7/66O0sHIIUFxc3ON9jq+fsunAIhGpUErZzjp2DOi9S0E03Q7LErLzS7liVNOP7UV9J/HvwreoteoIMxwN7aZlYYlwSb+ui++joqKaTdTUt5x9GxYZGYXNMDD8uLXl1KlTzJs3j9/85jfEx8c3e/MXKnuIladm7MPA7UBLS0DO9l2aEODfWwv5/MBJHpt3IfFRYW2/QRMQWvI5rfX3N774HMMIWEVDHedoupyaugMU/X/2zjy8qvLa/5937zOfzAMZScIokxMzKKgolapUhAK1VGtta29nO91bLbej9bb1em1v+2urta1Wi9JrHKpiUURQVEQGRcYwhpCEBDLnzGfv9/fHSQIhAxlOkpPk/TxPHpI9vescTlbWXnut76r7E5IQmnDiCe6mzvcG2QlfYcvhGmwWSU5GKUEDdGFDShODAFbNwaF/jkJKD1Zr+6F8c6Jnx7ZjeL2Bfn5lXadZ0N3j8eB2u/F4PFit1gu2i3aVwRTLdIaKcwYnSlg59khPTx/yPqerkZLZyb40wNfJfoUipjh6ppFab6iV/g5Aoi2Ra9OvISxDeE0fQTOEz/Djkz7GxI9iRsr0frNx5syZ6LpOOBxud79o+gqHwlgsOrNnz2p6ehcdhxEIBLjppptYvnw5t99+OwB5eXmUlZ0V5j4/Iz3A3A18lchTeAHcTyQQOgYcAb44cKYpOqLOF+L+dfu5PC+JFdNHDrQ5w5oL+ZxmwuEwuq4zc+bMqK7fVZ+TlZUV1XXPQcU5in5FSpPKxrVomgO7JQernoLdkoMpw1R5XuTNotPMHpXOvMzbcVgSMAgihUmKvYAbc35M8W4Ti7Xz+3m73YLfH6LiVP8OiegOSUlJrF69Gp/PR3l5OT6fj9WrV5OU1Pt23UEYy3SGinMGIUpYOfYYDj6nqxU824DPAS+2s28FENv1n71AygDS+w8QCQjnJ/rsl1OGjyO9T4ElD8jpkzUUEZr1d6YVJLfZd8vIm8lwZrCh4nXqw/XYRBwzUqdzY+YiLFr/lVUmJydzzTXX8Prrr3fqBCorK1i4cCEpKSlRW9s0TW699VbGjx/Pj3/845bt8+fP59ixYxw4cICCggIKCwtZs2ZN1NbtJZ8Dfgr8mkjA85yUcqcQ4j7gVSJ6GooY46HXiqj2BHnsczOjWn2m6D7NPmfDhg3k5HT8N6iiIuJzkpPb+s+e0h2f8+STT0Zt3fMYtnGOYmAImVWEjCrslta/b1YtlWNVxzhyOotbZ+ZxeeosLk6+kfpQObqwk2DNiMSi4g1El+dm9lnlW1SYP38+//jHP6ioqCAjIyMqN1qDNJbpDBXnDDKahZW/fPUYJawcYwx1n9PVO9afARuEEK8Ca4h0iFwnhPgmcAswv4/sG3iMSgjtAGzg/Bjg7JNlZOBtMBsg+AHQ+eQkRe/YUVxDksvK6LS2LQaa0LgyfS5z02YTMANYhbVfEzvn8o1vfIOioiJOnDjRUsLcTDgcpqKigry8PL7+9a/3eI3FixezdetWampqyMjI4J577mHSpEk899xzjBs3jgkTJgDws5/9jOXLl/PQQw+xaNEiDMNg1apVTJs2rdevM0qMBrZLKQ0hRJimX1QpZUgI8Wsi0yV+PID2Kc5jb1kdf3v3OJ+Znc+UnOGtCRYrDAafM3Xq1F6/zg4YvnGOYkAQTU3XUso2Dw+3H08F4Krx6QBYNBsp9vxWx4zMS2HvRyexdyIPGAiEcDisZGQmUnwiisb3AUlJSVG5yWpmw4YNgzGW6QwV5wwilLBy7DOUfU6X7lyllJuFEEuIZI3/0rT5F8BxYImU8r2+MS8G0HPA+RkQboTom+QOANapEN4H+mi6nndT9ITtxTVMy0vutBpLExpOvQ//v7tAYmIiv/vd7/jtb3/Lxo0bMQyjZZ+u6yxcuJCvf/3rJCb2/Ob4/HHFzXQktrp8+XKWL1/e4/X6kDrA0fR9GXARZ5+4W4DolTgpeo1pSv7z+T0ku2x852NKWDlWSExM5De/+Q2///3vY9bnnGtTNBnWcY6iT5AyCFgQov3qGYuWit2STciowaqf/RMVNCr54MRMMhMcjB3R8VP/G2+Zyr49pU0TtNrGjaZpEgyEmTv/Ilyu4adv9rGPfWwwxjKdoeKcQUSzsPIfVk1VwsrDhFjyOV3+xEkpXwZeFkKMJVJiUiWlPNhnlsUIQmgIe99rr2i2iUjrT4mU0W7u8/WGK9WeIEdPe/jktNyBNqVLJCYmsnr1ar761a+ybdu2FkGwmTNnRrVFYgiwC5gErG/6+okQwgeEgZ8DOwfQNsV5PLPzJDtP1PLfyy8l0Wm98AmKfmM4+5zhGucooos0KpC+FyB8BIQL6ViAsF3RJtEjhCA9biXl9Y8QCJ9EYEUSAlLZftzFoilpnT6ImjFrLJMuzmXfRycxDInNZkHTBFJKQiGDUMggJT2eWz97RR+/YkU/oeKcQYISVlYMNN1OKUopDwOH+8CWYU/bwR2KaLPrRJP+Tt7gulFJTk7m+uuvH2gzYplfEylfBvgRMBX4e9PPxcDXBsIoRVtqvUF+8coBZhQks2yq0huLVYazz1FxjqKnSOlDev4EZhC0bCAIvheQWBH2tpM47ZZsRiZ9D09gDyHzDDY9m4Onsqn3b2d+U3tWR9hsFr71Hzfy2wf/xeGDp/D7g0hTghBYbTq5eSnc+aUF5Bd0fh3FoEHFOYMEJaysGGg6TPAIIRZ050JSyo29N0fRn5gyRMj0YNMSOiwhHmpsL67Bogkuye16z6WUkoaQH6tmwWlR1QaxiJTytXO+PyWEmAmMAVzAfillaMCMU7Tiv189SJ0vxE9vnqICH8WAouIcRdQJH4roKerNyWs7aGkQ2AznJHi84RqON26j0n8Iq+Ygzz2dHNciNKHz1qEihIArx6ZdcLn0EQn8xw9vZveuYt7cuJ+6Wi9Wm86M2WOYOXssIzKVvtlQQcU5gwMlrKyIBTqr4NkALfL8HUXhsmmfBFT5ySAiYNRwsOavBI06kh0TGZ2wfFhUEO0ormFyTiJOW9de6/qT+3jyyPtU+OvRheDylJF8ZcJ8cuMGVwXQcENGmmDVE/gYY/fJWv7+3gk+N3cUE7MSBtochULFOYroIgPtbLSCrGv5yReuY9uZvxOSAVx6IoYMsa/uFepCZUxJuoG3Dp3mktwkkrqom+N225lz5XjmXDkewzDRNKGS58MAFefEHkpYWRErXKhFqwEobPry9L05iv6ixr+PoFGD05JNjX8fPvdpXJah3ScaMkw+LKll1az8Cx8MPF/8Ib/bv5k4i410exymlOysOsG33i/k/81eyQhnfB9b3Bqv19uih+Fyufp17cGAECIH+A6RaTepwGIp5R4hxN3Au0okdWBpFlZOi7Nz98JxA22OogsME5+j4hxF9NALIv/KEIimil/zDNhmtBxywrODkAwQb4lU6OjCilU4KPPuIVmbygcltXztmp7dHOr68KjGHq6oOCe2UcLKilihs0/fNcDtwDJgOfAc8LgqUR4a2PVkJCZ+4zS6sGPVhn4Z4d6yegJhk2n5F66+CRoGfzv8HolWBy5L5CmaJgTpjngqfA08e3wX/zax76fmhsNh3nnnHdauXcuePXsQIiKgOGXKFFauXMncuXNbjTIergghJgNvAQbwLnA50Pz4Mx+YCXx6YKxTADz9fgkfnqzjN5+6jASHanWMVcLhMG+//fZw8TkqzlFEFaGnI52fAF/ztDgJejbCsbDlmDOBozjOi7ma2+TfKDqBKWHsSJ2GkI9468BO81TEDirOiW2UsLIilugwSpNSbgY2CyG+CiwFbgPWCyHKiYh6/U1Kub9/zFREmyT7REYnrMAbLiPFcemwSPDsKI4ILE8vuHCCp6i+Ek84wAhH2yodl9XGO6eP9XmCp6GhgdWrV7Nr1y4cDgdZWVlomoZpmhw6dIjVq1dz+eWXc9999xEf37/VRDHIg2S19uUAACAASURBVMB+4HrADwTP2fcO8MuBMEoRodoT5FfrDzB7dAqfuDR7oM1RdMBw8zkqzlH0BZr9CqR1EhilIFyg57dqgbfr8TSGKrG2TLyOcCZg8JedxegWB2vPrOOVeicLMqdwy8iZ6EKjpqIWX6Mfm8NGanYymqaqdYYZKs6JYZSwsiKWuOBjOCmlH1gDrBFCZBHJDt8O/LsQ4g9SSqXaPggRQpDqvJRULh1oU/qNncU15CQ5yUhwXPBYXQikjAgst3HUUqL1sSh1OBxuudHKyclpZYOmaaSlpSGlZNeuXaxevZoHH3xwKD1V7wlXArdKKRtFWzGpCkA9ThlAfvWvAzT6w0pYOYYZzj5HxTmKaCClJGScxpR+bJYsNOuUdo/Ld09jZ/Uz2DQ3WtOfq9P+Gp4vCXCyzEpymg+7xUp9yMfTxe9wqvQ0SRsClBwsxQyboAlGjExj7s0zmDz3IuVThw8qzolRlLCyItbo7l1qFXC86UsCSmlWMSiQUrK9uLpL7VkA4xNGkGR30hgOttoupaQxHOSqjDF9YWYL77zzDjt37mxzo3UuQghycnLYuXMn7777bo/WWbFiBSkpKYwb11oTJScnh/HjxzNhwgSmTDkbpBYWFjJq1Cjy8vK49957e7RmH2F2si8N8PWXIYrW7DxRw9Pvl3DnlaMYnzH4qz6GKsrntKDiHEW3MUwP5fV/oqT2V5TW/Y4T1ffhDbavf5tmH8O4+Pl4wlU0hCqpD1XyYW2QuvpEgn4rIzKDWDWdOKsDzWvy3NFtvLdpF8V7T3LyUDklB8r46K39rPl5IW+sfZuI1u7g5vTp0zz88MN885vf5OGHH+b06dO9vqbX6+WSSy7hoosuYuzYsXzrW99q2RfDsUxnqDgnBlHCyoOToe5zupTgEUJcIYT4I1AOPA40AjcSKWdWKGKe0lofFfWBLrVnAeiaxr9ddCVeI0h1wEtYmviNMBWBRjKdCdySf3mf2rt27VqcTucFn8wJIXA6naxdu7ZH69x55528+OKL7e7bvHkzBw4cYM+ePUDkCf/dd9/NunXrKCoqorCwkJ07d/Zo3T5gG/C5DvatAN7uR1sUTRim5Icv7CEzwcE3rlXCyrHMYPE5u3bt6tG6F0LFOYreUOV5GV+oCJueg92SjRBWKhr+imF62xwrhGB0/FzmZ3yZy1KWMiP1U9QGM/FXR+KTtBGRSVzhYJi60lqCMkxdiok70YU7wYU7wQlScvpkFS8//BrH95b062uNNqdPn+auu+5izZo1HDp0iDVr1nDXXXf1+obL4XDw1ltvcfDgQfbu3cvrr7/Oxo0bYz2W6QwV58QgzcLK/3nTJCWsPEgYDj6nwwSPEGKsEOInQogjwJvARcB3gUwp5Sop5XopZWfZZIUiZmjW35ma1/WHsQuyJ3Df1MWMik+lNugjKMMsyp7Ir2d9kiR73wkfer1e9uzZQ0pKSpeOT0lJ4aOPPsLrbRtIXohFixaRlpbWpWM3b95MQUEBEydOxOFwsGzZMp555plur9lH/AxYLIR4lcgNmQSuE0I8DtwC/HwgjRuurHmvmD2l9ay+aSJxdhX4xCqDyecUFhZ2e82OUHGOIhpIadAY2I5Nz2pJkOpaHKYM4g8fa3VsdbCaD2p2s6vmA+pCPtLso0m2j0QXOnWnXbjiQjjdBgD11Q34vSGEpmG3WVuuLYTA5rARl+SmqryWDU++2b8vOMo8++yzVFdXk5OTQ1JSEjk5OVRXV/Pss8/26rqappGYmAhAMBgkHA4jhIj1WKYzVJwTYyhh5cHJc889N+R9TmcRdxFQDzwLfAEobto+Qggx4vyDpZRHo29ebOELV1LSsI505wySHZP7bB1D+imq+RsZrjkk2tVT72iwo7gGl01nQmb3WkRmphcwM70AU0oE9Euvu8fjQQjRZQFFTdMQQuDxeKI6yvjaa69FCMGdd97Jd77zHUpKSsjOPiuQO3LkSLZu3Rq19XqDlHKzEGIJ8GvgL02bf0GkzWKJGh3a/5xpDPDA+oNcOTaNGy/OGmhzFJ0wmHxOT1vDOkDFOYoooRO53z6fszHDh7W72Vb9PqJp2/vVO5iSOJk5qbOYkTKWf1RVkpt/NmlaX9UIDg1LENyVba+saRquODsfvrEXI2ygW86XZRkc7Nu3D7fb3Wqb2+1m3759vb52OBxmypQpnDhxgjvuuINrrrmGxx57LGZjmc5QcU7soYSVBycHDhwY8j7nQo9UE4A7gM924VqD8y9LN6gLHKTStw0To88SPFJKAkYNjaETeOtLuSz9nj5ZZ7ixo7iGy0YmYdF7Jo6s9aPjdrvdSCkxTbNLN1ymaSKlbOOsesPbb79NQUEBpaWlLFiwgMmTJ7fb5x9Lf9CklC8DLwshxgIjgCop5cEBNmvY8otXDuALGfzkZhX4xDqDyef0weQgFecoeoUQOgmO2dT538Sm5yCERtioQRdunNaIXl91sJpt1e+TbE1Gb9LHNaXJnro95LvzSPTnIs0z2FNq8IZCaJqGT4TAAVnvCSzh9j/3DreDqvIagv4gzrjBOVJ90qRJfPDBByQlJbVs83g8TJo0qdfXtlgsHDhwgDNnznDjjTeyffv2mI9lOkPFObGDElYevEyYMGHI+5zOEjwd9XkOW1KdU5FIEu3j+2wNIQS6sGNIP4n2i/tsneGEJxBmf3k9X7umrfjZSU8t71QepdxbR647ibkjRpPlShwAK8/icrmYMmUKhw4d6lIrQ3V1NRdffHFUn6QXFBQAEeHTm266iXfffZf58+dTVlbWcsz5T9djBSnlYaB9dUtFv7D9eDXP7DjJV1TgMygYTD4nKyuq1WAqzlEgzUak7xkIF4GWgnCuQFjyunWNFPciTOmjIbADAJuezoj4T6MJOwDFnhMIREtyB0ATGlbNxpGGI2w/moZFE3zsopEc8p4kLA2c9RpJ2w0ySzrOKxphA6vNgtbDh1exwNKlS/nXv/5FaWkpbrcbj8dDSkoKS5cujdoaaWlpzJs3jxdffHHQxDKdoeKcgUUJKw9ubrnlFl599dUh7XM6TPBIKR/v05UHIVbNTZZ7fp+vY9dTmJL6TRx61/QQFJ3zQUktpoSp503QOlhXweOHtmLRNNwWOx9Un2RXVQl3jp/L6Pj2b3LCpklxbQ0BwyDDHUdqFG9wzmXlypX84Ac/aH9M+zlIKfH5fKxcuTJqa9fX12OaJklJSdTX17Nx40ZWr17N/PnzOXbsGAcOHKCgoIDCwkLWrFkTtXW7ixBiQXeOl1Ju7CtbFGcJGyarn99DTpKTr6nAZ9AwWHzOk08+GbV1VZyjAJDepyB8FLQRYHqQnkch/jsIresPezRhZ0T8p0hx34iUQSxaMkKcTbqY7bZvgUBgYLC56DQzClL490tmUxv0EDBC/N//FbJj+4eQYe9wXU+dl5xxmdgctq6/4BgjPT2dRx55hGeffZZ9+/YxadIkli5dSnp6eq+uW1ZWhs1mIy0tDY/Hw6ZNm/jud78bc7FMZ6g4JzZpFlb+w6qpSlh5EDIcfI76VMYoTkvvPmSKs+workEIuPwcgWVTSl44sZt4q4M4ayR4clls1Ad9/PPEbr456Zo2NzlHa6p5cvcHNAaDIASmaTIjO4clEyZht0T3V2nu3LlMnTqVXbt2dTi2WEpJaWkpU6dOZc6cOT1aZ/HixWzdupWamhoyMjK45557uP7661myZAkAhmHwyU9+kmXLlgHw0EMPsWjRIgzDYNWqVUybNq3nL7L3bOCs6EFHd6SyaZ9EtVf0C397t5gDpxr442emqcBnEDFYfM7UqVN7/iIHGCHEWiJCzgBJQK2U8rJ2jjsONAAGEJZSTm/angKsBQqI6G6skFLW9LnhQxgpgxA+AloWCAEiAYwyME5BNxI8zVi09nX+cp057KjegSlNtKbEj5SSoBkg0czlwKmD/MeiCQAk2SKtjwtvvYp9bx6ksdZDXJK7RbunGV+jDyEE19121aBpMeqI9PR0vvSlL0X1miUlJdxxxx0YhoGUkiVLlvCpT30KiLlYpjNUnBNjKGHlocFQ9zkq+lYAUBusw2t4GGEfgUUbWh+LHcU1jB8RT6LT2rKtNuilJugl29k6gIu3Oij31dMYDhBvdbRsP+P18ujO7TitVrLjE4BIkmhb2Umsus7SidHVZLJYLNx3332sXr2anTt34nQ6SUlJQdM0TNOkuroan8/H1KlTue+++7D0MMHU0bjigwfbb+levnw5y5cv79FafUQDUNj05RlgW4Y9lfV+HnqtiKvGp3P95IyBNkfRDQaLzzEMo0frxgJSypayJyHEg0BdJ4dfI6U8c9627wOvSyl/IYT4ftPP/xF9S4cTFhB2wA84QZqACaJrejYh00+5bx+1wTISrCPIck7GrrfVphphT2dS4iT21u3DrtkQCPxmgNHuURwpjVTfzBvXunJ4zGUFzLxxGtvX76Kx1oOmaegWHdMwMQ0T3apz8fyJTL3ukl6+B0OTWbNmsX///nb3xWAs0xkqzokhlLCyoiNiyecMrTt5RY8o951iXfm/MDHJcmRwQ9bHW54wDXZMU7LzRA03XdK619Gq6SAjSZpzBZRNJEI07T+H7WUnMaQk3na2VFoTguy4BLaeLGHh6LHE2zsuo+4J8fHxPPjgg7z77rusXbuWjz76CCEEUkouvvhiVq5cyZw5c3p8ozUEuAa4HVgGLAeeAx5XJcoDx/3r9hMIm/xEBT6Dkvj4eH71q1+xbds25XP6EBH55VgBdKv9ArgZuLrp+8eBTagET68QQkM6l4H370SKIAywzQR95AXPDZl+3q9aQ0PoDDbh4JR/Hyc8O5mV9hnsemvtMSEEc1NnU+DK43DjEUxpMjpuNCNduXx7027S4mxMykpodY7FamHFdz+Bw2WjaPtRGmoaW4TQ3Qkuci/KZtndN+JO6JtWcUVMoOKcGEIJKysGCypKU3DMcwyBINWaQrn/FJ6wh3hr98aJxyqHKhtp8IeZdp7+TrzVwYSkTA7XVTLCefa1nvY3cGlyLg7d2ur447W1uK1te9z1pnHBVT5v1BM8EHmqPm/ePObNm4fX68Xj8eB2u6MqbjpYkVJuBjYLIb4KLAVuA9YLIcqBvwN/k1K2n0pXRJ2tR6t4/oMyvrFgLAVp0ZuupOhflM/pF+YBFVLKQx3sl8CrQggJPCylfKRpe4aUshxASlne3ij3ZoQQdwF3AWRkZLBp06aoGd8dGhsbO19b+oEwCAcDG5IuBBkCtKaKns0tezp6DSHTh89wo4tEwk3bAjLMJn0Tdi3unOMMfEYQQ5o4dCsO3YaG4DjHOCqPsnGvlynpOm++ubnNGgCZs5JJvewS/I0BwmEDTddwxtmxOWx8uP8D6OJfuQv+X0SR5OTkPqm2k1LGTBWflLLP308V58QOSlhZMZiImQRPV3rThRAXEek/b2Y08EMp5a+FED8Gvgicbtp3r5RyXd9aPTTIdmaxp24fZ0JVJFuTceqDc9Rme+wojsgTTD8vwQOwJO8SHju0lTJvHbKphXmkO4kbR05pc2yyw8GJujoSzkviSCkxpWw3+RNtXC6XuslqBymlH1gDrBFCZAGfJvLE69+FEH+QUn5tQA0cBoQMkx++sIfcZCdfaWdanWJwonxO9xFCbADaE2b4gZTyhabvbwWe6uQyV0gpy5oSOK8JIQ5IKd/sjh1NSaFHAKZPny6vvvrq7pweNTZt2kRHa5uBreB7iUhSxYmIvxuhtf1bPdB09Bp217zEGf8pXJazo3YDRiMui8mMtJsAeO/MITaXfYBVs6ALDZ/hJdeVwmdGzcOh2/joZB0N67ewYt4Urr48d0BeR1+we/dudD36cjCGYfTJdXuCEKLf3k8V5ww8SlhZMZiImU9oV3rTpZQHgcuajtGBUiLlis08JKX87z42dchR4C7gE9k30hhuJMeVM6Q0eLYXV5PqtpGf2vYmJdHm5GuTruJYQxW1QS8pdjcFcamtWraamZGTy/tlpYRNE4t2tn3tjM/LqKRk0tRNUKxQRUR89DgwGYi9u4UhyGNvH6eoopFHb5+OwxobwbdCMRBIKa/rbL8QwkLkSXyHCotSyrKmfyuFEM8BM4E3gQohRFZT9U4WUBk9yweA8P6IqLGWBEZ55KufEzxSRvRre9JSmmDN4JRvX6ttQekn05YFQH3Ix6vlu0mzJ7S0fSfj5qS3mp3Vx7g8aTSv7S8H4MqxarCGosuoOKefUcLKisFGzN3Jd6M3/VrgiJSyuO+tGvpkOoeWw2oMBSj11rLteBVT85M7DN50oTE24cKB1aikZD42ZiyvHjmMrmlYNQ1/OEyy08mKyRf3qd6I3+/nrbfeYuvWrTQ0NBAfH8/s2bOZN28eDofjwhcYBgghriBSurwcsAMvADcCrw2kXcOBU3V+fr2hiOsmjuC6SUpYeSigfE6fch1wQEp5sr2dQgg3oEkpG5q+/xjw06bd/wQ+C/yi6d8X2rvGoMEyHkJ7wfCCsIHef3GIlJJ6/7tUe9chZZgExxxS3TcSyb91jWznZEo8O6gPVWLXXASlD6uwk+e6HIASzxkkso2mn9W08tt3t9LQsI0jR+KId1s5VldJWlyu0i5TdIiKcwYOJaysGGzEXIKHC/emN/Mp2pY4f00IcTuwHfhOR+NDY6U3vSP6s0+6qwwmm7zhILVBHw1BOFlt4ZIkHxvfeKPdypzuYAOut9jxhUIY4TB23YLTkOzZtq1Ldp1LV/rTDcNgzZo1rF27Fq/Xi9VqRdd1DMNgw4YNuFwuVq5cyac//ekLliz3d996f/SmCyHGEgl2PkNkbPCbwHeB/5NSNvbp4ooW7nt5H2FT8sObojtJTtH/GIbBE0880crnWCwWwuFwi8+59dZbWbVqVcy0SQxC2sQuQohs4FEp5Q1ABvBc002EBVgjpfxX06G/AP4hhPg8cILIjd6gRdjmIkU8mGcQ1okILaXf1vYGD3La8ww2PQOBTq1/E7pwk+zutACrFTbdxcy0z3DS+yE1gRIybZnkuS7HaYlM54wIlLc+xx8M8+LOk9R6wziEFZ83GXtCHV9b9xI/W3Ad148dF82XqRjkqDhn4FHCyorBSL8meKLUm44QwgZ8ArjnnM1/AH5GRJzwZ8CDwJ3tnR8rvekd0Z990l1lsNhUHfDwwEcbSLO7KS4FCBDMM2gYncLN+ZcOmF3nc6H+dMMw+OUvf8n69evJyMggObltBW4gEODRRx+lpKSEe++994LX688bsn7qTS8C6oFngS8AzdV8I9oTH5VSHu1rg4Ybbx8+w0u7y/n2wvHktdMGqRg8GIbB/ffff0Gf88gjj1BcXHxBn6NoHynlHe1sKwNuaPr+KNDuHyspZRWR6uUhgRACYRuYEd/+8BE0bGgioqtn1VLxhPaSTNcSPIY0qA3WYdOsjIm/AtqZS1HgTseiafiNUMvghu3Hz1DtCRFvt4E/HhDEx4cImia/fPstrh09plUbuGLYo+KcAUQJKysGK/2a4IlGb3oTHwd2Sikrzrl2y/dCiD8BL/XC1AtSFahmZ80uLkmaQoaj79oSdtfuoaihiBkp08l35/XZOkOFYw1VmFJi0y2UnQ6iazA+y83O6pP9luCJBn//+99Zv349ubm5aB0Ee3a7ndzcXNavX09+fj633357P1sZEyQAdxBpV7gQ6m40igTCBv/5wh7yU13cNX/0QJuj6CXK5yiGExYtAZNgy8+G9GHXLjwaHeCUv4LXKzbiNXwgId+dx1Xp87DrrYcwuCx2bs6dzvMl71MbkmgIPiqtwqppOHUbNR4HQpjYnSHsWDnj8bCn8hSXZWZH9bUOFnw+H1VVVaSmpuJ0Dp1hH1FAxTkDhBJWHtoMZZ8Ta48JOu1NP4c2VT5NgoPN3ALsibJtrShqLGJX7Qfsru27ZYJmiK1V7+EJe9ha9V6frTOUsGk6zZ1YZZUGI1I1EBKHfmHH7AuFWLv3I7788j/5xisv8XLRAUIDMI7T7/fz1FNPkZGR0eGNVjOappGRkcFTTz2F3+/v9lorVqwgJSWFceNal4X/9Kc/ZezYsYwbN47Fixfj9XoBKCwsZNSoUeTl5XHvvfd2e70o87lzvu7swpciivx5yzGOnvbwk09MVsLKgxzlcxTDjTj7dByWPALhEgLhk2jCSorr4xc8L2AEWF/+KiBIs6WSakvhuKeY96t3tBwTMg02lh7mJ9tf4/H9u0kSI5iaNIYr0i/CoTlwWqwgIOC1Y3cFERqRuEUIqr3d/50a7BiGwcMPP8zNN9/Mbbfdxs0338zDDz8ctbbycDjMxIkTueaaa1q2DSK/EtU4RwjxFyFEpRBizznbUoQQrwkhDjX9m3zOvnuEEIeFEAeFENdH9ZXFOHUByYOvKmHlochw8DmxluBptzddCLHunJ9dwEIi5Yrn8ishxEdCiN3ANcC3+tLQyQmTuDLtCqYmX95na1iFhXx3HgEzwJg49YS8K4xJSCfOYue010NFlUlmmuBMoJF5GZ2XVnqDQb7xr5f4/fvvcbSmmgNnTvOrt7fwwzc2YJhmP1kf4a233sLr9WI/byR7R9jtdrxeL2+99Va317rzzjt58cUXW207duwYDz/8MB9++CGHDh3CMAz+/Oc/Ew6Hufvuu1m3bh1FRUUUFhayc+fObq8ZLaSUj3fnqyvXVMFP16jymfz29cNcPzmDqy9qUyWuGGQMJp+za9eubq+piC0a/Ds5UfMA5fV/JWzWD4gNuuYkO/HLZCZ8nsz4z5Kb9G1slgv7slP+U4RkCJceedorhCDFlszBxiJMGYkVnjmymxeL92HXdLJcCZR7PLxddoqJCflclJZG0DAIBzXCIQsOVwCAkGGgC8HFGcPPnz766KM88cQTJCQkkJWVRUJCAk888QSPPvpoVK5/3333tUoox1os0xl9EOc8Biw6b9v3gdellOOA15t+Rggxich92eSmc37fNMF4WPCPg0H8YSWsPBT5y1/+MuR9TkwleKSUd0gp/3jetrIm4cHmn71SylQpZd15x90mpbxYSnmJlPITUsryvrQ1wZrA3LTZpNr7ThRQCMHCjGu5rWAV05Kn9tk6QwmXxcbnx8/F8LgwTEhMCXN9zkTmjBjV6XnrjhRRVHWGTLebZIeTFKeLjDg3754sYVtpxwVlHl+Q9R8cZN3O/dR6fFF5DVu3bsVqtXbrHKvVynvvdb/Ka9GiRaSlpbXZbhgGHo+HUCiEz+cjNzeXzZs3U1BQwMSJE3E4HCxbtoxnnnmm22vGOI+hgp8LsuZAEInkh4uVsPJQYDD5nMLCwm6vqYgdQsZpKhufQsoA3uA+zjQ+P2C2aMKO2zYJt/1iLFpCl8+T7Wxrvv2r8Daw4/RJ8txJOCxWNCFId7pBSjaVHeGuqTOw6Dr19ZHfN5vLTyAUxhsOMy8vn3T38BJx9fl8FBYWkpmZic1mA8Bms5GZmUlhYSE+X+/iqqNHj7J+/Xq++MUvtmwbJrFMu0gp3wSqz9t8M9CcHHocWHLO9qellAEp5THgMDCzXwwdYLYfr+btsjBfmDdaCSsPMXw+H88+++yQ9zmqoTDG0YSGUx9afYFdJWyaNIYDuHQrti60WDWT5UokX+QBB7h//gKyEy8s/rr5+HEcFitCnM15akJDE4J3Sk4wZ2Rb/aM/vfYeT7y5i2AoDAJ0TWPx9InMTu5dpr+hoQGLpXu/mrqu09DQ0Kt1mxk1ahRf+9rXKCgowG63M3/+fG655RYee+wxsrPPagOMHDmSrVu3RmXNWEFK+aYQouC8zTcDVzd9/ziwCfgPzgl+gGNCiObg593+sHWg2HSwkh0VBt+7/iJykoanbxpqDCaf8+67Q/rXa8hjmF5Aogk3umYSNtsddtqneMO1BMxGnHoCDr3riR2ATEcmNmHFE/bitriQUlIdrGFK4iQ0oXHK14AQWpsn/kl2J4frzvCpaZdx96zZ3P/iUTQ9hF96saEzIzuHny3o+gSvoUJVVRWhUKjlRqsZm81GKBSiqqqK3NzcHl//K1/5Cg888AD19WcrxUpKSoZ8LNNNMpofikspy88Rb84Bzn1jTjZta0NPphPH4nReAMOU/PhdP8l2yWWWcjZtOjXQJrVLrL5/zfS3fV2ZTgxQWVnZqc+prKzstc/55S9/2eJzDMPgxIkTZGdnt9iXk5PDe++916G9HU0+7s6EYpXgUbTBb4SoDfhIdbixagNTkHDKW8/jh7dSG/Rh1XRWjJrKlOSuCw/uKK4hL8XVpeQOgE3XMM+fZwpIJNZ2JsX8c9s+Hn39fRw2CwkuBwBBI8yzW/cw5oretdPFx8cTDoe7dY5hGMTHtzPGowecPn2al156icOHD5OamspNN93EH/7wBxwOR5tjh0nZqgp+mggaktVv+8hwSi6SJWzadCG5tIEhFt+7ZgbKts6Cn576nLi4uB71rMsmX9t8brPPKSoqIjU1lcWLF/P73/++pWWs+TjTNBFC9DrwUQwcdksOTutF+EIHEegkuz/Zr+uf9O5mf92rAAgEFyctJsM5vsvn23U7i7KuZ2PlG1QFq5DAmLjRTE+JzAaJs9ppr8bHFw4xwhmpBLj14sv4xbOVzBjt5IZp+VySkcGlmVlow+PvaStSU1OxWq0Eg8FWN1zBYBCr1UpqamqPr/3000+Tnp7OlVdeybp1LUoPLf7nXIZJLNNd2ntT2itg69F04liczgvw2NvHKGnYx1cvc3D9dddc+IQBIlbfv2b6274LTSduZsSIEZ36nBEjRvR4Qmizz5k/f36Lzzn3Ws3fa5qGpmkdrtPR5OPuTChWCR5FK+qCPv54YAt1QR/ZrgS+eNGV2LtRPRMNTCl54sg2AkaYbFcifiPE00e3850p15Fsv3DCRkrJjuJa5o1r2wbQEYvGjGd7WRmGaaI3iYw2CywvGNU2YfOXN7Zj0QT2c55623QLplVS4/H1aiz57Nmz2bBhONk26AAAIABJREFUQ7fOCYVCzJo1q0frnc9LL71Efn5+yxOuJUuW8M4773DHHXdQVlbWctz5T8GGIcMu+Pnf1w9R6S3ie9OdXLdABT49YaBs6yz46anPmT17do/8XPPNVPO5r7zyCvn5+S1PzW655RbefffdFp/TfFxpaSnZ2dm9DnwUA4cQFrISPkfQqEAXbix6Ur+t7Tca2F/3Kk4tEYtmI2QG2FP7Mqn2fCxa1/SnADIcI1g5cjl1oTpsmg23xd2yryAumXRnHKd9jaQ53AghCJkGdUE/y8dERsLvOlGLN2jwuVnjWTQlq6NlhgVOp5Nly5bxxBNPtLRMBINBTp06xW233daryTZbtmzh1VdfJScnh0AgQGNjI0uWLOHrX/+6imVaUyGEyGp6gJUFVDZtPwmcO1ouFyhrc/YQ4nRDoEVYeXqGd6DNUfQBTqeTpUuX8uSTTw5pnxNTGjyKgedIwxlqgh6yXAmc9NZx0tO2fPqUt55nju3kzfJDGDL6AsQ+I0R1wNOSzHHoViRQFfB06fySah9nGgNMy0++8MFNLBg1mqvzR3Ha56HC00iFp5Eav48Vk6YwOb2t6GFFXQMOW1vNCrvNgmlKKusau7z2+cybNw+Xy0UgEOjS8YFAAJfLxbx583q85rkUFBSwY8cOGhoaME2TjRs3MmHCBObPn8+xY8c4cOAAfr+fwsJCli1bFpU1Y5yK5il9wzn4Kan28v/eOMyNl2QxOW1YSA0NGwaTz1m6dGlU1lQMHEJYsFty+jW5AxA0IjGERYs8tbVqdkwMgmb3b+Q0oZFsS26V3IFIq/adE2aQ7oyjzFNPmaeeKr+XJaMmMyk5A4A3i06ja4K5Y7v+EGoo84UvfIHbbruN+vp6ysvLqa+v57bbbuMLX/hCr677u9/9joqKCkpLS/nb3/7GnDlzeP7554dzLNMR/+TsCPbPAi+cs/1TQgi7EGIUMA7YNgD29Ru/eOWAElYeBtx5551D3ueoCh5FK9LsbkBQ5q3Dpumk2FsHLzUBL3e/9wynA41oUvKZsbP47LjZUbXBoVtwWWw0hgLEWe2ETAMTSYK1bYtQe2wvjujHdSfBo2saP7p6ATvLy3jrxHGsms61o0YzIS29XSdvtegYhkQ/L0VqmpECDrez608Dz8fhcHDrrbfyyCOPkJub2+nYYtM0qaio4K677mq3hepCLF68mK1bt1JTU0NGRgb33HMPd999N4sXL+aSSy7BYrEwZcoUvv3tb2O1WnnooYdYtGgRhmGwatUqpk2b1uPXOYhoDn5+QdvgZ40Q4n+AbIZ48POTF/eia4LVN07k4K7ui+sqYpfB5HOmTlUDBxQ9w2FJRBcWgoYXm+7CbzRg05zY9a6JqIZNg1P+WgSCTGcSumj/9yTN4eabF19JubcBvxEi0xWPy3K2FeDNQ6e5fGQSCY7uCZsPVXRd50tf+hK33347VVVVpKam9uop+oUYxrEMQoiniGgKpgkhTgI/IhLb/EMI8XngBLAcQEq5VwjxD2AfEAa+KqWMzhzpGGT78WoKd57ky1ePYUx6HCUDbZCizxgOPkcleBStyItL4Yvj53LCU8P4xBFtWqL21JRxwlONINJK9crJfVFP8OhCY9XoGfztyHs0+gKYSBbnTmGEs2saMzuKa4i3WxifEY+UknWHivjHvo9oDAaZnTuSz182jRRX21YvTQimZ+cwPbtdGZVWzBmbx6Z9R7BaHK0SQI2BYESXx9n9G59zWbVqFcXFxaxfv56MjIx2xxcHAgEqKiq4/vrrWbVqVY/WOX9ccTMPPfQQDz30UJvty5cvZ/ny5T1aazCggp/22bCvgg37K7n3hglkJTo5ONAGKaLOYPE5PdH8UQw83nAtB+o2EJRexsXNJ9VR0O822DQnlyUvZXfNCzSEKrHpbi5PXoouLpxoOeWr5enit2kI+ZFIUmxx3FpwBan21nFJ2DQxTBO7xUK2u62Ac7UnyEelddx9bdd1f4YLTqezV+KmnXHDDTdwww0tA3mHfCzTEVLKWzvYdW0Hx/8c+HnfWRQbhA2T/3xhL9mJDr6+YOxAm6PoJ4ayz1EJHkUbxiSkMyYhvd19Ns1C0DSwIAhKA9m+5EivGZ2QxnenXEd1wEOc1d6mkqgzdhTXcFleEromeOqjD/njjm24rDasmsbLhw6yt7KCPy2+pV3x5K7yvZvns+fkKc7Ue7HoGkJAMGwS77SRldx7sWNd17n33nvJz8/nqaee4syZM1itVnRdxzAMQqEQLpeLL33pS3z605/usd6PojUq+GmLP2Twk5f2Mm5EHJ+7YtRAm6PoI5p9zsiRI1m7dq3yOYqosqf2ZepDp7AIOx/UPMu8jC9j0/p/Cl+KPY/5GV8hZPqwak400bXP8dridwiZBhmORACqg408W7KNL4xZgBACTzDI+iOHeb/0JGHTZExKCovHTyAnoXWSZ8vhM0gJ88er9iyFIlZ4cmsx+8vr+cOqqbhs6tZYMfhRn2JFt8iNS+Li5CxO+xrRhODqzL57ChVntTdNpOg6Df4QBysaWDQlE9M0eWrPbhLtDlzWSHm002LleF0tW0+WMC+/oMe2pSXG8fdv3MrfNm/njb3HME2TWePyuHPBDA58uKPH1z0XXde5/fbbWbFiBW+99RbvvfceDQ0NxMfHM2vWLObNm9ejFgmFojv8ftMRSqp9PPXF2VjP70lUDCl0Xee2227jU5/6lPI5iqjiNWpxaPHowkZDuJKw6e/XBE9tsJYzwSpsmo1cZ06X27IAQtKgPuRrSe4AJFvdlHtrqAt5ibc4+euunRTX1ZER50YXgtL6en7//nt8a84VpJ1TMfxm0WmSXFYuye1f/SGFQtE+5worL5qSOdDmKBRRQSV4ooApJcWN1aQ74rqdkOguQSOMVdMHTPwr25nIylHT2VxxiGSbi6UFlw2IHR2x60QtUkb0d4KmSX0wQIbrbPWPEAKB4FRjQ6/XSopz8o0b5/GNG1sLjR7o9ZVb43A4WLhwIQsXLozylRWKzjl+xsMfNx/h5suymTOm5+NqFYML5XMU0WZM3BUt48kznRNx9qPAconnJK9WvIaUJiYwyl3AtRnXoHWgoXM+gsh0Till69hLRMSWj9XWUFxXS3Z8fMv+VJeLUw2NbCk+zrX5o3HYrFh0jbcOneaKsWnomhJwVShiASWsrBiKqARPFNhXW86fi95hbEI6X514VZ+t83rZQV4r28/U1DyWF1w+II5ICMGi3EkszJmAhog5Z7ijuAZNwGUjk3BYLIxMSOR0o4ekJvEswzQByaR2JmPFMoFAgNLSUnw+H06nk5ycnHY1MhSKaCGl5Ef/3ItN1/jBDRMH2hxFP6N8jiKajHRfRoo9D8MMEm8d0a+xw1tVW3DqLpy6AyklxzzHKPNdRK6ra9oLFqGT5Uqm0l9Hmi0eCVQG6hgXn0WC1ck+b6Tt6tzXZJomx/ZXsuVfRTxovoHNojNxTB4V9ZL541R7lkIRC5wvrKxQDBVUgicKJNqcxFntZDrbCupFk21njuPSbeysOsGS/EuxdbF3vC/oaHrEQLPzRA0XZSYQ3zSd4hsz5/CDja9R0diIEJFqq2tHjWHyiIwLXisYDrNl/3E+OlFOksvJtZeMIzc18YLnAYTDBrqu9TqILSsr48UXX+SFF17A5/MhhEBKidPpZMmSJdx0001kZ2f3ag2Foj3W761gc9Fp/vOmSYxIUG05w4Xy8nJefvll5XMUUcdtSen3NaWUeMM+kq2RiiEhBJrQCJjBbl1nRd4cXji5neOeSgAuSsjmppzIFJQEu53z/9S/+eZhTp6sxWbVSXTaCYUN3jx0BqypzBmlqiEVioFGCSsrhjIqwRMFRrqT+dFlN6L38ROpRTmTWF+6n3mZY7Fpw0vgUkpJXcCPYUqSnU60dt5rw5TsOlHLksvP3nzMyMnlT4tvYd2hg9T5/cwdmc8VeXkXXK/e5+eev/+LYxVVCKEhkRS+t4e7b7ySBRe3/4fA6wvyfy/vQPqq+MPX/0JuVhKrlsxk6iX5PXrNr7zyCg888ACmaZKSkkJS0tmSdr/fz5o1a3j66af53ve+x8c//vEeraFQtIc3GOanL+5lQmY8n53Ts8+vYvChfI4i2hjSYH/9QfKdI3FZ206v7GuEEIxyF3Ck8SipthT8ZgCBRpq9e0mWRJuL20fPpzHkRwiB23K2mm1sSirJTidnPF5SXU5qaryUltahWwTxDjtCCGxWC2HNjcUMsuvgMfLSpkT7pSoUim6ghJUVQxn1iY4SFq3vK1ouTx3J5akj+3ydWMMwTQr37+X9spOAYHxKKrdd2lb75+CpBhoDYabnt35KmJ+UxJdnzOrWmn9/cxdHT1WRkRRPcyrJEwjx23XvMGPsSOKdrVsVAsEQ//7zQoqOVrJ8YRYNHj8fHSznBw/8k69+9mpuuu7ibq3/yiuvcP/995Oent6uqKnD4SA7Oxu/38/9998PoG64FFHjdxsPU1bn5ze3Xo5FCSsPC5p9TlpaGk5nW/Fb5XMU3WVP3V5KfWWsLSokwZLAvRP/gxR7cr/bcWXaXARw3FuMS3fx8ayPkWi9cDWulJKiujNUB3w8degDpo/IZWxCapvKXJuu88Wp03l6z0ecqKvl6KHTACQ4HOhNsaEpwYcdt1nPxg8Oc/MVKsGjUAwUSlhZMdRRCR5FzLO9rJStJ0vITUhEAAerzvDa0SOcP4x8x4kaICKw3Fve3H+MRJeDc8M4t91KhT/AtsMlXHteFc9LGz7i4JFK4uLsaJqG0xGZ2uXzB3n06S0snDcBu93apbXLysp44IEHOkzunIvD4SA9PZ0HHniASy+9VLVOKHrN4cpG/vTWUZZNzWVGQf+3VCj6H+VzFH3BcyUvkEcOpmlSHazmjco3WDZyab/bYdftLMi4ptvnbS47yj+P72N6WLK3upz3T5/g5oIpXJU9us2x6W43X5s5izNeL481bqfiWC1W/WyltRc7EoHD9AH9X8kU63i9XrZs2cKpU6fIzMzkyiuvxOVS75Oib1DCyoqh7nPUo1lFpzQGg2w+fgxvKETYNAfEhtKGepwWK5qIiDon2h2U1NW1OW7H8WrS4+3kJvd+9GrYMNt1+gIIG0ab7a++uR9NF21axxwOKx5vkK27jnd57ZdeegnTNLs8jtjhcGCaJuvWrevyGgBHjhxh1qxZjB49mrFjx3Lfffe17FuxYgUpKSmMGzeu1TmFhYWMGjWKvLw87r333m6tp4h9pJT8+J97cVh1vv/xCQNtjqKfUD5H0RcEZRAJCAQmJj4jMNAmdRlvOMgrJQfJcsdj0TTSnXFkOhP4V8kB/OFQu+cIIUh3u1kycxIIgXFOrNAobQgkNtPPgsuV3se57N27l5UrV/Lzn/+cRx99lJ///OesXLmSvXv39vraOTk5jB8/ngkTJjBlytmqKeVXhi/NwspfmDdaCSsPU/bt2zfkfY5K8Cg65fEPd/LPov1U+3y8euRwm/3FtbV88Z/PsXTt33n6ow/7xIbMuHh84VDLmNL6YIDs+PPrdyIVPNPzk6OSjZ8xOpc6r6/VtkDIQNME00bntDne6wu2O/ZUIBACGhr9XVo3EAjw/PPPk5LSvcqJlJQUnn32WQKBrgfQFouF//mf/+Ho0aNs376dRx99lJ07dwJw55138uKLL7Y6PhwOc/fdd7Nu3TqKioooLCxsOV4xNHj5o3K2HD7D966/iPR4NTFpODDYfM6uXbu6Zadi4LgibS4aGlKYJOjxzEqZ0a/rG9LkTKCB2qAXKWW3zq0J+JBSYj1H79Cm6ximpCbo6+RMGJebzuwJ+TT6gviDIUzTpNG0YzN85KcncMNMlTxvxuv18v3vfx/DMMjJySE3N5ecnBwMw+D73/8+Pl/n73VX2Lx5MwcOHGDPnj2AimWGM0pYWeH1ern33nuHvM9RCR5Fh0gpOVFXR1ZcAromOFFX2+aYX7y9mSM11YRMk0d2budQ9Zmo2zEjO4fLMrMoa2igrLGB/KQkFo5u7Zgr6/2UVPui0p4FcNtV00h2u6ioa6TO6+dMo5dar4+Vcy8lLaFtxn/CmAxChtkmiDQME4Hgkkltk0Lt0TyWuKtP0ptxOBz4fD5KS0u7fE5+fj5XXHEFAElJSYwdO5YTJ04AsGjRItLSWo9y3bx5MwUFBUycOBGHw8GyZct45plnumWnInZpDIT52Uv7mJydwKpZSlh5uDDYfE5hYWG37FQMHPPSryTBmsD05Gncknszo+NH9dvannCAvx7ZxO+L1vObA+tYV7oLU3a9CjnZ7kQIQcg8W4UTMg10TZBku3CV8H99/uOsmH8pVl2nzm8QFFYmZ7j4493LsVqUOkIzW7Zsob6+vpWgO0T8Q319PVu2bIn6miqWGb40Cyv/502TlLDyMGW4+ByV4FF0iBCCawpGU9HYiCkl8/IK2hxT4/PhtFhxWa1IKan1da1SpTtYdZ3bLrmMf79iHt+ZcwX/Nm0mbput1TE7iiP6O1O7kODxBUNsP1zC9sMl+ILtl1pnpcTzv5+/meVzLmZkWhJTR2Xzk5ULWTX/8naP//SSGditOj5/CIhUGoXCBh5vkCkTs8nL7trT8eaxxD1BCIHf37P3/+DBg+zdu5errrqqw2NKSkpa6W2MHDmyWzd3itjmf18/REV9gJ8tmdJuNZpiaKJ8jqKvcFtcJFkT+eKYz3NNxtXoov+mf75e/hFlvmoy7IlkOBJ4r+ow++u6/tlxWWwsGjmecm89YWly2ueh3NPAopEX4bRcWE/ParHwzWXzWXf/F/i3T14PwH99Zj6J7u4lUoc6p06d6rC6SkrJqVOner3Gtddey+TJk3nwwQcB5VeGK0pYWQHDx+eo9KWiU64fM5ZpWdns3raNySNGtNl/65RL+N9tW/E2BrkoNY2LM/rGaTb3tnfEjuIabBaNKdmdT8bYV1LBfYUbqfVESvCS3E5WL1vApJEZbY5NjXfxuQVdKykflZfO97/6cX7z542YpqTRG0TXBZdNHsmPvnVjl64B4HQ6u11K3oyUsttP4QHq6upYunQpv/zlL0lO7jhB1p5dSpxuaFBU0cBfthxj5fSRTM3r/yk3ioFjsPkcrR8mViqiS38mdpop9VWTYHEhhEAgsGk6lf46JtP1SaRXZ48hy5XAvm3bmZySwfT0XMYlpl34xHPQNY1tx2vJSLAzPkPpfZxPZmZmh3GEEILMzN7FlG+//TYFBQWUlpayYMECJk+erGKZYYoSVlbA8PE5KsGj6JTmxIrewQfx5gmTuDQji2q/j0np6TgGqPR4x4kaLs1NxGbpOPgPGwb/9dwbeANBRiRGAq0aj4//eu4N/vrV5Vj03gWhV84Yw8xL83ltw0a+96UpjCkYwei87gWDOTk5OJ1O/H5/t26c/H4/TqeTnJyutYI1E/j/7N15fFT1ufjxzzMzWSEhCQkhCYSwiyjIUpeKgIAIKC4gKLUuv9al2tbSe621tLb2armKVrvbq7bX9nZRIC4oWNlka6HKoihLANkTloQkkACTZGa+vz9mEoYwSWaSSeZM8rxfr3klc+Z7znlmJvPkme853++pquLGG29kxowZ3H333Y22zc3NpaioqO5+/R5pFZ2MMTzx9ud0inPwfZ1YucOJtpyTlZUV0v5Ux5SVkMrW8gMkOmLxGA/VHjcZcckhbUNEuCi1G0fjEhnbP/DZu01xewzr9pRw3cWZ+qUygFGjRpGcnEx5efl5QybKy8tJTk5m1KhRLdp+Xl4e4M1zN954I+vXr2f06NFay3QwtRMrPzS2r06s3MF1lJyjh8JUi+WlpjI8K5v4IE5bbg3OGjefF55scnjW7qMnKK08Q0qnBATvFbFSOyVQWnmW3UfCM3dQbKyDTomxXDf64pA7dwDi4uK45ZZbKC0tDWm90tJSpk2bRlxc8BPjejweZs2axYABA3jyySebbD969Gj27dvHzp07cTqd5OfnM3369JDiVNaz6NMi/r2vlMcmDSStU2zTK6h2JdpyzrRpbX+ZbRU8Ywxbyw6y8OAGNp3YG7E4JmRdSlZCKsec5RxznuRLXftwcUqPJtercrn4YM9ufrZmFfP+uZb1hw61KI6th8s5ebaG0QMyWrSd9ioxMZFnnnkGu91OYWEhhw8fprCwELvdzjPPPENCQvOvinrq1CnKy8vrfl+5ciVDhgzRWqaD0YmVlb/ExETmzp3b7nOOnsHTCo5VVnKyykn/tK6tdsTmbE0Nb+3cjsvjYdqgwXSO7bhfzD4rPEmN2zCyV+Pz3MTYbRjjLUBr3xfjXUCMo+1PIW/IjTfeyOuvvx70EXWn04nNZmPKlCkh7Wf58uW89dZb9O/fn4su8p658dRTTzFjxgymTp3Khg0bKCsrIzMzkx/84AfMnj2bF198kUmTJuF2u7nzzjsZMWJEs56jsoZTzhqeXryDoT26cMeXciMdjoqQaMo5w4cPb9ZzVG1jb+Ux8g9tINEex9ayA3zZHZkhn50d8fy/PmMprz6N3WYjJaZTk/WYMYa/fvYp244fJ6NTJ2rcbuZv+4xxLRhitmZXCSIwql/oB3w6isGDBzN//nzWrVvH0aNH6d69O6NGjWrRFy3wTiB/yy23AOB2u7ntttvqvlRpLdNx1E6s/NKdw3ViZQXAxRdf3O5zjv6lh5nHGH778QYqqqv49uVX0Sc1tEvPBmtnSTEfFR3GGMOArl25skfH/XJWN8Fybkqj7fpmdiU3PYXC0pOkdU4EA6Wnz9AzPYW+mV3bItSgZGdn873vfY+5c+eSkZHR6Bcup9NJcXExc+bMCfl0v4kTJzY490b9yxXXmjFjBjNmzAhpP8q6frFsNyWVVfzhnpE6sXIH5p9z0tPTGy1yIp1z3G53wHbKGk5UVSAIqbGdqPG4qAnhylXh5rDZSY8PfljW0cpKdhQX0yM5ua4zKNZup6K0nGq3m1i7nZOnnWzZU0hhyUkyUzszvF8P0pITG9zmmt3FXJrTRc+ObEJCQgLXXXddWLc5aNAgCgoKAj6mtUzHoBMrq4a095yjHTxhJkD/tHQOnTpJanzLegIbk5WURFJsHB5jyElqfGLh9qigqJgTFWf4+7otrN9TRZ/0TnTt3PhQARHhR7eN55k3P2RfsXc4Qu+MNB6/9VrLjY2fPHkyAM899xwej4e0tLTzOnqcTielpaXYbDbmzJlT116pYO04coo/rd/PVy7PZUiPxjtHVfvnn3PKyso056hm6d05kzi7g6POcmzYiLe17dDtHWXH2Fh8mBixc012b3I6BV8fVVZXeydl9qsHYux2jDE4XTWUnjzD/37wEVU1bhLiYthTVML67Qe4Z+JIcrtdeKbSybM1fHKonIfG9A3Lc1NKhUYnVlYdlXbwhJmIcNfQy1p9P907J/GDUWMwxlxwyfD2buOeQ/wsfyU39OpM/qdb2HMmg+svCe5Ico+uXfjV12/mSNkpALJSk7FZ9MyFyZMnM3ToUJYsWcKbb75JWVkZIoIxhoSEBL761a8yZcoUnRxQhczj8U6s3CUhhu9dPzDS4SiLmDx5Mpdeein/+Mc/NOeoZsmIT+bBftdx1FlOt7hkPj+xuc32va30KH/Y+TGdHLG4jYdPS4v4zqXX0D0xKaj1s5KSEKTubB2Ak04nWXYbSbFxLPxwKwbonubdXpdO8RQXn+TXLy/n8j459OnXnUuH5hIb5y2t139RgttjdP4dpSJAJ1ZWHZl28ESxxJjITGocaa+t2oQHcNhtJHVKoqbSBq7TQa9vswk5XaPjrKfs7Gzuu+8+7rrrLgoLC+vmyMjJyQlpclOl/L25pZCNB8qYN30IKYkdq4NYNS4rK0tzjmqRtLjOpMW1/ReqNUf2kRwTR0qc9+zpotOn+KSkkEm5wV0dsHNsLLcOGsSbO7ZjjMEAcXY7qfEJnK2q4cDxMrqnnussOrzzODvW7cV5pprCTUew22306JnG7O/fQGb3LqzeVULnOAfDmhg+rpQKL51YWXV02sGjwmpnYTH/s3QDxhgeuO4KLu6ZGfZ9VJytIs43KfKpau/PtPjA8zq0ByUlJSxdupStW7dy5swZEhMTGTJkCNdffz1du1pn7iAVHU6eqeG/l+xgeG4Kt41o+qoyquPRnKOi0YXn4hokwNLGXNmjJ71TUtl94gQxdhuDMjLYvH4DNpsNQTAGRKCi9DSff7iHmqoaPB6ornZhA77YfYxfP/8+/zVvJmt2FXNV367E2PWCtUq1JZ1YWXV0+levwsbj8fDT+cs4XVUNwE8XLOevj8zC4QhvcfOlfj1YvGknHpPIiTNgw8N1l7a/SaYLCwt55ZVXWL16NR6Ph7i4OBwOBy6Xi/Xr1/PKK68wZswY7r//fnJyciIdrooSP19WQNmZav70tcstOzxRRUZhYSEvv/wya9as0Zyjos6Y7D68uuMjajwe3MaDw2ZnWEbof6eZnTuT2fn8M5DiYx1c1DODXYUldEvpzP5Pi6g6XY3NIcTE2omLdWAMVFfXULCjiDWbD1FYfpZvjNX5d5RqSzqxslLawaPCyOlycfKMk/SkTohA8anTnKmuJtnR9GV3Q/H1cZdT6ayG6jIq3Q76d0tk1EV5Yd1HpBUUFPDoo49SWVlJt27dsNsvvEyr2+1m9erVbNq0ieeff56BA3UuFdW4zwtP8pcNB7jryl5ckhMdwxRV26jNORUVFWRmZmrOUUFzeTws3LuVLSWF9E9O5ysDhpHoaPuhn4NSM/nGxVeyqaSQWJudL3fPo1tC+IaKTfrSRRwr38iRE6c4ur8UjzE47HYS473PVQTi4mKorHDyj08OAzCmv86/o1Rb0omVlQI9b1SFTWJsLFcN7EVJxWmOnzrNyH49SEoI/5wNCXExfP+WsXRLTeGsy87Uy3LbVRIvLCzk0UcfpaamhqysrIBftADsdjtZWVnU1NTw6KOPUlhY2MaRqmji8Rh+9PaWLCjnAAAgAElEQVTnpHWK5T8m6hdzdY5/zsnOztaco0Kyrewo/z52kG7xndlefpx/HT0QsVj6p2RwR7/LmNbn0qAnVw5WSucEHrrxKmaOGUpaciKxDjtJifHY/eqP2rl7tpc5yeuaSG7Xhi+hrryKiop46aWXmDp1KmPGjGHq1Km89NJLFBUVRTo0FWVqJ1a+75o+OrGyatCRI0fafc7RDh4VVnNuvZYnZkzgx7eN4ye3TWi1jhcRYe9JDwDDcy+8PGk0e+WVV6isrCQtLS2o9mlpaVRUVPDqq6+2cmQqms3feIhPDpXzg8mD6JLQMSdoV4FpzlEt4fJ4AMEmgl2Eao8r0iG1mtgYB4PzunPtmEHYbILb7al7zBhDVZWL2IRYdp2s5ho9e6dJGzdu5N577+WNN94gJiaG7OxsYmJieOONN7j33nvZuHFjpENUUaJ2YuUsnVhZNWLjxo187Wtfa/c5Rzt4VFjZ7TauGpDLVQPzsLfyxIK7yz3YbcJl7egKFSUlJaxevZpu3bqFtF63bt1YtWoVJ06cCHqdL774giuuuII+ffrQr18/nn766fMed7lcDBo0iGuvvbZuWX5+Pr179yY3N5c5c+aEFKOKnLLT1Tz7j518KS+VacN17hR1juactiMib4jIJ77bfhH5JECbgX5tPhGRUyIy2/fYkyJS6PfYlLZ/FhcanJpJ3y5pFJ05Rdf4RK7K7NWm+z9WWcmGQ4fYcqSIKlfbdC6NGT+Ynrnp1FS7OHu2GqezhrNna7DbhazhvXG6PHp59CYUFRUxZ84c4uLiyM7OJj4+HhEhPj6e7Oxs4uLimDNnTouOqs+cOZO0tDT69+9/3vKG8kp7yjcdTe3Eyj++8WKdWFkF1JFyjmU6eETkMhHZ4CtaNorI5Q20myQiBSKyR0Qe91ueJiLLRGS372f7Oq0jCB6PwZj2ezWp+vaUubk4K7ldJfKlS5fi8XgaHCLREIfDgcfj4YMPPghpnRdeeIG9e/eyceNGXn31VTZv3lz3+NNPP31egnK5XMyePZslS5awa9cu8vPzz2uvrGveBwWccrp46pZL2tVwRtVy0ZZztmzZElKcVmKMud0Yc5kx5jIgH3gzQJsCvzYjgDPAW35NXqx93BizpG0ib1y8I4aHB3+ZHw0fz6NDx5Ia13bDkvaWlfLi+n+ycMc2/rL1U17a+BFOV02r7ze7Ryq3feUKBg/pSVZ2CundkuiVl87wy/vQZXAuDptwVV+94lxj3nnnHaqrq0lKCjyULikpierqat59991m7+NrX/vaBes3VMtojRO9dGJlFYyOlHMs08EDzAN+6itqfuy7fx4RsQO/BSYDFwOzRORi38OPAyuMMf2BFb77EVftcvHHlRv5v9Wbcfmdyhtu+4+X8XT+Cn6/dANnq1u/uIk0l9vD3pMeRvRqX/14W7duJS6uefMWxcXF8dlnnwXdvlevXlx99dUApKSk0K9fPw4ePAjA3r17+eCDD7j//vvr2q9evZq8vDwGDRpEfHw806dPZ+HChc2KVbWdTw6V8/rHB7n3y3lc1D050uEoi4m2nJOfn9+sWK1EvL2sM4G/N9F0PPCFMSZyk9oEySZCl7gEHLa2LSvf2bmThJgYeiQn07NLFw6ePMm248Wtvl8RYcy4i7n/4fFMuXk4104YzG1fuZKHHpnI5iOVjOiVSue49nPwqTUsWbKkyWGhaWlpLF68uNn7mDRpEunp6ecta6iW0RoneunEyioYHSnnWKmDxwC13z66AIHOj7oc2GOM2WuMqQZeB272PXYz8Cff738CbmnFWIN26mwVu4+UsLPwOGeqq1ttP1sPHKHa5eZgSTlHyytabT9WsfNoBVVuGN7OOnjOnDmDw9G8otDhcHD69OlmrVtQUMC2bdsYM2YMAA8//DDPPffceUf1Dx06RHZ2dt39nj176iSrFuf2GJ54+3MyOscxe0L/pldQHY7mnIi4BjhmjNndRLs7uLAT6FsislVE/tgRz1Su76yrhji/v1+7SJucwQPeTp7cvHRuuHk4d9x1NaPGXES13c72I6d0eFYQTp061WTncmxsLKdOnQrrfhvKK+0437RrOrGyClZHyjlWOrwwG/hARJ7H2/H05QBtcoBDfvcPA1f4fs80xhwBMMYcEZEGJxQQkQeABwAyMzNZtWpVy6NvxNhuDkSEzf/eEFT7ysrKkGNKcLu5NLEGh93G/u1bObC9GYGGOabWtPyAt4CrKdrJqrJdEY7mfMG8Vqmpqbjd7guWJyQk4GrmHAIul4tOnToF3K4xJuBygJMnTzJt2jSeeeYZkpOT+fvf/05GRgZXXXUV77//PuC9PHLt+rU/PR4PItLg/qz099JR/f2jg3xWeJJf3nEZSfE6sbK6UGJiYotzTqhqc86zzz5Lamoqr7/+OhkZGYwaNYolS86NOgo05NjWxmeIhEpElgOBxgj80Bjzju/3WTRx9o6IxAI3AT/wW/wS8BTeA2JPAT8HvtbA+q1e5xjAY8x5V5Gqr7Vrh0uqqjhVVYXDZsNgyDCGM3u+YNXefU2ua4wvflvjR/xDeQ7/LPTWJp1OHWDVqsNBrdNW2rKOa6jG8ZecnIzT6SQhIaHBNlVVVSQnJ9dtq7FapiG1eaR+DdNQLRNMjVO7Xa1zIqt2YuVsnVhZBSE5OZmqqiri4+MbbFNdXU1ycnjPdg9Uy4hIg8vDoU07eBorfPCeivxdY0y+iMwE/gBMqL+JAOuGPOmMMeZl4GWAkSNHmrFjx4a6iVa1atUqNKbG5f99C2nxR5g+eVykQ7lAMK/V1q1bA855MXToUDZsCK4jsL6qqiouvfTSgNt1u90Bl1dVVXHzzTczY8YM7rnnHgD++c9/snTpUnJzc6mqqqKyspLp06fz7W9/m6KiorrtFBYWNnhJZRGx1N9LR3SisornPijgqj5duWlodtMrqA5pyJAhrF+/vlnr1uacUNe58cYbmTFjBnfffTcA69atY+nSpeTk5NTlnFtuuaUu59Q6dOgQWVlZzYq1rRhj6tct5xERBzAN7/w6jZkMbDbGHPPbdt3vIvIK8F4jcbRqnXOqysnvP/6YkrNnyOzUiQdGfImkAEdGW7t2cHk8LN2zm01HikiIiWHqgIEMTG/87Bm3x8OqT77gX9v34/Z4SEtK5JarLyG3W+ATokJ5Du+88QldOxVz99Rx2JroOGprbVnHNVTj+JsyZQpvvPFGox08ZWVlzJo1q25bDdUyjan9wlS7Xl5eXsBapqHlDe1P65zIq51Y+aU7h7er+ThV66jNOf5nzdRXWlrKrFmzwrrf3NzcC2qZ7OzsBpeHQ5seCjPGTDDGXBLg9g5wD+cmHFyAdzhWfYeBnn73e3BuKNcxEckC8P083jrPQlnB5gNl9Eux9pHc5pg4cSI2my3kI1Qulwubzcb1118f9Doej4dZs2YxYMAAnnzyybrlv/nNbzh27BiFhYX8+c9/5qqrruLtt99m9OjR7Nu3j507d+J0OsnPz2f69OkhxanazrP/2MnpKhf/dbOOSVcNi7acM23atJDitKAJwE5jTFOnd1xwlk9tjeNzK/B5mGML2saiIo6fPk12UhJFFRVsPXY0InE4bDamDBjIE2Ou5dEvj2qycwfgo4KDfPjpHlKTEumeloyzxsX/Ld9ExdmqFsXi8RjW7i5mVP90y3XuWNHNN99MbGwsFRWBpxWoqKggNjaWqVOnhnW/DdUyWuNEF51YWYWqI+UcK31DLgLG+H4fBwQam/4x0F9EevtOX74DWOR7bBHeTiJ8P98JsL5qBW63B4+n7a7edeTkWQrLz9I/JbSjONEgPT2dMWPGcPx4aP2Tx48fZ+zYsXTtGvxVO5YvX85bb73F2rVrueiii7joootYsGBBg+1jYmJ48cUXmTRpEv379+fWW29lxIimDkKrSNh0oJT5Gw/z9VG96Z8Z+GoBSkH05Zzhw4eHFKcFXTCvjohki8gSv/uJwHVceJWteSLymYhsBa4FvtvawTYkzm7HbTy4PB48xhAb4lkVkbRh+0G6JnfCYfeWwMmJ8VTVuNhTWNKi7W4/coqSympG99f5d4KRnZ3N3LlzqaqqoqioCKfTicfjwel0UlRURFVVFXPnzm3REe2pU6cyatQo9u3bR2ZmJr/4xS8arGW0xokuOrGyClVHyjlWOp/tfuCXvtOXnfjGjotINvCqMWaKMcYlIt8CPgDswB+NMdt86z8DzBeRrwMHgRlt/gw6oO27jvDu8q3ExjiYOXUEOd1TWn2fmw6UAdAv1Ur9k+Fz//33s2nTJkpLS5uc7R28pxMmJSVx3333hbSfiRMnBhz/6W/KlClMmTKl7v6MGTOYMUM/Wlbmcnt44u1tdE+O55HxOrGyalo05ZxQzzSyGmPMvQGWFQFT/O6fAS7oOTPG3NWqwYVgZHYO+8vL2V58nCt79OCy7tYeOuevxu0mLqZe+WsEdyNXOq2prsER42j0i+Sa3d6rd13TP73BNup8I0eO5LXXXuPdd99l8eLFlJaWkpyczKxZs5g6dWqLhys0dLnjhmoZrXGiQ+3Eyg+N7asTK6uQjBw5kj/+8Y8sXry4Xeccy3TwGGPWEWBMeoDCZwmwJEC7E3jn8VFtaMmHn9O5UxzOqho+/FcBX512RdMrtdCmA2XEx9jomdQ+O3hycnJ4/vnnefTRRykqKiIzMzPgGHCXy8Xx48dJSkri+eefJycnJwLRKqv5y4YDbD9yit9+ZTid9DK9Kgiac1So4hwO7hwyNNJhNMvwfjms2bqXrK7JiAjVNS5sNqF31vmdm2cqzrLhvU0UVxXz7O9/TY8B2Yy+7Sr6DOkVcLtrd5VwUfckuiU3PIGnulB2djYPPvggDz74YKRDUVFAJ1ZWLZWVldXuc45W/6pFkjrHU37yDDUuN0md26ao2XygjKE9UnDYWjZe3soGDhzI73//e1599VVWrVqFx+MhLi4Oh8OBy+WiqqoKm83Gtddey3333adftBRw/pj0KZfqmHQVvNqc88orr7B69WrNOarduubSPhwrq2TX4WJEwG6zcevVl9A1+dwV4arOVvHnJ99g69odXHHPJez77BAHth9m+4Zd3PPT2xl81cDztnm6ysXGA6V87erebf10lOpQdGJlpZqmnwzVIrdNGc6af+8iLi6GsVcOaPX9na12s63oFA+O6QNEZlLHtpKTk8NPfvITvvWtb7F48WI++ugjKioqSEpK4vLLL+eGG24Iaf4L1f7995IdOiZdNVtOTg5PPPEE3/72tzXnqHYrLsbBV8YNo+TkaU47q8lMTSIhLua8Np98+Dnr391E1ekq3G4PFaUVGGM4ffIMrz/zNk/mP4rdce4stw17T1DjNoweoPPvKNVadGJlpYKjHTyqRbqmduLWScPabH+fHi7H5TGM6JUKR9t3Bw/A/v37WbRoEYsXL6a6uhoRwRjD7t27KS8v56abbiIvLy/SYSoL+PfeE7y5pZBvXqtj0lXzHThwgPfee09zjmrXRISMlM401B3z4ev/5NSJU8TGxyIixMTFYDyG6rPV7N70BUV7j9FzwLm5GtbsKiY+xuatTVSzuN1unE4n8fHxIV8KXXUMOrGyCqf2nHO0g0dFldoJlofnpvJJO+7fcTqdPPfccyxfvhwRIT09ndjY2LrHq6urefPNN8nPz2fChAk89thjxMXFRTBiFUk1bg8/fmcbOSkJfPNaHZOuQqc5R6lzDu0oBCM4/CZjFpsQmxBL5ckzFB8qOa+DZ+3uEq7s05X4mPb1JaG11dTUsG7dOubPn8+2bdvqOpQHDx7MzJkzGTVqFDExMU1vSLV7OrGyCoeamhrWrl3LggUL2nXO0Q4eFVU2HSijX7fOpCTGNt04SjmdTr7//e+zZcsWsrOzsdkunEw6NjaW7OxsPB4Py5Yto6SkhHnz5ukXrg7qT//aT8GxCv7nrhE6Jl2FTHOOUudzxDnwcOEV34zHYBOI8ZvA/lDpGfaWnOarVwaefFkFVlBQwJw5cygpKSE+Pp6cnJy6L1t79+7lJz/5CRkZGcydO5eBAwc2vUHVbunEyiocOlLOaZ+XIbI4YwweT+OXilUX8ngMmw+WMSK3fZ8C/dxzz7FlyxZycnICftHyZ7PZyMnJYfPmzcybN6+NIlRWcuyUk18s383YgRlMvDgz0uGoKKQ5R6nzDby8P3abjaqz1XXLXDVuqpzVJKcnk9nr3OCu2suj6/w7wSsoKOCRRx7h9OnT5OTk0LVr17ohNyJC165d6dGjB6dPn+aRRx6hoKAgwhGrSKqdWPmJGy/Wg1iqWTpaztEOngh4c8kWfvmHlZwoO92q+zly/CSHisowpn10Ju0tqaT8TE27HuO+f/9+li9fTnZ2dtDji0WEnJwcli9fzv79+1s3QGU5P1u8g2q3h5/qmHTVDJpzlLrQ+K+MIrNXBnEJsRhjcJ6tQgSSUjox4rohZPRIr2u7ZlcxOSkJ9M3o1MgWVa2amhrmzJmDiJCWltZo27S0NESEOXPmUFNT00YRKivRiZVVS3XEnKMdPG3MGMPho+VUnHZScdrZavs5fKSM/33jX/xpwXoK9h5rtf00xO1yh71jqXb+nRF57beDZ9GiRYhIk0fR67PZbIgIixYtCnqdL774giuuuII+ffrQr18/nn76aQC2bt3KRRddVHfr3LkzTz31FAD5+fn07t2b3Nxc5syZE1KMKvz+taeERZ8W8Y0xfenVVb9cqNBpzlGNKTl5mvc/2sGHn+zhbHX0Fruh6jesN5O/Po7ci3KIiXWQ2SuD7L7dGXTVAGZ+7+a6ztAat4d/7TnBNf3TtYM9SOvWraO4uLjJL1q10tLSKC4uZt26dSHtp6F8Aw3nFc031qMTK6uW6og5Rzt42piIcPdtV3LvjKvolRPcH1pznHXWUOPyUON2c/Zs2xVlxhhWvb+VF598m7+8tJLTleHrxNp0oIyUxBj6pLfPL7JOp5PFixeTnp7edOMA0tPTWbx4MU5ncK+5w+HghRdeYO/evWzcuJFXX32VzZs3M2TIEHbu3MnOnTv5/PPPiY+P5/bbb8flcjF79myWLFnCrl27yM/PZ/Pmzc2KVbVctcvDE+98Ts+0BB4e2zfS4agoFI05Z8uWLc2KVYWuqsbFa0s/5qOdh1j5yR4W/WtbpENqM3a7nRsevI57n55F59TOXDllBLc8MoVv/PwesnqfGwr76aFyKqpcOjwrBPPnzychISGkdRISEpg/f35I6zSUbxqqZbTGsZ7aiZXvu6aPTqysmq0j5hwdyBgBqV0SSe2S2Kr76Nsrg6nXXYrL5ebSi7KbXiFMSksq2LhuFxndu3D0cCk7PjnIyFEDwrLtjQe88++01x78kpISqqurz7tyTShiY2Oprq6mpKSEHj16NNm+V69e9OrlnRQyJSWFfv36cfDgQYYPH17X5t133yU3N5cBAwawYsUK8vLyGDRoEADTp09n4cKF57VXbeeP/9zHF8Wn+cM9I/XKLapZojHn5OfnM3LkyGbFq0JTebaKyrNVdE9LptrlZv+x0kiH1KbsdjsDR/blSOUhbr19bMA2a3YVYxO4um/zOkk7GrfbzbZt28jJyQlpvbS0NLZt24bb7Q76csYN5ZuysrKAtUxDy7XGiQydWFmFQzTmnKFDh4YUayB6Bk87ZbMJwy/J5fLLeuNwtN2Xv9jYGOwOGxWnzuLxGOLDdLWr0tPV7C0+3a6HZ505cyYsnVdnzpwJeZ2CggK2bdvGmDFjzlv+97//nRkzZgBw6NAhsrPPdRb27NmTwsLClgWrmqWo/Cy/XL6bCYMyGT9IJ1ZWzaM5RzWmS6cEstKSKSw+yfGyCob2abuDRdFi9e4SLuuZQpfE6L+sbltwOp2ISMh5p7Z9sGcL1uefbxrKK5pvrEUnVlbh0FFzjn5iVFgldUng1q9+mS0b9tC9R1cGDc0Ny3a3HPTNv9OOr6CVmJgYlnmLEhNDOzvs5MmTTJs2jWeffZbU1HOvr9PpZNmyZbzwwgsAAWNrr2dTWd3Ti7fjMYafTL040qGoKBaNOSfUuYJU8znsNu6eOJLdh0uIi7HTP0eHIfkrO13N1sPlPDKuf6RDiRrx8fEYYzDGhFQ/1OaC+Pj4kPdZP980VMtojWMdOrGyCpeOmnO0g0eFXV7/7uT1D29C3nigDIdNGNIjJazbtZL09PS6IQ/NGTJRu14o82lUVVVx4403MmPGDO6+++7zHnvzzTcZPHhw3dCL3NxcioqK6h6v3/Os2saaXcUs+ewo/3ndAHqmte5QT9W+RWPOycrKCjlO1XwJsTEM6aOveSD//KIEY/Ty6KGw2+0MHjyYvXv30rVr16DXKy0tZfDgwUEPlagVKN80VMtojWMdOrGyCpeOmnP0UJiKCpsOlDE4pwsJse13rpH4+HhuuOEGSkpKmrV+SUkJN9xwQ9C9zR6Ph1mzZjFgwACefPLJCx7/29/+xsyZM+vujx49mn379rFz506cTif5+flMnz69WbGq5qlyufnJom3kdU3k/tF9Ih2OinLRmHOmTZvWrFiVCrc1u4pJjncwtEeXSIcSVWbOnMnZs2dDWsfpdJ6XG4LRUL5pqJbRGscadGJlFW4dMedoB49FeDwePB5Pq++npsbVJvsJpxq3h08Plbfr4Vm1brrpJowxIb9HHo8HYww33XRT0OssX76ct956i7Vr19ZdnnjBggUAVFRUsG7dOu6888669jExMbz44otMmjSJ/v37c+uttzJixIiQ4lQt88qavewrOc1Pb75EJ1ZWYRFtOUcnPFVWYIxhza4SRvVPx2HXUjoUo0aNIiMjg9LS4CbtLi0tJT09nVGjRoW0n4byTUO1jNY4kacTK6vW0BFzjg7RsoCzZ6r460sfEhfv4I4HxrbafvbtOso7f/0XqelJ3H7fGOITwjMBcmvbVnSKKpeHEb3afwdPXl4eEyZMYOnSpfTo0SOoU1ONMRQVFXHdddeRl5cX9L4mTpzY4PwbSUlJlJeXX7B8xowZdROgqrZ1qPQMv/lwD5Mv6c4YHRKgwiTaco7b7Q56f0q1lt3HKzl6ysk1/TUXhyomJoa5c+fyyCOPUFpaSlpaWoNtS0tLMcYwd+5cYmJiQvr8N5ZvGqpltMaJrNqJlV+6c7hOrKzCpiU5JxRWyjl62MECqs7WUHHyDGUnKnHXtN7ZNbu2FeJ2G44XlVNaXNFq+wm3TQe8EyyPbMdX0PL32GOPMXz4cA4fPtzkUXWPx0NhYSHDhg3jsccea6MIVST813vbEYQnbtSJlVV4ac5RKjRrdhUDOv9Ocw0cOJBf/epXdOrUicOHD3PixIm6L0bGGE6cOEFhYSGdOnXiV7/6FQMHDoxwxKq16cTKqjV1tJyj3aMWkNK1M3c+NA67wxa2y4oHMuzKvhw5eIL07l3oltW2kxWfOlHB0j+v4mylkwl3jiarT/CXdt58oIyclAQyk0OfyTwaxcXFMW/ePObNm8fy5csRkbrJUGtVV1dTUlKCMYaJEyfyve99j7i4uAhGrVrTyp3HWLb9GN+fdBHZKQmRDke1M5pzlArNmt0l9M3oRI7m42YbOHAgr7/+OuvWrWP+/Pls27at7rHBgwczc+ZMRo0aFfJRdBWddGJl1dpqc87atWtZsGBBu8452sFjEW3R4dItK4V7vzOx1fcTyIev/5P9nx0iLjGOt369hIdeuDfooQAbD5RyRe/gZz5vD+Li4njiiSe46667eOONN3jjjTeorKzE7XZjt9vp3Lkzt99+O7fffntIQyRU9HHWuHly0Xb6ZnTi66N6Rzoc1U7V5pw777yTBQsWaM5RqgHOGjf/3nuCr1yRG+lQol5MTAzDhg2jtLSUfv361Q2fyMvLY9iwYe3ii5ZqWu3Eyg+N7asTK6tWFRMTw9ChQykrK2vXOUc7eFSbcLnc2Ow2HLF2aqpdQa9XWH6WY6eqOszwLH8FBQXk5+ezYsUKOnXqRGLiuUtiiwhLly7F7XYzffr0qD+VUDXs96u/4GDpGf563xXEOnRUrWo9BQUFLFy4kJUrV2rOUaoBH+0rpcrlYbTOv9Mi/jWOy+XCbrdjt9txu924XC5+97vfMX78eM037ZxOrKzaSkfKOdrBo9rE2Jlf5r3SpZytdHLTQ9cHffpl7fw7wzvAFbT8vf/++8ybN69uqITDceFH1eVysXz5cpYtW8Zjjz3G5MmTIxCpak0HTpzmd6u+4MYhWVzdLz3S4ah2THOOUsFZu7uYWLuNK/o0PFGnapzmG1VLJ1ZWbaGj5Rz9JKk20TUrlXt+envI6206UEZirJ2Luie1QlTW9P777zN37lwyMjKIj2943iGHw0H37t1xOp3MnTsXIKqTkTqfMYYnF20jxib86AadWFm1Hs05SgVvza4SvtQ7Vb+MNlNL8s3EiZGZZkC1Dp1YWbWFjljj6Pn+ytI2HShjWG4KDnvH+FMtKChg3rx5TSYhf/Hx8WRkZDBv3jx27drVyhGq+kRkkogUiMgeEXk8XNvdctzNhwXFzJ4wgO5dOsYE46rtac5RKnhHTzopOFahw7OaqaX5Zs+ePa0coQqkteocnVhZtbaOWuN0jG/NUaqmuoaSwnOXcetoTle52HHkFCM60PCs/Px8RCToJFQrPj4eEWHhwoWtFJkKRETswG+BycDFwCwRafHpNmer3fx1RzUDMjtz79V5Ld2cUg3SnKOOHinn3//azb4vjnfYeiNYa3br5dFbQvNN9GmtOmd3mZv8zYe575o+OrGyajUdNedoB4+FLfvzal5+7P/4fN2OVtuHq8bFW79azK++9SoHth9qtf00xyeHyvEYGN6rY3TwlJeXs2LFCtLTmzfXSnp6OitWrKC8vDyo9mfOnGHIkCEMHDiQfv368d3vfrfusfz8fHr37k1ubi5z5sxpcnkHdjmwxxiz1xhTDbwO3NzSjf72wz2ccBr+6+ZLiOkgZ7TUyvEAACAASURBVK+ptqc5R5UUn+Kv/7uONSt3MP+v69m140ikQ7K0NbuKyUiK61DDxsMlHPlm5cqVmm/aXtjrHJfbw5+3V+vEyqpVdeQaR785WFhsfAw2u42YuNa7ZFvpkTIKNu7FXe1i84rPWm0/zbHpQBkiMKyDnMGzYsUKampqAk78FQyHw0FNTQ0rV64Mqn18fDxr166loKCAbdu2sWLFClauXInL5WL27NksWbKEXbt2kZ+fz+bNmxtc3sHlAP49o4d9y5qtpLKKl9fu5apsO1f26dqi4JRqTDTmnC1btjQrVhXYsaMn8Xg8ZHbvQnx8DPv3Ho90SJbl9hjW7Snhmv7pOpykGaIx32iNA7RCnbPo0yIOVXh44saLdS4r1Wo6cs7RT5WFjfvKNVx540g6p3RqtX2kZaXS97I8inYfYdi4S1ttP80x6/JchvToQpeE1uvgspIDBw40OwnVcjgc7N+/P6i2NpuNLl26AFBdXY3L5UJEWL16NXl5eQwaNAiA6dOns3DhQsrKygIuHz58eItijnKBqvwLxjiIyAPAAwCZmZmsWrWq0Y3+5/BYkjjbZLtIqaystGxsYO34IhVbamoqbrf7vGX79+8PS87Zt2/fBdtuSOfOnXG73TidTlwuFwCrVq2iV69eDBgwAIBp06axYMECSktLL1ien5/PsGHDLtiuMcay77mVdcvsgs1m4/ixk7hchtzeOvSoITaBv3z9CmIdemy0ObTGiVpN1jmh1jgpxnDfRYb4kp2sWlUQrjjDysp1BGh89bWXGmfBggUMGTIk4P5CqXO0g8fCbDZbq3buADhiHMz4j6kYYyx3RCojKY6xA7tFOow2U1FRgd1ub9E27HY7FRUVQbd3uVxccsklHDx4kHvvvZdrr72W1157jezs7Lo2PXv2ZMOGDRw6dCjg8g7uMNDT734PoKh+I2PMy8DLACNHjjRjx45tdKNj8f5DaKpdpFg5NrB2fJGKbevWrRfkl8rKyrDknFC2Uz/njBs3jtdee42cnJy6beTm5rJhwwYOHz58wfL169cH3JeIWPY9t7KMbsl85Z6r2be3mG6ZyfTtnxnpkCxLRLgkp0ukw4haWuNErSbrnFBrHACbhf9Pg7XrCND46msvNc6GDRsQkRbXOXoYQgFYrnOnI0pKSgq6h7ghbrebpKTg5wZwOBzs3LmTgwcPsmnTJjZu3Bhwkk0RaXB5B/cx0F9EeotILHAHsCjCMSkVlGjMOTabli3hlpWTypevGUC/Ad01p6tWE435Rj8PgNY5Kkp15JyjlZJSFtGrV6+60/may+VykZeXF/J66enpXHPNNbz77rvk5uZSVHTu4EztUa2GlndkxhgX8C3gA2AHMN8Ysy2yUSkVnGjMOVlZWS2KVykVGdGYbzp6jQNa56jo1ZFzjnbwKGUR48ePJyYmptnJyOVyERMTw7hx44JqX1RURElJCQCnT59m1apVDBo0iNGjR7Nv3z527tyJ0+kkPz+f6dOnN7i8ozPGLDHGDDDG9DXG/CzS8SgVrGjMOdOmTWtWrEqpyIrGfKM1jpfWOSoadeSco3PwKGURKSkpjB8/nuXLl9O9e/eQ1y8pKWHChAmkpKQE1f7QoUPce++9uN1ujDHccsst3HHHHQC8+OKLTJo0CbfbzZ133smIESMaXa6Uij7RmHN0wlOlolM48s348eO1xlFKBSUaa5wRI0a0eFgZWKiDR0QuA34PxAMu4GFjzEf12vQE/gx0BzzAy8aYX/oeexK4Hyj2NZ9jjFnSNtErFR7Tp09n2bJlOJ1O4uPjg17P6XRijOG2224Lep0rrriCHTt2BHxsxowZzJgxI+jlSqnoFG05JxyFj1IqMqIt3yiloltHzTlWGqI1D/ipMeYy4Me++/W5gP80xgwCrgS+KSIX+z3+ojHmMt9NO3dU1Bk4cCCPPfYYxcXFOJ3OoNZxOp0UFxfz2GOP1V1qTymlgqE5RynVVlqab/r169fKESql2pOOWuNY5gwewADJvt+7EPhSw0eAI77fK0RkB5ADbG+rIJVqbZMnTwZg3rx5iAjp6ek4HBd+VF0uFyUlJRhjmDNnTt16SikVCv+cA5CRkaE5RynVKlpS4+gZfEqpUHXE71VW6uCZDXwgIs/jPbPoy401FpE8YBjwb7/F3xKRu4GNeM/0KWtg3QeABwAyMzNZtWpVS2MPq8rKSo0pCFaMCYKLKzU1FZfL1eDl8CZOnEifPn1YuHAhK1eupKamBofDgd1ux+121038NX78eG677Tb69evXaOFjjGmzwsgYgzHGku+NUh2Zx+Np8DLjkydPpm/fvixYsKDRnDNhwgRuu+02Sx3V8ng8kQ4hKMEMRfe1mwT8ErADrxpjnvEtTwPeAPKA/cDMhuocpSItmHyzcOFCVqxYEVX5BqIn5yjVkWjOOadNO3hEZDne+XPq+yEwHviuMSZfRGYCfwAmNLCdzkA+MNsYc8q3+CXgKbxnAj0F/Bz4WqD1jTEvAy8DjBw50owdO7a5T6lVrFq1Co2paVaMCYKLa9u2bZw4cYKMjIwGk9HAgQP54Q9/yDe/+U1WrlzJ/v37qaioICkpiby8PMaNGxf0xF9utxu73R7qUwmZx+OhpKQEh8NhyfdGqY7KbrdTXFzcaM4ZMGAAjz/+eFhyTlvxeDwUFxe3SX4Lg9qh6O+LyBTf/bH+DUTEDvwWuA44DHwsIouMMduBx4EVxphnRORx3/3vt+UTUCoYweabOXPm8PDDD0dNvoGoyzlKdQih5JwHH3yQ1atXt+uc06YdPMaYgB02ACLyZ+A7vrsLgFcbaBeDt3Pnr8aYN/22fcyvzSvAe+GIWanW0KdPH/bu3cuxY8eabgz069fvgrHnBw8e5ODBg0Gtb4xp8GyhcLPb7fTp06dN9qWUCk6wOac2V7Q057SWQLksinJOk0PRgcuBPcaYvQAi8jpwM96h6DdzrkPoT8AqtINHWVBr1ThtWcs0JopyjlIdQig5xxhj2RoHwlPnWGmIVhEwBm/BMg7YXb+BeJ/tH4AdxpgX6j2W5ZujB+BW4PNWjVapFkhISGDw4MFttj+rnu2klGobweYcq+cKq8fXhGCGoucAh/zuHwau8P2eWVvnGGOOiEi3hnZklaHoVh1KHYr28BygfTyPyspKOnfuHOkwACguLm66kVKqTYTyvcrqdUQ44rNSB8/9wC9FxAE48RUmIpKNdwz6FOBq4C7gMxH5xLde7eXQ5/nGtxu8Y9MfbOP4lVJKKdWBhWEoeqDTE0yocVhlKLrVC+lgtIfnAO3jebSH56CUUq3NMh08xph1wIgAy4uAKX5tAp6baYy5q1UDVEoppZRqRBiGoh8Gevrd78G5oVzHas9WFpEs4HgYQlZKKaVUOxJ4FiKllFJKKRVOtUPRoYGh6MDHQH8R6S0iscAdwCLfY4uAe3y/3wO804qxKqWUUioKiTEhn/nbrohIMXAg0nHUkw6URDqIejSm4FkxLivE1MsYkxHhGCIuhJxjhfesIVaODawdn5Vjg/YVn6VyjoiMwnv589qh6A8bYzbVG4qO7wpbv8B7mfQ/GmN+5lveFZgP5AIHgRnGmNIg9hvJOsfqf0/BaA/PAdrH87D6c7BUzomEdlLjgMbXUlaOz8qxQRjqnA7fwWNFIrLRGDMy0nH405iCZ8W4rBiTapyV3zMrxwbWjs/KsYHGp8KrPbxf7eE5QPt4Hu3hOSgvq7+XGl/LWDk+K8cG4YlPh2gppZRSSimllFJKRTnt4FFKKaWUUkoppZSKctrBY00vRzqAADSm4FkxLivGpBpn5ffMyrGBteOzcmyg8anwag/vV3t4DtA+nkd7eA7Ky+rvpcbXMlaOz8qxQRji0zl4lFJKKaWUUkoppaKcnsGjlFJKKaWUUkopFeW0g0cppZRSSimllFIqymkHj0WIyAwR2SYiHhEZWe+xH4jIHhEpEJHrIxjjUBFZLyKfici7IpIcqVj8YrpMRDaIyCcislFELo90TAAi8oYvpk9EZL+IfBLpmABE5Nu+v6NtIjIv0vGoC4nIJN97tEdEHo9QDH8UkeMi8rnfsjQRWSYiu30/U/0ea7McJSI9ReRDEdnh+zv+jsXiixeRj0TkU198P7VSfL792UVki4i8Z8HY9vv+x3wiIhutFp8KnRVrh1BZtdYIlVVrk1BpLRPdtM5pMjatc1oeY8euc4wxerPADRgEDARWASP9ll8MfArEAb2BLwB7hGL8GBjj+/1rwFMWeN2WApN9v08BVkU6pgAx/hz4sQXiuBZYDsT57neLdEx6u+A9svs+432AWN9n/+IIxDEaGA587rdsHvC47/fHgWd9v7dpjgKygOG+35OAXb4YrBKfAJ19v8cA/wautEp8vn3+B/A34D0rvbe+fe4H0usts0x8emvWe2q52qEZz8HytUYznpMlapNmxK21TBTftM4JKjatc1oeY4euc/QMHoswxuwwxhQEeOhm4HVjTJUxZh+wB4jUkaOBwBrf78uA6RGKw58Bao8GdgGKIhjLBUREgJnA3yMdC/AQ8IwxpgrAGHM8wvGoC10O7DHG7DXGVAOv480BbcoYswYorbf4ZuBPvt//BNzit7zNcpQx5ogxZrPv9wpgB5BjofiMMabSdzfGdzNWiU9EegA3AK/6LbZEbI2wenyqcVasHUJl6VojVBarTUKltUx00zqn6di0zmkBrXN0iFY0yAEO+d0/7FsWCZ8DN/l+nwH0jFAc/mYDz4nIIeB54AcRjqe+a4BjxpjdkQ4EGABcIyL/FpHVIvKlSAekLmClz3t9mcaYI+AtPoBuvuURi1lE8oBheI8eWSY+36nBnwDHgWXGGCvF9wvgMcDjt8wqsYG3SFwqIptE5AELxqdCZ8XaIVRWrzVCZaXaJFRay0Q3K+dty/2v0TqnWTp8neMIY7CqCSKyHOge4KEfGmPeaWi1AMta7dr2jcWI99TqX4nIj4FFQHVrxRFCTOOB7xpj8kVkJvAHYEKk4/J7P2fRhkfImnitHEAq3tMovwTMF5E+xncOoLKENv28h0lEYhaRzkA+MNsYc8p7QDpw0wDLWjU+Y4wbuExEUoC3ROSSRpq3WXwiciNw3BizSUTGBrNKgGWt/d5ebYwpEpFuwDIR2dlI22j8vLRLVqwdQmXVWiNUVqxNQqW1TLsWjXlb65z6G9c6pyVavc7RDp42ZIxpTjFwmPOPdvWgFU8NDiLGiQAiMgDv6W+trrGYROTPwHd8dxdw/ul4raqp10pEHMA0YETbRNTka/UQ8KavCPpIRDxAOlDcVvGpJrXp5z1Ex0QkyxhzRESy8B61gQjELCIxeIuevxpj3rRafLWMMeUisgqYZJH4rgZuEpEpQDyQLCJ/sUhsABhjinw/j4vIW3hPRbZMfCowK9YOobJqrREqK9YmodJapl2zct62zP8arXOaTescdIhWNFgE3CEicSLSG+gPfBSJQHw9jYiIDfgR8PtIxFFPETDG9/s4wEqnG08AdhpjDkc6EJ+38b5GtUV2LFAS0YhUfR8D/UWkt4jEAnfgzQFWsAi4x/f7PcA7fsvbLEf55o74A7DDGPOCBePL8B3RQkQS8OUBK8RnjPmBMaaHMSYP79/WSmPMV60QG4CIdBKRpNrf8XYKfG6V+FTzWLR2CJWVa41QWa02CZXWMtFN65wmaJ3TfFrn+JhWnCVabyHNqH0r3l66KuAY8IHfYz/EO2t2Ab6rOEQoxu/gncl9F/AMIBZ43UYBm/DOMP5vYESkY/KL7TXgG5GOwy+eWOAvvkSyGRgX6Zj0FvB9muL7jH2B95T6SMTwd+AIUOPLS18HugIr8H6xWQGk+bVvsxzl+8wbYCvwie82xULxDQG2+OL7HN9VaqwSn98+x3Lu6hKWiA3vVVU+9d221f79WyU+vTX7fbVc7dCM52DZWqMZz8VStUkz4tdaJspvWuc0GZvWOeGJs8PWOeJbUSmllFJKKaWUUkpFKR2ipZRSSimllFJKKRXltINHKaWUUkoppZRSKsppB49SSimllFJKKaVUlNMOHqWUUkoppZRSSqkopx08SimllFJKKaWUUlFOO3giSETuFRHjd6sQkU9F5Fsi4mjlfef59nmv37LXRGR/iNsZKyJPikhY/5Z822zyEm8isqrea+h/+0U4Y7IaEZkuIsdEJNFvmRGRp/3u3yoiR0Wkc2SiVK1Jc0ij2w02h9hF5AkR2SciVSKyW0RmN9D2IRHZ6Wt3UESeEpGYem2GichHvvdiuYj0qve4w/cefS+I2BrLb/63vCa206L3SkRyROS0iIwMpn0ofK/XGRHJDfe2VeRpjmp0u1rnNEHrnI5N80ej29Ua59x2tMapRzt4rGEGcBUwHfgI+DXw4wjE8RRwa4jrjAV+QmT/lrbiff3q316MYEytyvePbS7wnDHmTCNN3waOAk0mWhXVNIc03++AHwF/AG4EFgDPi8iP/BuJyA+A3wKLfe1+Dfwn8JJfGwewEPgCmAbYgT/V29+3fcuDyU8Pc35OWwIUc2GuO9LEdo742i0OYp+BPAV8aIzZ2Mz1G2SM2QIs8+1DtV+ao1pG65yGaZ3T/mn+aD6tcZrW7mqcVu39VEH7xBizx/f7UhHpB8ymgeTl6011GWOa7LkNhTHmi3Burw1VGGM2RDoIEYkzxlS10e5uBvKAPzbWyBhjRORl4CkR+W9jjLMtglNtTnNIM/iOqNwHPGWMqT0ivExEkoEfisjvjDGlIhIPzAH+bIz5T792BpgnIi8aY7YBA4E+wFXGmOMiUgH8S0QSjTFnRCQbeBKYaoxxNRWfMWZ7vXiLgepQ850vLzUrR4pIJvBVQi9qQ/E/wDsi8gNjTFEr7kdFjuaoltE6pwFa53QImj+aQWucprXXGkfP4LGmj4EkEenmd9rZwyIyT0SKgCogBUBEponIBt/pX+UisqD+aWAikigivxOREyJSKSKLgB71dxrodDYR6SQiz4jIF75T9o6KSL6IZIrIk3h7pQFqak+lq7ffZ32nBVb7fv6w/mmKvtPX1oqIU0QKReQJQFr8Kp6/j1Uisk5EJojIZt/r9bmI3BKg7VARWSQiZSJyVkT+KSLX1GvzmogcFpGrRORfInIWmOd7rI+ILPHt47iI/FxEHvA/zVBE3hORzQH23VtEPCLyYBNP6T7gH8aY0iCe/ny8fy/Tgmir2gfNIcHlkMvx/h98v97yfwDxwGTf/UuAzg20E6A2j8T6fp71/TztezzOd/8F4C1jzJogYguKeE9VXy8ipb73b4OI3FCvzQWnL4fgXqAC+KDeNveLyGsB4jG+97X2/gARecuXC53iPe17gZx/ev1S4JRvX6pj0ByldY7WOaq5NH9ojePfRmucevQMHmvqDbiBSqB23PEP8Sa0B/Ce+uYUkW/gPXXuf4H/ApLw9pyuFpEhxpgK37r/A9wO/NS3jeuAvzUVhIjE4j2t7DLgv/H2jnYBrgdSgVfxJsCvA6N8Mdeu68D7YbkY72lpnwFXAk8AaXhP+0NE0oGVeE+vvQdvUv4eENJYRQk8Ftddr/e+L/BL33Mp8cWwUEQuqj0yICLDgbXAFuB+4AzwDWC5iHzZGLPJb3tdgNeB5/H2fJ/1e83i8Z56eBxvkXJbvdh+BywWkcuNMR/5LX8Ab8Js8P0RkTi8p3w+0VAbf8aYEhHZAUxqbLuqXdEcElwOqd1fdb3ltUeoLwmxXQFQDswWkd8A3wF2GWPKRGQ8MBHvEbBwysP7Ou7H+z99KvCeiEwxxtQv1ppjErA+mKNxDXgP72vyEN68mwNMwe8AkzHGJSLrffua27JwVZTQHKV1jtY5qrk0f2iNozVOY4wxeovQDW9PnsH7YXDgTQYP4v2gve1rk+drsxkQv3U7AyeBP9bbZh7eD+hs3/2Bvu09Xq/dS77t3uu37DVgv9/9r/na3NTIc3jS18ZRb/ldvuWj6y3/oS++br77P/Pdz/Vr0wnvh8QE8Rqu8u0n0O22eu1qgP5+y7r5Xps5fstWADuAWL9ldt+yt+u9Vga4uV48D/iWX+63TIBPfcvzfMtseMew/sGvXQzeBP77Jp7zFb5tXRfgMQM8HWD5/+FNwhH/u9db+G6aQ1qWQ/AWVgZ4qN7yH/uW/4/fa+UGnq3X7m5fuw/8lt2O9wuT8cVwDd6jXjuBB1v4fr8GHG7kcZvv72Ap8E6997TR96qB7YnvufwswGP7gdcCLDfAk77f05t6//3WewpwArZIf670Fr6b5iitc9A6R2/NvGn+0Bqn3uNa4wR50yFa1rAT7z/lUrxHPP6KN2n4e9v4/kJ8rgKSgb+Kd8Zyh683+LBve6N97a7A+4GYX297rwcR10TgqDFmUShPxmcScADv2Ez/+Jbi/Qd/pd/z2GCMOVi7ojHmNPBuCPv6FPhSgNuKeu12G2N2++3nON4jT7kAIpIAjME7AZnHL2YBlnPuNa3lwttz6+9K4KDxO1rle9/y/RsZYzx4jxjcISJdfItvATJ9yxuT7ftZ3EQ7f8V+66n2R3NIM3KI8Y7/Xgb8VESuF5EUEbkV79h+AI+vXSXeeSC+JSJ3+Npdi/eInbu2na/tG3j/6Q8Cso0xa4FH8Raar4jIZeIdRlEm3mERQ5rx2tQRkRHiHQpxDG9OqsF79DEcR9FSgARCyzX+TgB7gWdE5H4R6d9I22K8p3mnNXNfyto0R2mdA1rnqObR/KE1jtY4IdAhWtZwK96EUwEcMIEniKs/g3g338/lDWyzzPczy/fzWL3H698PpCtQGES7QLoBvfB+EBvaNnjj+zzA48HEV6vSBDfzeaBx3FV4TzMG74fOjvf0yICnBYuIzVe0ABw3xrjrNcnCW0zVF+j5/AHv6aB3Ab/Be4r0R8Y743pjauMNZaLDs37rqfZHc8iFgs0h/w9vsfgP3/1TwGPA7zn/NftP3z7/hvfLkBPvUbDH6rXDeK/4shNAvJcQfRzvcAM78BbeQuo637pv+YZPNPQ8GyQiPfF+wduO98oVB/EWQE/hLb5aqjm5po4xxojIdXiPYP430FVE9uG9Ks5L9ZrXjulPaM6+lOVpjrqQ1jkN0zpH+dP8cSGtcVqu3dY42sFjDZ+bc7PDN8TUu3/C9/NeYFuA9rXjSms/lJl4exnxu9+UEs6NuwzVCWAfMLOBx/f7fh5pIJZg4gu3cry91L8F/hyogV/RAxe+J+B9PhcHWH7B8zHGnBCRBcCD/7+9ewuVqo7iOP5dCaWWRFKRhHG8IFSWkFiRFEZCZnaVrJdKIYhAqBTR6EJJVBKpJRlBapaYUfggkihZpqcyCYmi8HT1kj1kWInl5cHVw/pvzz57LmfGM2oz/j6wH9zzn7332TN7zfJ/NbO1wPXEOPbuZJ/9OTWUzfTPvU9aj2JIqZpiiLvvBsZYrP7QnxhSkLU4tefK7QPuNLPzgAvS+fsSk462U9krRDffrWZ2GdGVeL67HzCzuURiMIzyn0F3xhHj/Se5+6/ZTjPrW/ktdakWaw7SOeFidt6Slil3/xm4z8wMGAFMBRaa2XbvOn4+e+8fPb5q+T9SjCqlPKcy5TmSp/hRSjlOz7VsjqMKnub1GRGchrr70irlviB+zCcBL+T231PDOdYRXWtvcfdKXQGzWs8+dAZLiJriiUSr07Yq5/gcmGFmA919F8SM9MQkWieUu/9jZpuIB3RrIcmp1WZgSn5SwfTQT6xQfiFxD94gatVr6RKa3c/BxPegFoOIydFEMoohOR5LV/6WntdHiOdsQ5lye0jdec3sceLH+r1yx7RY6eFKYhx73pnE33pWVrSea83JkpyjLWNmNgwYTbR29oi7H06tUYPLvLyD0sR2QpVjOfCVmU0jJpwcTtcVOwYBu9z9QLn3yylJMarBlOfIKUTxI0c5TqlWznFUwdOk3H2fmc0AXk21rWuI8Y8XEuOrN7j7cnfvMLPlwGyLZfey2eHH13CaZcQKC++Y2fNEEOxHzA4/PwWk71LZ6Wa2hljR4UuiO+AUYL2ZvUSMHz+dWOHhVuD21MVvHrEKwzqLZeey2eHreQD6mdnVZfb/6e71/thPAzYCa81sEVFzfi5wBdDL3Wd18/43gZnAyhQY9xCtVVntcJdkyt03Wywjeh2wIN2Tqtx9p5ntIILqsnJF8v9IwXwUMWGcCKAYkjGzh4iWml+IVqv7idUubsj/58fM7iZaYDqI5/kOYrLBid65Ekf+uL2BBcB0d/877e4AdgELzOx1YtWFHcD3tVxrGR8S3ZXfSvdoADEcYic0bI69jUSsKVoBLDazecQcHSMoLAGaxt6/DLwL/Eh0356crvmjwvGuSucSARSjCpTnFIrk/6E8R4oUP4JynG61Zo7jJ2AmZ20VZ9SeTPxIDa1Spi2VeaDC6+OBj4lWkQPEF2wxcEmuTF/iR28vsaTgKqL2s9sZx4na1xeJB/QwkQi8T+fs7r2Irr6/Ez/qnntvb6Jr3jYiIO0lAufT5GaTJ5KKTUQA2k2MC38mf6wq92cDlVeXWF0o117m/dspzJJOjOtckf6mQ0Qt8SpgfOFelZ3pnQjOH6TPYw/x8M9M13R2mfKPpdcureO7Mwf4ubCvTzrOE4X92Wc9/GR/57U1dlMMOVquJzFkKpGUHEzHX1nuWSRa974hVlzYR7Tcja5y3NnA+jL7RwFbiGWCtwAj6/i8S+JOuq5t6fq/JVodu3wOHOMKE6ncTelzaSvsP40Yn78j3ZO1ROzLrzBxPrCUSO7+Tff3E+DGwrEGpnNMONnPlLbGbihGZeWU5yjP0VbnpvhxtJxyHOU4dW2WTiwix5GZrQYudvchZV77FDji7tfWcbwhRMAe4+7tad/lRAvAve6+LFf2NSLpqfn4IiIQE64CPwBL3P3Z43SOmURL3xAvndBVRJqA8hwRaTatmuNoiJZIg6Xxl/uJgNEPuAu4mXi4szJnEDXyY4FrgNvqOYe7/2Rmm456/QAAAOVJREFUS4BZZjYFGEm0kO2nc6Z8zCzrjjmuB3+SiJyi3P2ImT0FzDWzuV7D8Ip6pG7eDwOzVLkj0hyU54hIK2jVHEcVPCKNdwh4FLiI6JrZQXQdXZQrM4CYAO4v4Dl3X3UM53kSeJBIrOYAXwNj3T0/Q3sbMT5Wc1uIyLFaTsxb0EbnfAKN0kYM73i7wccVkeNHeY6ItIqWy3E0REtEREREREREpMk1agZqERERERERERE5SVTBIyIiIiIiIiLS5FTBIyIiIiIiIiLS5FTBIyIiIiIiIiLS5FTBIyIiIiIiIiLS5P4DCRG9D8gn7AkAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=8000, lr=.1, rqps=100000, rtail='99', \n", + " mpred=['energy', 'time'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 93, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n", + "mse_loss_time=0.7063962132539772 loss_time=0.84047 us zeta=58.75666427612305 alpha=0.4095919132232666 phi=0.4501851797103882\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mse_loss_time=0.05258496150790794 loss_time=0.22931 us zeta=119.68704223632812 alpha=-1.7928632497787476 phi=0.7653049230575562\n", + "mse_loss_time=0.037903563303001694 loss_time=0.19469 us zeta=178.44497680664062 alpha=-1.3998749256134033 phi=0.7265582084655762\n", + "mse_loss_time=0.028653752967152106 loss_time=0.16927 us zeta=235.4379119873047 alpha=-1.122263789176941 phi=0.7052175402641296\n", + "mse_loss_time=0.022066000569903876 loss_time=0.14855 us zeta=293.00311279296875 alpha=-0.9001413583755493 phi=0.6923113465309143\n", + "mse_loss_time=0.01711212282671693 loss_time=0.13081 us zeta=352.0680847167969 alpha=-0.7109043598175049 phi=0.6843900084495544\n", + "mse_loss_time=0.013514371016960498 loss_time=0.11625 us zeta=412.8857421875 alpha=-0.5608503818511963 phi=0.6902008652687073\n", + "mse_loss_time=0.01034085348416501 loss_time=0.10169 us zeta=475.2510681152344 alpha=-0.39682334661483765 phi=0.6781690120697021\n", + "mse_loss_time=0.008076851267400981 loss_time=0.08987 us zeta=538.729248046875 alpha=-0.2592134177684784 phi=0.6734082698822021\n", + "mse_loss_time=0.006309608728328136 loss_time=0.07943 us zeta=602.6909790039062 alpha=-0.14312918484210968 phi=0.6797636151313782\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([117])) that is different to the input size (torch.Size([1, 117])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=132.26405152130178 loss_energy=11.500610919481703J gamma=0.24812674522399902 beta=0.595555305480957\n", + "loss_energy=2.366172669481497 loss_energy=1.5382368704076421J gamma=-7.627729892730713 beta=-3.2790396213531494\n", + "loss_energy=2.154213232121057 loss_energy=1.467723826924213J gamma=-9.891667366027832 beta=-0.7962259650230408\n", + "loss_energy=2.1274531413060354 loss_energy=1.4585791515396191J gamma=-10.884321212768555 beta=0.29239726066589355\n", + "loss_energy=2.126915760525779 loss_energy=1.4583949261176752J gamma=-11.040889739990234 beta=0.4641020894050598\n", + "loss_energy=2.1269151306024123 loss_energy=1.4583947101530548J gamma=-11.046404838562012 beta=0.47015172243118286\n", + "loss_energy=2.1269151305868457 loss_energy=1.4583947101477177J gamma=-11.04642105102539 beta=0.47016939520835876\n", + "loss_energy=2.1269151305839085 loss_energy=1.4583947101467107J gamma=-11.046426773071289 beta=0.4701755940914154\n", + "loss_energy=2.126915130582066 loss_energy=1.458394710146079J gamma=-11.04643440246582 beta=0.4701838791370392\n", + "loss_energy=2.126915130582066 loss_energy=1.458394710146079J gamma=-11.04643440246582 beta=0.4701838791370392\n", + "measurement tensor(-6.9706, dtype=torch.float64) tensor(1.0650, dtype=torch.float64)\n", + "measurement tensor(356.7556, dtype=torch.float64) tensor(77.6052, dtype=torch.float64)\n", + "SYS linux_tuned\n", + "mse_loss_time=0.5576192719308424 loss_time=0.74674 us zeta=185.45164489746094 alpha=0.42133188247680664 phi=0.8375769853591919\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " zeta = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "mse_loss_time=0.019929630491325675 loss_time=0.14117 us zeta=210.9813995361328 alpha=-1.4780915975570679 phi=0.6359609961509705\n", + "mse_loss_time=0.01772182487312631 loss_time=0.13312 us zeta=253.4729461669922 alpha=-1.3016544580459595 phi=0.6178131103515625\n", + "mse_loss_time=0.01560013120309461 loss_time=0.1249 us zeta=305.685302734375 alpha=-1.121045470237732 phi=0.6004408597946167\n", + "mse_loss_time=0.013739955767639428 loss_time=0.11722 us zeta=364.3681640625 alpha=-0.9512237906455994 phi=0.5852888822555542\n", + "mse_loss_time=0.012171423387612868 loss_time=0.11032 us zeta=427.0919189453125 alpha=-0.79744553565979 phi=0.5726999640464783\n", + "mse_loss_time=0.010879135853166714 loss_time=0.1043 us zeta=491.48291015625 alpha=-0.6606760025024414 phi=0.5621813535690308\n", + "mse_loss_time=0.00980667778089382 loss_time=0.09903 us zeta=556.9212646484375 alpha=-0.5389081835746765 phi=0.5536491274833679\n", + "mse_loss_time=0.008908985367173982 loss_time=0.09439 us zeta=623.0147094726562 alpha=-0.4293878376483917 phi=0.5465350151062012\n", + "mse_loss_time=0.008152292358506128 loss_time=0.09029 us zeta=689.4158325195312 alpha=-0.3303106129169464 phi=0.5406104922294617\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([75])) that is different to the input size (torch.Size([1, 75])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy=156.53206452482686 loss_energy=12.511277493718492J gamma=0.34488630294799805 beta=1.4379456043243408\n", + "loss_energy=0.3158698005582037 loss_energy=0.562022953764527J gamma=-6.370461940765381 beta=-4.369227886199951\n", + "loss_energy=0.27661806944211764 loss_energy=0.5259449300469752J gamma=-7.347050189971924 beta=-3.389430522918701\n", + "loss_energy=0.2332069954295604 loss_energy=0.4829151016789187J gamma=-8.719482421875 beta=-2.012488603591919\n", + "loss_energy=0.20102864553473876 loss_energy=0.4483621812048143J gamma=-10.247498512268066 beta=-0.4794517755508423\n", + "loss_energy=0.1872637262251068 loss_energy=0.43273979043428257J gamma=-11.51304817199707 beta=0.790255606174469\n", + "loss_energy=0.1847046957126514 loss_energy=0.42977284199057J gamma=-12.181934356689453 beta=1.461340308189392\n", + "loss_energy=0.18457346476815378 loss_energy=0.42962014008674426J gamma=-12.356884956359863 beta=1.636865258216858\n", + "loss_energy=0.18457259800161746 loss_energy=0.42961913132636154J gamma=-12.37208080291748 beta=1.652111530303955\n", + "loss_energy=0.18457259781612687 loss_energy=0.42961913111048355J gamma=-12.372286796569824 beta=1.652317762374878\n", + "measurement tensor(-7.4312, dtype=torch.float64) tensor(0.5554, dtype=torch.float64)\n", + "measurement tensor(403.0347, dtype=torch.float64) tensor(56.1946, dtype=torch.float64)\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=10000, lr=.1, rqps=200000, rtail='99', \n", + " mpred=['energy', 'time'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 95, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2.5231561490262545e-06 6.14421235332821e-06\n" + ] + } + ], + "source": [ + "print(math.exp(-12.89), math.exp(-12))" + ] + }, + { + "cell_type": "code", + "execution_count": 247, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " AA = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " BB = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy 650023.282771559 -0.9005858898162842\n", + "loss_energy 1781.3906453932466 288.5133361816406\n", + "loss_energy 1781.3704988446007 288.5961608886719\n", + "loss_energy 1781.37049804096 288.596435546875\n", + "loss_energy 1781.3704977830323 288.5965881347656\n", + "loss_energy 1782.110535853954 288.595458984375\n", + "loss_energy 1781.3704976513366 288.5967712402344\n", + "SYS linux_tuned\n", + "loss_energy 618320.9215949354 -1.3064892292022705\n", + "loss_energy 1158.7568419057247 388.2728576660156\n", + "loss_energy 1145.158582090512 390.5665283203125\n", + "loss_energy 1145.158580522677 390.5669250488281\n", + "loss_energy 1145.1585840246396 390.5675048828125\n", + "loss_energy 1145.3135048206834 390.5669250488281\n", + "loss_energy 1198.5300801030876 390.58154296875\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=7000, lr=1, rqps=600000, rtail='90', \n", + " mpred=['energy'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " AA = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " BB = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy 920247.299989853 -1.836015224456787\n", + "loss_energy 8542.277643926016 422.97113037109375\n", + "loss_energy 8442.651885842135 427.45745849609375\n", + "loss_energy 8442.651882791135 427.45782470703125\n", + "loss_energy 8442.651881373737 427.4580993652344\n", + "loss_energy 8442.651880908845 427.458251953125\n", + "loss_energy 8442.651880740501 427.4583435058594\n", + "SYS linux_tuned\n", + "loss_energy 1624253.6814025042 1.827260971069336\n", + "loss_energy 21957.93905157295 552.2310180664062\n", + "loss_energy 16744.13391287446 585.412353515625\n", + "loss_energy 16744.118224289323 585.4678955078125\n", + "loss_energy 16744.11821034165 585.46875\n", + "loss_energy 16744.11820501437 585.4692993164062\n", + "loss_energy 16744.118203285358 585.4696044921875\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=7000, lr=1, rqps=200000, rtail='90', \n", + " mpred=['energy'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 241, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " AA = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " BB = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy 55837.02530966488 -1.1267814636230469\n", + "loss_energy 14591.49312039786 90.1004867553711\n", + "loss_energy 14591.49312039786 90.1004867553711\n", + "loss_energy 14591.493120396959 90.10047912597656\n", + "loss_energy 14591.493120396959 90.10047912597656\n", + "loss_energy 14591.493120396632 90.10047149658203\n", + "loss_energy 14591.493120396632 90.10047149658203\n", + "loss_energy 14591.493120396632 90.10047149658203\n", + "SYS linux_tuned\n", + "loss_energy 198472.27790596543 -0.3984062671661377\n", + "loss_energy 16725.156715829336 193.77761840820312\n", + "loss_energy 16725.156715721296 193.77767944335938\n", + "loss_energy 16725.15671564918 193.77774047851562\n", + "loss_energy 16725.156715618654 193.7777862548828\n", + "loss_energy 16725.156715612968 193.77780151367188\n", + "loss_energy 16725.15671560953 193.77781677246094\n", + "loss_energy 16725.15671560833 193.77783203125\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=8000, lr=1, rqps=400000, rtail='90', \n", + " mpred=['energy'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 242, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "SYS ebbrt_tuned\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " AA = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " BB = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":27: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_energy 20526.91971893835 -0.07694077491760254\n", + "loss_energy 17282.64256848035 -25.68421745300293\n", + "loss_energy 17282.64256848035 -25.68421745300293\n", + "loss_energy 17282.64256848035 -25.68421745300293\n", + "loss_energy 17282.64256848035 -25.68421745300293\n", + "loss_energy 17282.64256848035 -25.68421745300293\n", + "loss_energy 17282.64256848037 -25.684219360351562\n", + "loss_energy 17282.64256848037 -25.684219360351562\n", + "SYS linux_tuned\n", + "loss_energy 46337.521686803106 1.054931402206421\n", + "loss_energy 13646.841189580799 76.79198455810547\n", + "loss_energy 13646.841189580799 76.79198455810547\n", + "loss_energy 13646.841189579627 76.7919921875\n", + "loss_energy 13646.841189579121 76.79199981689453\n", + "loss_energy 13646.841189579121 76.79199981689453\n", + "loss_energy 13646.841189579121 76.79199981689453\n", + "loss_energy 13646.841189579121 76.79199981689453\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=8000, lr=1, rqps=600000, rtail='90', \n", + " mpred=['energy'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_q = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "429.2119140625 -0.5882140398025513 -0.9464408159255981 35.02513128982781\n", + "211.00961303710938 3.6269314289093018 0.9249256253242493 0.24131148425789484\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_comb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrqps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtail\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'90'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0mrun_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_comb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrqps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m400000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtail\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'90'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mrun_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_comb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrqps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m600000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtail\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'90'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36mrun_time\u001b[0;34m(df_comb, n_iter, lr, rqps, rtail, msys)\u001b[0m\n\u001b[1;32m 10\u001b[0m \u001b[0;31m#plt.plot(d[:,0], d[:,1], 'p')\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 12\u001b[0;31m \u001b[0mpred_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmax_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitr_suppress\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minference_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'mcd'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprint_freq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 13\u001b[0m \u001b[0mtnum\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 14\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mrqps\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m200000\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36minference_time\u001b[0;34m(d, n_iter, lr, workload, sys, print_freq)\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 43\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0m_\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mn_iter\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 44\u001b[0;31m \u001b[0mp_busy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mp_static_busy\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mp_busy_min\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mdvfs\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0mbeta\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 45\u001b[0m \u001b[0mt_busy\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mmax_time\u001b[0m \u001b[0;34m/\u001b[0m \u001b[0mdvfs\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0malpha\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 46\u001b[0m \u001b[0mpred_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mitr_suppress\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0mitr\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mt_busy\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "def run_time(df_comb, n_iter=2000, lr=1, rqps=400000, rtail='99', msys=['ebbrt_tuned']): \n", + " df_comb = df_comb[df_comb['QPS'] == rqps]\n", + "\n", + " for sys in msys:\n", + " df = df_comb[(df_comb['sys']==sys)].copy()\n", + " rt = 'read_'+rtail+'th_mean'\n", + " df = df[['joules_mean','itr', 'dvfs', 'QPS', rt]]\n", + " d = df.values\n", + " d = torch.tensor(d)\n", + " #plt.plot(d[:,0], d[:,1], 'p')\n", + "\n", + " pred_time, max_time, alpha, itr_suppress = inference_time(d, n_iter, lr, 'mcd', sys, print_freq=1000)\n", + " tnum = 0\n", + " if rqps == 200000:\n", + " if sys == 'linux_tuned':\n", + " tnum=345\n", + " else:\n", + " tnum=246\n", + " elif rqps == 400000:\n", + " if sys == 'linux_tuned':\n", + " tnum=318\n", + " #df[f'pre_energy lr={lr}'] = pred_energy.view(318, 1).detach().numpy()\n", + " #df[f'pre_time lr={lr}'] = pred_time.view(318, 1).detach().numpy()\n", + " else:\n", + " tnum=245\n", + " #df[f'pre_energy lr={lr}'] = pred_energy.view(245, 1).detach().numpy()\n", + " #df[f'pre_time lr={lr}'] = pred_time.view(245, 1).detach().numpy()\n", + " if rqps == 600000:\n", + " if sys == 'linux_tuned':\n", + " tnum=202\n", + " else:\n", + " tnum=246\n", + " df[f'pre_time lr={lr}'] = pred_time.view(tnum, 1).detach().numpy()\n", + "\n", + "# for pred_name in ['time', 'energy']:\n", + "# if pred_name == 'energy':\n", + "# pred = pred_energy\n", + "# qps = d[:,3]\n", + "# yvalue = d[:,0]/(qps*20)\n", + "# else:\n", + " pred = pred_time\n", + " yvalue = d[:,4]\n", + " fig, ax = plt.subplots()\n", + " plt.title(f'pred:time mcd sys={sys} lr={lr} tail={rtail} \\n QPS={rqps} max_time={round(max_time.item(),2)} \\n alpha={round(alpha.item(),2)} itr_suppress={round(itr_suppress.item(),2)}')\n", + " plt.xlabel(u\"predictions\")\n", + " plt.ylabel('time')\n", + " scatter = ax.scatter(pred.detach().numpy()[0], yvalue, marker = 'o', s = d[:,1], c = d[:,2], alpha=0.3)\n", + " legend1 = ax.legend(*scatter.legend_elements(),loc=\"upper left\", title=\"dvfs\")\n", + " ax.add_artist(legend1)\n", + " handles, labels = scatter.legend_elements(prop=\"sizes\", alpha=0.6)\n", + " legend2 = plt.legend(handles, labels, loc=\"lower right\", title=\"itr\")\n", + " ax.add_artist(legend2)\n", + " \n", + "run_time(df_comb, rqps=200000, rtail='90')\n", + "run_time(df_comb, rqps=400000, rtail='90')\n", + "run_time(df_comb, rqps=600000, rtail='90')" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":16: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True)\n", + ":17: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":18: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":19: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":20: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n" + ] + }, + { + "ename": "AttributeError", + "evalue": "'float' object has no attribute 'item'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mAttributeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun_energy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_comb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrqps\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m200000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrtail\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'90'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mrun_energy\u001b[0;34m(df_comb, n_iter, lr, rqps, rtail, msys)\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 39\u001b[0m \u001b[0mfig\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 40\u001b[0;31m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'pred:energy mcd sys={sys} lr={lr} tail={rtail} \\n QPS={rqps} max_time={round(max_time.item(),2)} \\n alpha={round(alpha.item(),2)} beta={round(beta.item(),2)} \\n p_detect={round(p_detect.item(),2)}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 41\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxlabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mu\"predictions\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 42\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mylabel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'energy'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mAttributeError\u001b[0m: 'float' object has no attribute 'item'" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/analysis/run_netpipe.ipynb b/analysis/run_netpipe.ipynb new file mode 100644 index 0000000..c68ccbd --- /dev/null +++ b/analysis/run_netpipe.ipynb @@ -0,0 +1,2165 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('../bayesopt')\n", + "\n", + "import read_agg_data\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.autograd as auto\n", + "import torch.optim as optim\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as plt\n", + "import pandas as pd\n", + "\n", + "import pdb\n", + "import math\n", + "\n", + "dvfs_dict = {\n", + " \"0xc00\" : 1.2,\n", + " \"0xd00\" : 1.3,\n", + " \"0xe00\" : 1.4,\n", + " \"0xf00\" : 1.5,\n", + " \"0x1000\" : 1.6,\n", + " \"0x1100\" : 1.7,\n", + " \"0x1200\" : 1.8,\n", + " \"0x1300\" : 1.9,\n", + " \"0x1400\" : 2.0,\n", + " \"0x1500\" : 2.1,\n", + " \"0x1600\" : 2.2,\n", + " \"0x1700\" : 2.3,\n", + " \"0x1800\" : 2.4,\n", + " \"0x1900\" : 2.5,\n", + " \"0x1a00\" : 2.6,\n", + " \"0x1b00\" : 2.7,\n", + " \"0x1c00\" : 2.8,\n", + " \"0x1d00\" : 2.9,\n", + " \"0xffff\" : 3.0,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.11199999999999999\n", + "Index(['sys', 'i', 'msg', 'itr', 'dvfs', 'rapl', 'time', 'tput', 'joules',\n", + " 'rx_bytes', 'tx_bytes', 'instructions', 'cycles', 'ref_cycles',\n", + " 'llc_miss', 'c1', 'c1e', 'c3', 'c6', 'c7', 'num_interrupts'],\n", + " dtype='object')\n", + "[ 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 2 4]\n", + "[1.2 1.5 1.6 1.7 1.8 2. 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9 1.3 1.9 2.1]\n", + "[ 8192 65536 524288 64]\n", + "['ebbrt_tuned' 'linux_tuned']\n", + "1259\n" + ] + } + ], + "source": [ + "#df_comb, _, _ = read_agg_data.start_analysis('mcd') #DATA\n", + "#df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: int(x, base=16))\n", + "\n", + "df_comb = pd.read_csv('/home/handong/jupyter/jupyter-notebooks/nic-tuning-experiments/bayesopt/summary_data/netpipe_combined.csv', sep=' ')\n", + "df_comb = df_comb[(df_comb['i'] == 1) & (df_comb['rapl'] == 135)]\n", + "#df_comb = df_comb[(df_comb['rapl'] == 135)]\n", + "\n", + "df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: dvfs_dict[x])\n", + "df_comb = df_comb[(df_comb['itr']!=1) & (df_comb['dvfs']!=65535)] #filter out linux dynamic\n", + "\n", + "print(df_comb['time'].min())\n", + "print(df_comb.columns)\n", + "print(df_comb['itr'].unique())\n", + "print(df_comb['dvfs'].unique())\n", + "print(df_comb['msg'].unique())\n", + "print(df_comb['sys'].unique())\n", + "print(df_comb.shape[0])\n", + "\n", + "# df_comb['dvfs'] = df_comb['dvfs'].astype(float) / df_comb['dvfs'].min()\n", + "# print(df_comb['dvfs'].unique())\n", + "# df_comb['itr'] = df_comb['itr'].astype(float) / df_comb['itr'].min()\n", + "# print(df_comb['itr'].unique())\n", + "#print(10**6)" + ] + }, + { + "cell_type": "code", + "execution_count": 161, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "7.5\n", + "******* ebbrt_tuned 6 8192\n", + " joules itr dvfs time num_interrupts\n", + "1 4.36 6 1.2 0.234 19979\n", + "143 4.88 6 1.5 0.244 21176\n", + "325 4.76 6 1.7 0.220 17655\n", + "391 5.36 6 1.8 0.243 20307\n", + "468 4.68 6 2.2 0.217 25768\n", + "501 4.79 6 2.3 0.218 25520\n", + "528 4.84 6 2.4 0.218 25491\n", + "561 4.85 6 2.5 0.217 25406\n", + "588 4.95 6 2.6 0.218 25589\n", + "647 5.24 6 2.9 0.221 25753\n", + "\n", + "7.5\n", + "******* ebbrt_tuned 8 8192\n", + " joules itr dvfs time num_interrupts\n", + "12 5.54 8 1.2 0.297 20094\n", + "154 5.55 8 1.5 0.278 17962\n", + "334 5.61 8 1.7 0.259 18201\n", + "400 6.10 8 1.8 0.279 19294\n", + "474 4.99 8 2.2 0.240 21966\n", + "504 5.05 8 2.3 0.238 21867\n", + "534 4.92 8 2.4 0.230 20924\n", + "564 5.05 8 2.5 0.232 21062\n", + "594 5.10 8 2.6 0.230 20958\n", + "653 5.17 8 2.9 0.227 20976\n", + "\n", + "7.5\n", + "******* ebbrt_tuned 10 8192\n", + " joules itr dvfs time num_interrupts\n", + "23 5.44 10 1.2 0.291 15615\n", + "165 5.80 10 1.5 0.291 16875\n", + "343 6.22 10 1.7 0.289 19092\n", + "409 6.18 10 1.8 0.283 17883\n", + "480 5.07 10 2.2 0.245 19950\n", + "507 5.07 10 2.3 0.242 19857\n", + "540 5.09 10 2.4 0.242 20014\n", + "567 5.13 10 2.5 0.242 20105\n", + "600 5.15 10 2.6 0.242 20211\n", + "659 5.35 10 2.9 0.241 20150\n", + "\n", + "7.5\n", + "******* ebbrt_tuned 12 8192\n", + " joules itr dvfs time num_interrupts\n", + "34 5.46 12 1.2 0.291 18432\n", + "176 6.06 12 1.5 0.304 17847\n", + "352 6.48 12 1.7 0.300 19192\n", + "418 6.33 12 1.8 0.290 18254\n", + "486 5.42 12 2.2 0.265 19486\n", + "510 5.41 12 2.3 0.263 19390\n", + "546 5.39 12 2.4 0.262 19444\n", + "570 5.41 12 2.5 0.262 19704\n", + "606 5.43 12 2.6 0.262 19778\n", + "665 5.38 12 2.9 0.264 20282\n", + "\n", + "7.5\n", + "******* ebbrt_tuned 16 8192\n", + " joules itr dvfs time num_interrupts\n", + "53 5.55 16 1.2 0.296 17505\n", + "194 6.64 16 1.5 0.334 18015\n", + "361 5.84 16 1.7 0.326 19845\n", + "427 5.89 16 1.8 0.326 19849\n", + "489 6.13 16 2.2 0.326 19844\n", + "513 6.04 16 2.3 0.326 19835\n", + "549 6.05 16 2.4 0.325 19798\n", + "573 6.09 16 2.5 0.325 19783\n", + "609 6.03 16 2.6 0.325 19750\n", + "668 6.03 16 2.9 0.324 19741\n", + "\n", + "7.5\n", + "******* ebbrt_tuned 20 8192\n", + " joules itr dvfs time num_interrupts\n", + "68 4.80 20 1.2 0.270 13082\n", + "209 4.57 20 1.5 0.257 12489\n", + "367 4.65 20 1.7 0.262 12791\n", + "433 4.49 20 1.8 0.252 12289\n", + "492 4.52 20 2.2 0.256 12462\n", + "516 4.55 20 2.3 0.257 12521\n", + "552 4.66 20 2.4 0.262 12791\n", + "576 4.47 20 2.5 0.252 12299\n", + "612 4.39 20 2.6 0.248 12075\n", + "671 4.65 20 2.9 0.262 12795\n", + "\n", + "7.5\n", + "******* ebbrt_tuned 24 8192\n", + " joules itr dvfs time num_interrupts\n", + "84 7.50 24 1.2 0.401 14915\n", + "223 4.39 24 1.5 0.248 10078\n", + "373 4.42 24 1.7 0.250 10170\n", + "438 4.37 24 1.8 0.247 10053\n", + "495 4.40 24 2.2 0.249 10111\n", + "519 4.56 24 2.3 0.258 10463\n", + "555 4.42 24 2.4 0.248 10079\n", + "579 4.41 24 2.5 0.248 10062\n", + "615 4.39 24 2.6 0.248 10070\n", + "674 4.41 24 2.9 0.248 10077\n", + "\n", + "7.5\n", + "******* ebbrt_tuned 28 8192\n", + " joules itr dvfs time num_interrupts\n", + "100 5.08 28 1.2 0.287 10009\n", + "234 5.07 28 1.5 0.288 10021\n", + "379 5.09 28 1.7 0.287 10006\n", + "444 5.06 28 1.8 0.287 10006\n", + "498 5.10 28 2.2 0.287 10007\n", + "522 5.08 28 2.3 0.287 10006\n", + "558 5.10 28 2.4 0.287 10005\n", + "582 5.09 28 2.5 0.287 10004\n", + "618 5.10 28 2.6 0.287 10004\n", + "677 5.10 28 2.9 0.287 10007\n", + "\n" + ] + } + ], + "source": [ + "#6 8 10 12 16 20 24 28\n", + "for itr in [6, 8, 10, 12, 16, 20, 24, 28]:\n", + " for sys in ['ebbrt_tuned']:\n", + " for msg in [8192]:\n", + " df = df_comb[(df_comb['sys']==sys) & (df_comb['msg'] == msg)].copy()\n", + " #print(df.shape[0])\n", + " print(df['joules'].max())\n", + " #df['joules_per_interrupt'] = df['joules']/df['num_interrupts']\n", + " df = df[['joules','itr', 'dvfs', 'time', 'num_interrupts']]\n", + " #print(df.shape[0])\n", + " #print('')\n", + " \n", + " dfi = df[df['itr']==itr]\n", + " #dfi = dfi.drop_duplicates(subset = [\"itr\", \"dvfs\"])\n", + " #dfi['joules_mean'] = dfi['joules_mean']/dfi['joules_mean'].max()\n", + " #print(dfi.diff())\n", + " print('*******', sys, itr, msg)\n", + " print(dfi.sort_values(by=['dvfs']))\n", + " #print(dfi.sort_values(by=['dvfs']).diff())\n", + " print('')\n", + " #plt.plot(dfi['dvfs'], dfi['joules_per_interrupt'])\n", + " #print(dfi)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.5536e-06\n" + ] + } + ], + "source": [ + "print(8192*8/(10**10))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 218, + "metadata": {}, + "outputs": [], + "source": [ + "def inference(d, n_iter, lr, workload, sys, print_freq=10):\n", + " # p_busy_min = 20\n", + " p_static = {\n", + " 'c1':1.5, \n", + " 'c3':0.5,\n", + " 'c4':0.25,\n", + " 'c7':34, # 34 Watts\n", + " 'busy': 10\n", + " }\n", + " chosen_sleep = 'c7'\n", + "\n", + " p_q = p_static[chosen_sleep]/10**6 # joules/us idle\n", + " # p_detect = p_static[chosen_sleep]\n", + "\n", + " #:16: UserWarning: To copy construct from a tensor, \n", + " # it is recommended to use sourceTensor.clone().detach() \n", + " # or sourceTensor.clone().detach().requires_grad_(True), \n", + " # rather than torch.tensor(sourceTensor).\n", + " \n", + " #starts randomly\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(0, 0.2), requires_grad=True)\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(0.6, 0.7), requires_grad=True)\n", + " eta = torch.tensor(torch.Tensor(1,1).uniform_(3, 3), requires_grad=True)\n", + " #beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2)).clone().detach()\n", + " #p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35)).clone().detach()\n", + " #p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35)).clone().detach()\n", + " #p_q = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " #p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35)).clone().detach()\n", + " #itr_suppress = torch.rand(1, requires_grad=True)\n", + " #itr_suppress = torch.tensor(1., requires_grad=True)\n", + " \n", + " AA = torch.tensor(torch.Tensor(1,1).uniform_(20, 21), requires_grad=True)\n", + " BB = torch.tensor(torch.Tensor(1,1).uniform_(2, 3), requires_grad=True)\n", + " CC = torch.tensor(torch.Tensor(1,1).uniform_(12, 14), requires_grad=True)\n", + " #gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1)).clone().detach()\n", + " \n", + " #df[['joules_mean','itr', 'dvfs', 'QPS', 'read_99th_mean']]\n", + " #df[['joules', 'itr', 'dvfs', 'time', 'num_interrupts']]\n", + " ninterrupts = d[:,4]\n", + " energy = (d[:,0]/5000) ## joules/num_interrupts\n", + " itr = d[:,1]\n", + " dvfs = d[:,2]\n", + " time = (d[:,3]/5000)\n", + " msgsize = d[:,5]\n", + " \n", + " current_loss_time = -100\n", + " fixed_max_time = -100\n", + " fixed_alpha = -100\n", + " fixed_beta = -100\n", + " fixed_gamma = -100\n", + " \n", + " criterion = nn.MSELoss()\n", + " #optimizer_time = optim.Adam([max_time, alpha, gamma, delta], lr=lr)\n", + " optimizer_time = optim.Adam([max_time, alpha, beta, gamma], lr=lr)\n", + " optimizer_energy = optim.Adam([AA, BB, CC, eta], lr=lr)\n", + "\n", + " for i in range(n_iter): \n", + " t_busy = (torch.exp(max_time) / dvfs**(1+alpha)) ## dvfs impact on processing\n", + " \n", + " #pred_time = itr_suppress*itr + t_busy\n", + " #pred_time = ((2*((itr*itr_suppress)**beta))/(10**6)) + (gamma*(2*((msgsize*8)/(10**10)))) + (2*t_busy)\n", + " #pred_time = (gamma*itr*itr_suppress)*(dvfs**beta)\n", + " #pred_time = ((2*(((itr**beta))))/(10**6)) + (2*t_busy) \n", + " \n", + " #pred_time = ((2*(((itr**beta))))/(10**6)) + (2*t_busy) + (2*((msgsize*8)/(10**10)))\n", + " beta = gamma*dvfs+delta\n", + " pred_time = ((2*(((itr**beta))))/(10**6)) + (2*t_busy) + (2*((msgsize*8)/(10**10)))\n", + " \n", + " #pred_time = A(itr)**beta*(dvfs**gamma)\n", + " #pred_time = 2*itr**(alpha*dvfs)\n", + " \n", + " #import pdb\n", + " #pdb.set_trace()\n", + " \n", + " #loss_time = criterion(pred_time/time, torch.ones((1,pred_time.shape[1])).double())\n", + " loss_time = criterion(pred_time, time)\n", + " \n", + " if i % 1000 == 0:\n", + " print(f'MSE_loss_time={loss_time.item()} loss_time={round(math.sqrt(loss_time.item())*10**6, 5)} us '\n", + " + f'max_time={max_time.item()} alpha={alpha.item()} gamma={gamma.item()} delta={delta.item()} ')\n", + " \n", + " optimizer_time.zero_grad()\n", + " loss_time.backward()\n", + " optimizer_time.step()\n", + " \n", + " if(current_loss_time == -100):\n", + " current_loss_time = loss_time.item()\n", + " else:\n", + " if(current_loss_time >= loss_time.item()):\n", + " current_loss_time = loss_time.item()\n", + "\n", + " alpha.requires_grad = False\n", + " gamma.requires_grad = False\n", + " delta.requires_grad = False\n", + " max_time.requires_grad = False\n", + "\n", + "# fixed_max_time = max_time.item()\n", + "# fixed_alpha = alpha.item()\n", + "# fixed_delta = delta.item()\n", + "# fixed_gamma = gamma.item()\n", + " \n", + " for i in range(n_iter):\n", + " #fixed_t_busy = (torch.exp(fixed_max_time) / dvfs**(1+fixed_alpha)) ## dvfs impact on processing\n", + " #fixed_beta = fixed_gamma*dvfs+fixed_delta\n", + " #pred_energy = AA*((2*(((itr**fixed_beta))))/(10**6)) + BB*(2*fixed_t_busy) + CC*(2*((msgsize*8)/(10**10)))\n", + " \n", + " t_busy = (torch.exp(max_time) / dvfs**(1+alpha)) ## dvfs impact on processing\n", + " beta = gamma*dvfs+delta\n", + " pred_energy = AA*((2*(((itr**beta))))/(10**6)) + (BB*dvfs**eta+CC)*(2*t_busy) + AA*(2*((msgsize*8)/(10**10)))\n", + " \n", + " ## energy is function of power and work\n", + " ## \n", + " \n", + " #p_busy = (p_q*dvfs**(2+beta))\n", + " #t_busy_energy = (max_time / dvfs**(1+beta))\n", + " #t_q_energy = itr#(fixed_itr_suppress*itr)\n", + " #t_q_energy = (interarrival_time - (fixed_itr_suppress*itr) - t_busy_energy)\n", + " \n", + " #pred_energy = (p_q * t_q_energy) + (p_busy * t_busy_energy)\n", + " #pred_energy = AA*(fixed_itr_suppress*itr)**gamma + BB*dvfs**beta\n", + " #loss_energy = criterion(pred_energy/energy, torch.ones((1,pred_energy.shape[1])).double())\n", + " #pred_energy = ((fixed_itr_suppress*itr*p_q)*((20*(10**6))/(fixed_itr_suppress*itr))) + (dvfs*beta)\n", + " #pred_energy = (gamma*(fixed_itr_suppress*itr))*(dvfs**beta) #+ (AA*(dvfs**beta))\n", + " \n", + " #pred_energy = 2*(gamma+(np.log(itr)))+(2*(beta*np.log(dvfs)))\n", + " \n", + " #pred_energy = (*itr + t_busy_energy)*p_q\n", + " loss_energy = criterion(pred_energy, energy)\n", + "\n", + " if i % 1000 == 0:\n", + " print(f'MSE_loss_energy={loss_energy.item()} loss_energy={math.sqrt(loss_energy.item())}J AA={AA.item()} BB={BB.item()} CC={CC.item()} eta={eta.item()}')\n", + " #print(pred_energy)\n", + " \n", + " optimizer_energy.zero_grad()\n", + " loss_energy.backward(retain_graph=True)\n", + " optimizer_energy.step()\n", + " return pred_energy, pred_time" + ] + }, + { + "cell_type": "code", + "execution_count": 237, + "metadata": {}, + "outputs": [], + "source": [ + "def run(df_comb, n_iter=2000, lr=1, rmsg=64, msys=['ebbrt_tuned'], mpred=['energy', 'time']): \n", + " df_comb = df_comb[df_comb['msg'] == rmsg]\n", + " i=1\n", + " \n", + " for sys in msys:\n", + " df = df_comb[(df_comb['sys']==sys)].copy()\n", + " #df = df[['joules','itr', 'dvfs', 'QPS', 'read_99th', 'num_interrupts']]\n", + " print(df['itr'].unique())\n", + " df = df[['joules', 'itr', 'dvfs', 'time', 'num_interrupts', 'msg']]\n", + " \n", + " tnum = df.shape[0]\n", + " d = df.values\n", + " d = torch.tensor(d)\n", + " print('SYS', sys)\n", + " \n", + " #pred_energy, max_time, alpha, beta, p_detect, p_q = inference_energy(d, n_iter, lr, 'mcd', sys, print_freq=1000)\n", + " pred_energy, pred_time = inference(d, n_iter, lr, 'netpipe', sys, print_freq=1000)\n", + " for pred_name in mpred:\n", + " if pred_name == 'energy':\n", + " #pred_energy = inference(d, n_iter, lr, 'netpipe', sys, print_freq=1000)\n", + " pred = pred_energy\n", + " #yvalue = (d[:,0]/d[:,4]).log()\n", + " yvalue = (d[:,0]/5000)\n", + " #yvalue = d[:,0]\n", + " else:\n", + " #pred_time = inference_time(d, n_iter, lr, 'netpipe', sys, print_freq=1000)\n", + " pred = pred_time\n", + " yvalue = d[:,3]/5000\n", + "\n", + " #fig, ax = plt.subplots()\n", + " ax = plt.subplot(1, len(msys)*len(mpred), i)\n", + " \n", + " if sys == 'ebbrt_tuned':\n", + " plt.title(f'EbbRT @ {rmsg} B', fontsize=18)\n", + " else:\n", + " plt.title(f'Linux @ {rmsg} B', fontsize=18)\n", + " \n", + " if pred_name == 'energy':\n", + " plt.ylabel('Measured Energy (J)', fontsize=18)\n", + " plt.xlabel('Pred. Energy (J)', fontsize=18)\n", + " else:\n", + " plt.ylabel('Measured Time (s)', fontsize=18)\n", + " plt.xlabel('Pred. Time (s)', fontsize=18)\n", + " \n", + " \n", + " #if pred_name == 'time':\n", + " tmax = yvalue.max().item()\n", + " plt.plot(np.linspace(0, tmax, 10), np.linspace(0, tmax, 10))\n", + " \n", + " scatter = ax.scatter(pred.detach().numpy(), yvalue, marker = 'o', \n", + " s = d[:,1], c = d[:,2], alpha=0.5)\n", + " #scatter = ax.scatter(pred.detach().numpy(), yvalue, marker = 'o', alpha=0.3)\n", + " plt.ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(0,0))\n", + " plt.ticklabel_format(axis=\"x\", style=\"sci\", scilimits=(0,0))\n", + " \n", + " legend1 = ax.legend(*scatter.legend_elements(),loc=\"upper left\", title=\"dvfs\")\n", + " ax.add_artist(legend1)\n", + " handles, labels = scatter.legend_elements(prop=\"sizes\", alpha=0.6)\n", + " legend2 = plt.legend(handles, labels, loc=\"lower right\", title=\"itr\")\n", + " ax.add_artist(legend2)\n", + " \n", + " plt.grid(True)\n", + " plt.tight_layout()\n", + " i += 1\n", + " plt.subplots_adjust(wspace=0.3, hspace=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 245, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5.888053484147942e-05 1.9211528815372642e-05 3.073833454742418e-05 2.254493791321221e-05\n" + ] + } + ], + "source": [ + "print(math.exp(-9.74), math.exp(-10.86), math.exp(-10.39), math.exp(-10.7))\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 238, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 10 12 14 16 20 24 28 30]\n", + "SYS ebbrt_tuned\n", + "MSE_loss_time=3.0218504222293235e-10 loss_time=17.38347 us max_time=-10.145421028137207 alpha=0.10499846935272217 gamma=0.10147801786661148 delta=0.6083422303199768 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(0, 0.2), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(0.6, 0.7), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " eta = torch.tensor(torch.Tensor(1,1).uniform_(3, 3), requires_grad=True)\n", + ":35: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " AA = torch.tensor(torch.Tensor(1,1).uniform_(20, 21), requires_grad=True)\n", + ":36: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " BB = torch.tensor(torch.Tensor(1,1).uniform_(2, 3), requires_grad=True)\n", + ":37: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " CC = torch.tensor(torch.Tensor(1,1).uniform_(12, 14), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([49])) that is different to the input size (torch.Size([1, 49])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=5.340832578946276e-11 loss_time=7.3081 us max_time=-10.677175521850586 alpha=-0.21166090667247772 gamma=0.1268179714679718 delta=0.6083422303199768 \n", + "MSE_loss_time=4.376403461759977e-11 loss_time=6.61544 us max_time=-10.755624771118164 alpha=-0.5100135803222656 gamma=0.11052645742893219 delta=0.6083422303199768 \n", + "MSE_loss_time=3.912409090754213e-11 loss_time=6.25493 us max_time=-10.817151069641113 alpha=-0.7148424386978149 gamma=0.100267194211483 delta=0.6083422303199768 \n", + "MSE_loss_time=3.70452771260743e-11 loss_time=6.08648 us max_time=-10.862042427062988 alpha=-0.8507832884788513 gamma=0.09431173652410507 delta=0.6083422303199768 \n", + "MSE_loss_energy=1.2256786277872293e-07 loss_energy=0.0003500969334037688J AA=20.33348274230957 BB=2.751593589782715 CC=12.721452713012695 eta=3.0\n", + "MSE_loss_energy=1.0893079061410922e-08 loss_energy=0.00010436991454155226J AA=18.1312255859375 BB=3.0495898723602295 CC=12.076119422912598 eta=2.5464365482330322\n", + "MSE_loss_energy=1.0681075665653341e-08 loss_energy=0.00010334928962336094J AA=17.582189559936523 BB=4.057217597961426 CC=12.564849853515625 eta=2.217498540878296\n", + "MSE_loss_energy=1.0534018900826634e-08 loss_energy=0.00010263536866415317J AA=17.106246948242188 BB=4.954024314880371 CC=13.014800071716309 eta=2.0012946128845215\n", + "MSE_loss_energy=1.0429801517873887e-08 loss_energy=0.00010212639971072066J AA=16.698612213134766 BB=5.735225200653076 CC=13.414148330688477 eta=1.85045325756073\n", + "[ 6 8 10 12 14 16 20 24 28 30]\n", + "SYS linux_tuned\n", + "MSE_loss_time=5.364051760086207e-09 loss_time=73.23969 us max_time=-11.903712272644043 alpha=-0.780055046081543 gamma=0.11331408470869064 delta=0.6915727257728577 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(0, 0.2), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(0.6, 0.7), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " eta = torch.tensor(torch.Tensor(1,1).uniform_(3, 3), requires_grad=True)\n", + ":35: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " AA = torch.tensor(torch.Tensor(1,1).uniform_(20, 21), requires_grad=True)\n", + ":36: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " BB = torch.tensor(torch.Tensor(1,1).uniform_(2, 3), requires_grad=True)\n", + ":37: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " CC = torch.tensor(torch.Tensor(1,1).uniform_(12, 14), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([50])) that is different to the input size (torch.Size([1, 50])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=4.355920927282618e-11 loss_time=6.59994 us max_time=-9.790987014770508 alpha=-0.3873150050640106 gamma=0.10078710317611694 delta=0.6915727257728577 \n", + "MSE_loss_time=4.062206576178975e-11 loss_time=6.37354 us max_time=-9.746745109558105 alpha=-0.25214552879333496 gamma=0.11091745644807816 delta=0.6915727257728577 \n", + "MSE_loss_time=4.0599879528252786e-11 loss_time=6.3718 us max_time=-9.74301528930664 alpha=-0.2403123825788498 gamma=0.11184757202863693 delta=0.6915727257728577 \n", + "MSE_loss_time=4.0599703090881e-11 loss_time=6.37179 us max_time=-9.742691993713379 alpha=-0.2392820566892624 gamma=0.11192911863327026 delta=0.6915727257728577 \n", + "MSE_loss_energy=2.121346262212193e-08 loss_energy=0.00014564842128262815J AA=20.979084014892578 BB=2.2917745113372803 CC=12.156291007995605 eta=3.0\n", + "MSE_loss_energy=1.726046689300761e-08 loss_energy=0.0001313790961036329J AA=18.13074493408203 BB=3.6570587158203125 CC=13.996180534362793 eta=2.4422571659088135\n", + "MSE_loss_energy=1.70306534511581e-08 loss_energy=0.00013050154578072285J AA=17.01474952697754 BB=4.132777690887451 CC=14.729840278625488 eta=2.360226631164551\n", + "MSE_loss_energy=1.7001574295450266e-08 loss_energy=0.0001303900851117533J AA=16.64207649230957 BB=4.213076591491699 CC=15.074294090270996 eta=2.358264684677124\n", + "MSE_loss_energy=1.6995594484764323e-08 loss_energy=0.00013036715262965714J AA=16.520654678344727 BB=4.169751167297363 CC=15.274040222167969 eta=2.377548933029175\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df_comb = pd.read_csv('/home/handong/jupyter/jupyter-notebooks/nic-tuning-experiments/bayesopt/summary_data/netpipe_combined.csv', sep=' ')\n", + "df_comb = df_comb[(df_comb['i'] == 1) & (df_comb['rapl'] == 135)]\n", + "#df_comb = df_comb[(df_comb['rapl'] == 135)]\n", + "\n", + "df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: dvfs_dict[x])\n", + "df_comb = df_comb[(df_comb['itr']!=1) & (df_comb['dvfs']!=65535)] #filter out linux dynamic\n", + "df_comb = df_comb[(df_comb['dvfs'] < 1.9)]\n", + "df_comb = df_comb[(df_comb['dvfs'] != 1.3)]\n", + "#[ 6 8 10 12 14 16 20 24 28 30]\n", + "#[ 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40]\n", + "df_comb = df_comb[(df_comb['itr'] > 5) & (df_comb['itr'] < 31) & (df_comb['itr'] != 18) & (df_comb['itr'] != 22) & (df_comb['itr'] != 26)]\n", + "#df_comb = df_comb[(df_comb['dvfs'] == 1.3) | (df_comb['dvfs'] == 1.5)| (df_comb['dvfs'] == 1.7) | (df_comb['dvfs'] == 1.9) | (df_comb['dvfs'] == 2.1) | (df_comb['dvfs'] == 2.3) | (df_comb['dvfs'] == 2.5) | (df_comb['dvfs'] == 2.7) | (df_comb['dvfs'] == 2.9)]\n", + "\n", + "#run(df_comb, n_iter=5000, lr=.1, rmsg=65536, mpred=['time', 'energy'], msys=['linux_tuned'])\n", + "plt.rcParams['figure.figsize'] = 16, 6\n", + "run(df_comb, n_iter=5000, lr=.1, rmsg=65536, mpred=['time', 'energy'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 239, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 10 12 16 20 24 28]\n", + "SYS ebbrt_tuned\n", + "MSE_loss_time=2.400763157620155e-10 loss_time=15.4944 us max_time=-10.405628204345703 alpha=0.31501662731170654 gamma=0.09384206682443619 delta=0.6889466643333435 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(0, 0.2), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(0.6, 0.7), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " eta = torch.tensor(torch.Tensor(1,1).uniform_(3, 3), requires_grad=True)\n", + ":35: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " AA = torch.tensor(torch.Tensor(1,1).uniform_(20, 21), requires_grad=True)\n", + ":36: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " BB = torch.tensor(torch.Tensor(1,1).uniform_(2, 3), requires_grad=True)\n", + ":37: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " CC = torch.tensor(torch.Tensor(1,1).uniform_(12, 14), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([32])) that is different to the input size (torch.Size([1, 32])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=5.66985625545491e-11 loss_time=7.52984 us max_time=-10.647892951965332 alpha=-0.054774001240730286 gamma=-0.043771304190158844 delta=0.6889466643333435 \n", + "MSE_loss_time=4.71281007279464e-11 loss_time=6.86499 us max_time=-10.705870628356934 alpha=-0.3536141812801361 gamma=-0.08803153038024902 delta=0.6889466643333435 \n", + "MSE_loss_time=4.294676161784141e-11 loss_time=6.55338 us max_time=-10.751740455627441 alpha=-0.548661470413208 gamma=-0.11911696940660477 delta=0.6889466643333435 \n", + "MSE_loss_time=4.140775609822422e-11 loss_time=6.43489 us max_time=-10.78447437286377 alpha=-0.6657953262329102 gamma=-0.13610464334487915 delta=0.6889466643333435 \n", + "MSE_loss_energy=9.304592907921847e-08 loss_energy=0.0003050343080363559J AA=20.347665786743164 BB=2.9713759422302246 CC=13.417655944824219 eta=3.0\n", + "MSE_loss_energy=2.5519205770692145e-08 loss_energy=0.00015974731850861267J AA=19.074033737182617 BB=2.66755747795105 CC=14.15481185913086 eta=1.5190255641937256\n", + "MSE_loss_energy=2.5274847306562115e-08 loss_energy=0.00015898065073008765J AA=17.649700164794922 BB=2.97647762298584 CC=14.603144645690918 eta=1.4188002347946167\n", + "MSE_loss_energy=2.5047391838927942e-08 loss_energy=0.00015826367820484882J AA=16.261302947998047 BB=3.2922587394714355 CC=15.02282428741455 eta=1.331946849822998\n", + "MSE_loss_energy=2.483793660005215e-08 loss_energy=0.00015760056027835735J AA=14.922731399536133 BB=3.6062493324279785 CC=15.418517112731934 eta=1.2570703029632568\n", + "[ 6 8 10 12 14 16 20 24 28 30]\n", + "SYS linux_tuned\n", + "MSE_loss_time=1.009720585006311e-10 loss_time=10.04849 us max_time=-10.504953384399414 alpha=-0.33561789989471436 gamma=0.19852399826049805 delta=0.6601803302764893 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(0, 0.2), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(0.6, 0.7), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " eta = torch.tensor(torch.Tensor(1,1).uniform_(3, 3), requires_grad=True)\n", + ":35: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " AA = torch.tensor(torch.Tensor(1,1).uniform_(20, 21), requires_grad=True)\n", + ":36: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " BB = torch.tensor(torch.Tensor(1,1).uniform_(2, 3), requires_grad=True)\n", + ":37: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " CC = torch.tensor(torch.Tensor(1,1).uniform_(12, 14), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([50])) that is different to the input size (torch.Size([1, 50])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=1.5295458416055712e-11 loss_time=3.91094 us max_time=-10.435206413269043 alpha=-0.22466744482517242 gamma=0.1316995620727539 delta=0.6601803302764893 \n", + "MSE_loss_time=1.481050121438907e-11 loss_time=3.84844 us max_time=-10.414266586303711 alpha=-0.1592252254486084 gamma=0.13410526514053345 delta=0.6601803302764893 \n", + "MSE_loss_time=1.4664510932807418e-11 loss_time=3.82943 us max_time=-10.4030122756958 alpha=-0.12323295325040817 gamma=0.1354716569185257 delta=0.6601803302764893 \n", + "MSE_loss_time=1.4620012476757754e-11 loss_time=3.82361 us max_time=-10.396893501281738 alpha=-0.10334795713424683 gamma=0.1362440437078476 delta=0.6601803302764893 \n", + "MSE_loss_energy=3.3536464718685247e-08 loss_energy=0.00018312963910488452J AA=20.879465103149414 BB=2.52333664894104 CC=13.815570831298828 eta=3.0\n", + "MSE_loss_energy=5.144879960429955e-09 loss_energy=7.172781859522813e-05J AA=19.566043853759766 BB=2.6061506271362305 CC=13.8846435546875 eta=2.2196929454803467\n", + "MSE_loss_energy=5.125097113530944e-09 loss_energy=7.158978358349007e-05J AA=19.2902889251709 BB=2.858675956726074 CC=13.751870155334473 eta=2.133897066116333\n", + "MSE_loss_energy=5.117221455818939e-09 loss_energy=7.15347569774228e-05J AA=19.235214233398438 BB=3.039240598678589 CC=13.567177772521973 eta=2.0689361095428467\n", + "MSE_loss_energy=5.110814263484433e-09 loss_energy=7.148995917948502e-05J AA=19.22467041015625 BB=3.197415828704834 CC=13.383009910583496 eta=2.0134341716766357\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=5000, lr=.1, rmsg=8192, mpred=['time', 'energy'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 240, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 10 12 14 20 24 28 30 16]\n", + "SYS ebbrt_tuned\n", + "MSE_loss_time=5.402645578169643e-09 loss_time=73.50269 us max_time=-10.856658935546875 alpha=-0.845221996307373 gamma=0.02741457335650921 delta=0.6020134687423706 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(0, 0.2), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(0.6, 0.7), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " eta = torch.tensor(torch.Tensor(1,1).uniform_(3, 3), requires_grad=True)\n", + ":35: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " AA = torch.tensor(torch.Tensor(1,1).uniform_(20, 21), requires_grad=True)\n", + ":36: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " BB = torch.tensor(torch.Tensor(1,1).uniform_(2, 3), requires_grad=True)\n", + ":37: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " CC = torch.tensor(torch.Tensor(1,1).uniform_(12, 14), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([24])) that is different to the input size (torch.Size([1, 24])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=2.7924372202524506e-11 loss_time=5.28435 us max_time=-9.80671215057373 alpha=-1.0148134231567383 gamma=0.010376421734690666 delta=0.6020134687423706 \n", + "MSE_loss_time=2.7908666347007417e-11 loss_time=5.28287 us max_time=-9.804288864135742 alpha=-1.0055655241012573 gamma=0.012113479897379875 delta=0.6020134687423706 \n", + "MSE_loss_time=2.7908633794285966e-11 loss_time=5.28286 us max_time=-9.804195404052734 alpha=-1.0051735639572144 gamma=0.012208165600895882 delta=0.6020134687423706 \n", + "MSE_loss_time=2.790863378907421e-11 loss_time=5.28286 us max_time=-9.804195404052734 alpha=-1.0051727294921875 gamma=0.012208874337375164 delta=0.6020134687423706 \n", + "MSE_loss_energy=1.0596956517583327e-06 loss_energy=0.0010294151989155458J AA=20.03651237487793 BB=2.660541296005249 CC=12.469120979309082 eta=3.0\n", + "MSE_loss_energy=3.14617344329506e-07 loss_energy=0.000560907607658789J AA=17.948144912719727 BB=6.6240129470825195 CC=12.44192123413086 eta=2.557882785797119\n", + "MSE_loss_energy=3.087394897188609e-07 loss_energy=0.0005556433115937426J AA=17.030683517456055 BB=10.814407348632812 CC=14.579850196838379 eta=1.9361456632614136\n", + "MSE_loss_energy=3.0420672848745813e-07 loss_energy=0.0005515493889829433J AA=16.08246612548828 BB=14.947054862976074 CC=17.28221893310547 eta=1.57926344871521\n", + "MSE_loss_energy=3.001447956314168e-07 loss_energy=0.0005478547212824005J AA=15.095080375671387 BB=19.1424503326416 CC=20.352365493774414 eta=1.3367338180541992\n", + "[28 30]\n", + "SYS linux_tuned\n", + "MSE_loss_time=8.565726592095544e-09 loss_time=92.55121 us max_time=-10.58541488647461 alpha=-0.32757866382598877 gamma=0.14850349724292755 delta=0.6673144102096558 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(0, 0.2), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(0.6, 0.7), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " eta = torch.tensor(torch.Tensor(1,1).uniform_(3, 3), requires_grad=True)\n", + ":35: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " AA = torch.tensor(torch.Tensor(1,1).uniform_(20, 21), requires_grad=True)\n", + ":36: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " BB = torch.tensor(torch.Tensor(1,1).uniform_(2, 3), requires_grad=True)\n", + ":37: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " CC = torch.tensor(torch.Tensor(1,1).uniform_(12, 14), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([7])) that is different to the input size (torch.Size([1, 7])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=3.7465522912855256e-12 loss_time=1.9356 us max_time=-9.50551700592041 alpha=-0.40142154693603516 gamma=0.20441022515296936 delta=0.6673144102096558 \n", + "MSE_loss_time=3.741934407245959e-12 loss_time=1.93441 us max_time=-9.505964279174805 alpha=-0.3948402404785156 gamma=0.20570772886276245 delta=0.6673144102096558 \n", + "MSE_loss_time=3.738652128992638e-12 loss_time=1.93356 us max_time=-9.506332397460938 alpha=-0.3892652690410614 gamma=0.20679178833961487 delta=0.6673144102096558 \n", + "MSE_loss_time=3.736328285193816e-12 loss_time=1.93296 us max_time=-9.506634712219238 alpha=-0.3845630884170532 gamma=0.20769570767879486 delta=0.6673144102096558 \n", + "MSE_loss_energy=5.13570804716215e-07 loss_energy=0.0007166385453743156J AA=20.80026626586914 BB=2.424548387527466 CC=13.849628448486328 eta=3.0\n", + "MSE_loss_energy=1.287722854045583e-08 loss_energy=0.00011347787687675439J AA=18.480554580688477 BB=8.813693046569824 CC=9.280960083007812 eta=2.8883984088897705\n", + "MSE_loss_energy=9.90294650912968e-09 loss_energy=9.951354937459361e-05J AA=18.13921546936035 BB=12.30472183227539 CC=7.045486927032471 eta=2.470735549926758\n", + "MSE_loss_energy=8.616454084536105e-09 loss_energy=9.282485704021366e-05J AA=17.952880859375 BB=14.757478713989258 CC=5.265254497528076 eta=2.256196975708008\n", + "MSE_loss_energy=7.885284512201945e-09 loss_energy=8.879912450132571e-05J AA=17.841901779174805 BB=16.61887550354004 CC=3.7719533443450928 eta=2.1200294494628906\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.rcParams['figure.figsize'] = 16, 6\n", + "run(df_comb, n_iter=5000, lr=.1, rmsg=524288, mpred=['time', 'energy'], msys=['ebbrt_tuned', 'linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 174, + "metadata": {}, + "outputs": [], + "source": [ + "def inference_time(d, n_iter, lr, workload, sys, print_freq=10): \n", + " #starts randomly\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + " \n", + " ninterrupts = d[:,4]\n", + " energy = (d[:,0]/ninterrupts).log() ## joules/num_interrupts\n", + " itr = d[:,1]\n", + " dvfs = d[:,2]\n", + " time = (d[:,3]/5000)\n", + " msgsize = d[:,5]\n", + " \n", + " criterion = nn.MSELoss()\n", + " optimizer_time = optim.Adam([max_time, alpha, gamma, delta], lr=lr)\n", + "\n", + " for i in range(n_iter): \n", + " t_busy = (torch.exp(max_time) / dvfs**(1+alpha)) ## dvfs impact on processing\n", + " \n", + " #pred_time = itr_suppress*itr + t_busy\n", + " #pred_time = ((2*((itr*itr_suppress)**beta))/(10**6)) + (gamma*(2*((msgsize*8)/(10**10)))) + (2*t_busy)\n", + " #pred_time = (gamma*itr*itr_suppress)*(dvfs**beta)\n", + " #pred_time = ((2*(((itr**beta))))/(10**6)) + (2*t_busy) \n", + " \n", + " #pred_time = ((2*(((itr**beta))))/(10**6)) + (2*t_busy) + (2*((msgsize*8)/(10**10)))\n", + " beta = gamma*dvfs+delta\n", + " pred_time = ((2*(((itr**beta))))/(10**6)) + (2*t_busy) + (2*((msgsize*8)/(10**10)))\n", + " ## short circuting of ITR, i.e. slow-to-stay-busy effect, send ACK packets during processing\n", + " \n", + " #pred_time = A(itr)**beta*(dvfs**gamma)\n", + " #pred_time = 2*itr**(alpha*dvfs)\n", + " \n", + " #import pdb\n", + " #pdb.set_trace()\n", + " \n", + " #loss_time = criterion(pred_time/time, torch.ones((1,pred_time.shape[1])).double())\n", + " loss_time = criterion(pred_time, time)\n", + " \n", + " if i % 1000 == 0:\n", + " print(f'MSE_loss_time={loss_time.item()} loss_time={round(math.sqrt(loss_time.item())*10**6, 5)} us '\n", + " + f'max_time={max_time.item()} alpha={alpha.item()} gamma={gamma.item()} delta={delta.item()} ')\n", + " \n", + " optimizer_time.zero_grad()\n", + " loss_time.backward()\n", + " optimizer_time.step()\n", + "\n", + "\n", + " return pred_time" + ] + }, + { + "cell_type": "code", + "execution_count": 175, + "metadata": {}, + "outputs": [], + "source": [ + "def inference_energy(d, n_iter, lr, workload, sys, print_freq=10):\n", + " #starts randomly\n", + " #max_time = torch.tensor(torch.Tensor(1,1).uniform_(-5, 5), requires_grad=True)\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " \n", + " ninterrupts = d[:,4]\n", + " energy = (d[:,0]/5000) #(d[:,0]/ninterrupts).log() ## joules/num_interrupts\n", + " itr = d[:,1]\n", + " dvfs = d[:,2]\n", + " time = (d[:,3]/5000)\n", + " msgsize = d[:,5]\n", + " \n", + " criterion = nn.MSELoss()\n", + " optimizer_energy = optim.Adam([alpha, beta], lr=lr)\n", + " \n", + " for i in range(n_iter):\n", + " #p_busy = (p_q*dvfs**(2+beta))\n", + " #t_busy_energy = (max_time / dvfs**(1+beta))\n", + " #t_q_energy = itr#(fixed_itr_suppress*itr)\n", + " #t_q_energy = (interarrival_time - (fixed_itr_suppress*itr) - t_busy_energy)\n", + " \n", + " #pred_energy = (p_q * t_q_energy) + (p_busy * t_busy_energy)\n", + " #pred_energy = AA*(fixed_itr_suppress*itr)**gamma + BB*dvfs**beta\n", + " #loss_energy = criterion(pred_energy/energy, torch.ones((1,pred_energy.shape[1])).double())\n", + " #pred_energy = ((fixed_itr_suppress*itr*p_q)*((20*(10**6))/(fixed_itr_suppress*itr))) + (dvfs*beta)\n", + " #pred_energy = ((alpha*(itr**gamma))*(delta*(dvfs**beta))) #+ (AA*(dvfs**beta))\n", + " \n", + " #pred_energy = (alpha*itr*((msgsize*8)/(10**10)))+(dvfs*beta)\n", + " #pred_energy = alpha*(itr+dvfs)\n", + " #pred_energy = alpha+np.log(itr)+np.log(dvfs)\n", + " \n", + " #pred_energy = (gamma*(fixed_itr_suppress*itr))*(dvfs**beta) #+ (AA*(dvfs**beta)) \n", + " #pred_energy = 2*(gamma+(np.log(itr)))+(2*(beta*np.log(dvfs)))\n", + " \n", + " beta = gamma*dvfs+delta\n", + " pred_time = ((2*(((itr**beta))))/(10**6)) + (2*t_busy) + (2*((msgsize*8)/(10**10)))\n", + " \n", + " loss_energy = criterion(pred_energy, energy)\n", + "\n", + " if i % 1000 == 0:\n", + " print(f'MSE_loss_energy={loss_energy.item()} alpha={alpha.item()} beta={beta.item()}')\n", + " \n", + " optimizer_energy.zero_grad()\n", + " loss_energy.backward(retain_graph=True)\n", + " optimizer_energy.step()\n", + " return pred_energy" + ] + }, + { + "cell_type": "code", + "execution_count": 199, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 202, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40]\n", + "SYS linux_tuned\n", + "MSE_loss_time=6.105263736153823e-08 loss_time=247.08832 us max_time=-11.391225814819336 alpha=0.7308746576309204 gamma=0.3672170639038086 delta=0.6828203201293945 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " eta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":35: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " AA = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":36: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " BB = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":37: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " CC = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=1.771637031682223e-10 loss_time=13.31029 us max_time=-9.615437507629395 alpha=-0.022785352542996407 gamma=0.13984856009483337 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714590118254352e-10 loss_time=13.30962 us max_time=-9.611628532409668 alpha=-0.01429019309580326 gamma=0.1401815116405487 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589440242342e-10 loss_time=13.30962 us max_time=-9.611572265625 alpha=-0.01415804959833622 gamma=0.1401870846748352 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589440238258e-10 loss_time=13.30962 us max_time=-9.611572265625 alpha=-0.014157860539853573 gamma=0.1401870995759964 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589440238258e-10 loss_time=13.30962 us max_time=-9.611572265625 alpha=-0.014157860539853573 gamma=0.1401870995759964 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589440238573e-10 loss_time=13.30962 us max_time=-9.611572265625 alpha=-0.014157861471176147 gamma=0.1401870995759964 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589440238258e-10 loss_time=13.30962 us max_time=-9.611572265625 alpha=-0.014157860539853573 gamma=0.1401870995759964 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589435661429e-10 loss_time=13.30962 us max_time=-9.611571311950684 alpha=-0.014156253077089787 gamma=0.1401871293783188 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589435661429e-10 loss_time=13.30962 us max_time=-9.611571311950684 alpha=-0.014156253077089787 gamma=0.1401871293783188 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589435661429e-10 loss_time=13.30962 us max_time=-9.611571311950684 alpha=-0.014156253077089787 gamma=0.1401871293783188 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589435661429e-10 loss_time=13.30962 us max_time=-9.611571311950684 alpha=-0.014156253077089787 gamma=0.1401871293783188 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589435661429e-10 loss_time=13.30962 us max_time=-9.611571311950684 alpha=-0.014156253077089787 gamma=0.1401871293783188 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589435661429e-10 loss_time=13.30962 us max_time=-9.611571311950684 alpha=-0.014156253077089787 gamma=0.1401871293783188 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589435661429e-10 loss_time=13.30962 us max_time=-9.611571311950684 alpha=-0.014156253077089787 gamma=0.1401871293783188 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589435661429e-10 loss_time=13.30962 us max_time=-9.611571311950684 alpha=-0.014156253077089787 gamma=0.1401871293783188 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589435661429e-10 loss_time=13.30962 us max_time=-9.611571311950684 alpha=-0.014156253077089787 gamma=0.1401871293783188 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589435661429e-10 loss_time=13.30962 us max_time=-9.611571311950684 alpha=-0.014156253077089787 gamma=0.1401871293783188 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589435661429e-10 loss_time=13.30962 us max_time=-9.611571311950684 alpha=-0.014156253077089787 gamma=0.1401871293783188 delta=0.6828203201293945 \n", + "MSE_loss_time=1.7714589435661429e-10 loss_time=13.30962 us max_time=-9.611571311950684 alpha=-0.014156253077089787 gamma=0.1401871293783188 delta=0.6828203201293945 \n", + "loss_energy=2.742938315524056e-05 AA=-1.558868646621704 BB=1.7780506610870361 CC=1.5318777561187744 eta=-0.624751091003418\n", + "loss_energy=1.2896842998016547e-07 AA=10.541085243225098 BB=9.923327445983887 CC=13.26382827758789 eta=1.9530826807022095\n", + "loss_energy=9.898899601075295e-08 AA=14.280488014221191 BB=7.920267105102539 CC=11.377359390258789 eta=2.0297958850860596\n", + "loss_energy=8.329494965732697e-08 AA=17.58542823791504 BB=5.652066230773926 CC=10.52133560180664 eta=2.2014379501342773\n", + "loss_energy=7.779071780087028e-08 AA=19.50945281982422 BB=3.6457278728485107 CC=11.325690269470215 eta=2.505409002304077\n", + "loss_energy=7.639589310537323e-08 AA=20.173030853271484 BB=2.5241403579711914 CC=12.603333473205566 eta=2.802511692047119\n", + "loss_energy=7.630711393312337e-08 AA=20.219968795776367 BB=2.280395269393921 CC=13.071125984191895 eta=2.891216516494751\n", + "loss_energy=7.630584030945464e-08 AA=20.205366134643555 BB=2.2596230506896973 CC=13.139484405517578 eta=2.9000275135040283\n", + "loss_energy=7.63058319393323e-08 AA=20.20335578918457 BB=2.258408308029175 CC=13.145193099975586 eta=2.9005942344665527\n", + "loss_energy=7.63058319148721e-08 AA=20.20330238342285 BB=2.2583558559417725 CC=13.145390510559082 eta=2.9006173610687256\n", + "loss_energy=7.63058319148721e-08 AA=20.20330238342285 BB=2.2583558559417725 CC=13.145390510559082 eta=2.9006173610687256\n", + "loss_energy=7.630583191273894e-08 AA=20.203289031982422 BB=2.258357048034668 CC=13.145406723022461 eta=2.9006175994873047\n", + "loss_energy=7.630583191280847e-08 AA=20.203289031982422 BB=2.258356809616089 CC=13.145406723022461 eta=2.9006173610687256\n", + "loss_energy=7.630583190849225e-08 AA=20.203256607055664 BB=2.258362054824829 CC=13.145442008972168 eta=2.9006168842315674\n", + "loss_energy=7.630583190824978e-08 AA=20.20325469970703 BB=2.25836181640625 CC=13.145444869995117 eta=2.9006171226501465\n", + "loss_energy=7.630583190603286e-08 AA=20.203203201293945 BB=2.2583653926849365 CC=13.145512580871582 eta=2.9006190299987793\n", + "loss_energy=7.63058319050722e-08 AA=20.203195571899414 BB=2.258359909057617 CC=13.145536422729492 eta=2.9006214141845703\n", + "loss_energy=7.630583190441042e-08 AA=20.20320701599121 BB=2.258359432220459 CC=13.145520210266113 eta=2.900620698928833\n", + "loss_energy=7.630583190441042e-08 AA=20.20320701599121 BB=2.258359432220459 CC=13.145520210266113 eta=2.900620698928833\n", + "loss_energy=7.630583190909945e-08 AA=20.203262329101562 BB=2.2583565711975098 CC=13.14544677734375 eta=2.9006192684173584\n", + "yvalue torch.Size([340])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 209, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40]\n", + "SYS linux_tuned\n", + "MSE_loss_time=1.46251835276159e-06 loss_time=1209.34625 us max_time=-11.885858535766602 alpha=0.07132470607757568 gamma=0.8096492290496826 delta=-0.11642897129058838 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([340])) that is different to the input size (torch.Size([1, 340])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=7.072609998480563e-10 loss_time=26.59438 us max_time=-9.570508003234863 alpha=-0.6329886317253113 gamma=0.177914097905159 delta=-0.7460958957672119 \n", + "MSE_loss_time=7.07224096347386e-10 loss_time=26.59369 us max_time=-9.570474624633789 alpha=-0.632748007774353 gamma=0.180347740650177 delta=-0.7442581057548523 \n", + "MSE_loss_time=7.071351205860702e-10 loss_time=26.59201 us max_time=-9.570417404174805 alpha=-0.6322575807571411 gamma=0.18532580137252808 delta=-0.7403994798660278 \n", + "MSE_loss_time=7.068707956200416e-10 loss_time=26.58704 us max_time=-9.570279121398926 alpha=-0.6310902833938599 gamma=0.19618730247020721 delta=-0.7316680550575256 \n", + "MSE_loss_time=7.054387198317444e-10 loss_time=26.5601 us max_time=-9.56977653503418 alpha=-0.6269953846931458 gamma=0.22700776159763336 delta=-0.7059494853019714 \n", + "MSE_loss_time=4.3142341745005496e-10 loss_time=20.77073 us max_time=-9.473012924194336 alpha=-0.21366718411445618 gamma=0.5000976324081421 delta=-0.3887026607990265 \n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mrun\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_comb\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10000\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m.1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mrmsg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m65536\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmpred\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'time'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'energy'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsys\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'linux_tuned'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m\u001b[0m in \u001b[0;36mrun\u001b[0;34m(df_comb, n_iter, lr, rmsg, msys, mpred)\u001b[0m\n\u001b[1;32m 23\u001b[0m \u001b[0;31m#yvalue = d[:,0]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 24\u001b[0m \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 25\u001b[0;31m \u001b[0mpred_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0minference_time\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0md\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mn_iter\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'netpipe'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprint_freq\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1000\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 26\u001b[0m \u001b[0mpred\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpred_time\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 27\u001b[0m \u001b[0myvalue\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0md\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m/\u001b[0m\u001b[0;36m5000\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m\u001b[0m in \u001b[0;36minference_time\u001b[0;34m(d, n_iter, lr, workload, sys, print_freq)\u001b[0m\n\u001b[1;32m 37\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 38\u001b[0m \u001b[0;31m#loss_time = criterion(pred_time/time, torch.ones((1,pred_time.shape[1])).double())\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 39\u001b[0;31m \u001b[0mloss_time\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcriterion\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpred_time\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtime\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 40\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 41\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;36m1000\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m_call_impl\u001b[0;34m(self, *input, **kwargs)\u001b[0m\n\u001b[1;32m 1100\u001b[0m if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks\n\u001b[1;32m 1101\u001b[0m or _global_forward_hooks or _global_forward_pre_hooks):\n\u001b[0;32m-> 1102\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mforward_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 1103\u001b[0m \u001b[0;31m# Do not call functions when jit is used\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1104\u001b[0m \u001b[0mfull_backward_hooks\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnon_full_backward_hooks\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py\u001b[0m in \u001b[0;36mforward\u001b[0;34m(self, input, target)\u001b[0m\n\u001b[1;32m 518\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 519\u001b[0m \u001b[0;32mdef\u001b[0m \u001b[0mforward\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mTensor\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 520\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mF\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmse_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mreduction\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 521\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 522\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;32m~/anaconda3/lib/python3.8/site-packages/torch/nn/functional.py\u001b[0m in \u001b[0;36mmse_loss\u001b[0;34m(input, target, size_average, reduce, reduction)\u001b[0m\n\u001b[1;32m 3110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3111\u001b[0m \u001b[0mexpanded_input\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpanded_target\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mbroadcast_tensors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0minput\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtarget\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3112\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mtorch\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_C\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_nn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmse_loss\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mexpanded_input\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexpanded_target\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0m_Reduction\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_enum\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mreduction\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 3113\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3114\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], + "source": [ + "run(df_comb, n_iter=10000, lr=.1, rmsg=65536, mpred=['time', 'energy'], msys=['linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 205, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40]\n", + "SYS linux_tuned\n", + "MSE_loss_time=4.084167250518678e-09 loss_time=63.90749 us max_time=-11.584077835083008 alpha=0.680945634841919 gamma=-0.299230694770813 delta=-0.21227359771728516 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " eta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":35: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " AA = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":36: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " BB = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":37: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " CC = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([340])) that is different to the input size (torch.Size([1, 340])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=3.311145496338428e-10 loss_time=18.19655 us max_time=-10.1016206741333 alpha=-0.7089433073997498 gamma=-0.06271976232528687 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.3941358974396467e-10 loss_time=15.473 us max_time=-10.194531440734863 alpha=-0.5634053349494934 gamma=0.372245192527771 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.064105298894164e-10 loss_time=14.36699 us max_time=-9.966798782348633 alpha=-0.13345687091350555 gamma=0.4011845588684082 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.060588272502824e-10 loss_time=14.35475 us max_time=-9.944465637207031 alpha=-0.08817222714424133 gamma=0.40380439162254333 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.060549843694182e-10 loss_time=14.35462 us max_time=-9.942144393920898 alpha=-0.08344041556119919 gamma=0.40407276153564453 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.060549415605705e-10 loss_time=14.35461 us max_time=-9.941930770874023 alpha=-0.08298047631978989 gamma=0.4041007459163666 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.0605494080817157e-10 loss_time=14.35461 us max_time=-9.941917419433594 alpha=-0.08295129984617233 gamma=0.4041025638580322 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.0605494080817157e-10 loss_time=14.35461 us max_time=-9.941917419433594 alpha=-0.08295129984617233 gamma=0.4041025638580322 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.0605494080817157e-10 loss_time=14.35461 us max_time=-9.941917419433594 alpha=-0.08295129984617233 gamma=0.4041025638580322 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.0605494080817157e-10 loss_time=14.35461 us max_time=-9.941917419433594 alpha=-0.08295129984617233 gamma=0.4041025638580322 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.0605494080817157e-10 loss_time=14.35461 us max_time=-9.941917419433594 alpha=-0.08295129984617233 gamma=0.4041025638580322 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.0605494080817157e-10 loss_time=14.35461 us max_time=-9.941917419433594 alpha=-0.08295129984617233 gamma=0.4041025638580322 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.0605494080817157e-10 loss_time=14.35461 us max_time=-9.941917419433594 alpha=-0.08295129984617233 gamma=0.4041025638580322 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.0605494080817157e-10 loss_time=14.35461 us max_time=-9.941917419433594 alpha=-0.08295129984617233 gamma=0.4041025638580322 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.0605494080817157e-10 loss_time=14.35461 us max_time=-9.941917419433594 alpha=-0.08295129984617233 gamma=0.4041025638580322 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.0605494080817157e-10 loss_time=14.35461 us max_time=-9.941917419433594 alpha=-0.08295129984617233 gamma=0.4041025638580322 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.0605494080817157e-10 loss_time=14.35461 us max_time=-9.941917419433594 alpha=-0.08295129984617233 gamma=0.4041025638580322 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.0605494080817157e-10 loss_time=14.35461 us max_time=-9.941917419433594 alpha=-0.08295129984617233 gamma=0.4041025638580322 delta=-0.21227359771728516 \n", + "MSE_loss_time=2.0605494080817157e-10 loss_time=14.35461 us max_time=-9.941917419433594 alpha=-0.08295129984617233 gamma=0.4041025638580322 delta=-0.21227359771728516 \n", + "loss_energy=3.639260237167787e-06 AA=0.9379985332489014 BB=0.06702136993408203 CC=-0.37714576721191406 eta=0.8119533061981201\n", + "loss_energy=8.922517621527084e-08 AA=15.794493675231934 BB=6.2542338371276855 CC=9.919940948486328 eta=1.4807237386703491\n", + "loss_energy=7.637099306860095e-08 AA=23.73980140686035 BB=6.184631824493408 CC=9.020633697509766 eta=1.1812316179275513\n", + "loss_energy=7.448442813371063e-08 AA=26.994993209838867 BB=6.4220685958862305 CC=8.413445472717285 eta=0.9992974996566772\n", + "loss_energy=7.427719186048705e-08 AA=27.886863708496094 BB=6.810821533203125 CC=7.89439058303833 eta=0.9183956384658813\n", + "loss_energy=7.419009933605477e-08 AA=28.05515480041504 BB=7.276706695556641 CC=7.359184741973877 eta=0.8732330203056335\n", + "loss_energy=7.411059906413705e-08 AA=28.064592361450195 BB=7.781205177307129 CC=6.797974586486816 eta=0.8371169567108154\n", + "loss_energy=7.40372691101815e-08 AA=28.047788619995117 BB=8.297758102416992 CC=6.229086875915527 eta=0.8046742677688599\n", + "loss_energy=7.397150573984758e-08 AA=28.02999496459961 BB=8.808273315429688 CC=5.670307159423828 eta=0.7753594517707825\n", + "loss_energy=7.391339945171462e-08 AA=28.013872146606445 BB=9.302027702331543 CC=5.1327290534973145 eta=0.7491702437400818\n", + "loss_energy=7.386230697018455e-08 AA=28.000064849853516 BB=9.773818969726562 CC=4.6214704513549805 eta=0.7258649468421936\n", + "loss_energy=7.38173222484972e-08 AA=27.988357543945312 BB=10.222033500671387 CC=4.137786865234375 eta=0.7051100134849548\n", + "loss_energy=7.377752961633994e-08 AA=27.978368759155273 BB=10.647071838378906 CC=3.68082332611084 eta=0.6865555644035339\n", + "loss_energy=7.37421095916105e-08 AA=27.96944808959961 BB=11.050314903259277 CC=3.2487521171569824 eta=0.6698934435844421\n", + "loss_energy=7.371036938193612e-08 AA=27.96181297302246 BB=11.433491706848145 CC=2.8393959999084473 eta=0.6548234820365906\n", + "loss_energy=7.368173397850316e-08 AA=27.954864501953125 BB=11.798445701599121 CC=2.450561046600342 eta=0.6411263942718506\n", + "loss_energy=7.365573561415036e-08 AA=27.94904327392578 BB=12.146883964538574 CC=2.0801987648010254 eta=0.6285831332206726\n", + "loss_energy=7.363199080361682e-08 AA=27.943321228027344 BB=12.480411529541016 CC=1.7264814376831055 eta=0.6170689463615417\n", + "loss_energy=7.361018684609929e-08 AA=27.938343048095703 BB=12.800421714782715 CC=1.3877578973770142 eta=0.6064205169677734\n", + "loss_energy=7.35900660499868e-08 AA=27.934062957763672 BB=13.108175277709961 CC=1.0626022815704346 eta=0.5965251326560974\n", + "yvalue torch.Size([340])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=20000, lr=.1, rmsg=8192, mpred=['energy'], msys=['linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 203, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 10 12 14 16 20 24 28 30]\n", + "SYS ebbrt_tuned\n", + "MSE_loss_time=5.307760841592641e-10 loss_time=23.03858 us max_time=-10.477350234985352 alpha=0.3237651586532593 gamma=-0.8744559288024902 delta=-0.7463735342025757 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":26: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " eta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":35: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " AA = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":36: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " BB = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":37: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " CC = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([49])) that is different to the input size (torch.Size([1, 49])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=8.419945084611864e-11 loss_time=9.17603 us max_time=-10.413800239562988 alpha=-0.6822545528411865 gamma=-0.8796855211257935 delta=-0.7463735342025757 \n", + "MSE_loss_time=7.27466836252827e-11 loss_time=8.52917 us max_time=-10.541411399841309 alpha=-0.981916069984436 gamma=-0.8847626447677612 delta=-0.7463735342025757 \n", + "MSE_loss_time=7.106648381934523e-11 loss_time=8.43009 us max_time=-10.591769218444824 alpha=-1.0968222618103027 gamma=-0.8894336223602295 delta=-0.7463735342025757 \n", + "MSE_loss_time=7.079398064850203e-11 loss_time=8.41392 us max_time=-10.61220645904541 alpha=-1.1429905891418457 gamma=-0.8938983678817749 delta=-0.7463735342025757 \n", + "MSE_loss_time=7.074674451276259e-11 loss_time=8.41111 us max_time=-10.62060546875 alpha=-1.1618759632110596 gamma=-0.8982492089271545 delta=-0.7463735342025757 \n", + "MSE_loss_time=7.073716147837419e-11 loss_time=8.41054 us max_time=-10.624076843261719 alpha=-1.1696840524673462 gamma=-0.9025225639343262 delta=-0.7463735342025757 \n", + "MSE_loss_time=7.073405430027385e-11 loss_time=8.41035 us max_time=-10.625505447387695 alpha=-1.1728907823562622 gamma=-0.9067390561103821 delta=-0.7463735342025757 \n", + "MSE_loss_time=7.07320786570974e-11 loss_time=8.41024 us max_time=-10.62612247467041 alpha=-1.174283504486084 gamma=-0.9109067916870117 delta=-0.7463735342025757 \n", + "MSE_loss_time=7.073035058639655e-11 loss_time=8.41013 us max_time=-10.626333236694336 alpha=-1.1747692823410034 gamma=-0.9150195121765137 delta=-0.7463735342025757 \n", + "loss_energy=8.575445265670614e-06 AA=0.11140751838684082 BB=0.2537527084350586 CC=0.7303256988525391 eta=-0.6071197986602783\n", + "loss_energy=1.7644464260666056e-08 AA=13.991302490234375 BB=10.240640640258789 CC=13.625411033630371 eta=0.8960652351379395\n", + "loss_energy=1.7439872121438884e-08 AA=14.209967613220215 BB=10.344348907470703 CC=13.814176559448242 eta=0.7936506867408752\n", + "loss_energy=1.7439729045588235e-08 AA=14.20068359375 BB=10.359709739685059 CC=13.819127082824707 eta=0.7924329042434692\n", + "loss_energy=1.743952370342962e-08 AA=14.186927795410156 BB=10.381858825683594 CC=13.825409889221191 eta=0.7908950448036194\n", + "loss_energy=1.743924914490544e-08 AA=14.168622970581055 BB=10.411788940429688 CC=13.833380699157715 eta=0.7888195514678955\n", + "loss_energy=1.743890640805154e-08 AA=14.145844459533691 BB=10.44961929321289 CC=13.842659950256348 eta=0.7862211465835571\n", + "loss_energy=1.7438504535548217e-08 AA=14.119134902954102 BB=10.49449634552002 CC=13.853048324584961 eta=0.7831588387489319\n", + "loss_energy=1.74380568252119e-08 AA=14.089259147644043 BB=10.545018196105957 CC=13.86436939239502 eta=0.779735267162323\n", + "loss_energy=1.7437579911652977e-08 AA=14.057308197021484 BB=10.599480628967285 CC=13.876047134399414 eta=0.7760780453681946\n", + "yvalue torch.Size([49])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=10000, lr=.1, rmsg=65536, mpred=['energy'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 147, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40]\n", + "SYS linux_tuned\n", + "MSE_loss_energy=10.401746502436177 alpha=0.26566147804260254 beta=1.5014312267303467\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([340])) that is different to the input size (torch.Size([1, 340])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_energy=3.262613680156139e-07 alpha=-1.1357345581054688 beta=0.0009362245909869671\n", + "MSE_loss_energy=3.0685046480679154e-07 alpha=-0.9034810662269592 beta=0.0009217746555805206\n", + "MSE_loss_energy=2.754881005294182e-07 alpha=-0.5081182718276978 beta=0.0008971766801550984\n", + "MSE_loss_energy=2.3110499279456789e-07 alpha=0.10404948145151138 beta=0.0008590901270508766\n", + "MSE_loss_energy=1.7641721148758873e-07 alpha=0.9801543951034546 beta=0.0008045823778957129\n", + "MSE_loss_energy=1.2128311679971452e-07 alpha=2.108976125717163 beta=0.0007343515753746033\n", + "MSE_loss_energy=8.071682083613856e-08 alpha=3.3447418212890625 beta=0.0006574671133421361\n", + "MSE_loss_energy=6.308644321431112e-08 alpha=4.339838027954102 beta=0.0005955544183962047\n", + "MSE_loss_energy=6.190181196842276e-08 alpha=4.802731990814209 beta=0.0005897650262340903\n", + "yvalue torch.Size([340])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=10000, lr=.1, rmsg=8192, mpred=['energy'], msys=['linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 179, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40]\n", + "SYS linux_tuned\n", + "MSE_loss_time=1.0429160477799717e-09 loss_time=32.29421 us max_time=-10.445191383361816 alpha=-0.2974517345428467 gamma=0.13766062259674072 delta=0.7391911745071411 \n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([340])) that is different to the input size (torch.Size([1, 340])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=1.6557064500533677e-10 loss_time=12.86743 us max_time=-9.785359382629395 alpha=-0.21617451310157776 gamma=0.06116277351975441 delta=0.8767921328544617 \n", + "MSE_loss_time=1.6480417360827935e-10 loss_time=12.83761 us max_time=-9.745709419250488 alpha=-0.1549433320760727 gamma=0.07286328822374344 delta=0.8514975905418396 \n", + "MSE_loss_time=1.6478498169560058e-10 loss_time=12.83686 us max_time=-9.739546775817871 alpha=-0.14528939127922058 gamma=0.07468517124652863 delta=0.8475677371025085 \n", + "MSE_loss_time=1.647845454530269e-10 loss_time=12.83684 us max_time=-9.73862075805664 alpha=-0.143840029835701 gamma=0.0749572142958641 delta=0.8469799757003784 \n", + "MSE_loss_time=1.647845370474955e-10 loss_time=12.83684 us max_time=-9.73852825164795 alpha=-0.14368705451488495 gamma=0.07498495280742645 delta=0.8469218611717224 \n", + "MSE_loss_time=1.6478453680299063e-10 loss_time=12.83684 us max_time=-9.738523483276367 alpha=-0.14367903769016266 gamma=0.07498624920845032 delta=0.8469192385673523 \n", + "MSE_loss_time=1.6478453680299063e-10 loss_time=12.83684 us max_time=-9.738523483276367 alpha=-0.14367903769016266 gamma=0.07498624920845032 delta=0.8469192385673523 \n", + "MSE_loss_time=1.6478453680299063e-10 loss_time=12.83684 us max_time=-9.738523483276367 alpha=-0.14367903769016266 gamma=0.07498624920845032 delta=0.8469192385673523 \n", + "MSE_loss_time=1.6478453680299063e-10 loss_time=12.83684 us max_time=-9.738523483276367 alpha=-0.14367903769016266 gamma=0.07498624920845032 delta=0.8469192385673523 \n", + "yvalue torch.Size([340])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=10000, lr=.1, rmsg=65536, mpred=['time'], msys=['linux_tuned'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40]\n", + "SYS linux_tuned\n", + "MSE_loss_energy=3.1736209974731158 alpha=1.6539652347564697 beta=-0.8273105621337891\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([340])) that is different to the input size (torch.Size([1, 340])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_energy=8.609881749776227e-07 alpha=2.1432385444641113 beta=0.0013114969478920102\n", + "MSE_loss_energy=4.824321421179702e-07 alpha=1.61918044090271 beta=0.001572378329001367\n", + "MSE_loss_energy=3.5453072235186565e-07 alpha=1.2246168851852417 beta=0.0017687961226329207\n", + "MSE_loss_energy=3.4125223926614626e-07 alpha=1.071757197380066 beta=0.0018448913469910622\n", + "MSE_loss_energy=3.4100761104064225e-07 alpha=1.0487926006317139 beta=0.0018563230987638235\n", + "MSE_loss_energy=3.410073301453634e-07 alpha=1.047995924949646 beta=0.0018567200750112534\n", + "MSE_loss_energy=1.2136040151154726e-06 alpha=1.048421859741211 beta=0.002291131531819701\n", + "MSE_loss_energy=3.410102254985726e-07 alpha=1.0479880571365356 beta=0.001857515424489975\n", + "MSE_loss_energy=3.410073301154088e-07 alpha=1.047987937927246 beta=0.0018567241495475173\n", + "yvalue torch.Size([340])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=10000, lr=.1, rmsg=65536, mpred=['energy'], msys=['linux_tuned'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 148, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[28 30 32 34 36 38 40]\n", + "SYS linux_tuned\n", + "MSE_loss_energy=5.199010363610722 alpha=1.655785083770752 beta=-1.607767105102539\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([29])) that is different to the input size (torch.Size([1, 29])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_energy=3.3289138809710734e-05 alpha=2.7812421321868896 beta=-0.013115008361637592\n", + "MSE_loss_energy=1.4360415481617989e-05 alpha=1.9579185247421265 beta=-0.0049598123878240585\n", + "MSE_loss_energy=3.8635257924247674e-06 alpha=1.1497951745986938 beta=0.003044822718948126\n", + "MSE_loss_energy=1.4008444447896256e-06 alpha=0.6610794067382812 beta=0.00788565631955862\n", + "MSE_loss_energy=1.232328208547477e-06 alpha=0.5111297965049744 beta=0.009370938874781132\n", + "MSE_loss_energy=1.2306223463538695e-06 alpha=0.49479347467422485 beta=0.009532756172120571\n", + "MSE_loss_energy=1.2306216016418088e-06 alpha=0.4944464862346649 beta=0.009536191821098328\n", + "MSE_loss_energy=1.2306216417763603e-06 alpha=0.49444490671157837 beta=0.009536066092550755\n", + "MSE_loss_energy=1.2306610980935978e-06 alpha=0.49444931745529175 beta=0.009540589526295662\n", + "yvalue torch.Size([29])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=10000, lr=.1, rmsg=524288, mpred=['energy'], msys=['linux_tuned'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 155, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 10 12 16 20 24 28]\n", + "SYS ebbrt_tuned\n", + "MSE_loss_time=9.3518833865048e-10 loss_time=30.58085 us max_time=-10.931262969970703 alpha=0.8639646768569946 gamma=-0.49780237674713135 delta=-0.21662914752960205\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([80])) that is different to the input size (torch.Size([1, 80])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=4.7445300777785e-11 loss_time=6.88805 us max_time=-10.515900611877441 alpha=-0.5818349123001099 gamma=-0.4936071038246155 delta=-0.2227908968925476\n", + "MSE_loss_time=4.236176593303981e-11 loss_time=6.50859 us max_time=-10.630703926086426 alpha=-0.748201847076416 gamma=-0.5089138746261597 delta=-0.23197980225086212\n", + "MSE_loss_time=4.217741654004542e-11 loss_time=6.49441 us max_time=-10.652167320251465 alpha=-0.7784810662269592 gamma=-0.5206874012947083 delta=-0.2379435896873474\n", + "MSE_loss_time=4.21571846459416e-11 loss_time=6.49286 us max_time=-10.65610408782959 alpha=-0.7841393947601318 gamma=-0.531475305557251 delta=-0.24318993091583252\n", + "MSE_loss_time=4.2143893041436884e-11 loss_time=6.49183 us max_time=-10.656660079956055 alpha=-0.7850796580314636 gamma=-0.5417852997779846 delta=-0.2481829822063446\n", + "MSE_loss_time=4.2131668979084197e-11 loss_time=6.49089 us max_time=-10.656660079956055 alpha=-0.7851954102516174 gamma=-0.5517299771308899 delta=-0.25301480293273926\n", + "MSE_loss_time=4.2120232537629125e-11 loss_time=6.49001 us max_time=-10.656532287597656 alpha=-0.7851487398147583 gamma=-0.5613399744033813 delta=-0.25770172476768494\n", + "MSE_loss_time=4.2109499922781713e-11 loss_time=6.48918 us max_time=-10.656356811523438 alpha=-0.785022497177124 gamma=-0.5706393718719482 delta=-0.2622584402561188\n", + "MSE_loss_time=4.209941524421303e-11 loss_time=6.48841 us max_time=-10.656184196472168 alpha=-0.7848891615867615 gamma=-0.5796449184417725 delta=-0.2666912376880646\n", + "yvalue torch.Size([80])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=10000, lr=.1, rmsg=8192, mpred=['time'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 159, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 10 12 14 16 20 24 28 30]\n", + "SYS ebbrt_tuned\n", + "MSE_loss_energy=1.8716230579680093 alpha=-0.8316636085510254 beta=0.874535083770752\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([49])) that is different to the input size (torch.Size([1, 49])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_energy=6.246585413241833e-07 alpha=-1.3500171899795532 beta=0.0026435942854732275\n", + "MSE_loss_energy=2.92997325904283e-07 alpha=-0.7278295755386353 beta=0.002298724604770541\n", + "MSE_loss_energy=1.1255511871187007e-07 alpha=-0.123292937874794 beta=0.001963638933375478\n", + "MSE_loss_energy=7.152672242858627e-08 alpha=0.2361350953578949 beta=0.001764413551427424\n", + "MSE_loss_energy=6.885477372127496e-08 alpha=0.34346234798431396 beta=0.0017049235757440329\n", + "MSE_loss_energy=6.88297931080648e-08 alpha=0.35467055439949036 beta=0.0016987109556794167\n", + "MSE_loss_energy=6.882978363556487e-08 alpha=0.3548932373523712 beta=0.001698593609035015\n", + "MSE_loss_energy=4.516908398239483e-07 alpha=0.35528793931007385 beta=0.0020928415469825268\n", + "MSE_loss_energy=6.883143400136191e-08 alpha=0.35489335656166077 beta=0.0016977684572339058\n", + "MSE_loss_energy=6.882978354508891e-08 alpha=0.3548942506313324 beta=0.001698586973361671\n", + "MSE_loss_energy=3.676757241138263e-05 alpha=0.35874953866004944 beta=0.005558536853641272\n", + "MSE_loss_energy=6.883012188926645e-08 alpha=0.3548939824104309 beta=0.001698216306976974\n", + "MSE_loss_energy=4.077934111563847e-07 alpha=0.3545233905315399 beta=0.0013276224490255117\n", + "MSE_loss_energy=6.884899453212272e-08 alpha=0.3548913896083832 beta=0.0016957942862063646\n", + "yvalue torch.Size([49])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=15000, lr=.1, rmsg=65536, mpred=['energy'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 10 12 14 16 20 24 28 30]\n", + "SYS ebbrt_tuned\n", + "MSE_loss_time=6.258170179356339e-10 loss_time=25.01634 us max_time=-11.112141609191895 alpha=-0.20667457580566406 gamma=0.20616579055786133 delta=0.14654159545898438\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([49])) that is different to the input size (torch.Size([1, 49])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=3.8352846966180634e-11 loss_time=6.19297 us max_time=-10.703995704650879 alpha=-0.4967239797115326 gamma=0.32468709349632263 delta=0.23750276863574982\n", + "MSE_loss_time=3.739892863921175e-11 loss_time=6.11547 us max_time=-10.730485916137695 alpha=-0.5888569355010986 gamma=0.3261134624481201 delta=0.22593876719474792\n", + "MSE_loss_time=3.6967499875839745e-11 loss_time=6.08009 us max_time=-10.750848770141602 alpha=-0.6498305797576904 gamma=0.3184581995010376 delta=0.23383992910385132\n", + "MSE_loss_time=3.6707352629978575e-11 loss_time=6.05866 us max_time=-10.767232894897461 alpha=-0.6932665705680847 gamma=0.3067343533039093 delta=0.25083649158477783\n", + "MSE_loss_time=3.651504617565465e-11 loss_time=6.04277 us max_time=-10.781044006347656 alpha=-0.7266898155212402 gamma=0.29346156120300293 delta=0.2716030478477478\n", + "MSE_loss_time=3.63587941477605e-11 loss_time=6.02983 us max_time=-10.793082237243652 alpha=-0.7542036175727844 gamma=0.2799374759197235 delta=0.2933439016342163\n", + "MSE_loss_time=3.6227245503133124e-11 loss_time=6.01891 us max_time=-10.803948402404785 alpha=-0.7780500650405884 gamma=0.2668508291244507 delta=0.31466519832611084\n", + "MSE_loss_time=3.6115336238321904e-11 loss_time=6.0096 us max_time=-10.813849449157715 alpha=-0.7994301319122314 gamma=0.2544863820075989 delta=0.3348894417285919\n", + "MSE_loss_time=3.6019766740820316e-11 loss_time=6.00165 us max_time=-10.823001861572266 alpha=-0.8189905881881714 gamma=0.2429746687412262 delta=0.3537555932998657\n", + "yvalue torch.Size([49])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=10000, lr=.1, rmsg=65536, mpred=['time'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40]\n", + "SYS linux_tuned\n", + "MSE_loss_energy=18.681472224736677 alpha=-0.0990293025970459 J\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + ":5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([680])) that is different to the input size (torch.Size([1, 680])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_energy=3.349001683366263e-07 alpha=5.4724056099075824e-05 J\n", + "MSE_loss_energy=3.3489944894610644e-07 alpha=5.466543007059954e-05 J\n", + "MSE_loss_energy=3.3489944894458e-07 alpha=5.466537913889624e-05 J\n", + "MSE_loss_energy=3.3489944894409114e-07 alpha=5.4665350035065785e-05 J\n", + "MSE_loss_energy=3.34899448943987e-07 alpha=5.466534275910817e-05 J\n", + "MSE_loss_energy=3.348994489439332e-07 alpha=5.466533184517175e-05 J\n", + "MSE_loss_energy=3.3489944894389087e-07 alpha=5.466532820719294e-05 J\n", + "MSE_loss_energy=1.0592082571550955e-06 alpha=2.1330277377273887e-05 J\n", + "MSE_loss_energy=3.440480151089373e-07 alpha=5.838388460688293e-05 J\n", + "MSE_loss_energy=3.349623944952624e-07 alpha=5.497341408045031e-05 J\n", + "MSE_loss_energy=7.164122409006158e-07 alpha=3.072250183322467e-05 J\n", + "MSE_loss_energy=4.36903829727795e-06 alpha=-2.3160711862146854e-05 J\n", + "MSE_loss_energy=3.348995113745342e-07 alpha=5.4675008868798614e-05 J\n", + "MSE_loss_energy=3.3489944900058766e-07 alpha=5.466504080686718e-05 J\n", + "MSE_loss_energy=3.349039223646735e-07 alpha=5.474726276588626e-05 J\n", + "MSE_loss_energy=4.061640660713825e-07 alpha=6.500710878754035e-05 J\n", + "MSE_loss_energy=3.348994494259767e-07 alpha=5.466617221827619e-05 J\n", + "MSE_loss_energy=0.0005069936531984957 alpha=-0.0008173314854502678 J\n", + "MSE_loss_energy=3.350801006186595e-07 alpha=5.4144624300533906e-05 J\n", + "MSE_loss_energy=3.348994490512316e-07 alpha=5.466493166750297e-05 J\n", + "MSE_loss_energy=0.00018620293333671006 alpha=0.0005828178254887462 J\n", + "MSE_loss_energy=3.3517760300184166e-07 alpha=5.401921953307465e-05 J\n", + "MSE_loss_energy=3.3489944900273695e-07 alpha=5.466563015943393e-05 J\n", + "MSE_loss_energy=0.0022667512125380484 alpha=0.0018989448435604572 J\n", + "MSE_loss_energy=3.3505803472038723e-07 alpha=5.5153177527245134e-05 J\n", + "MSE_loss_energy=3.348994522280068e-07 alpha=5.4663098126184195e-05 J\n", + "MSE_loss_energy=3.34899449064474e-07 alpha=5.4665750212734565e-05 J\n", + "MSE_loss_energy=3.348994628415158e-07 alpha=5.4660758905811235e-05 J\n", + "MSE_loss_energy=3.348996230872932e-07 alpha=5.464915375341661e-05 J\n", + "yvalue torch.Size([680])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=30000, lr=.01, rmsg=65536, mpred=['energy'], msys=['linux_tuned'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40]\n", + "SYS linux_tuned\n", + "MSE_loss_time=2.607992334315835e-09 loss_time=51.06851 us\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([1020])) that is different to the input size (torch.Size([1, 1020])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=1.4321045374656487e-10 loss_time=11.96706 us\n", + "MSE_loss_time=1.1229138646877116e-10 loss_time=10.59676 us\n", + "MSE_loss_time=9.833010400099115e-11 loss_time=9.91615 us\n", + "MSE_loss_time=9.200676189518122e-11 loss_time=9.59202 us\n", + "MSE_loss_time=8.793716094145902e-11 loss_time=9.37748 us\n", + "MSE_loss_time=8.473511251011704e-11 loss_time=9.20517 us\n", + "MSE_loss_time=8.21104176869666e-11 loss_time=9.06148 us\n", + "MSE_loss_time=7.996481491071716e-11 loss_time=8.9423 us\n", + "MSE_loss_time=7.822415766882217e-11 loss_time=8.84444 us\n", + "MSE_loss_time=7.682671397251105e-11 loss_time=8.76508 us\n", + "MSE_loss_time=7.571198302318803e-11 loss_time=8.70126 us\n", + "MSE_loss_time=7.48307368101092e-11 loss_time=8.65048 us\n", + "MSE_loss_time=7.413629515511132e-11 loss_time=8.61024 us\n", + "MSE_loss_time=7.359265064749525e-11 loss_time=8.57862 us\n", + "MSE_loss_time=7.316875728823207e-11 loss_time=8.55387 us\n", + "MSE_loss_time=7.283833685100317e-11 loss_time=8.53454 us\n", + "MSE_loss_time=7.258367014843486e-11 loss_time=8.51961 us\n", + "MSE_loss_time=7.238630113228117e-11 loss_time=8.50801 us\n", + "MSE_loss_time=7.223392324688401e-11 loss_time=8.49905 us\n", + "MSE_loss_time=7.211650266127755e-11 loss_time=8.49214 us\n", + "MSE_loss_time=7.202590025360469e-11 loss_time=8.48681 us\n", + "MSE_loss_time=7.195628706055052e-11 loss_time=8.48271 us\n", + "MSE_loss_time=7.190357369142107e-11 loss_time=8.4796 us\n", + "MSE_loss_time=7.186187512867872e-11 loss_time=8.47714 us\n", + "MSE_loss_time=7.183138556239405e-11 loss_time=8.47534 us\n", + "MSE_loss_time=7.18069740090079e-11 loss_time=8.4739 us\n", + "MSE_loss_time=7.178859771846951e-11 loss_time=8.47282 us\n", + "MSE_loss_time=7.177498299049814e-11 loss_time=8.47201 us\n", + "MSE_loss_time=7.176399229696884e-11 loss_time=8.47136 us\n", + "yvalue torch.Size([1020])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "df_comb = pd.read_csv('/home/handong/jupyter/jupyter-notebooks/nic-tuning-experiments/bayesopt/summary_data/netpipe_combined.csv', sep=' ')\n", + "#df_comb = df_comb[(df_comb['i'] == 1) & (df_comb['rapl'] == 135)]\n", + "df_comb = df_comb[(df_comb['rapl'] == 135)]\n", + "\n", + "df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: dvfs_dict[x])\n", + "df_comb = df_comb[(df_comb['itr']!=1) & (df_comb['dvfs']!=65535)] #filter out linux dynamic\n", + "\n", + "run_energy(df_comb, n_iter=30000, lr=.01, rmsg=8192, mpred=['time'], msys=['linux_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 10 12 14 16 20 24 28 30]\n", + "SYS ebbrt_tuned\n", + "loss_time=2.57520754163816e-06 max_time=-11.216626167297363 alpha=0.3909003734588623 gamma=0.9856512546539307 delta=0.5502802133560181\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([49])) that is different to the input size (torch.Size([1, 49])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_time=4.969348230600252e-10 max_time=-11.678744316101074 alpha=0.705072283744812 gamma=0.5869538187980652 delta=0.15162181854248047\n", + "loss_time=3.1589909805032055e-10 max_time=-11.555351257324219 alpha=0.6717889904975891 gamma=0.5569562315940857 delta=0.1252904087305069\n", + "loss_time=2.596477904439758e-10 max_time=-11.356971740722656 alpha=0.6064764261245728 gamma=0.545141339302063 delta=0.11924216151237488\n", + "loss_time=1.9554310192149585e-10 max_time=-11.130919456481934 alpha=0.5296098589897156 gamma=0.5335561037063599 delta=0.11535559594631195\n", + "loss_time=1.3196472427766278e-10 max_time=-10.906689643859863 alpha=0.4496612846851349 gamma=0.5181466341018677 delta=0.10872402787208557\n", + "loss_time=8.627199477662976e-11 max_time=-10.721221923828125 alpha=0.37631598114967346 gamma=0.5000279545783997 delta=0.09867574274539948\n", + "loss_time=6.440469141453763e-11 max_time=-10.59963607788086 alpha=0.31640923023223877 gamma=0.483280211687088 delta=0.08744902908802032\n", + "loss_time=5.6820670550361005e-11 max_time=-10.535999298095703 alpha=0.2690556049346924 gamma=0.4716368019580841 delta=0.07784360647201538\n", + "loss_time=5.4216592815039454e-11 max_time=-10.509061813354492 alpha=0.22995366156101227 gamma=0.4655805826187134 delta=0.07032199203968048\n", + "loss_time=5.286372026998652e-11 max_time=-10.50101375579834 alpha=0.195633664727211 gamma=0.4635092616081238 delta=0.06384226679801941\n", + "loss_time=5.1800428024282384e-11 max_time=-10.501510620117188 alpha=0.16407504677772522 gamma=0.46370741724967957 delta=0.05754728242754936\n", + "loss_time=5.083597624839159e-11 max_time=-10.505500793457031 alpha=0.1342552751302719 gamma=0.46502685546875 delta=0.051180534064769745\n", + "loss_time=4.9938671417926936e-11 max_time=-10.510814666748047 alpha=0.10571003705263138 gamma=0.46680256724357605 delta=0.04482342302799225\n", + "loss_time=4.91025316692642e-11 max_time=-10.516536712646484 alpha=0.07820577919483185 gamma=0.4686674177646637 delta=0.03866315633058548\n", + "loss_time=4.832613422323807e-11 max_time=-10.522258758544922 alpha=0.051686327904462814 gamma=0.47043007612228394 delta=0.03281768411397934\n", + "loss_time=4.760580725417142e-11 max_time=-10.52798080444336 alpha=0.02610282227396965 gamma=0.47202613949775696 delta=0.027391334995627403\n", + "loss_time=4.693878430241124e-11 max_time=-10.53353500366211 alpha=0.0014243152691051364 gamma=0.47339460253715515 delta=0.022414973005652428\n", + "loss_time=4.6322023707385866e-11 max_time=-10.538923263549805 alpha=-0.022347141057252884 gamma=0.4745330810546875 delta=0.017890719696879387\n", + "loss_time=4.575165545118472e-11 max_time=-10.544201850891113 alpha=-0.04523705691099167 gamma=0.47545745968818665 delta=0.013821450062096119\n", + "yvalue torch.Size([49])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=20000, lr=0.01, rmsg=65536, mpred=['time'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 6 8 10 12 14 18 20 22 24 26 28 30 32 34 36 38 40 16]\n", + "SYS ebbrt_tuned\n", + "loss_time=1.3230961388258921e-08 max_time=-11.893648147583008 alpha=0.6270890235900879 gamma=0.18047595024108887 delta=-0.09962725639343262\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(-12, -10), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([45])) that is different to the input size (torch.Size([1, 45])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "loss_time=1.8958488275455196e-09 max_time=-11.03542423248291 alpha=0.3844453692436218 gamma=0.24624042212963104 delta=0.8157742619514465\n", + "loss_time=4.0103061025381426e-10 max_time=-9.965204238891602 alpha=0.015366299077868462 gamma=0.09874476492404938 delta=0.8807123303413391\n", + "loss_time=1.2521801763705126e-10 max_time=-9.682907104492188 alpha=-0.24912947416305542 gamma=0.09442934393882751 delta=0.7038712501525879\n", + "loss_time=8.856843543575778e-11 max_time=-9.702617645263672 alpha=-0.4068114459514618 gamma=0.1354701966047287 delta=0.6141543984413147\n", + "loss_time=6.996599469468223e-11 max_time=-9.724239349365234 alpha=-0.5223428010940552 gamma=0.16338804364204407 delta=0.55540931224823\n", + "loss_time=6.025628725733556e-11 max_time=-9.740906715393066 alpha=-0.6078701615333557 gamma=0.18102142214775085 delta=0.5154772996902466\n", + "loss_time=5.5122739095906116e-11 max_time=-9.75363540649414 alpha=-0.6712750792503357 gamma=0.19214054942131042 delta=0.4882328510284424\n", + "loss_time=5.2386910228608144e-11 max_time=-9.763300895690918 alpha=-0.7183417081832886 gamma=0.19906334578990936 delta=0.4697312116622925\n", + "loss_time=5.0919445579404864e-11 max_time=-9.770689964294434 alpha=-0.7533528804779053 gamma=0.2032381147146225 delta=0.4573560655117035\n", + "yvalue torch.Size([45])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/handong/anaconda3/lib/python3.8/site-packages/matplotlib/collections.py:988: UserWarning: Collection without array used. Make sure to specify the values to be colormapped via the `c` argument.\n", + " warnings.warn(\"Collection without array used. Make sure to \"\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=10000, lr=0.01, rmsg=524288, mpred=['time'], msys=['ebbrt_tuned'])" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/analysis/run_nodejs.ipynb b/analysis/run_nodejs.ipynb new file mode 100644 index 0000000..d819eed --- /dev/null +++ b/analysis/run_nodejs.ipynb @@ -0,0 +1,821 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 118, + "metadata": {}, + "outputs": [], + "source": [ + "import sys\n", + "sys.path.append('../bayesopt')\n", + "\n", + "import read_agg_data\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.autograd as auto\n", + "import torch.optim as optim\n", + "\n", + "import numpy as np\n", + "import matplotlib.pylab as plt\n", + "import pandas as pd\n", + "\n", + "import pdb\n", + "import math\n", + "\n", + "dvfs_dict = {\n", + " \"0xc00\" : 1.2,\n", + " \"0xd00\" : 1.3,\n", + " \"0xe00\" : 1.4,\n", + " \"0xf00\" : 1.5,\n", + " \"0x1000\" : 1.6,\n", + " \"0x1100\" : 1.7,\n", + " \"0x1200\" : 1.8,\n", + " \"0x1300\" : 1.9,\n", + " \"0x1400\" : 2.0,\n", + " \"0x1500\" : 2.1,\n", + " \"0x1600\" : 2.2,\n", + " \"0x1700\" : 2.3,\n", + " \"0x1800\" : 2.4,\n", + " \"0x1900\" : 2.5,\n", + " \"0x1a00\" : 2.6,\n", + " \"0x1b00\" : 2.7,\n", + " \"0x1c00\" : 2.8,\n", + " \"0x1d00\" : 2.9,\n", + " \"0xffff\" : 3.0,\n", + "}" + ] + }, + { + "cell_type": "code", + "execution_count": 119, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5.5998\n", + "Index(['sys', 'i', 'itr', 'dvfs', 'rapl', 'lat50', 'lat75', 'lat90', 'lat99',\n", + " 'requests', 'time', 'joules', 'rx_desc', 'rx_bytes', 'tx_desc',\n", + " 'tx_bytes', 'instructions', 'cycles', 'ref_cycles', 'llc_miss', 'c1',\n", + " 'c1e', 'c3', 'c6', 'c7', 'num_interrupts', 'msg'],\n", + " dtype='object')\n", + "148\n", + "[ 0 2 4 6 8 12 16 20 24 28 32 36 40 80 50 60 70]\n", + "[2.9 2.7 2.5 2.1 1.3 2.3 1.9 1.7 1.5]\n", + "['ebbrt_tuned' 'linux_tuned']\n", + "284\n" + ] + } + ], + "source": [ + "#df_comb, _, _ = read_agg_data.start_analysis('mcd') #DATA\n", + "#df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: int(x, base=16))\n", + "\n", + "df_comb = pd.read_csv('/home/handong/jupyter/jupyter-notebooks/nic-tuning-experiments/bayesopt/summary_data/node_combined.csv', sep=' ')\n", + "df_comb['msg'] = 148\n", + "df_comb = df_comb[(df_comb['i'] == 1) & (df_comb['rapl'] == 135)]\n", + "#df_comb = df_comb[(df_comb['rapl'] == 135)]\n", + "\n", + "df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: dvfs_dict[x])\n", + "df_comb = df_comb[(df_comb['itr']!=1) & (df_comb['dvfs']!=65535)] #filter out linux dynamic\n", + "\n", + "print(df_comb['time'].min())\n", + "print(df_comb.columns)\n", + "print(df_comb['msg'].min())\n", + "print(df_comb['itr'].unique())\n", + "print(df_comb['dvfs'].unique())\n", + "print(df_comb['sys'].unique())\n", + "print(df_comb.shape[0])\n", + "\n", + "# df_comb['dvfs'] = df_comb['dvfs'].astype(float) / df_comb['dvfs'].min()\n", + "# print(df_comb['dvfs'].unique())\n", + "# df_comb['itr'] = df_comb['itr'].astype(float) / df_comb['itr'].min()\n", + "# print(df_comb['itr'].unique())\n", + "#print(10**6)" + ] + }, + { + "cell_type": "code", + "execution_count": 120, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "425.38\n", + "******* ebbrt_tuned 6 8192\n", + " joules itr dvfs time num_interrupts msg\n", + "93 188.94 6 1.3 9.7990 1712 148\n", + "90 174.40 6 1.5 8.4992 5823 148\n", + "87 185.23 6 1.7 8.4995 109308 148\n", + "84 200.15 6 1.9 8.5994 177416 148\n", + "81 207.64 6 2.1 8.3997 196605 148\n", + "78 176.84 6 2.3 6.7997 199814 148\n", + "75 193.73 6 2.5 7.0996 195838 148\n", + "72 210.69 6 2.7 7.2999 190382 148\n", + "70 239.24 6 2.9 7.9001 175002 148\n", + "\n", + "425.38\n", + "******* ebbrt_tuned 8 8192\n", + " joules itr dvfs time num_interrupts msg\n", + "117 194.78 8 1.3 10.1000 1646 148\n", + "112 180.96 8 1.7 8.2999 86818 148\n", + "109 202.30 8 1.9 8.7001 173028 148\n", + "104 179.90 8 2.3 6.8989 200344 148\n", + "101 198.81 8 2.5 7.2995 195309 148\n", + "98 218.95 8 2.7 7.5995 189618 148\n", + "96 247.14 8 2.9 8.1996 169298 148\n", + "\n", + "425.38\n", + "******* ebbrt_tuned 10 8192\n", + "Empty DataFrame\n", + "Columns: [joules, itr, dvfs, time, num_interrupts, msg]\n", + "Index: []\n", + "\n", + "425.38\n", + "******* ebbrt_tuned 12 8192\n", + " joules itr dvfs time num_interrupts msg\n", + "144 185.19 12 1.3 9.5990 1662 148\n", + "141 172.63 12 1.5 8.3995 6293 148\n", + "138 183.21 12 1.7 8.3995 98556 148\n", + "135 200.05 12 1.9 8.5995 170858 148\n", + "132 207.76 12 2.1 8.3995 193619 148\n", + "129 182.67 12 2.3 6.9997 199782 148\n", + "126 194.77 12 2.5 7.0998 200085 148\n", + "123 214.59 12 2.7 7.3999 195411 148\n", + "120 240.23 12 2.9 7.8999 182770 148\n", + "\n", + "425.38\n", + "******* ebbrt_tuned 16 8192\n", + " joules itr dvfs time num_interrupts msg\n", + "169 189.01 16 1.3 9.7993 1674 148\n", + "164 176.60 16 1.7 8.0995 85705 148\n", + "161 197.68 16 1.9 8.4996 152857 148\n", + "158 205.32 16 2.1 8.2996 194353 148\n", + "155 177.43 16 2.3 6.7998 199835 148\n", + "152 189.30 16 2.5 6.9000 198825 148\n", + "149 216.23 16 2.7 7.4990 188902 148\n", + "146 241.60 16 2.9 7.9993 171038 148\n", + "\n", + "425.38\n", + "******* ebbrt_tuned 20 8192\n", + " joules itr dvfs time num_interrupts msg\n", + "192 189.17 20 1.3 9.7995 1718 148\n", + "187 172.26 20 1.7 7.9002 67731 148\n", + "184 197.73 20 1.9 8.4993 156571 148\n", + "182 205.51 20 2.1 8.2994 192535 148\n", + "179 180.12 20 2.3 6.8995 200505 148\n", + "177 189.99 20 2.5 6.8996 200880 148\n", + "174 213.89 20 2.7 7.3999 191387 148\n", + "171 236.77 20 2.9 7.7995 179331 148\n", + "\n", + "425.38\n", + "******* ebbrt_tuned 24 8192\n", + " joules itr dvfs time num_interrupts msg\n", + "217 189.15 24 1.3 9.7998 1710 148\n", + "214 176.78 24 1.5 8.5999 2551 148\n", + "211 176.78 24 1.7 8.1000 84467 148\n", + "208 202.08 24 1.9 8.7001 175929 148\n", + "205 205.45 24 2.1 8.3001 192710 148\n", + "202 200.31 24 2.3 7.7992 198545 148\n", + "199 230.28 24 2.5 8.6002 188181 148\n", + "197 252.77 24 2.7 8.8992 181625 148\n", + "\n", + "425.38\n", + "******* ebbrt_tuned 28 8192\n", + " joules itr dvfs time num_interrupts msg\n", + "241 193.06 28 1.3 9.9989 1683 148\n", + "238 176.65 28 1.5 8.5996 3280 148\n", + "235 172.22 28 1.7 7.8991 55584 148\n", + "231 228.58 28 2.1 9.5994 196180 148\n", + "228 260.31 28 2.3 10.3994 186459 148\n", + "226 271.37 28 2.5 10.2993 165233 148\n", + "223 282.55 28 2.7 10.1002 158516 148\n", + "220 293.10 28 2.9 9.9000 154426 148\n", + "\n" + ] + } + ], + "source": [ + "#6 8 10 12 16 20 24 28\n", + "for itr in [6, 8, 10, 12, 16, 20, 24, 28]:\n", + " for sys in ['ebbrt_tuned']:\n", + " df = df_comb[(df_comb['sys']==sys)].copy()\n", + " #print(df.shape[0])\n", + " print(df['joules'].max())\n", + " #df['joules_per_interrupt'] = df['joules']/df['num_interrupts']\n", + " df = df[['joules','itr', 'dvfs', 'time', 'num_interrupts', 'msg']]\n", + " #print(df.shape[0])\n", + " #print('')\n", + "\n", + " dfi = df[df['itr']==itr]\n", + " #dfi = dfi.drop_duplicates(subset = [\"itr\", \"dvfs\"])\n", + " #dfi['joules_mean'] = dfi['joules_mean']/dfi['joules_mean'].max()\n", + " #print(dfi.diff())\n", + " print('*******', sys, itr, msg)\n", + " print(dfi.sort_values(by=['dvfs']))\n", + " #print(dfi.sort_values(by=['dvfs']).diff())\n", + " print('')\n", + " #plt.plot(dfi['dvfs'], dfi['joules_per_interrupt'])\n", + " #print(dfi)" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6.5536e-06\n" + ] + } + ], + "source": [ + "print(8192*8/(10**10))" + ] + }, + { + "cell_type": "code", + "execution_count": 102, + "metadata": {}, + "outputs": [], + "source": [ + "def inference(d, n_iter, lr, workload, sys, print_freq=10):\n", + " # p_busy_min = 20\n", + " p_static = {\n", + " 'c1':1.5, \n", + " 'c3':0.5,\n", + " 'c4':0.25,\n", + " 'c7':34, # 34 Watts\n", + " 'busy': 10\n", + " }\n", + " chosen_sleep = 'c7'\n", + "\n", + " p_q = p_static[chosen_sleep]/10**6 # joules/us idle\n", + " # p_detect = p_static[chosen_sleep]\n", + "\n", + " #:16: UserWarning: To copy construct from a tensor, \n", + " # it is recommended to use sourceTensor.clone().detach() \n", + " # or sourceTensor.clone().detach().requires_grad_(True), \n", + " # rather than torch.tensor(sourceTensor).\n", + " \n", + " #starts randomly\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(1, 2), requires_grad=True)\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 0), requires_grad=True)\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(0, 1), requires_grad=True)\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(0, 1), requires_grad=True)\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(2, 3), requires_grad=True)\n", + " \n", + " #beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2)).clone().detach()\n", + " #p_static_busy = torch.tensor(torch.Tensor(1,1).uniform_(0, 35)).clone().detach()\n", + " #p_detect = torch.tensor(torch.Tensor(1,1).uniform_(0, 35)).clone().detach()\n", + " #p_q = torch.tensor(torch.Tensor(1,1).uniform_(0, 35), requires_grad=True)\n", + " #p_busy_min = torch.tensor(torch.Tensor(1,1).uniform_(0, 35)).clone().detach()\n", + " #itr_suppress = torch.rand(1, requires_grad=True)\n", + " #itr_suppress = torch.tensor(1., requires_grad=True)\n", + " \n", + " #AA = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2)).clone().detach()\n", + " #BB = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2)).clone().detach()\n", + " #gamma = torch.tensor(torch.Tensor(1,1).uniform_(-1, 1)).clone().detach()\n", + " \n", + " #df[['joules_mean','itr', 'dvfs', 'QPS', 'read_99th_mean']]\n", + " #df[['joules', 'itr', 'dvfs', 'time', 'num_interrupts']]\n", + " ninterrupts = d[:,4]\n", + " energy = (d[:,0]/ninterrupts).log() ## joules/num_interrupts\n", + " itr = d[:,1]\n", + " dvfs = d[:,2]\n", + " time = d[:,3]\n", + " msgsize = d[:,5]\n", + " \n", + " #current_loss_time = -100\n", + " #fixed_max_time = -100\n", + " #fixed_alpha = -100\n", + " #fixed_itr_suppress = -100\n", + " \n", + " criterion = nn.MSELoss()\n", + " #optimizer_time = optim.Adam([max_time, alpha, gamma, delta], lr=lr)\n", + " optimizer_time = optim.Adam([max_time, alpha, gamma, delta], lr=lr)\n", + " #optimizer_energy = optim.Adam([gamma, beta], lr=lr)\n", + "\n", + " for i in range(n_iter): \n", + " t_busy = (torch.exp(max_time) / dvfs**(1+alpha)) ## dvfs impact on processing\n", + " \n", + " #pred_time = itr_suppress*itr + t_busy\n", + " #pred_time = ((2*((itr*itr_suppress)**beta))/(10**6)) + (gamma*(2*((msgsize*8)/(10**10)))) + (2*t_busy)\n", + " #pred_time = (gamma*itr*itr_suppress)*(dvfs**beta)\n", + " #pred_time = ((2*(((itr**beta))))/(10**6)) + (2*t_busy) \n", + " \n", + " #pred_time = (((2*(((itr**beta))))/(10**6)) + (2*t_busy) + (2*((msgsize*8)/(10**10)))*gamma)\n", + " beta = gamma*dvfs+delta\n", + " pred_time = ((2*(((itr**beta))))/(10**6)) + (2*t_busy) + (2*((msgsize*8)/(10**10)))\n", + " \n", + " #pred_time = A(itr)**beta*(dvfs**gamma)\n", + " #pred_time = 2*itr**(alpha*dvfs)\n", + " \n", + " #import pdb\n", + " #pdb.set_trace()\n", + " \n", + " #loss_time = criterion(pred_time/time, torch.ones((1,pred_time.shape[1])).double())\n", + " loss_time = criterion(pred_time, time)\n", + " \n", + " if i % 1000 == 0:\n", + " #print(f'MSE_loss_time={loss_time.item()} loss_time={round(math.sqrt(loss_time.item())*10**6, 5)} us')\n", + " print(f'MSE_loss_time={loss_time.item()} max_time={max_time.item()} '+ \n", + " f'alpha={alpha.item()} gamma={gamma.item()} delta={delta.item()}')\n", + " #itr_suppress={itr_suppress.item()}\n", + " \n", + " optimizer_time.zero_grad()\n", + " loss_time.backward()\n", + " optimizer_time.step()\n", + "\n", + "# if(current_loss_time == -100):\n", + "# current_loss_time = loss_time.item()\n", + "# else:\n", + "# if(current_loss_time >= loss_time.item()):\n", + "# current_loss_time = loss_time.item()\n", + "# fixed_max_time = max_time.item()\n", + "# fixed_alpha = alpha.item()\n", + " #fixed_itr_suppress = itr_suppress.item()\n", + " \n", + "# for i in range(n_iter):\n", + "# #p_busy = (p_q*dvfs**(2+beta))\n", + "# #t_busy_energy = (max_time / dvfs**(1+beta))\n", + "# #t_q_energy = itr#(fixed_itr_suppress*itr)\n", + "# #t_q_energy = (interarrival_time - (fixed_itr_suppress*itr) - t_busy_energy)\n", + " \n", + "# #pred_energy = (p_q * t_q_energy) + (p_busy * t_busy_energy)\n", + "# #pred_energy = AA*(fixed_itr_suppress*itr)**gamma + BB*dvfs**beta\n", + "# #loss_energy = criterion(pred_energy/energy, torch.ones((1,pred_energy.shape[1])).double())\n", + "# #pred_energy = ((fixed_itr_suppress*itr*p_q)*((20*(10**6))/(fixed_itr_suppress*itr))) + (dvfs*beta)\n", + "# #pred_energy = (gamma*(fixed_itr_suppress*itr))*(dvfs**beta) #+ (AA*(dvfs**beta))\n", + " \n", + "# pred_energy = gamma+(np.log(fixed_itr_suppress)+np.log(itr))+(beta*np.log(dvfs))\n", + " \n", + "# #pred_energy = (*itr + t_busy_energy)*p_q\n", + "# loss_energy = criterion(pred_energy, energy)\n", + "\n", + "# if i % 1000 == 0:\n", + "# print(f'loss_energy={loss_energy.item()} gamma={gamma.item()} beta={beta.item()}')\n", + "# #print(pred_energy)\n", + " \n", + "# optimizer_energy.zero_grad()\n", + "# loss_energy.backward(retain_graph=True)\n", + "# optimizer_energy.step()\n", + " pred_energy = None\n", + " return pred_energy, pred_time" + ] + }, + { + "cell_type": "code", + "execution_count": 103, + "metadata": {}, + "outputs": [], + "source": [ + "def run_energy(df_comb, n_iter=2000, lr=1, msys=['ebbrt_tuned'], mpred=['energy', 'time']): \n", + " for sys in msys:\n", + " df = df_comb[(df_comb['sys']==sys)].copy()\n", + " #df = df[['joules','itr', 'dvfs', 'QPS', 'read_99th', 'num_interrupts']]\n", + " print(df['itr'].unique())\n", + " df = df[['joules', 'itr', 'dvfs', 'time', 'num_interrupts', 'msg']]\n", + " \n", + " tnum = df.shape[0]\n", + " d = df.values\n", + " d = torch.tensor(d)\n", + " print('SYS', sys)\n", + " #pred_energy, max_time, alpha, beta, p_detect, p_q = inference_energy(d, n_iter, lr, 'mcd', sys, print_freq=1000)\n", + " pred_energy, pred_time = inference(d, n_iter, lr, 'nodejs', sys, print_freq=1000)\n", + " \n", + " #df[f'pre_energy lr={lr}'] = pred_energy.view(tnum, 1).detach().numpy()\n", + " df[f'pre_time lr={lr}'] = pred_time.view(tnum, 1).detach().numpy()\n", + " \n", + " for pred_name in mpred:\n", + " if pred_name == 'energy':\n", + " pred = pred_energy\n", + " yvalue = (d[:,0]/d[:,4]).log()\n", + " #yvalue = d[:,0]\n", + " else:\n", + " pred = pred_time\n", + " yvalue = d[:,3]\n", + "\n", + " fig, ax = plt.subplots()\n", + " plt.title(f'pred:{pred_name} NODEJS sys={sys} lr={lr} \\n ')\n", + " #plt.title(f'pred:{pred_name} mcd sys={sys} lr={lr} tail={rtail} \\n QPS={rqps} max_time={round(max_time,2)} \\n alpha={round(alpha,2)} beta={round(beta.item(),2)} \\n p_detect={round(p_detect.item(),2)}')\n", + " plt.xlabel(u\"predictions\")\n", + " plt.ylabel(f'{pred_name}')\n", + " print('yvalue', yvalue.shape)\n", + " \n", + " tmax = yvalue.max().item()\n", + " plt.plot(np.linspace(0, tmax, 10), np.linspace(0, tmax, 10))\n", + " \n", + " scatter = ax.scatter(pred.detach().numpy(), yvalue, marker = 'o', \n", + " s = d[:,1], c = d[:,2], alpha=0.3)\n", + " #scatter = ax.scatter(pred.detach().numpy(), yvalue, marker = 'o', alpha=0.3)\n", + " plt.ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(0,0))\n", + " plt.ticklabel_format(axis=\"x\", style=\"sci\", scilimits=(0,0))\n", + " \n", + " \n", + " legend1 = ax.legend(*scatter.legend_elements(),loc=\"upper left\", title=\"dvfs\")\n", + " ax.add_artist(legend1)\n", + " handles, labels = scatter.legend_elements(prop=\"sizes\", alpha=0.6)\n", + " legend2 = plt.legend(handles, labels, loc=\"lower right\", title=\"itr\")\n", + " ax.add_artist(legend2)" + ] + }, + { + "cell_type": "code", + "execution_count": 104, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 4 6 8 12 16 20 24 28 32 36 40 50 60 70 80 0]\n", + "SYS linux_tuned\n", + "MSE_loss_time=16934.131648371065 max_time=1.7223296165466309 alpha=-0.8468027710914612 gamma=0.7089595794677734 delta=2.5453941822052\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":21: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(1, 2), requires_grad=True)\n", + ":22: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 0), requires_grad=True)\n", + ":23: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(0, 1), requires_grad=True)\n", + ":24: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(0, 1), requires_grad=True)\n", + ":25: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(2, 3), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([147])) that is different to the input size (torch.Size([1, 147])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=3.6468175795986975 max_time=2.436983108520508 alpha=-0.05861497297883034 gamma=0.4673231542110443 delta=2.3069944381713867\n", + "MSE_loss_time=3.208974976795988 max_time=2.419663190841675 alpha=-0.10797525942325592 gamma=0.4502891004085541 delta=2.2959377765655518\n", + "MSE_loss_time=3.176673309166506 max_time=2.413864850997925 alpha=-0.12180129438638687 gamma=0.44367823004722595 delta=2.2992711067199707\n", + "MSE_loss_time=3.1519975359633263 max_time=2.4131555557250977 alpha=-0.12288451194763184 gamma=0.4389410614967346 delta=2.3112282752990723\n", + "MSE_loss_time=3.110279664852155 max_time=2.4130501747131348 alpha=-0.1219663918018341 gamma=0.43181103467941284 delta=2.3321962356567383\n", + "MSE_loss_time=3.0384135945703035 max_time=2.41284441947937 alpha=-0.1203802302479744 gamma=0.4197843670845032 delta=2.3676090240478516\n", + "MSE_loss_time=2.911691805252428 max_time=2.412307024002075 alpha=-0.11781749129295349 gamma=0.39922478795051575 delta=2.427990436553955\n", + "MSE_loss_time=2.6796363669180017 max_time=2.410703659057617 alpha=-0.11394764482975006 gamma=0.3633503317832947 delta=2.5327839851379395\n", + "MSE_loss_time=2.2377340665703134 max_time=2.405068874359131 alpha=-0.10998459905385971 gamma=0.29916730523109436 delta=2.7178826332092285\n", + "yvalue torch.Size([147])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run_energy(df_comb, n_iter=10000, lr=0.01, mpred=['time'], msys=['linux_tuned'])\n", + "\n", + "##8.6% errorfrom mean" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "###test" + ] + }, + { + "cell_type": "code", + "execution_count": 139, + "metadata": {}, + "outputs": [], + "source": [ + "def inference_time(d, n_iter, lr, workload, sys, print_freq=10): \n", + " #starts randomly\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(1, 2), requires_grad=True)\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 0), requires_grad=True)\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(0, 1), requires_grad=True)\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(0, 1), requires_grad=True)\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(2, 3), requires_grad=True)\n", + " \n", + " ninterrupts = d[:,4]\n", + " itr = d[:,1]\n", + " dvfs = d[:,2]\n", + " time = d[:,3]\n", + " msgsize = d[:,5]\n", + " \n", + " criterion = nn.MSELoss()\n", + " optimizer_time = optim.Adam([max_time, alpha, gamma, delta], lr=lr)\n", + "\n", + " for i in range(n_iter): \n", + " t_busy = (torch.exp(max_time) / dvfs**(1+alpha)) ## dvfs impact on processing\n", + " \n", + " #pred_time = itr_suppress*itr + t_busy\n", + " #pred_time = ((2*((itr*itr_suppress)**beta))/(10**6)) + (gamma*(2*((msgsize*8)/(10**10)))) + (2*t_busy)\n", + " #pred_time = (gamma*itr*itr_suppress)*(dvfs**beta)\n", + " #pred_time = ((2*(((itr**beta))))/(10**6)) + (2*t_busy) \n", + " \n", + " #pred_time = ((2*(((itr**beta))))/(10**6)) + (2*t_busy) + (2*((msgsize*8)/(10**10)))\n", + " beta = gamma*dvfs+delta\n", + " pred_time = ((2*(((itr**beta))))/(10**6)) + (2*t_busy) + (2*((msgsize*8)/(10**10)))\n", + " \n", + " #pred_time = A(itr)**beta*(dvfs**gamma)\n", + " #pred_time = 2*itr**(alpha*dvfs)\n", + " \n", + " #import pdb\n", + " #pdb.set_trace()\n", + " \n", + " #loss_time = criterion(pred_time/time, torch.ones((1,pred_time.shape[1])).double())\n", + " loss_time = criterion(pred_time, time)\n", + " \n", + " if i % 1000 == 0:\n", + " print(f'MSE_loss_time={loss_time.item()} loss_time={round(math.sqrt(loss_time.item())*10**6, 5)} us '\n", + " + f'max_time={max_time.item()} alpha={alpha.item()} gamma={gamma.item()} delta={delta.item()}')\n", + " \n", + " optimizer_time.zero_grad()\n", + " loss_time.backward()\n", + " optimizer_time.step()\n", + " return pred_time\n", + "\n", + "def inference_energy(d, n_iter, lr, workload, sys, print_freq=10):\n", + " #starts randomly\n", + " #max_time = torch.tensor(torch.Tensor(1,1).uniform_(-5, 5), requires_grad=True)\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " #gamma = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " #delta = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True)\n", + " \n", + " ninterrupts = d[:,4]\n", + " energy = (d[:,0]/ninterrupts) #(d[:,0]/ninterrupts).log() ## joules/num_interrupts\n", + " itr = d[:,1]\n", + " dvfs = d[:,2]\n", + " time = d[:,3]\n", + " msgsize = d[:,5]\n", + " \n", + " criterion = nn.MSELoss()\n", + " optimizer_energy = optim.Adam([alpha, beta], lr=lr)\n", + " \n", + " for i in range(n_iter):\n", + " #p_busy = (p_q*dvfs**(2+beta))\n", + " #t_busy_energy = (max_time / dvfs**(1+beta))\n", + " #t_q_energy = itr#(fixed_itr_suppress*itr)\n", + " #t_q_energy = (interarrival_time - (fixed_itr_suppress*itr) - t_busy_energy)\n", + " \n", + " #pred_energy = (p_q * t_q_energy) + (p_busy * t_busy_energy)\n", + " #pred_energy = AA*(fixed_itr_suppress*itr)**gamma + BB*dvfs**beta\n", + " #loss_energy = criterion(pred_energy/energy, torch.ones((1,pred_energy.shape[1])).double())\n", + " #pred_energy = ((fixed_itr_suppress*itr*p_q)*((20*(10**6))/(fixed_itr_suppress*itr))) + (dvfs*beta)\n", + " #pred_energy = ((alpha*(itr**gamma))*(delta*(dvfs**beta))) #+ (AA*(dvfs**beta))\n", + " \n", + " pred_energy = (alpha*itr*((msgsize*8)/(10**10)))+(dvfs*beta)\n", + " #pred_energy = alpha*(itr+dvfs)\n", + " #pred_energy = alpha+np.log(itr)+np.log(dvfs)\n", + " \n", + " #pred_energy = (gamma*(fixed_itr_suppress*itr))*(dvfs**beta) #+ (AA*(dvfs**beta)) \n", + " #pred_energy = 2*(gamma+(np.log(itr)))+(2*(beta*np.log(dvfs)))\n", + " \n", + " loss_energy = criterion(pred_energy, energy)\n", + "\n", + " if i % 1000 == 0:\n", + " print(f'MSE_loss_energy={loss_energy.item()} alpha={alpha.item()} beta={beta.item()}')\n", + " \n", + " optimizer_energy.zero_grad()\n", + " loss_energy.backward(retain_graph=True)\n", + " optimizer_energy.step()\n", + " return pred_energy\n", + "\n", + "def run(df_comb, n_iter=2000, lr=1, rmsg=64, msys=['ebbrt_tuned'], mpred=['energy', 'time']): \n", + " for sys in msys:\n", + " df = df_comb[(df_comb['sys']==sys)].copy()\n", + " #df = df[['joules','itr', 'dvfs', 'QPS', 'read_99th', 'num_interrupts']]\n", + " print(df['itr'].unique())\n", + " df = df[['joules', 'itr', 'dvfs', 'time', 'num_interrupts', 'msg']]\n", + " \n", + " tnum = df.shape[0]\n", + " d = df.values\n", + " d = torch.tensor(d)\n", + " print('SYS', sys)\n", + " \n", + " #pred_energy, max_time, alpha, beta, p_detect, p_q = inference_energy(d, n_iter, lr, 'mcd', sys, print_freq=1000)\n", + " #pred_energy, pred_time = inference(d, n_iter, lr, 'netpipe', sys, print_freq=1000)\n", + " for pred_name in mpred:\n", + " if pred_name == 'energy':\n", + " pred_energy = inference_energy(d, n_iter, lr, 'netpipe', sys, print_freq=1000)\n", + " pred = pred_energy\n", + " #yvalue = (d[:,0]/d[:,4]).log()\n", + " yvalue = d[:,0]/d[:,4]\n", + " #yvalue = d[:,0]\n", + " else:\n", + " pred_time = inference_time(d, n_iter, lr, 'netpipe', sys, print_freq=1000)\n", + " pred = pred_time\n", + " yvalue = d[:,3]\n", + "\n", + " fig, ax = plt.subplots()\n", + " plt.title(f'pred:{pred_name} mcd sys={sys} lr={lr} \\n MSG={rmsg}')\n", + " #plt.title(f'pred:{pred_name} mcd sys={sys} lr={lr} tail={rtail} \\n QPS={rqps} max_time={round(max_time,2)} \\n alpha={round(alpha,2)} beta={round(beta.item(),2)} \\n p_detect={round(p_detect.item(),2)}')\n", + " plt.xlabel(u\"predictions\")\n", + " plt.ylabel(f'{pred_name}')\n", + " print('yvalue', yvalue.shape)\n", + " \n", + " \n", + " #if pred_name == 'time':\n", + " tmax = yvalue.max().item()\n", + " plt.plot(np.linspace(0, tmax, 10), np.linspace(0, tmax, 10))\n", + " \n", + " scatter = ax.scatter(pred.detach().numpy(), yvalue, marker = 'o', \n", + " s = d[:,1], c = d[:,2], alpha=0.3)\n", + " #scatter = ax.scatter(pred.detach().numpy(), yvalue, marker = 'o', alpha=0.3)\n", + " plt.ticklabel_format(axis=\"y\", style=\"sci\", scilimits=(0,0))\n", + " plt.ticklabel_format(axis=\"x\", style=\"sci\", scilimits=(0,0))\n", + " \n", + " legend1 = ax.legend(*scatter.legend_elements(),loc=\"upper left\", title=\"dvfs\")\n", + " ax.add_artist(legend1)\n", + " handles, labels = scatter.legend_elements(prop=\"sizes\", alpha=0.6)\n", + " legend2 = plt.legend(handles, labels, loc=\"lower right\", title=\"itr\")\n", + " ax.add_artist(legend2)" + ] + }, + { + "cell_type": "code", + "execution_count": 133, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 2 4 6 8 12 16 20 24 28 32 36 40 50 60 70 80 0]\n", + "SYS linux_tuned\n", + "MSE_loss_time=22.17882687873267 loss_time=4709440.1874 us max_time=1.8131587505340576 alpha=-0.5364206433296204 gamma=0.31824231147766113 delta=2.3066892623901367\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(1, 2), requires_grad=True)\n", + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 0), requires_grad=True)\n", + ":5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(0, 1), requires_grad=True)\n", + ":6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(0, 1), requires_grad=True)\n", + ":7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(2, 3), requires_grad=True)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=1.035170186285164 loss_time=1017433.13603 us max_time=2.328415870666504 alpha=-0.179485023021698 gamma=0.056357771158218384 delta=3.358389139175415\n", + "yvalue torch.Size([147])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=2000, lr=0.1, mpred=['time'], msys=['linux_tuned'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": 146, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 2 4 6 8 12 16 20 24 28 32 36 40 80 50 60 70]\n", + "SYS ebbrt_tuned\n", + "MSE_loss_time=9.679488415016243 loss_time=3111187.62131 us max_time=1.5235276222229004 alpha=-0.5037246346473694 gamma=0.5070372223854065 delta=2.2511630058288574\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + ":3: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " max_time = torch.tensor(torch.Tensor(1,1).uniform_(1, 2), requires_grad=True)\n", + ":4: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " alpha = torch.tensor(torch.Tensor(1,1).uniform_(-1, 0), requires_grad=True)\n", + ":5: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " beta = torch.tensor(torch.Tensor(1,1).uniform_(0, 1), requires_grad=True)\n", + ":6: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " gamma = torch.tensor(torch.Tensor(1,1).uniform_(0, 1), requires_grad=True)\n", + ":7: UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).\n", + " delta = torch.tensor(torch.Tensor(1,1).uniform_(2, 3), requires_grad=True)\n", + "/home/handong/anaconda3/lib/python3.8/site-packages/torch/nn/modules/loss.py:520: UserWarning: Using a target size (torch.Size([137])) that is different to the input size (torch.Size([1, 137])). This will likely lead to incorrect results due to broadcasting. Please ensure they have the same size.\n", + " return F.mse_loss(input, target, reduction=self.reduction)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "MSE_loss_time=2.854975601040078 loss_time=1689667.30484 us max_time=1.507089614868164 alpha=-0.9582613706588745 gamma=0.2201293259859085 delta=2.9038984775543213\n", + "MSE_loss_time=2.85497560103439 loss_time=1689667.30484 us max_time=1.507089376449585 alpha=-0.9582617282867432 gamma=0.22012904286384583 delta=2.9038991928100586\n", + "MSE_loss_time=2.854975601031157 loss_time=1689667.30484 us max_time=1.5070887804031372 alpha=-0.9582623839378357 gamma=0.22012877464294434 delta=2.903899908065796\n", + "MSE_loss_time=2.8549756011879626 loss_time=1689667.30488 us max_time=1.5070887804031372 alpha=-0.9582623839378357 gamma=0.22012904286384583 delta=2.903900623321533\n", + "MSE_loss_time=2.854975601031157 loss_time=1689667.30484 us max_time=1.5070887804031372 alpha=-0.9582623839378357 gamma=0.22012877464294434 delta=2.903899908065796\n", + "MSE_loss_time=2.8549756010310454 loss_time=1689667.30484 us max_time=1.5070886611938477 alpha=-0.9582625031471252 gamma=0.22012867033481598 delta=2.903900146484375\n", + "MSE_loss_time=2.8549756010313967 loss_time=1689667.30484 us max_time=1.5070886611938477 alpha=-0.9582624435424805 gamma=0.22012870013713837 delta=2.903900146484375\n", + "MSE_loss_time=2.8549756012794063 loss_time=1689667.30491 us max_time=1.5070884227752686 alpha=-0.9582621455192566 gamma=0.22012828290462494 delta=2.903899669647217\n", + "MSE_loss_time=2.8549756010344365 loss_time=1689667.30484 us max_time=1.5070886611938477 alpha=-0.9582624435424805 gamma=0.22012875974178314 delta=2.903900146484375\n", + "MSE_loss_time=2.8549849292261524 loss_time=1689670.0652 us max_time=1.5069819688796997 alpha=-0.9581586122512817 gamma=0.22006329894065857 delta=2.903837203979492\n", + "MSE_loss_time=2.8549756010822045 loss_time=1689667.30485 us max_time=1.5070888996124268 alpha=-0.9582627415657043 gamma=0.22012880444526672 delta=2.903900384902954\n", + "MSE_loss_time=2.8549756046286063 loss_time=1689667.3059 us max_time=1.5070871114730835 alpha=-0.9582607746124268 gamma=0.22012722492218018 delta=2.9038987159729004\n", + "MSE_loss_time=2.8549756033117237 loss_time=1689667.30551 us max_time=1.5070877075195312 alpha=-0.9582613706588745 gamma=0.22012749314308167 delta=2.9038987159729004\n", + "MSE_loss_time=2.854975601748759 loss_time=1689667.30505 us max_time=1.5070894956588745 alpha=-0.9582632184028625 gamma=0.2201293557882309 delta=2.903900623321533\n", + "MSE_loss_time=2.8549756017541785 loss_time=1689667.30505 us max_time=1.5070894956588745 alpha=-0.9582631587982178 gamma=0.22012928128242493 delta=2.9039008617401123\n", + "MSE_loss_time=2.8550361760305325 loss_time=1689685.22987 us max_time=1.5068960189819336 alpha=-0.9580489993095398 gamma=0.21993421018123627 delta=2.903714179992676\n", + "MSE_loss_time=2.8549756059459743 loss_time=1689667.30629 us max_time=1.5070868730545044 alpha=-0.9582606554031372 gamma=0.22012701630592346 delta=2.903898239135742\n", + "MSE_loss_time=2.8549809624626206 loss_time=1689668.89137 us max_time=1.5070098638534546 alpha=-0.9582092761993408 gamma=0.2200758308172226 delta=2.9038424491882324\n", + "MSE_loss_time=2.8549756010899645 loss_time=1689667.30485 us max_time=1.5070886611938477 alpha=-0.9582624435424805 gamma=0.22012843191623688 delta=2.903899908065796\n", + "yvalue torch.Size([137])\n" + ] + }, + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "run(df_comb, n_iter=20000, lr=.01, mpred=['time'], msys=['ebbrt_tuned'])\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.8.3" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/analysis/timefit.py b/analysis/timefit.py new file mode 100644 index 0000000..5a681c8 --- /dev/null +++ b/analysis/timefit.py @@ -0,0 +1,115 @@ +import sys +sys.path.append('../bayesopt') + +import read_agg_data +import torch +import torch.nn as nn +import torch.autograd as auto +import torch.optim as optim + +import numpy as np +import matplotlib.pylab as plt + +import pdb + +plt.ion() + +def inference(d, itr, n_iter, lr, workload, sys, print_freq=10): + #starts randomly + #max_time = torch.tensor(torch.Tensor(1,1).uniform_(10, 500), requires_grad=True) + #alpha = torch.tensor(torch.Tensor(1,1).uniform_(-2, 2), requires_grad=True) + + log_max_time = torch.rand(1, requires_grad=True) + alpha = torch.rand(1, requires_grad=True) + itr_suppress = torch.rand(1, requires_grad=True) + + t_latency = d[:,0] + dvfs = d[:,1] + + criterion = nn.MSELoss() + optimizer = optim.Adam([log_max_time, alpha, itr_suppress], lr=lr) + + for _ in range(n_iter): + max_time = torch.exp(log_max_time) + pred = itr_suppress*itr + max_time/dvfs**(1+alpha) + loss = criterion(pred, t_latency) + + optimizer.zero_grad() + loss.backward() + optimizer.step() + + if _ % print_freq == 0: + if _==0: + print(f'{"max_time":^10} {"alpha":^10} {"itr_suppress":^10} {"loss":^10}') + + print(f'{max_time.item():^10.3f} {alpha.item():^10.3f} {itr_suppress.item():^10.3f} {loss.item():^10.3f}') + + return pred, {'max_time': max_time.item(), 'alpha': alpha.item(), 'itr_suppress': itr_suppress.item(), 'sqrt_loss': np.sqrt(loss.item())} + +def run_all(n_iter=1000, lr=1e-2, qps=200000): + #for qps in [200000,400000, 60000]: + data = [] + for sys in ['linux_tuned', 'ebbrt_tuned']: + for target_col in [f'read_{i}th_mean' for i in [5, 10, 50, 90, 99]]: + d = run(n_iter=n_iter, lr=lr, sys=sys, qps=qps, target_col=target_col) + data.append(d) + + return data + + +def run(n_iter=2000, + lr=1e-1, + target_col='read_99th_mean', + sys='ebbrt_tuned', + qps=400000): + + #read linux_mcd.csv + for workload in ['mcd']: + #read raw data: TODO check all preprocessing is correct + df_comb, _, _ = read_agg_data.start_analysis(workload) + df_comb['dvfs'] = df_comb['dvfs'].apply(lambda x: int(x, base=16)) + df_comb = df_comb[(df_comb['itr']!=1) | (df_comb['dvfs']!=65535)] #filter out linux dynamic + df_comb['dvfs'] = df_comb['dvfs'].astype(float) / df_comb['dvfs'].min() + df_comb = df_comb[df_comb['QPS'] == qps] + + #filter to system + df = df_comb[(df_comb['sys']==sys)].copy() + + #interate over different itr values + unique_itr = df['itr'].unique() + for itr in unique_itr: + df_itr = df[df['itr'] == itr] + + df_itr = df_itr[[target_col, 'dvfs']] + d = df_itr.values + d = torch.tensor(d) + + #fitting + print(f'----------{workload} {sys} itr={itr} QPS={qps} {target_col}-------------') + if df.shape==0: + raise ValueError('Empty Dataframe') + pred, params = inference(d, itr, n_iter, lr, workload, sys, print_freq=500) + # df[f'prediction lr={lr}'] = pred.detach().numpy() + + #plotting + fig, ax = plt.subplots() + plt.title(f"{workload} {sys} {qps} {target_col}\n itr={itr} maxtime={params['max_time']:.2f} alpha={params['alpha']:.2f} itr_suppress={params['itr_suppress']:.2f} loss={params['sqrt_loss']:.2f}") + plt.xlabel(u"predictions") + plt.ylabel(u"actual values") + + scatter = plt.scatter(pred.detach().numpy(), d[:,0], s = [10**n for n in d[:,1]]) + + handles, labels = scatter.legend_elements(prop="sizes", alpha=0.6) + legend2 = plt.legend(handles, labels, loc="lower right", title="dvfs") + ax.add_artist(legend2) + + ax.set_xlim(ax.get_xlim()[0], ax.get_xlim()[1]) + ax.set_ylim(ax.get_ylim()[0], ax.get_ylim()[1]) + ax.grid() + + ax.plot(np.arange(0, int(ax.get_xlim()[1]+100)), np.arange(0, int(ax.get_xlim()[1]+100))) + + plt.savefig(f'plots/timefit/diff_itr/{itr}_{workload}_{sys}_{target_col}_{qps}_{lr}.png') + plt.close() + + return {**params, 'sys': sys, 'workload': workload, 'qps': qps, 'target_col': target_col} \ No newline at end of file diff --git a/bayesopt/__init__.py b/bayesopt/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/bayesopt/read_agg_data.py b/bayesopt/read_agg_data.py index a1dfb04..e1f543b 100644 --- a/bayesopt/read_agg_data.py +++ b/bayesopt/read_agg_data.py @@ -129,7 +129,8 @@ def scale_to_requests(d): def start_mcd_analysis(drop_outliers=False, scale_requests=True): '''TODO: Merge with start_nodejs_analysis''' - df_linux = pd.read_csv(os.path.join(Locations.aggregate_files_loc, 'mcd_combined.csv'), sep=' ') + df_linux = pd.read_csv(os.path.join('/home/handong/jupyter/jupyter-notebooks/nic-tuning-experiments/bayesopt/summary_data/mcd_combined.csv'), sep=' ') + #df_linux = pd.read_csv(os.path.join(Locations.aggregate_files_loc, 'mcd_combined.csv'), sep=' ') #df_ebbrt = pd.read_csv(os.path.join(Locations.aggregate_files_loc, 'ebbrt_mcd.csv'), sep=' ') #df = pd.concat([df_linux, df_ebbrt], axis=0) df = df_linux @@ -167,11 +168,12 @@ def scale_to_requests(d): 'joules', 'instructions', 'cycles', - 'refcyc', + #'refcyc', 'llc_miss', 'c3', 'c6', - #'c7'] + #'c7' + ] #SCALE_FACTOR = 5000000. / (d['QPS_uncorrected']*d['time']) SCALE_FACTOR = 5000000. / (d['measure_QPS']*d['time']) @@ -283,4 +285,4 @@ def scale_to_requests(d): df.reset_index(inplace=True) return df, dfr, outlier_list - \ No newline at end of file + diff --git a/bayesopt/summary_data/mcdsilo_combined.csv b/bayesopt/summary_data/mcdsilo_combined.csv index 4cca9a7..1ca9c70 100644 --- a/bayesopt/summary_data/mcdsilo_combined.csv +++ b/bayesopt/summary_data/mcdsilo_combined.csv @@ -1,100 +1,313 @@ sys i itr dvfs rapl read_5th read_10th read_50th read_90th read_95th read_99th measure_QPS target_QPS time joules rx_desc rx_bytes tx_desc tx_bytes instructions cycles ref_cycles llc_miss c1 c1e c3 c6 c7 num_interrupts -ebbrt_tuned 5 50 0xf00 95 55.9 59.3 92.9 206.5 266.4 336.3 50051.1 50000 20 986.24 1426446 135832800 1855804 72376308 70293247704 84688618820 169664993580 154244778 0 0 0 0 368757 2074686 -ebbrt_tuned 6 50 0xf00 95 56.2 59.6 92.8 204.2 264.4 333.2 50022.7 50000 20 1066.4 1536776 146362994 2000709 78025548 76225243423 92126023640 184416979278 167863895 0 0 0 0 399677 2240761 -ebbrt_tuned 7 50 0xf00 95 56.2 59.7 93.4 206.1 268.4 334.5 50031.6 50000 20 1066.16 1532864 146167465 2001154 78043612 75918644563 91832970461 183822776093 165936874 0 0 0 0 396626 2233534 -ebbrt_tuned 4 50 0xf00 55 56.1 59.4 91.7 200.3 255.7 323.6 49993.5 50000 20 1056.78 1541845 146664306 1999599 77982674 75127851918 89489482184 179609451063 156844398 0 0 0 0 397654 2235863 -ebbrt_tuned 5 50 0xf00 55 56.3 59.7 92.5 200.0 259.8 328.2 49875.1 50000 20 1020.7 1445480 137570425 1878934 73277858 70888902140 84916765325 170290707817 150679195 0 0 0 0 376382 2107784 -ebbrt_tuned 6 50 0xf00 55 56.1 59.5 92.3 202.3 260.8 329.2 50059.7 50000 20 1064.13 1543321 146793733 2002231 78085702 75753862045 90646631175 181715308654 162794821 0 0 0 0 396837 2238187 -ebbrt_tuned 7 50 0xf00 55 55.9 59.2 91.7 202.1 263.1 332.0 50076.9 50000 20 988.84 1426835 135895671 1858006 72461804 70209933095 84195264651 168712730788 149621084 0 0 0 0 369770 2077400 -ebbrt_tuned 4 50 0x1300 95 53.6 58.3 92.8 207.2 248.2 333.1 100140.1 100000 20 1354.36 2263558 244264589 4004795 156177640 151116473475 169254808941 266570855978 332699139 0 0 0 0 346619 3326236 -ebbrt_tuned 5 50 0x1300 95 52.8 57.4 92.1 205.6 247.5 327.1 100120.1 100000 20 1355.16 2258977 243958346 4004038 156147854 151328974139 170186337662 268058841831 338067893 0 0 0 0 346638 3325574 -ebbrt_tuned 6 50 0x1300 95 52.8 57.4 91.8 203.5 245.7 324.8 100045.7 100000 20 1305.99 2229435 240457898 3941551 153709754 149039407366 167555043309 263825354261 331408377 0 0 0 0 344221 3282060 -ebbrt_tuned 7 50 0x1300 95 52.5 56.8 91.7 206.8 247.5 326.7 99967.9 100000 20 1353.22 2262009 243990359 3997986 155910834 151233522463 169298860879 266606984207 333435659 0 0 0 0 348884 3329896 -ebbrt_tuned 4 200 0x1300 75 71.0 85.8 181.6 295.6 344.1 437.7 99985.7 100000 20 1343.57 2208966 240793215 3998739 155942078 150491050563 163002972465 257969837879 327004072 0 0 0 0 466055 1429102 -ebbrt_tuned 5 200 0x1300 75 72.3 86.5 183.5 297.0 347.7 436.8 100088.4 100000 20 1342.71 2212427 241123856 4002867 156102476 150195982498 162660359545 257713395577 327739321 0 0 0 0 474064 1429998 -ebbrt_tuned 6 200 0x1300 75 72.8 88.4 184.8 299.7 351.6 444.8 100013.3 100000 20 1344.26 2208534 240768144 3999745 155981762 149957006389 163018877784 258113817398 326086348 0 0 0 0 466625 1429723 -ebbrt_tuned 7 200 0x1300 75 72.3 86.7 184.9 298.7 349.9 440.5 99901.1 100000 20 1344.79 2207879 240674234 3995116 155796618 149932085114 163305054896 258668266310 329556700 0 0 0 0 470813 1429893 -ebbrt_tuned 4 100 0x1400 135 62.8 74.0 135.1 269.3 321.5 442.4 199899.0 200000 20 1599.99 4276849 473122588 7992401 311650734 297416094467 298274331174 434205352324 641850414 0 0 0 0 351954 2753481 -ebbrt_tuned 5 100 0x1400 135 62.9 74.0 134.8 268.4 318.0 435.6 199857.7 200000 20 1600.12 4275555 472965296 7990612 311583138 297251475665 297988235716 433804131669 644860072 0 0 0 0 354761 2752314 -ebbrt_tuned 6 100 0x1400 135 63.3 74.9 137.2 275.1 325.5 445.3 200117.4 200000 20 1601.69 4282399 473640997 8000934 311981376 298329221670 300266894543 437026370215 651069250 0 0 0 0 355854 2751714 -ebbrt_tuned 7 100 0x1400 135 62.8 74.3 136.0 270.6 321.4 440.6 200144.6 200000 20 1599.98 4282985 473688073 8001918 312022190 298432992084 299299017906 435599299123 648626887 0 0 0 0 354750 2752499 -ebbrt_tuned 4 50 0x1500 55 51.8 55.9 85.4 166.0 212.9 271.1 50006.7 50000 20 1320.17 1568980 148276620 2000129 78003126 76188852492 99963693747 156899056468 173194778 0 0 0 0 402891 2253893 -ebbrt_tuned 5 50 0x1500 55 51.5 55.7 84.7 164.5 211.4 271.4 49976.7 50000 20 1322.25 1566420 148117693 1998865 77954064 76070122880 100022169495 157078661501 173306519 0 0 0 0 403873 2257759 -ebbrt_tuned 6 50 0x1500 55 53.3 57.8 87.3 171.1 218.0 275.3 49981.0 50000 20 1323.06 1568745 148238840 1999066 77961332 76241944719 99615652385 156415730694 171408074 0 0 0 0 404572 2259935 -ebbrt_tuned 4 50 0x1500 75 51.1 55.2 87.2 187.6 227.6 297.5 99897.2 100000 20 1466.83 2265646 244102477 3995189 155802336 151062167346 172621606015 250610185660 334747517 0 0 0 0 356020 3336925 -ebbrt_tuned 6 50 0x1500 75 51.3 55.4 87.7 186.6 227.2 293.9 99950.6 100000 20 1464.63 2269447 244408784 3997192 155881054 150884429468 171531929198 248982354122 331676976 0 0 0 0 355982 3341130 -ebbrt_tuned 7 50 0x1500 75 51.7 55.8 87.9 187.9 228.5 296.1 100114.3 100000 20 1468.46 2270368 244616289 4003758 156135200 151491750905 172350803200 250076924902 333790207 0 0 0 0 355399 3340006 -ebbrt_tuned 4 50 0x1500 55 51.1 55.3 87.5 187.8 227.3 298.9 99974.4 100000 20 1465.82 2274552 244708676 3998184 155917776 150687447111 171500505368 249181850081 331575248 0 0 0 0 355867 3340480 -ebbrt_tuned 5 50 0x1500 55 51.4 55.6 87.5 185.7 226.6 294.5 99894.1 100000 20 1467.25 2268433 244228770 3995041 155797536 150866625249 172300449317 250088049940 333150958 0 0 0 0 358138 3344801 -ebbrt_tuned 6 50 0x1500 55 51.1 55.3 87.0 185.6 226.3 298.6 99839.2 100000 20 1467.01 2274093 244553499 3992927 155714756 150895521366 171989172663 249886651900 334422821 0 0 0 0 356371 3338907 -ebbrt_tuned 7 50 0x1500 55 51.7 56.0 88.6 188.9 227.6 298.1 99994.6 100000 20 1466.95 2269636 244465279 3999057 155954648 151134822978 172854876439 250759276661 333017079 0 0 0 0 357390 3346516 -ebbrt_tuned 4 50 0x1500 55 54.0 60.9 102.3 232.4 275.8 389.1 200005.5 200000 20 1682.68 4245231 471303912 7996676 311818622 300250619591 311647311763 432077128802 666069785 0 0 0 0 296056 4600816 -ebbrt_tuned 5 50 0x1500 55 54.4 61.2 103.6 234.5 281.4 394.3 200112.1 200000 20 1681.7 4244550 471347688 8000399 311960898 300692675499 310940719068 431062195444 660789074 0 0 0 0 292974 4591915 -ebbrt_tuned 6 50 0x1500 55 53.3 60.0 101.1 230.4 273.3 389.5 200033.8 200000 20 1678.13 4243860 471242390 7997837 311863518 301185281432 312316705852 432956150760 667762553 0 0 0 0 291643 4589207 -ebbrt_tuned 7 50 0x1500 55 53.7 60.4 102.5 234.4 276.8 393.8 200090.7 200000 20 1681.51 4247517 471538280 8000033 311947662 300812909848 311661836627 432053006196 666453392 0 0 0 0 292339 4592072 -ebbrt_tuned 4 100 0x1500 135 62.3 73.1 131.6 247.3 291.5 393.8 199772.0 200000 20 1650.32 4261411 472066654 7987190 311447724 294599012670 289891250597 403497354607 603099598 0 0 0 0 377404 2780692 -ebbrt_tuned 5 100 0x1500 135 62.0 73.5 132.7 254.5 298.8 401.7 199941.8 200000 20 1664.06 4265019 472432218 7992195 311614208 296244872448 296030428703 411550073157 625761020 0 0 0 0 372623 2774015 -ebbrt_tuned 7 100 0x1500 135 60.4 70.9 129.2 251.2 295.7 400.7 200052.2 200000 20 1667.9 4271432 472929103 7998057 311868970 297075613868 299340348503 415907041248 637473602 0 0 0 0 367998 2768591 -ebbrt_tuned 4 50 0x1700 55 50.0 54.4 85.9 176.2 213.0 274.5 99982.1 100000 20 1582.01 2278987 244974644 3998451 155929908 150977875072 175438396842 237398541719 331443773 0 0 0 0 361104 3348983 -ebbrt_tuned 5 50 0x1700 55 49.5 54.0 84.7 175.4 212.4 275.0 100042.9 100000 20 1581.54 2277346 244958885 4001009 156030438 151034048572 175134702398 236950238839 328723110 0 0 0 0 358310 3336760 -ebbrt_tuned 6 50 0x1700 55 50.7 55.2 86.6 178.3 214.9 275.4 100108.3 100000 20 1580.92 2276626 244977552 4003740 156137160 150894169829 175485262910 237476940841 332070992 0 0 0 0 361438 3350183 -ebbrt_tuned 7 50 0x1700 55 50.1 54.4 85.6 177.1 213.4 276.8 99958.0 100000 20 1584.04 2277790 244893232 3997686 155901424 150837926376 175648380106 237596516570 330729089 0 0 0 0 358901 3341441 -ebbrt_tuned 4 50 0x1800 95 52.0 58.0 94.8 203.4 240.0 332.0 199964.7 200000 20 1764.5 3918902 435838811 7409666 288930000 278583615098 297506119688 363212816085 618054914 0 0 0 0 292256 4302411 -ebbrt_tuned 5 50 0x1800 95 51.8 57.7 95.2 206.4 243.5 335.8 199916.3 200000 20 1899.16 4227049 470134098 7992740 311658960 300942834095 319673745406 390179891725 662424776 0 0 0 0 316162 4641283 -ebbrt_tuned 6 50 0x1800 95 50.1 56.3 91.9 200.5 237.3 330.7 199928.5 200000 20 1904.52 4227917 470198680 7993484 311692132 300807229528 321145284751 392028502467 665937376 0 0 0 0 310581 4627805 -ebbrt_tuned 5 50 0x1900 95 52.1 58.2 94.4 199.0 234.6 320.7 199919.9 200000 20 1988.93 4222350 469811982 7993382 311691356 300796626824 324442864723 381325670972 673773527 0 0 0 0 318834 4647936 -ebbrt_tuned 6 50 0x1900 95 50.5 56.6 92.4 196.9 231.4 318.1 200074.2 200000 20 1991.33 4228147 470353606 7999442 311926338 301176962107 324842308512 381537677357 666085311 0 0 0 0 318529 4649263 -ebbrt_tuned 7 50 0x1900 95 50.4 56.5 92.0 196.9 230.7 322.2 200089.5 200000 20 1992.15 4226426 470260886 7999630 311932532 300822471279 323959414639 380629295741 667580191 0 0 0 0 319275 4648491 -ebbrt_tuned 4 100 0x1900 135 54.9 62.1 114.6 194.6 227.9 292.5 100186.9 100000 20 1701.29 2249840 243474179 4006858 156259034 149418036052 171977144156 221523413890 314186720 0 0 0 0 390059 2455258 -ebbrt_tuned 5 100 0x1900 135 54.2 61.5 113.8 193.6 228.7 293.1 100125.2 100000 20 1700.3 2246485 243175541 4003945 156140370 149203981439 172915579313 222565978071 318042295 0 0 0 0 386998 2449326 -ebbrt_tuned 6 100 0x1900 135 54.1 61.8 115.1 196.1 231.4 296.7 100118.9 100000 20 1700.88 2249309 243355362 4004019 156146994 149306966227 173335414064 222773490094 318029284 0 0 0 0 388240 2449420 -ebbrt_tuned 7 100 0x1900 135 53.6 61.2 113.3 193.7 227.7 293.7 100010.7 100000 20 1701.94 2254005 243492919 3999750 155981468 149256017812 173006127496 222508827626 317760545 0 0 0 0 388897 2451602 -ebbrt_tuned 4 100 0x1900 95 54.8 62.3 115.0 195.9 230.3 296.8 99991.3 100000 20 1704.79 2245417 242991357 3998943 155950202 149108922284 174162744694 223614821245 319930314 0 0 0 0 388042 2450159 -ebbrt_tuned 5 100 0x1900 95 54.3 61.6 113.4 193.1 229.2 293.4 99921.1 100000 20 1700.84 2252788 243346267 3996114 155839316 149467267408 173239746999 222636578484 319126256 0 0 0 0 388405 2450022 -ebbrt_tuned 6 100 0x1900 95 54.6 61.9 114.2 195.9 230.6 295.4 99928.1 100000 20 1702.47 2250869 243228624 3996507 155854848 149258017646 173853371783 223145881646 320821637 0 0 0 0 385885 2448965 -ebbrt_tuned 7 100 0x1900 95 55.6 63.3 116.0 199.0 234.4 296.7 99932.3 100000 20 1698.52 2251191 243257866 3996462 155852298 149124128552 173448094945 222747527322 320047735 0 0 0 0 388560 2450848 -ebbrt_tuned 4 50 0x1a00 95 49.3 54.0 84.4 164.8 198.1 254.9 100095.1 100000 20 1785.95 2290355 245815102 4002875 156100276 150009138359 180171868740 223652996207 328709598 0 0 0 0 366061 3359888 -ebbrt_tuned 5 50 0x1a00 95 48.4 53.1 83.2 162.4 195.4 250.5 99959.8 100000 20 1780.1 2282684 245189464 3997699 155901460 150050911250 179386383937 222763238855 326763794 0 0 0 0 367031 3356105 -ebbrt_tuned 6 50 0x1a00 95 49.5 54.1 83.8 162.0 195.7 250.9 99888.2 100000 20 1776.47 2286216 245343559 3994919 155793156 149781892423 178783183940 221986746658 324207414 0 0 0 0 366705 3355875 -ebbrt_tuned 7 50 0x1a00 95 49.2 54.0 84.2 164.9 197.4 256.5 100001.4 100000 20 1782.48 2284779 245356000 3999250 155961634 150229684876 179979344072 223351919657 329034462 0 0 0 0 365223 3354228 -ebbrt_tuned 4 50 0x1a00 75 50.1 56.2 90.8 189.8 222.9 303.0 200112.7 200000 20 2073.35 4225202 470189988 8000678 311971680 301345499374 328941900547 372868467614 678142565 0 0 0 0 322558 4660433 -ebbrt_tuned 5 50 0x1a00 75 50.6 56.6 91.1 192.2 226.1 306.7 199981.3 200000 20 2071.51 4220981 469828564 7995534 311772796 300621771819 328042875171 371922464269 676154420 0 0 0 0 322555 4654733 -ebbrt_tuned 6 50 0x1a00 75 49.9 55.7 90.1 190.9 223.7 304.5 199907.9 200000 20 2072.51 4221694 469771628 7992492 311653198 300840400930 328329526587 371935679888 666022330 0 0 0 0 324472 4660145 -ebbrt_tuned 7 50 0x1a00 75 50.9 56.7 91.4 191.3 226.1 309.1 199894.0 200000 20 2077.76 4219806 469642835 7991913 311630356 301790622760 331010884226 375007223994 679503175 0 0 0 0 323035 4658424 -ebbrt_tuned 4 300 0x1a00 135 79.0 101.4 233.7 364.6 404.3 490.1 199919.1 200000 20 2023.74 4373193 478892873 7992921 311668322 300646970677 309167699223 358497925702 632983299 0 0 0 0 672278 976522 -ebbrt_tuned 5 300 0x1a00 135 77.3 98.8 230.7 361.4 400.7 484.5 200022.9 200000 20 2028.92 4375706 479130973 7997306 311841984 300822607276 311447289839 360393142083 638560360 0 0 0 0 674077 976522 -ebbrt_tuned 6 300 0x1a00 135 77.6 99.3 231.3 363.4 402.9 489.7 199950.9 200000 20 2028.06 4371748 478812152 7993950 311705694 301311314650 311657705939 360188457763 642652417 0 0 0 0 666836 976514 -ebbrt_tuned 7 300 0x1a00 135 79.4 101.3 234.6 365.7 406.7 491.2 200107.4 200000 20 2030.69 4376562 479258697 8000529 311968900 301507285126 312683767782 360513192513 646543423 0 0 0 0 673823 976513 -ebbrt_tuned 4 50 0x1b00 75 49.3 54.1 84.2 161.5 193.7 248.7 100108.3 100000 20 1857.88 2290169 245820655 4003603 156130078 150628239188 182634823361 220641763894 331568592 0 0 0 0 366509 3361027 -ebbrt_tuned 5 50 0x1b00 75 48.2 53.0 83.0 160.8 194.4 254.0 100083.0 100000 20 1858.48 2286723 245566614 4002602 156093046 150692390612 182867262060 220741046322 330558066 0 0 0 0 365543 3355757 -ebbrt_tuned 6 50 0x1b00 75 49.2 54.2 84.1 163.8 197.0 250.0 100024.3 100000 20 1856.45 2289948 245728510 4000201 155996664 150624157982 182274075828 219948327697 328627376 0 0 0 0 367627 3360687 -ebbrt_tuned 7 50 0x1b00 75 47.8 52.4 82.2 158.7 190.8 245.7 100168.8 100000 20 1789.28 2178455 233799252 3810726 148610664 143729172528 174422775098 210401242917 316929614 0 0 0 0 347689 3194343 -ebbrt_tuned 4 50 0x1b00 95 50.1 56.2 90.5 186.7 220.1 297.5 200164.0 200000 20 2167.17 4224094 470169679 8002835 312055958 301762097006 333049138895 364735845915 685847314 0 0 0 0 326224 4667557 -ebbrt_tuned 5 50 0x1b00 95 50.1 55.9 90.2 187.0 220.7 298.7 199941.0 200000 20 2164.92 4219719 469683675 7994098 311719958 301904951327 333778285931 365442632639 682821736 0 0 0 0 325675 4660676 -ebbrt_tuned 6 50 0x1b00 95 49.5 55.4 88.9 183.6 215.4 296.7 200134.1 200000 20 2168.77 4226449 470346905 8001780 312017198 301874605224 334381939609 366076852605 688825727 0 0 0 0 324862 4662928 -ebbrt_tuned 7 50 0x1b00 95 50.4 56.2 90.7 186.9 219.2 298.9 200123.4 200000 20 2009.71 3955586 440252873 7498554 292396750 283229856055 313587292815 343240096484 644427148 0 0 0 0 305731 4370557 -ebbrt_tuned 4 100 0x1b00 95 52.1 58.6 104.2 179.2 207.8 271.9 50019.4 50000 20 1646.08 1536012 146331587 2000653 78023930 75669018587 103021658208 141135510924 167667044 0 0 0 0 414704 1855303 -ebbrt_tuned 5 100 0x1b00 95 51.8 58.0 103.4 178.9 206.8 271.3 50044.8 50000 20 1643.51 1531385 146058641 2001576 78060062 75880265204 103137810402 141199463435 169205539 0 0 0 0 415634 1858906 -ebbrt_tuned 6 100 0x1b00 95 51.0 57.3 102.1 177.0 204.3 268.5 49975.3 50000 20 1644.48 1538069 146395757 1998889 77955066 75711341245 103053606535 140937859829 167425092 0 0 0 0 415001 1857769 -ebbrt_tuned 7 100 0x1b00 95 51.6 58.1 103.1 179.6 207.5 270.1 49947.4 50000 20 1646.62 1529422 145840734 1997747 77910516 75762909357 103341531681 141250140326 167971466 0 0 0 0 412480 1853046 -ebbrt_tuned 4 200 0x1b00 75 65.9 79.8 172.7 267.9 298.9 368.2 100139.7 100000 20 1833.96 2216398 241418015 4004982 156185808 150056341386 174832998363 212250729488 325819749 0 0 0 0 499568 1430633 -ebbrt_tuned 5 200 0x1b00 75 66.5 80.5 173.0 268.9 301.2 371.8 100009.8 100000 20 1836.86 2215154 241203534 3999691 155978402 149797982365 175113538354 212493996945 327166309 0 0 0 0 497894 1430085 -ebbrt_tuned 6 200 0x1b00 75 65.4 79.1 172.3 268.3 299.9 369.6 100012.4 100000 20 1838.47 2214534 241200339 3999654 155977208 149830460222 175363317308 213054958677 328388760 0 0 0 0 501251 1430848 -ebbrt_tuned 7 200 0x1b00 75 66.1 80.2 173.6 269.4 301.2 368.8 99968.3 100000 20 1834.73 2214553 241098052 3998046 155915008 149688080933 174794016922 212318444285 328658653 0 0 0 0 498792 1429789 -ebbrt_tuned 4 50 0x1c00 95 49.8 55.8 89.3 181.9 212.3 287.9 200007.1 200000 20 2261.21 4218189 469664618 7996215 311796592 301260189572 335153293160 355460227467 678831844 0 0 0 0 330997 4672849 -ebbrt_tuned 5 50 0x1c00 95 50.3 56.1 89.8 182.0 213.4 286.5 200094.5 200000 20 2258.17 4219042 469828159 8000229 311957212 301576835078 333102658469 353245528729 670116805 0 0 0 0 331400 4674646 -ebbrt_tuned 6 50 0x1c00 95 50.1 56.0 89.6 182.9 214.0 289.9 199889.0 200000 20 2260.17 4214631 469333310 7991909 311630594 301525863499 334063309466 354238478434 675510869 0 0 0 0 331545 4673169 -ebbrt_tuned 7 50 0x1c00 95 49.2 55.0 88.7 180.8 211.3 285.8 199927.4 200000 20 2256.66 4214319 469353039 7993255 311685130 301482790805 333852942743 354051201596 675163273 0 0 0 0 330471 4671489 -ebbrt_tuned 4 50 0x1d00 95 47.2 51.8 81.6 153.8 185.1 240.3 100000.1 100000 20 2020.39 2289347 245629730 3999308 155963354 151047300389 188047094794 215763069476 335718903 0 0 0 0 367670 3356499 -ebbrt_tuned 5 50 0x1d00 95 48.4 53.3 82.7 157.2 188.7 242.7 100030.5 100000 20 2018.39 2290915 245738834 4000328 156002130 151016221425 187447762378 215025036006 334198679 0 0 0 0 367524 3358018 -ebbrt_tuned 6 50 0x1d00 95 49.3 54.3 83.9 158.2 190.3 245.2 99982.6 100000 20 2019.37 2290890 245710571 3998676 155939432 151373524824 188045437873 215812536603 339416549 0 0 0 0 367232 3355144 -ebbrt_tuned 4 50 0x1d00 135 48.5 54.5 87.0 177.0 206.7 276.4 200131.9 200000 20 2361.02 4218426 469807702 8001529 312005610 302385935560 339720938775 349011194214 682678065 0 0 0 0 333254 4681500 -ebbrt_tuned 5 50 0x1d00 135 49.3 55.2 88.0 178.6 207.5 280.6 200046.5 200000 20 2360.81 4217452 469700708 7998177 311877046 301413340164 339593417921 348966998040 684780104 0 0 0 0 333393 4684985 -ebbrt_tuned 6 50 0x1d00 135 48.0 53.4 84.7 163.7 192.7 255.4 199979.4 200000 20 2303.69 4211308 469246701 7995424 311766014 294335831394 316348680537 329139692907 603190543 0 0 0 0 341968 4704643 -ebbrt_tuned 4 100 0x1d00 55 53.7 61.1 113.4 190.5 223.8 293.3 99920.4 100000 20 1969.23 2249545 243148696 3996164 155841246 150758322123 182735113007 215488228878 333563542 0 0 0 0 389777 2449631 -ebbrt_tuned 5 100 0x1d00 55 52.9 60.3 111.3 183.3 210.4 267.1 100024.6 100000 20 1948.54 2252363 243417285 4000036 155990320 147676059014 173251198522 206410140711 296968784 0 0 0 0 394805 2457529 -ebbrt_tuned 6 100 0x1d00 55 52.7 60.0 111.9 184.2 214.1 276.1 99915.7 100000 20 1959.19 2255769 243543796 3995914 155831852 148172001944 175415173333 209868298607 308956050 0 0 0 0 393480 2457737 -ebbrt_tuned 7 100 0x1d00 55 53.1 60.3 112.5 186.0 217.2 285.8 100052.9 100000 20 1956.75 2250632 243396857 4001349 156043716 148413593130 175805555343 210463955098 310981393 0 0 0 0 392740 2457658 +ebbrt_poll 0 666 0xc00 135 40.9 43.2 62.3 153.1 222.9 334.1 50052.3 50000 20 2244.57 1607461 150634793 2001928 78073662 825598016408 580441479558 844972521123 276194870 0 0 0 0 0 298090 +ebbrt_poll 1 666 0xc00 135 42.0 44.2 63.7 155.0 227.7 335.9 49881.9 50000 20 2247.03 1487894 139312456 1848404 72087130 768516823970 581162654414 845396335301 339125861 0 0 0 0 0 298238 +ebbrt_poll 0 666 0xc00 135 41.6 44.4 72.5 213.5 295.8 427.7 100054.4 100000 20 2262.87 2294618 245999804 4001410 156046560 840582363820 582463301202 848066550437 364918347 0 0 0 0 0 296264 +ebbrt_poll 1 666 0xc00 135 41.9 44.7 72.8 218.0 297.0 424.3 100071.4 100000 20 2264.93 2285316 245428792 4002067 156071778 840758714921 582088784519 847575488333 363036265 0 0 0 0 0 296240 +ebbrt_poll 0 666 0xd00 135 40.8 43.2 60.8 143.4 201.9 305.5 49966.1 50000 20 2283.52 1619052 151245946 1998419 77936452 837776858033 596756664672 846211801149 300158498 0 0 0 0 0 298349 +ebbrt_poll 1 666 0xd00 135 41.1 43.3 61.2 146.2 205.0 306.3 49930.0 50000 20 2288.25 1620265 151266417 1997021 77882334 836149390464 598071818993 847415449281 304348114 0 0 0 0 0 298371 +ebbrt_poll 0 666 0xd00 135 41.4 44.1 68.0 188.5 262.8 366.9 99986.5 100000 20 2301.57 2292311 245783860 3998668 155939570 863481498652 597359025413 847012642239 366340196 0 0 0 0 0 296674 +ebbrt_poll 1 666 0xd00 135 41.7 44.5 68.1 187.8 261.0 367.5 99929.7 100000 20 2301.89 2299602 246180759 3996397 155849966 863264170023 597961994160 847515467323 366337116 0 0 0 0 0 296687 +ebbrt_poll 0 666 0xf00 135 40.2 42.4 59.0 134.7 181.3 268.8 50078.6 50000 20 2369.15 1642347 152760949 2002975 78114180 877704449345 628682563740 846784316945 316329881 0 0 0 0 0 298694 +ebbrt_poll 1 666 0xf00 135 40.2 42.5 58.6 133.3 180.6 268.4 50056.1 50000 20 2370.01 1644338 152871119 2002000 78075738 871582636323 627303488496 845454905847 323330921 0 0 0 0 0 298678 +ebbrt_poll 0 666 0xf00 135 41.6 43.9 65.2 169.1 226.4 316.0 99832.2 100000 20 2385.85 2315309 247025447 3992497 155699068 911964655508 629230015995 847007443757 370277281 0 0 0 0 0 297402 +ebbrt_poll 1 666 0xf00 135 41.3 43.6 64.5 168.2 226.6 313.6 99967.2 100000 20 2385.09 2316383 247221796 3997931 155910474 907783508976 628287925345 846589559673 374884681 0 0 0 0 0 297385 +ebbrt_poll 0 666 0x1100 135 40.3 42.2 57.1 123.5 163.1 235.6 50048.5 50000 20 2463.61 1539751 142632307 1855731 72372430 926704956410 660285050580 846878484469 313538326 0 0 0 0 0 299001 +ebbrt_poll 1 666 0x1100 135 39.7 41.7 56.6 121.4 160.2 234.2 49973.4 50000 20 2465.07 1655321 153439792 1998742 77949198 913761559573 658800139733 845323448615 333361429 0 0 0 0 0 298952 +ebbrt_poll 0 666 0x1100 135 40.5 42.5 60.6 150.5 194.7 267.6 99957.6 100000 20 2471.15 2165703 230258484 3705047 144489606 898284313242 658934016511 845772487254 428215548 0 0 0 0 0 298041 +ebbrt_poll 1 666 0x1100 135 40.1 42.3 60.7 151.0 197.5 270.0 100025.8 100000 20 2477.67 2328631 248029425 4000209 155998026 958572140977 660561680320 847570409012 371532046 0 0 0 0 0 297868 +ebbrt_poll 0 666 0x1100 135 41.8 44.6 75.2 206.4 254.0 389.1 199850.2 200000 20 2487.02 4218574 469510129 7990607 311585336 902057220655 661905608483 849699737231 661918640 0 0 0 0 0 295707 +ebbrt_poll 1 666 0x1100 135 41.9 44.8 76.3 211.9 264.5 402.3 199734.8 200000 20 2487.38 4215349 469214916 7985819 311397846 900672368699 661943882570 849593803044 664781578 0 0 0 0 0 295698 +ebbrt_poll 0 666 0x1300 135 39.6 41.6 56.4 123.3 158.7 225.2 50158.2 50000 20 2563.32 1664137 154172514 2006125 78236700 953978119149 689752737320 845464297961 355651521 0 0 0 0 0 298991 +ebbrt_poll 1 666 0x1300 135 41.6 42.9 57.8 126.7 162.7 226.3 49992.0 50000 20 2571.03 1661363 153816161 1999471 77978078 954474077807 688091166115 843550651326 350886333 0 0 0 0 0 299008 +ebbrt_poll 0 666 0x1300 135 39.9 42.0 59.8 145.1 181.9 248.1 100065.2 100000 20 2583.39 2338984 248676770 4001577 156049166 1000431814127 691674410255 847548826342 391158322 0 0 0 0 0 298001 +ebbrt_poll 1 666 0x1300 135 40.4 42.3 60.4 148.3 187.7 253.3 100137.3 100000 20 2582.04 2344149 249084813 4004786 156178584 996367235985 690622561103 846413340303 396145077 0 0 0 0 0 297995 +ebbrt_poll 0 666 0x1300 135 41.7 44.3 74.0 200.4 247.9 371.6 200127.6 200000 20 2592.1 4218145 469784844 8001381 312004502 934412389580 692424557040 848853558962 697774900 0 0 0 0 0 295933 +ebbrt_poll 1 666 0x1300 135 40.7 43.1 67.3 175.1 211.5 305.9 200093.6 200000 20 2563.24 4203967 468888167 7999154 311905096 966056589366 691835368816 848029955395 614078568 0 0 0 0 0 296628 +ebbrt_poll 0 666 0x1500 135 40.9 42.2 56.2 118.6 154.6 206.2 50136.0 50000 20 2684.62 1670961 154533110 2005187 78200556 1027861635494 724061691852 848997591667 329172471 0 0 0 0 0 299123 +ebbrt_poll 1 666 0x1500 135 38.8 40.5 54.7 115.5 151.9 203.2 50084.1 50000 20 2683.51 1678029 154899854 2003184 78122736 1028412472110 721008874044 845617795193 322993757 0 0 0 0 0 299120 +ebbrt_poll 0 666 0x1500 135 40.1 42.0 58.6 137.0 170.6 224.8 99974.5 100000 20 2693.17 2342211 248783777 3998131 155917932 1055118164771 721355653002 845904680970 379504583 0 0 0 0 0 298282 +ebbrt_poll 1 666 0x1500 135 40.6 42.2 59.0 137.6 171.6 226.7 99981.3 100000 20 2692.13 2351234 249325345 3998521 155933484 1052977057072 721986811400 846620295903 386298894 0 0 0 0 0 298233 +ebbrt_poll 0 666 0x1500 135 41.8 43.9 69.8 179.4 217.7 320.4 199901.0 200000 20 2703.78 4205688 468802218 7992518 311658462 986221574212 723375076388 848352187503 693521809 0 0 0 0 0 296468 +ebbrt_poll 1 666 0x1500 135 40.2 42.7 67.6 174.3 210.5 305.1 200078.3 200000 20 2703.11 4205887 468985238 7999525 311930940 987651691170 723859467639 848847690377 693005667 0 0 0 0 0 296478 +ebbrt_poll 0 666 0x1700 135 38.8 40.4 53.8 111.1 146.9 189.9 49953.8 50000 20 2811.81 1678996 154830186 1998021 77921544 1075551321476 751859726292 844847564183 324030595 0 0 0 0 0 299213 +ebbrt_poll 1 666 0x1700 135 39.0 40.8 54.2 109.7 146.0 191.6 50013.3 50000 20 2811.54 1687028 155385326 2000331 78011244 1067711915594 751296387027 844254876683 333981626 0 0 0 0 0 299215 +ebbrt_poll 0 666 0x1700 135 40.0 41.9 57.8 131.8 164.8 217.9 100086.5 100000 20 2822.02 2356035 249732096 4002727 156096768 1097249305320 753664533566 847128974840 392603385 0 0 0 0 0 298439 +ebbrt_poll 1 666 0x1700 135 38.8 40.3 56.4 129.0 162.1 212.2 100071.2 100000 20 2825.88 2361956 250049939 4001954 156065166 1097528442110 751857751917 845320115848 385134556 0 0 0 0 0 298449 +ebbrt_poll 0 666 0x1700 135 40.7 42.8 65.2 164.2 197.0 277.7 200110.3 200000 20 2838.0 4202244 468813380 8000496 311965884 1038425183661 754538521349 848099601563 684484949 0 0 0 0 0 296879 +ebbrt_poll 1 666 0x1700 135 39.7 42.0 64.4 163.0 195.1 274.1 199886.5 200000 20 2836.32 4197811 468303836 7991852 311631888 1036550345905 754434351473 848037012632 682535541 0 0 0 0 0 296857 +ebbrt_poll 0 666 0x1700 135 41.8 45.4 85.0 222.1 280.5 420.5 300023.5 300000 20 2845.41 6404369 709206340 11989854 467442366 946815270549 756369497311 850253215571 1030115047 0 0 0 0 0 295145 +ebbrt_poll 1 666 0x1700 135 42.0 46.2 87.0 224.7 281.6 422.5 300151.8 300000 20 2843.12 6409200 709532223 11994284 467612712 946742981944 756670446128 850546495761 1046103882 0 0 0 0 0 295177 +ebbrt_poll 0 666 0x1900 135 38.3 39.5 52.4 106.8 142.1 180.1 49988.3 50000 20 2947.62 1563104 143920615 1852089 72230242 1115857553995 783362875336 845676181879 327318116 0 0 0 0 0 299342 +ebbrt_poll 1 666 0x1900 135 38.5 39.8 52.7 105.7 142.4 180.0 50045.0 50000 20 2950.18 1691110 155627849 2001619 78061416 1121255369943 783505863429 845649504228 328597386 0 0 0 0 0 299298 +ebbrt_poll 0 666 0x1900 135 38.6 40.1 55.4 123.8 154.0 198.2 99918.3 100000 20 2960.39 2369004 250325698 3996000 155834970 1154468222034 784699852040 846941507066 379965396 0 0 0 0 0 298589 +ebbrt_poll 1 666 0x1900 135 38.8 40.4 55.5 123.7 154.6 197.1 99879.3 100000 20 2957.31 2372109 250479862 3994283 155768326 1140065367769 784371696200 846683985935 397220451 0 0 0 0 0 298601 +ebbrt_poll 0 666 0x1900 135 39.4 41.6 62.3 154.6 183.9 256.9 200067.6 200000 20 2967.35 4198561 468574797 7998660 311892990 1090358277058 785898940982 848323278302 673150466 0 0 0 0 0 297180 +ebbrt_poll 1 666 0x1900 135 39.1 41.0 61.5 152.9 181.5 255.2 199955.4 200000 20 2965.96 3886927 433840608 7405928 288783718 1093184582976 785243374355 847593496927 634071825 0 0 0 0 0 297379 +ebbrt_poll 0 666 0x1900 135 42.2 45.1 78.0 199.7 246.9 358.8 299977.1 300000 20 2970.77 6380698 707693447 11987935 467369400 991745416814 787513726335 850115382399 1036554842 0 0 0 0 0 295597 +ebbrt_poll 1 666 0x1900 135 40.4 43.8 77.3 197.8 247.1 361.0 299932.5 300000 20 2971.65 6377430 707475148 11986147 467303646 999051316048 787473107606 850041048932 1021633532 0 0 0 0 0 295599 +ebbrt_poll 0 666 0x1b00 135 38.1 39.1 51.3 101.0 135.4 168.3 50018.8 50000 20 3103.83 1700468 156209593 2000550 78019500 1167106412252 814177320632 845213816145 330882394 0 0 0 0 0 299365 +ebbrt_poll 1 666 0x1b00 135 38.4 39.6 51.8 102.9 138.8 170.0 49892.1 50000 20 3102.85 1695060 155719861 1995519 77823786 1162756207201 814825488334 845960222698 339261881 0 0 0 0 0 299362 +ebbrt_poll 0 666 0x1b00 135 38.7 40.1 54.5 117.7 148.3 185.3 100141.8 100000 20 3114.0 2359127 248955050 3966360 154679168 1198072978705 816912910971 848109292261 387421145 0 0 0 0 0 298741 +ebbrt_poll 1 666 0x1b00 135 39.1 41.0 55.0 117.7 147.9 186.1 100126.9 100000 20 3114.22 2382002 251308852 4004042 156147560 1194848151361 813101206279 844176525440 383795939 0 0 0 0 0 298723 +ebbrt_poll 0 666 0x1b00 135 39.3 41.4 60.8 148.4 173.6 242.7 199883.0 200000 20 3125.62 4189718 467816055 7991031 311592978 1138705965831 816658642237 847833417929 666344319 0 0 0 0 0 297394 +ebbrt_poll 1 666 0x1b00 135 38.6 40.2 59.6 145.9 170.8 240.1 199957.6 200000 20 3124.09 4193162 468138667 7994500 311735896 1138264098527 816793382167 847973294818 674317503 0 0 0 0 0 297408 +ebbrt_poll 0 666 0x1b00 135 40.1 43.5 76.0 187.8 230.2 327.6 300038.8 300000 20 3128.13 6354936 706208965 11989029 467396420 1049422430993 818509242912 849766942933 1010288146 0 0 0 0 0 296005 +ebbrt_poll 1 666 0x1b00 135 40.2 43.5 75.2 186.8 231.4 327.7 300133.4 300000 20 3129.96 6359747 706646699 11993921 467601942 1048900896282 818730053280 850002189164 1019495762 0 0 0 0 0 295993 +ebbrt_poll 0 666 0x1d00 135 38.4 39.6 50.7 95.8 127.5 153.9 50049.5 50000 20 3234.16 1720689 157434409 2001741 78065944 1203685512315 843617039661 843617039779 334000954 0 0 0 0 0 299482 +ebbrt_poll 1 666 0x1d00 135 38.6 40.1 51.2 97.0 129.2 154.6 49982.7 50000 20 3258.78 1709792 156701554 1999058 77961794 1210579333243 845194683236 845194682728 331524497 0 0 0 0 0 299466 +ebbrt_poll 0 666 0x1d00 135 38.1 39.3 52.1 110.2 137.2 169.3 99897.5 100000 20 3279.99 2397868 252059051 3995083 155799042 1251288048321 845725886849 845725885704 369234209 0 0 0 0 0 298914 +ebbrt_poll 1 666 0x1d00 135 38.1 39.1 52.2 110.5 137.8 169.6 99877.9 100000 20 3280.6 2394982 251848166 3994329 155769966 1247433841085 846253344943 846253343794 376045043 0 0 0 0 0 298891 +ebbrt_poll 0 666 0x1d00 135 38.4 40.0 57.9 137.3 160.2 221.0 200080.9 200000 20 3292.98 4189217 468011406 7999270 311918198 1195794761056 847799109048 847799109363 653257089 0 0 0 0 0 297708 +ebbrt_poll 1 666 0x1d00 135 38.6 40.2 58.1 136.6 160.3 219.3 199759.6 200000 20 3295.88 4186309 467505354 7986624 311426016 1190645403566 848041862123 848041861780 657119660 0 0 0 0 0 297659 +ebbrt_poll 0 666 0x1d00 135 39.6 42.3 69.4 170.8 207.8 298.3 300033.5 300000 20 3305.32 6336432 705118582 11990077 467455550 1108461966240 849121230786 849121230905 977504624 0 0 0 0 0 296402 +ebbrt_poll 1 666 0x1d00 135 39.5 42.4 70.7 173.8 212.3 303.4 300005.1 300000 20 3300.63 6338243 705258224 11989391 467429034 1105988430796 849192979971 849192979196 987196249 0 0 0 0 0 296407 +ebbrt_tuned 0 2 0x1900 135 44.9 49.5 94.4 244.0 304.1 443.8 300374.7 300000 20 2244.02 6440025 711721351 12003587 467977230 452872640656 485841621086 563571687369 1046695086 0 0 0 0 5913827 9942895 +ebbrt_tuned 0 4 0x1900 135 45.2 49.9 93.6 244.2 304.5 451.5 300036.8 300000 20 2243.15 6436690 711149606 11990148 467450578 452844476207 488779411659 566979433840 1059531279 0 0 0 0 6014184 9960685 +ebbrt_tuned 0 10 0x1900 135 49.8 54.2 101.1 255.4 319.0 464.1 300036.4 300000 20 2239.28 6443070 711496961 11990591 467473900 451870352768 485980115756 563732080076 1052497165 0 0 0 0 5842379 8925220 +ebbrt_tuned 0 20 0x1900 135 49.7 54.9 98.3 246.3 305.0 445.9 299706.7 300000 20 2235.38 6438162 710843910 11976668 466928472 451187150767 481685343161 558750503228 1046130364 0 0 0 0 5522343 8132519 +ebbrt_tuned 0 30 0x1900 135 51.6 57.5 101.9 250.6 312.5 446.2 300119.9 300000 20 2234.45 6460747 712692704 11993594 467586074 451894949206 481251687018 558247805222 1048517537 0 0 0 0 5011306 6984625 +ebbrt_tuned 0 40 0x1900 135 52.3 58.9 105.4 255.1 320.4 462.2 299985.3 300000 20 2232.75 6468601 713036252 11988206 467381474 451798384945 477899066957 554359185246 1046196354 0 0 0 0 4564882 5875921 +ebbrt_tuned 0 50 0x1900 135 55.7 63.3 114.1 264.0 325.6 467.5 299894.7 300000 20 2227.93 6477431 713422424 11984337 467231106 452394704104 474879124302 550856268141 1048027289 0 0 0 0 4240379 4983450 +ebbrt_tuned 0 100 0x1900 135 64.3 75.9 143.8 292.3 351.3 488.6 299844.6 300000 20 2224.62 6545013 717456483 11982895 467172690 453154804516 471782813674 547265433650 1039920488 0 0 0 0 3268289 2815502 +ebbrt_tuned 0 200 0x1900 135 71.7 88.4 190.2 321.2 370.3 487.5 300004.4 300000 20 2152.92 6638894 723195601 11988436 467386430 441470199585 430996265731 502006310528 905960025 0 0 0 0 2883179 1464692 +ebbrt_tuned 0 2 0x1b00 135 43.3 46.8 80.7 208.3 258.1 380.5 299896.1 300000 20 2443.7 6385815 707900295 11984634 467235352 451767898300 489940369211 526233864744 1026495766 0 0 0 0 6708167 10132319 +ebbrt_tuned 0 4 0x1b00 135 43.1 46.7 81.9 207.7 258.9 372.6 300094.2 300000 20 2443.22 6391305 708471731 11992404 467542916 452002955037 492428498859 528906401881 1037316150 0 0 0 0 6828413 10112663 +ebbrt_tuned 0 10 0x1b00 135 46.3 49.8 83.7 209.8 258.8 367.7 299936.3 300000 20 2445.21 6398577 708790825 11985750 467274708 451799602027 492584926447 529074863635 1033269985 0 0 0 0 6554516 9170306 +ebbrt_tuned 0 20 0x1b00 135 48.0 52.5 86.3 213.2 264.9 385.1 299904.6 300000 20 2440.26 6403129 708966549 11984772 467242354 451102757685 489068086609 525297215668 1039334142 0 0 0 0 6101426 8277614 +ebbrt_tuned 0 30 0x1b00 135 49.7 54.9 89.3 213.4 261.5 379.7 300227.1 300000 20 2436.69 6422310 710485490 11997876 467756374 452076789163 486526686659 522567176258 1041831886 0 0 0 0 5569431 7086758 +ebbrt_tuned 0 40 0x1b00 135 52.2 58.5 99.6 226.5 277.0 397.3 299872.8 300000 20 2430.62 6425341 710290378 11982383 467136614 451964241346 482634637064 518386496876 1034843206 0 0 0 0 5102157 5937294 +ebbrt_tuned 0 50 0x1b00 135 53.9 60.8 105.8 235.8 287.1 406.0 300020.1 300000 20 2430.32 6444770 711597053 11990055 467455628 452578405840 481946223242 517646865738 1033966641 0 0 0 0 4661248 5044487 +ebbrt_tuned 0 100 0x1b00 135 61.9 73.2 135.0 259.3 308.6 432.8 299958.5 300000 20 2422.73 6512215 715544204 11986914 467326448 452613156930 477282966887 512637665607 1029250160 0 0 0 0 3616063 2843265 +ebbrt_tuned 0 200 0x1b00 135 72.2 88.7 190.7 322.6 371.4 485.9 300210.1 300000 20 2414.36 6649104 724115210 11996374 467689670 453212806576 471617187023 506551950212 1029922487 0 0 0 0 2906359 1464700 +ebbrt_tuned 0 2 0x1d00 135 42.3 44.9 73.5 182.5 222.0 319.9 300012.2 300000 20 2454.17 5981292 664696731 11291121 440201524 422816427972 462941863372 462943147490 943660134 0 0 0 0 7058449 9762583 +ebbrt_tuned 0 4 0x1d00 135 43.2 46.3 76.1 185.8 226.5 324.7 300235.6 300000 20 2652.2 6361702 706857666 11998613 467785812 450488685989 494991988762 494993471049 1003053461 0 0 0 0 7605325 10343138 +ebbrt_tuned 0 10 0x1d00 135 45.0 48.1 75.5 183.8 224.4 323.9 299726.0 300000 20 2647.53 6357269 706008939 11977813 466980516 448785942522 491195284670 491196762049 995458880 0 0 0 0 7256669 9387033 +ebbrt_tuned 0 20 0x1d00 135 46.6 50.6 79.3 188.4 227.8 326.5 300243.5 300000 20 2649.1 6377182 707792736 11998147 467765042 449788051818 490131396033 490132831132 1000479306 0 0 0 0 6733803 8443116 +ebbrt_tuned 0 30 0x1d00 135 47.8 52.8 84.0 191.5 233.5 330.6 300091.2 300000 20 2642.66 6385459 708098600 11992076 467521638 449886117380 487080395527 487081685336 995330976 0 0 0 0 6151455 7185784 +ebbrt_tuned 0 40 0x1d00 135 50.1 56.2 91.5 202.3 244.7 345.4 300159.2 300000 20 2641.45 6400644 709149065 11995619 467668716 450250135964 485951703090 485952885305 1004533872 0 0 0 0 5543185 6002820 +ebbrt_tuned 0 50 0x1d00 135 52.9 59.3 98.4 207.0 248.3 349.7 299912.0 300000 20 2634.91 6409664 709380292 11985563 467274492 450885840681 482039198228 482040356656 996799701 0 0 0 0 5142893 5111933 +ebbrt_tuned 0 100 0x1d00 135 59.6 70.0 128.5 237.2 278.7 379.0 299989.9 300000 20 2623.07 6480464 713727175 11988730 467401178 450766786405 474805773884 474806566061 994792167 0 0 0 0 3954462 2866185 +ebbrt_tuned 0 200 0x1d00 135 69.2 85.4 183.4 301.4 349.1 449.8 299957.2 300000 20 2620.46 6615494 721745601 11987287 467346036 451030488457 471108647929 471109282256 996595076 0 0 0 0 3101993 1464733 +ebbrt_tuned 0 2 0x1d00 135 40.3 42.2 59.3 141.2 166.3 233.1 200124.1 200000 20 2373.72 4194555 468366904 8000853 311979628 298161838671 329884798950 329886308376 633261016 0 0 0 0 7265283 7544310 +ebbrt_tuned 1 2 0x1d00 135 40.8 42.5 60.0 143.7 168.3 228.5 199766.0 200000 20 2382.43 4187029 467544491 7986986 311439272 298016505454 331648475738 331650003290 635379342 0 0 0 0 7244594 7537862 +ebbrt_tuned 0 4 0x1d00 135 41.3 42.9 61.1 147.3 170.8 234.5 199900.8 200000 20 2388.4 4189767 467819594 7992433 311655974 298997269329 335777258221 335779060004 651149389 0 0 0 0 7374355 7547457 +ebbrt_tuned 1 4 0x1d00 135 41.5 43.2 61.6 146.8 171.3 240.1 200040.7 200000 20 2388.26 4189406 467959338 7996257 311780314 299062620010 334956892358 334958510746 646107316 0 0 0 0 7358684 7536483 +ebbrt_tuned 0 10 0x1d00 135 44.5 46.7 64.5 148.9 173.9 239.8 200111.7 200000 20 2387.5 4199617 468670506 8000550 311965966 299515152140 334978467810 334980135930 651993784 0 0 0 0 7083842 7043107 +ebbrt_tuned 1 10 0x1d00 135 44.4 46.6 64.2 148.1 173.5 233.3 199931.5 200000 20 2385.96 4192938 468076558 7993622 311697596 299319879213 333914201177 333915804117 650963658 0 0 0 0 7085154 7038887 +ebbrt_tuned 0 2 0xd00 135 54.9 57.3 88.2 221.3 293.2 370.7 50013.9 50000 20 995.12 1531432 146035403 2000402 78013878 76107223708 91555651811 206128539459 170728347 0 0 0 0 2806198 2428924 +ebbrt_tuned 0 2 0xd00 135 54.0 56.8 94.5 272.2 325.4 453.8 100044.0 100000 20 1072.65 2241383 242836596 4001040 156032254 149992052724 156669631203 350020092933 321040393 0 0 0 0 3440441 3863472 +ebbrt_tuned 0 4 0xd00 135 54.9 57.3 87.6 215.4 289.8 370.1 50060.0 50000 20 995.7 1535651 146361815 2002233 78085032 76330227412 91590256910 206182811770 168957765 0 0 0 0 2825462 2441733 +ebbrt_tuned 0 4 0xd00 135 56.1 58.7 98.4 277.5 329.8 468.9 99825.3 100000 20 1073.16 2236617 242271565 3991354 155639094 149546675704 156917286858 350578950512 323120588 0 0 0 0 3447155 3878397 +ebbrt_tuned 0 8 0xd00 135 56.9 60.0 90.6 220.9 295.6 371.3 49963.7 50000 20 995.74 1526121 145649384 1998366 77934754 76154933346 91912793112 206951622505 169129202 0 0 0 0 2942950 2374189 +ebbrt_tuned 0 8 0xd00 135 57.7 61.2 100.9 281.7 334.0 476.4 99899.4 100000 20 1034.51 2155391 233743554 3857516 150433950 144716971142 152928973601 341688206086 314796153 0 0 0 0 3546569 3593952 +ebbrt_tuned 0 2 0xf00 135 52.5 55.6 82.3 194.6 256.0 322.6 50067.5 50000 20 1066.25 1562004 147923402 2002528 78097112 75978128878 93378181635 186580130023 166326830 0 0 0 0 2961055 2479138 +ebbrt_tuned 0 2 0xf00 135 51.6 54.3 84.5 227.9 274.4 378.7 99854.9 100000 20 1154.21 2262225 243861663 3993410 155731816 149606747859 159299576881 310375882473 316313603 0 0 0 0 3735118 3939590 +ebbrt_tuned 0 4 0xf00 135 52.3 55.3 82.0 189.8 254.5 325.6 50087.0 50000 20 1065.99 1563165 148041567 2003347 78129168 76101129364 93827357555 187358569512 167548868 0 0 0 0 2957467 2482961 +ebbrt_tuned 0 4 0xf00 135 51.9 54.7 86.1 232.2 279.3 389.7 99854.2 100000 20 1155.98 2255673 243452859 3993359 155729886 149449448901 160052655363 311765535416 317809320 0 0 0 0 3755053 3952019 +ebbrt_tuned 0 8 0xf00 135 55.8 58.3 86.3 197.9 262.3 328.6 50018.9 50000 20 1066.01 1551393 147220428 2000561 78020636 76207166034 94907456152 189550746647 169039429 0 0 0 0 3062301 2409491 +ebbrt_tuned 0 8 0xf00 135 55.2 57.5 89.4 235.5 284.4 393.3 100011.1 100000 20 1157.48 2254271 243528936 3999543 155972128 149989692835 161999409336 315618116251 321827466 0 0 0 0 3972259 3801692 +ebbrt_tuned 0 2 0x1100 135 49.8 51.1 70.6 159.2 219.8 272.9 49958.5 50000 20 1176.01 1604333 150358793 1998181 77927516 77348659694 88775415617 151439854376 177810831 0 0 0 0 3226525 2545707 +ebbrt_tuned 0 2 0x1100 135 48.1 50.7 76.1 199.1 244.7 330.7 99993.8 100000 20 1215.72 2226390 239266466 3900847 152126176 148272789634 159169422718 271523786220 324480089 0 0 0 0 3948532 3928209 +ebbrt_tuned 0 4 0x1100 135 48.1 50.4 69.6 159.0 220.4 274.3 50005.9 50000 20 1175.5 1604706 150440549 2000044 77999226 77463944014 89144897387 152070160676 178329702 0 0 0 0 3230932 2547093 +ebbrt_tuned 0 4 0x1100 135 49.1 51.6 77.6 196.8 236.9 315.5 100067.8 100000 20 1244.28 2283627 245409330 4002016 156070480 148544018284 160122386445 278621310426 297815267 0 0 0 0 4076881 4043592 +ebbrt_tuned 0 8 0x1100 135 53.3 56.4 81.0 178.4 232.0 293.0 50078.7 50000 20 1146.38 1569080 148382378 2003004 78115770 75892842793 95852792032 174144186202 161738751 0 0 0 0 3181628 2439848 +ebbrt_tuned 0 8 0x1100 135 52.7 55.7 82.2 202.7 246.3 329.5 100034.6 100000 20 1251.9 2274199 244789511 4000683 156018284 149126910126 163328512544 284033910562 311967097 0 0 0 0 4190795 3868140 +ebbrt_tuned 0 2 0x1300 135 46.1 47.7 64.7 144.4 198.2 247.0 49982.4 50000 20 1282.38 1622717 151485991 1999082 77962954 77457463788 90425665265 138019830268 177708939 0 0 0 0 3332390 2572522 +ebbrt_tuned 0 2 0x1300 135 46.8 48.9 71.8 179.3 222.1 293.5 100013.3 100000 20 1376.91 2295937 246033057 3999832 155985500 152113312975 165741978356 252976582789 334256115 0 0 0 0 4303749 4087023 +ebbrt_tuned 0 4 0x1300 135 46.6 48.5 65.5 144.5 199.3 246.7 49954.8 50000 20 1283.38 1623513 151502910 1997930 77917482 77372057771 90573815511 138245922330 175877017 0 0 0 0 3371231 2575876 +ebbrt_tuned 0 4 0x1300 135 46.3 48.4 70.9 177.1 220.2 293.3 100040.7 100000 20 1375.74 2299567 246270553 4000903 156026240 152096759349 165718939055 252941460454 331809362 0 0 0 0 4322882 4088062 +ebbrt_tuned 0 8 0x1300 135 51.1 52.9 70.4 149.7 204.8 250.5 49955.7 50000 20 1283.22 1612384 150818484 1998107 77924726 77337180537 90635907412 138340769121 177246917 0 0 0 0 3432575 2500265 +ebbrt_tuned 0 2 0x1500 135 44.1 46.0 60.8 129.0 178.8 222.2 50071.6 50000 20 1395.73 1648235 153112593 2002713 78104172 77517687692 90996088991 125660479246 176655346 0 0 0 0 3469758 2607300 +ebbrt_tuned 0 2 0x1500 135 44.9 46.7 66.1 157.2 200.0 259.0 99904.2 100000 20 1493.56 2316424 247182031 3995330 155808176 151781260282 166484164986 229906617281 329213968 0 0 0 0 4526607 4132765 +ebbrt_tuned 0 4 0x1500 135 45.5 46.7 61.8 133.7 183.5 225.5 50104.2 50000 20 1396.93 1642521 152811793 2003975 78153424 77605179179 91483583037 126333732864 178892090 0 0 0 0 3501248 2604995 +ebbrt_tuned 0 4 0x1500 135 44.5 46.5 65.7 156.5 199.4 260.0 99933.7 100000 20 1492.87 2313143 246990753 3995847 155815308 151957366305 166410266855 229804594121 330940436 0 0 0 0 4560427 4128034 +ebbrt_tuned 0 8 0x1500 135 48.6 50.5 65.0 136.0 187.1 227.7 50021.1 50000 20 1396.11 1632563 152115159 2000640 78023572 77527779551 91174756797 125906662711 175677783 0 0 0 0 3555794 2527482 +ebbrt_tuned 0 8 0x1500 135 48.1 50.5 69.5 162.7 203.9 264.8 99890.2 100000 20 1492.25 2309603 246745798 3994882 155790858 151870997263 166888384161 230465068048 332526723 0 0 0 0 4614506 3969456 +ebbrt_tuned 0 2 0x1700 135 42.6 44.2 58.4 120.6 165.9 205.4 49981.9 50000 20 1518.97 1664243 153983511 1999062 77962106 77402380297 92307501648 116391136369 176712576 0 0 0 0 3541244 2625531 +ebbrt_tuned 0 2 0x1700 135 43.4 45.5 62.8 144.3 183.5 233.4 100024.0 100000 20 1627.7 2322551 247657815 3998632 155912876 152197315994 170280482297 214705454846 334917732 0 0 0 0 4699854 4161960 +ebbrt_tuned 0 4 0x1700 135 42.7 44.5 58.4 121.8 168.8 206.4 50097.7 50000 20 1522.2 1660710 153899616 2003666 78140900 77774326109 93136286019 117436039951 179882950 0 0 0 0 3586559 2626386 +ebbrt_tuned 0 4 0x1700 135 42.6 44.4 61.4 140.5 180.6 228.3 99959.8 100000 20 1627.7 2336284 248419400 3997666 155899254 152161935279 169476913059 213692488761 331621371 0 0 0 0 4775868 4176357 +ebbrt_tuned 0 8 0x1700 135 46.3 47.9 61.7 123.9 172.0 207.7 49990.5 50000 20 1521.13 1647033 152945102 1999472 77978000 77619261618 92755600550 116955984130 178537988 0 0 0 0 3621879 2546637 +ebbrt_tuned 0 8 0x1700 135 46.7 48.8 65.7 147.1 185.7 232.8 99961.5 100000 20 1628.99 2321042 247507935 3997733 155903212 151906035452 169841066140 214151701267 334484940 0 0 0 0 4840442 4010848 +ebbrt_tuned 0 2 0x1900 135 41.6 42.8 55.7 111.4 155.1 190.9 50021.2 50000 20 1660.29 1672947 154546575 2000696 78025074 77117789667 93593413630 108566031250 177811494 0 0 0 0 3629989 2640946 +ebbrt_tuned 0 2 0x1900 135 42.3 43.8 59.5 133.2 170.0 216.5 99919.2 100000 20 1774.25 2347720 249046598 3995819 155826100 151289680233 170552289606 197837440619 329015728 0 0 0 0 4913464 4205817 +ebbrt_tuned 0 4 0x1900 135 41.9 43.0 56.1 111.4 154.8 193.2 49998.1 50000 20 1657.96 1672611 154492176 1999734 77987316 77315340410 93659967535 108643175484 177433042 0 0 0 0 3677049 2640847 +ebbrt_tuned 0 4 0x1900 135 42.0 43.4 58.9 130.8 169.6 212.2 99962.1 100000 20 1771.78 2348728 249157537 3997755 155902876 151576103710 170965967861 198317223521 330434168 0 0 0 0 4932983 4204393 +ebbrt_tuned 0 8 0x1900 135 45.6 46.8 59.2 115.6 159.2 197.5 49962.1 50000 20 1657.54 1657050 153534710 1998313 77932422 77254769553 93293365832 108217859164 175783309 0 0 0 0 3655275 2559335 +ebbrt_tuned 0 8 0x1900 135 45.9 47.5 63.0 136.2 174.6 220.0 99963.6 100000 20 1774.96 2331407 248127738 3997695 155901362 151858177239 172853032799 200506342403 336087866 0 0 0 0 4915892 4034034 +ebbrt_tuned 0 2 0x1b00 135 41.3 42.3 54.2 104.4 143.3 179.2 49984.5 50000 20 1814.71 1684950 155216800 1999166 77965738 77028780838 94478739608 101476950916 175546607 0 0 0 0 3715581 2656218 +ebbrt_tuned 0 2 0x1b00 135 41.7 42.9 57.2 122.6 157.7 197.6 100035.0 100000 20 1938.68 2366977 250324556 4000780 156021990 151088912886 173175121906 186002811827 328214732 0 0 0 0 5060091 4243138 +ebbrt_tuned 0 4 0x1b00 135 41.7 42.8 55.0 106.1 146.1 181.5 50013.8 50000 20 1817.03 1681377 155036043 2000344 78010874 77071261581 94863647836 101890293890 176917881 0 0 0 0 3730505 2653121 +ebbrt_tuned 0 4 0x1b00 135 41.5 42.6 56.9 121.3 156.4 191.8 99993.3 100000 20 1865.97 2283227 241435451 3862383 150625600 146094185089 167598551362 180013067176 318480805 0 0 0 0 4907042 4092284 +ebbrt_tuned 0 8 0x1b00 135 43.6 45.7 57.1 107.1 146.8 183.5 50082.3 50000 20 1817.88 1668876 154357349 2003037 78115964 77117245187 94593647441 101600414124 175518911 0 0 0 0 3721704 2575487 +ebbrt_tuned 0 8 0x1b00 135 44.6 46.3 60.0 125.6 160.9 201.0 99855.7 100000 20 1941.29 2339314 248483613 3992177 155665640 151132229997 172509238634 185287623426 325124043 0 0 0 0 5013354 4054819 +ebbrt_tuned 0 2 0x1d00 135 42.1 43.4 54.9 100.3 136.7 169.7 50026.6 50000 20 1990.11 1696571 155977032 2000754 78027376 76954250143 95286577935 95286708037 173585089 0 0 0 0 3782900 2671341 +ebbrt_tuned 0 2 0x1d00 135 40.0 41.8 54.9 114.1 146.9 183.4 100037.0 100000 20 2122.63 2378574 251019006 4000879 156025866 151022807888 174316002490 174316060502 322160370 0 0 0 0 5167682 4264280 +ebbrt_tuned 0 4 0x1d00 135 39.8 41.5 52.5 97.0 135.9 168.7 49955.1 50000 20 1911.8 1623663 149355591 1919461 74857050 73757713761 91604503517 91604485614 167409867 0 0 0 0 3649215 2558873 +ebbrt_tuned 0 4 0x1d00 135 39.9 41.7 54.8 112.8 145.7 183.2 100111.9 100000 20 2122.74 2380677 251236789 4003874 156142514 151063615456 175927464425 175927581425 328943462 0 0 0 0 5241576 4269388 +ebbrt_tuned 0 8 0x1d00 135 45.5 46.6 57.6 103.4 142.7 175.3 49929.8 50000 20 1988.01 1670363 154283186 1996809 77870212 77082902430 95072856083 95073012083 172712876 0 0 0 0 3718615 2575319 +ebbrt_tuned 0 8 0x1d00 135 43.4 45.6 58.3 117.2 150.3 186.6 99887.6 100000 20 1964.92 2180338 231087902 3702804 144401676 139798778203 161523622892 161523869110 300275530 0 0 0 0 4753484 3783531 +ebbrt_tuned 1 10 0x1900 135 45.5 47.9 72.4 177.8 207.2 296.9 200069.6 200000 20 1986.86 4208525 469159556 7998706 311892736 296852025726 321769271739 377961322363 646182170 0 0 0 0 311037 6866322 +ebbrt_tuned 2 10 0x1900 135 45.8 48.1 71.9 176.2 206.6 292.9 199944.5 200000 20 1916.69 3939339 439057346 7483646 291790070 278419204386 302588103048 355248617831 605962533 0 0 0 0 293010 6431943 +ebbrt_tuned 3 10 0x1900 135 45.6 47.9 71.8 174.7 204.1 286.8 200223.4 200000 20 1991.79 4214021 469655799 8005316 312153684 297698455597 324210866010 380677350771 654841630 0 0 0 0 311511 6873048 +ebbrt_tuned 4 10 0x1900 135 45.6 48.1 72.4 178.0 208.2 297.3 199962.9 200000 20 1996.29 4208927 469094724 7994122 311709368 298293052057 326548834977 383280803499 663651360 0 0 0 0 311150 6859540 +ebbrt_tuned 0 10 0x1300 135 51.4 54.4 79.4 168.2 222.5 283.4 49991.9 50000 20 1234.38 1573717 148544631 1999504 77978830 76446531638 99346326394 165735760496 173528505 0 0 0 0 410730 2443073 +ebbrt_tuned 1 10 0x1300 135 51.5 54.4 79.4 170.9 224.8 285.3 50058.3 50000 20 1231.56 1575516 148724554 2002210 78084722 76851185000 99633807981 166171275681 175414931 0 0 0 0 413287 2446654 +ebbrt_tuned 0 10 0x1300 75 51.1 53.9 79.0 168.1 223.0 280.0 49971.9 50000 20 1231.12 1574099 148550077 1998785 77951408 76576104114 99731034391 166347087079 176622766 0 0 0 0 412765 2444433 +ebbrt_tuned 1 10 0x1300 75 51.3 54.3 79.6 169.9 223.1 277.6 50012.1 50000 20 1232.1 1578129 148839691 2000351 78012266 76780299405 99382585102 165703817750 174791633 0 0 0 0 410948 2447575 +ebbrt_tuned 0 10 0x1300 55 50.4 53.2 77.9 165.0 218.6 278.7 49932.2 50000 20 1231.77 1576854 148671842 1997120 77885830 76586740515 99105892390 165261183181 173159609 0 0 0 0 409786 2442687 +ebbrt_tuned 1 10 0x1300 55 50.7 53.4 78.6 168.3 220.3 278.1 49933.3 50000 20 1232.03 1577214 148694860 1997221 77890172 76681659568 99750216173 166298651134 176469833 0 0 0 0 411090 2444699 +ebbrt_tuned 0 10 0x1300 135 50.8 53.1 79.8 196.5 236.6 313.5 99902.2 100000 20 1354.63 2275596 244743158 3995416 155811542 151167683362 170817151882 268680751036 332892100 0 0 0 0 355574 3876887 +ebbrt_tuned 1 10 0x1300 135 50.4 52.6 78.7 192.0 234.7 318.3 100082.4 100000 20 1356.78 2277088 245002621 4002534 156089978 151557976829 171693257353 269840943857 334555809 0 0 0 0 355554 3881648 +ebbrt_tuned 0 10 0x1300 75 50.0 52.3 78.6 192.6 233.1 307.7 99930.4 100000 20 1356.47 2275027 244683305 3996567 155857604 151480567143 171625967124 269808808667 333642613 0 0 0 0 356286 3876714 +ebbrt_tuned 1 10 0x1300 75 50.2 52.5 78.4 191.8 233.4 313.2 99944.0 100000 20 1355.74 2275896 244790133 3997019 155874136 151223208035 171113496983 269008267495 333108969 0 0 0 0 357976 3883095 +ebbrt_tuned 0 10 0x1300 55 50.8 53.1 79.5 195.1 236.8 319.2 100030.2 100000 20 1355.27 2278452 244989226 4000545 156012872 151422641106 170919084313 268763837167 334130030 0 0 0 0 357520 3885450 +ebbrt_tuned 1 10 0x1300 55 50.1 52.4 78.3 189.3 233.6 305.8 100028.5 100000 20 1355.79 2282341 245223891 4000431 156006482 151131233818 171115553911 269046736923 332615717 0 0 0 0 356022 3885161 +ebbrt_tuned 0 20 0x1300 135 51.5 54.9 80.4 171.8 225.0 284.4 50066.2 50000 20 1186.94 1513367 142877524 1924521 75055082 73951061532 95887742101 159902013283 167959274 0 0 0 0 392194 2334860 +ebbrt_tuned 1 20 0x1300 135 51.1 54.3 79.6 170.8 224.8 281.2 49981.1 50000 20 1228.78 1570013 148307319 1999100 77962966 76721005918 99331910604 165802410790 174609499 0 0 0 0 408672 2425291 +ebbrt_tuned 0 20 0x1300 75 51.5 54.7 80.2 173.6 226.3 288.8 49908.1 50000 20 1229.59 1569654 148205693 1996218 77851228 76676341187 99429708606 165883729864 174524394 0 0 0 0 407644 2422311 +ebbrt_tuned 1 20 0x1300 75 52.3 55.7 81.5 172.4 225.5 283.8 50024.0 50000 20 1229.59 1575720 148704108 2000763 78028386 76896162996 99731087414 166416788608 175402973 0 0 0 0 407745 2428547 +ebbrt_tuned 0 20 0x1300 55 51.6 54.8 80.4 170.5 223.3 284.2 50028.5 50000 20 1229.04 1569875 148363827 2000937 78033768 76928095229 99454584319 165932561137 174692105 0 0 0 0 410607 2427160 +ebbrt_tuned 1 20 0x1300 55 51.4 54.5 80.1 171.7 227.1 285.1 50077.7 50000 20 1231.45 1575724 148765407 2002972 78114176 76993894134 100109645525 166941573652 176707827 0 0 0 0 410444 2432168 +ebbrt_tuned 0 20 0x1300 135 50.5 53.1 79.8 192.6 236.0 315.4 100055.5 100000 20 1353.1 2275058 244819382 4001519 156051100 151527942293 170590497058 268381892348 332199001 0 0 0 0 353324 3827873 +ebbrt_tuned 1 20 0x1300 135 50.1 52.7 79.3 194.0 236.8 317.5 100011.8 100000 20 1354.47 2275283 244794526 3999842 155984448 151482281395 170961636521 268891132522 333345210 0 0 0 0 351371 3823460 +ebbrt_tuned 0 20 0x1300 75 50.3 52.8 79.3 192.0 235.5 310.2 99983.4 100000 20 1354.24 2280087 245067859 3998567 155935996 151342458897 170399155360 268061022428 332131164 0 0 0 0 353460 3829762 +ebbrt_tuned 1 20 0x1300 75 50.6 53.2 80.0 194.8 238.3 316.7 99989.0 100000 20 1356.7 2281046 245121990 3998858 155946232 151535156227 171713658728 270141772834 337887652 0 0 0 0 352126 3830486 +ebbrt_tuned 0 20 0x1300 55 50.3 52.9 79.5 193.1 235.6 314.2 99986.2 100000 20 1356.1 2266407 244322198 3992646 155604164 151276339953 171337131990 269464502136 337253475 0 0 0 0 351317 3810507 +ebbrt_tuned 1 20 0x1300 55 50.1 52.7 78.9 191.6 236.2 311.3 100087.5 100000 20 1357.37 2276720 245008736 4002870 156102998 151692631722 171501718881 269678360954 337081779 0 0 0 0 354993 3833061 +ebbrt_tuned 0 30 0x1300 135 51.9 55.3 82.0 173.4 225.7 288.5 49944.7 50000 20 1232.8 1563372 147883515 1997694 77908806 76787080792 99796784556 166497107994 176738192 0 0 0 0 404574 2381768 +ebbrt_tuned 1 30 0x1300 135 51.9 55.4 82.1 174.0 228.3 288.1 49996.3 50000 20 1232.25 1566582 148131338 1999701 77987132 76795047112 99613349601 166198641328 175978338 0 0 0 0 402275 2382829 +ebbrt_tuned 0 30 0x1300 75 52.0 55.5 82.6 174.8 227.6 288.1 50053.0 50000 20 1232.36 1569004 148325558 2001973 78075096 77068427200 99823630235 166512442266 177231712 0 0 0 0 405049 2389141 +ebbrt_tuned 1 30 0x1300 75 51.3 54.8 81.2 169.8 224.9 285.9 49950.0 50000 20 1144.24 1452154 137301854 1854056 72308138 71257749694 92607832352 154530626149 164631369 0 0 0 0 376043 2213444 +ebbrt_tuned 0 30 0x1300 55 51.5 54.9 81.3 171.0 225.3 285.7 49943.5 50000 20 1234.19 1568673 148213575 1997608 77905020 76845571873 100070120869 166895761946 177221870 0 0 0 0 404798 2386318 +ebbrt_tuned 0 30 0x1300 135 51.3 54.4 82.6 197.3 239.8 321.1 99930.6 100000 20 1351.06 2275314 244706574 3996542 155855720 151279414080 170754073746 268652630461 337721332 0 0 0 0 351211 3698766 +ebbrt_tuned 1 30 0x1300 135 50.7 53.7 81.8 196.6 239.3 315.2 100090.2 100000 20 1353.98 2273026 244756356 4002993 156108050 151382896915 170720714899 268452687177 334799756 0 0 0 0 352569 3701796 +ebbrt_tuned 0 30 0x1300 55 50.7 53.6 82.1 197.7 240.2 319.0 99817.6 100000 20 1350.01 2269946 244267853 3992066 155681658 151145451468 170513749595 268177637141 334843169 0 0 0 0 349458 3691737 +ebbrt_tuned 1 30 0x1300 55 51.0 54.0 83.0 200.1 241.2 320.0 100169.9 100000 20 1356.44 2277817 245146151 4006071 156228120 151504590303 171523029455 269561427125 334736185 0 0 0 0 349611 3705423 +ebbrt_tuned 0 40 0x1300 135 51.3 54.9 81.5 165.6 211.3 268.7 50048.1 50000 20 1217.01 1573947 148644421 2001741 78066338 75211092558 94675427360 159753473804 155527352 0 0 0 0 405628 2340067 +ebbrt_tuned 1 40 0x1300 135 52.2 56.0 83.0 167.8 215.9 272.3 50030.3 50000 20 1223.45 1571737 148485325 2001087 78040834 75557082847 95506206573 160877966001 160070475 0 0 0 0 405586 2338427 +ebbrt_tuned 0 40 0x1300 75 52.1 55.9 82.6 169.6 218.3 274.1 50057.0 50000 20 1224.99 1571841 148511302 2002112 78080606 75755085322 96111861656 161798958383 162986481 0 0 0 0 402338 2333694 +ebbrt_tuned 1 40 0x1300 75 53.0 56.8 84.1 171.1 221.4 279.2 50039.6 50000 20 1224.24 1569291 148369015 2001450 78054864 75628495102 96075327757 161615780231 163335564 0 0 0 0 402461 2330707 +ebbrt_tuned 0 40 0x1300 55 51.9 55.7 82.7 167.0 217.6 276.0 50047.1 50000 20 1225.65 1569962 148370342 2001715 78065216 75627485103 96698026226 162553772181 165305970 0 0 0 0 405389 2338738 +ebbrt_tuned 1 40 0x1300 55 51.2 54.9 81.5 166.3 217.3 276.3 49933.7 50000 20 1137.47 1454227 137406886 1852352 72240722 70209590319 89781183838 150852531093 153760420 0 0 0 0 375958 2164853 +ebbrt_tuned 0 40 0x1300 135 51.4 55.0 85.1 196.3 235.9 309.8 99972.8 100000 20 1348.86 2272542 244650217 3998197 155919356 149747697329 166093308439 262260312798 317655245 0 0 0 0 352424 3540465 +ebbrt_tuned 1 40 0x1300 135 51.1 54.7 84.6 194.0 235.6 307.2 100019.8 100000 20 1348.85 2269911 244464666 4000182 155998196 149598482507 166737623336 263255287631 320274796 0 0 0 0 351607 3537784 +ebbrt_tuned 0 40 0x1300 75 51.3 54.7 84.5 194.3 235.5 313.2 100025.8 100000 20 1349.39 2266886 244312541 4000124 155993194 149825400984 166847511465 263267501851 321097267 0 0 0 0 351719 3535741 +ebbrt_tuned 1 40 0x1300 75 51.2 54.8 85.1 195.9 237.2 311.9 100037.4 100000 20 1351.25 2265567 244240331 4000833 156023106 149861158331 167429131926 264138300453 324327732 0 0 0 0 351722 3537832 +ebbrt_tuned 0 40 0x1300 55 51.3 54.8 85.1 195.0 237.9 315.7 100030.3 100000 20 1351.19 2268728 244448918 3999924 155978046 149652083876 167316258173 264032075222 324439067 0 0 0 0 349238 3531279 +ebbrt_tuned 0 10 0x1500 135 50.0 53.0 77.0 154.7 203.9 259.8 49887.3 50000 20 1319.97 1587067 149222299 1995345 77816852 76021333409 99968384525 156232635192 169543074 0 0 0 0 416282 2460874 +ebbrt_tuned 1 10 0x1500 135 50.3 53.2 77.2 155.7 204.3 258.4 49883.2 50000 20 1322.94 1588264 149297023 1995110 77807778 76037441109 100097322028 156334110426 169320990 0 0 0 0 416275 2460853 +ebbrt_tuned 0 10 0x1500 75 49.0 52.3 76.5 156.1 205.3 259.8 49991.2 50000 20 1325.36 1588903 149457750 1999529 77980176 76252709463 100448353934 156833718888 171123794 0 0 0 0 415337 2463249 +ebbrt_tuned 1 10 0x1500 75 49.7 52.7 77.2 156.9 205.1 262.4 49983.2 50000 20 1322.0 1586303 149286356 1999166 77966090 76138917795 100088186443 156257362825 169266493 0 0 0 0 413972 2459220 +ebbrt_tuned 0 10 0x1500 55 50.3 53.2 77.1 155.9 205.3 263.6 50040.7 50000 20 1326.9 1584881 149258572 2001493 78056682 76393167152 101099522252 157878422022 175819364 0 0 0 0 416966 2461395 +ebbrt_tuned 1 10 0x1500 55 50.5 53.4 77.3 156.6 205.1 263.5 49988.3 50000 20 1322.6 1590272 149532850 1999357 77973590 76238355512 100284839197 156652006541 172323849 0 0 0 0 412760 2462431 +ebbrt_tuned 0 10 0x1500 135 47.6 50.5 74.0 171.8 211.9 274.1 99975.9 100000 20 1465.3 2290922 245717763 3998277 155924134 150297193409 172162379314 249716149416 328534552 0 0 0 0 360967 3915930 +ebbrt_tuned 1 10 0x1500 135 47.4 50.3 73.7 171.4 212.6 276.9 99953.0 100000 20 1466.13 2295533 245974327 3997412 155888906 150518693617 172088381959 249469263078 327447429 0 0 0 0 362384 3922901 +ebbrt_tuned 0 10 0x1500 75 47.6 50.4 74.2 172.4 212.8 274.9 100074.0 100000 20 1466.74 2290578 245792883 4002201 156077472 150503288023 173201087948 250953063651 331106801 0 0 0 0 363139 3921625 +ebbrt_tuned 1 10 0x1500 75 48.4 51.2 75.1 173.0 214.5 280.9 100085.8 100000 20 1466.11 2293226 245969164 4002768 156098736 150701394048 173738201556 251778950243 334118936 0 0 0 0 361289 3918746 +ebbrt_tuned 0 10 0x1500 55 49.0 51.6 75.9 179.1 217.8 284.7 100002.6 100000 20 1467.11 2287906 245543276 3999339 155965182 150853839214 173306681909 250986376560 331792545 0 0 0 0 360246 3915390 +ebbrt_tuned 1 10 0x1500 55 47.5 50.4 74.4 173.9 214.9 279.6 99950.1 100000 20 1468.22 2294613 245929943 3997378 155888690 150944008372 173815964808 251785370785 333787919 0 0 0 0 361842 3918299 + +ebbrt_tuned 1 20 0x1500 135 50.1 53.3 78.2 160.2 207.6 265.1 50008.7 50000 20 1326.19 1586377 149325095 2000199 78005850 76773269777 101585702334 158517672966 176149770 0 0 0 0 414984 2446444 +ebbrt_tuned 0 20 0x1500 75 50.4 53.5 78.3 160.3 207.3 265.9 50009.7 50000 20 1327.43 1588162 149426196 2000197 78006048 76820105844 101618566772 158519387011 176637328 0 0 0 0 414453 2448723 +ebbrt_tuned 1 20 0x1500 75 50.0 53.2 77.6 156.8 207.2 265.7 49967.3 50000 20 1327.86 1583142 149090957 1998527 77940748 76680482384 101081910545 157804418604 174060989 0 0 0 0 412163 2440135 +ebbrt_tuned 0 20 0x1500 55 51.6 54.8 79.4 162.6 211.4 269.2 50018.7 50000 20 1326.68 1581668 149029759 2000564 78020358 76876170019 101429971576 158265388974 175295697 0 0 0 0 411797 2439060 +ebbrt_tuned 1 20 0x1500 55 50.4 53.7 78.3 159.6 208.5 263.6 50082.7 50000 20 1327.05 1585189 149348612 2003189 78122856 76893902682 101779208802 158769739603 176825608 0 0 0 0 412808 2445775 +ebbrt_tuned 0 20 0x1500 135 71.5 86.9 188.1 297.2 342.2 425.6 100059.5 100000 20 1450.29 2212582 241120224 4001585 156053484 151921688165 165991655284 242884089586 328731508 0 0 0 0 486029 1344440 +ebbrt_tuned 1 20 0x1500 135 48.5 51.6 76.1 177.6 216.5 286.8 100219.1 100000 20 1466.82 2291244 246007417 4008012 156302226 151442666645 173687021291 251554761451 330940544 0 0 0 0 357130 3859088 +ebbrt_tuned 0 20 0x1500 75 49.0 51.9 76.8 178.9 218.5 285.1 100138.7 100000 20 1468.31 2288723 245707112 4004801 156178962 151388476443 173576687983 251294810816 329716710 0 0 0 0 360697 3862966 +ebbrt_tuned 1 20 0x1500 75 48.9 51.9 77.1 180.1 218.9 286.0 99976.0 100000 20 1468.06 2283772 245294637 3998452 155930966 151020060587 174397139043 252579052471 335367012 0 0 0 0 362476 3857190 +ebbrt_tuned 0 20 0x1500 55 48.5 51.6 76.5 178.2 218.2 283.9 100056.7 100000 20 1463.89 2286525 245556405 4001554 156050926 151413445734 173804492709 251766838364 334310831 0 0 0 0 360076 3856820 +ebbrt_tuned 1 20 0x1500 55 49.3 52.1 77.1 180.9 220.1 288.8 100090.7 100000 20 1467.27 2284121 245422690 4002801 156101016 151542777993 174463291633 252700039166 336309309 0 0 0 0 358778 3856000 +ebbrt_tuned 0 30 0x1500 135 50.9 54.5 80.0 161.5 210.8 269.1 50020.7 50000 20 1324.12 1581109 149031914 2000702 78025856 76844350658 101210187739 158002815289 175157668 0 0 0 0 407161 2400140 +ebbrt_tuned 1 30 0x1500 135 51.5 55.1 80.5 163.9 212.2 269.4 49950.8 50000 20 1323.0 1577734 148739143 1997902 77916582 76727833618 101233610600 158004326421 174331084 0 0 0 0 407233 2396266 +ebbrt_tuned 0 30 0x1500 75 50.6 54.0 79.3 160.5 210.2 265.0 49997.4 50000 20 1324.7 1581792 149039711 1999712 77986888 76788382931 101264972459 158037480700 175589275 0 0 0 0 408223 2400053 +ebbrt_tuned 1 30 0x1500 75 50.2 53.8 79.2 163.4 211.1 267.8 50027.3 50000 20 1275.96 1532143 144433160 1943339 75788944 74791396230 98736699234 153992780056 170530400 0 0 0 0 396682 2330826 +ebbrt_tuned 0 30 0x1500 55 50.5 53.9 79.4 161.5 211.1 269.7 50029.0 50000 20 1324.77 1579708 148948152 2001051 78039534 76883023953 101545115124 158456240874 176335450 0 0 0 0 407424 2399573 +ebbrt_tuned 1 30 0x1500 55 50.4 53.9 78.8 160.7 209.5 268.8 50154.8 50000 20 1324.87 1582879 149287248 2006032 78233760 77143526424 101790913487 158766555200 177234180 0 0 0 0 407624 2405532 +ebbrt_tuned 0 30 0x1500 135 48.9 52.2 77.8 176.6 217.0 284.9 99949.5 100000 20 1468.17 2281953 245147483 3997188 155878880 151227748073 173825284657 251726802559 334558060 0 0 0 0 357649 3719214 +ebbrt_tuned 1 30 0x1500 135 49.2 52.4 78.1 176.8 217.7 281.5 100026.6 100000 20 1468.65 2281581 245182793 4000293 156002104 151516362137 173740437597 251580400032 333986661 0 0 0 0 357217 3722109 +ebbrt_tuned 0 30 0x1500 75 47.8 51.2 75.6 166.0 202.6 261.2 100221.2 100000 20 1450.48 2297725 246385149 4008116 156307212 148658016317 165340994992 241727493983 301508860 0 0 0 0 363579 3749652 +ebbrt_tuned 1 30 0x1500 75 48.9 52.3 77.6 173.4 210.2 271.0 100058.4 100000 20 1456.58 2292061 245889179 4001440 156043290 149157238858 166808619634 243315349195 309013437 0 0 0 0 363331 3741894 +ebbrt_tuned 0 30 0x1500 55 48.8 52.2 77.5 174.1 211.9 276.3 100087.3 100000 20 1460.46 2291230 245845969 4002722 156096902 149266664077 168532139247 245582512297 317695003 0 0 0 0 363921 3741269 +ebbrt_tuned 1 30 0x1500 55 79.7 100.6 234.3 365.9 404.7 494.7 100038.8 100000 20 1439.37 2202223 240458870 4000775 156021562 150914707037 161031392350 237522597890 313792813 0 0 0 0 541424 960113 +ebbrt_tuned 0 40 0x1500 135 50.7 54.5 80.5 158.7 205.3 265.0 49977.7 50000 20 1322.58 1575252 148638042 1998986 77958834 75874383307 99753970160 156685544142 170027910 0 0 0 0 412271 2341700 +ebbrt_tuned 1 40 0x1500 135 51.1 55.0 81.2 159.6 207.1 264.7 50031.0 50000 20 1320.26 1576804 148783740 2001050 78039774 76082683614 99415741017 156157186617 170471772 0 0 0 0 409225 2336939 +ebbrt_tuned 0 40 0x1500 75 49.7 53.7 79.6 156.5 205.1 261.5 50050.2 50000 20 1320.49 1579593 148974505 2001908 78073310 76059524726 99355545951 156023317484 168863289 0 0 0 0 409700 2338255 +ebbrt_tuned 1 40 0x1500 75 50.5 54.5 81.1 160.9 209.0 266.3 50072.2 50000 20 1323.97 1574377 148687871 2002668 78101624 76229466176 100142719038 157050449315 171164346 0 0 0 0 410705 2337483 +ebbrt_tuned 1 40 0x1500 55 50.6 54.7 81.2 161.4 209.8 268.1 49946.1 50000 20 1222.49 1469770 138571252 1862130 72621904 70779975609 92823784566 145727920648 158903257 0 0 0 0 383288 2179858 +ebbrt_tuned 0 40 0x1500 135 49.9 53.6 81.8 182.3 220.9 286.3 100004.1 100000 20 1466.26 2280041 245078208 3999340 155965790 149736455166 170802072557 248312621887 324171584 0 0 0 0 359243 3548300 +ebbrt_tuned 1 40 0x1500 135 49.5 53.1 81.0 180.8 217.8 282.0 100003.0 100000 20 1464.64 2278028 244954255 3999388 155967308 150236378330 170872144829 248375783684 325237112 0 0 0 0 359077 3547627 +ebbrt_tuned 0 40 0x1500 75 49.0 52.9 81.0 179.4 218.7 284.4 100089.7 100000 20 1462.84 2286047 245576405 4002951 156106038 150040647958 170398004212 247740646535 324248069 0 0 0 0 359187 3552352 +ebbrt_tuned 1 40 0x1500 75 50.3 53.9 82.2 181.3 220.2 285.6 100028.9 100000 20 1465.37 2277769 244997786 4000449 156007958 150063433762 171008735347 248421175441 326227297 0 0 0 0 359146 3547334 +ebbrt_tuned 0 40 0x1500 55 49.9 53.4 81.4 178.2 216.7 279.0 99969.5 100000 20 1464.18 2275941 244808689 3998063 155915718 150045272158 171073147028 248570855374 327348622 0 0 0 0 358656 3547881 +ebbrt_tuned 1 40 0x1500 55 49.9 53.7 82.2 183.5 221.7 287.1 99930.0 100000 20 1464.01 2279529 244954960 3996393 155849718 150039771649 170520449036 247826920549 325617037 0 0 0 0 358732 3547395 +ebbrt_tuned 0 10 0x1700 135 48.4 52.0 75.6 146.5 193.8 246.4 49983.4 50000 20 1420.1 1594433 149790268 1999207 77967304 76484135346 103256476651 152073061285 173961793 0 0 0 0 425044 2473510 +ebbrt_tuned 1 10 0x1700 135 47.9 51.4 74.9 146.1 192.6 245.7 50073.4 50000 20 1419.85 1594387 149890604 2002735 78105430 76560006867 103068911317 151740777226 173288135 0 0 0 0 420631 2472078 +ebbrt_tuned 0 10 0x1700 75 48.5 51.9 75.4 148.0 194.4 247.0 50148.1 50000 20 1420.43 1596338 150068984 2005748 78222778 76655409120 103690486931 152640920203 176226039 0 0 0 0 423967 2476960 +ebbrt_tuned 1 10 0x1700 75 47.8 51.2 74.5 148.7 193.4 245.3 50027.2 50000 20 1420.59 1596556 149942486 2000961 78035972 76543321444 103553669813 152451242629 175116142 0 0 0 0 423628 2474010 +ebbrt_tuned 0 10 0x1700 55 48.3 51.8 75.3 147.2 193.9 246.9 50041.6 50000 20 1421.15 1593536 149799035 2001504 78057442 76508673938 103482152949 152307111382 175636648 0 0 0 0 422665 2472061 +ebbrt_tuned 1 10 0x1700 55 48.0 51.5 75.0 147.1 194.0 248.1 50022.9 50000 20 1420.12 1596127 149900970 2000815 78030450 76593741951 103165052557 151886756382 175174203 0 0 0 0 419865 2473161 +ebbrt_tuned 0 10 0x1700 135 46.6 49.0 71.6 160.8 199.3 258.9 100099.9 100000 20 1582.63 2301505 246489569 4003410 156124320 150957574624 177166780284 238728266046 329944791 0 0 0 0 368683 3947468 +ebbrt_tuned 1 10 0x1700 135 46.5 48.9 71.9 161.0 199.7 260.9 99805.4 100000 20 1581.55 2298759 245984740 3991569 155662162 150737215327 177157835358 238783160494 330912948 0 0 0 0 368452 3937663 +ebbrt_tuned 0 10 0x1300 135 50.8 53.8 92.6 246.4 300.3 435.7 199895.2 200000 20 1557.37 4243211 471006754 7991799 311627296 299936891986 312164288963 477057480463 663107218 0 0 0 0 265007 6493448 +ebbrt_tuned 0 10 0x1300 75 51.1 54.3 96.5 253.7 314.2 448.0 199952.7 200000 20 1558.66 4243238 471126619 7994489 311733196 300541591600 312383233792 477380124284 663588896 0 0 0 0 264836 6492104 +ebbrt_tuned 0 10 0x1300 55 50.3 53.6 93.5 248.8 302.7 438.2 199943.2 200000 20 1559.03 4242784 471093496 7993850 311708172 300342652597 312095688190 476945102911 664729302 0 0 0 0 266224 6496507 +ebbrt_tuned 0 20 0x1300 135 51.8 56.0 95.9 252.2 308.5 439.8 199856.8 200000 20 1556.89 4245527 471143340 7990450 311571390 300063191399 311629243747 476246654992 664392764 0 0 0 0 265133 6261258 +ebbrt_tuned 0 20 0x1300 75 51.4 55.6 97.4 256.7 316.2 454.7 199929.2 200000 20 1444.64 4029623 447150332 7570491 295200962 284759595949 296379954923 452931830971 630261620 0 0 0 0 247782 5915265 +ebbrt_tuned 0 20 0x1300 55 51.5 55.4 96.1 252.0 309.1 451.0 200234.0 200000 20 1555.8 4253290 472027979 8005705 312168180 301289973053 313896843073 479714857212 676818767 0 0 0 0 261510 6247123 +ebbrt_tuned 0 30 0x1300 135 52.4 57.6 99.5 255.7 310.7 449.4 200208.9 200000 20 1553.79 4256970 472224555 8004761 312132654 301373834232 311933554641 476710452456 671475012 0 0 0 0 262588 5724057 +ebbrt_tuned 0 30 0x1300 75 52.7 58.1 102.2 260.5 317.7 452.6 199816.1 200000 20 1443.8 3943531 437294230 7410021 288945160 278703145276 291252492809 445059044603 627154681 0 0 0 0 240842 5296685 +ebbrt_tuned 0 30 0x1300 55 52.1 57.5 100.3 258.4 317.9 460.5 200116.9 200000 20 1555.08 4253822 471933521 8001152 311993960 301164563922 311455156772 475951651303 667338207 0 0 0 0 259966 5695647 +ebbrt_tuned 0 40 0x1300 135 53.6 60.1 103.8 257.7 315.2 455.3 200313.1 200000 20 1556.27 4263966 472757826 8008292 312260508 301734881667 312944189143 478256780509 676753565 0 0 0 0 262069 5124499 +ebbrt_tuned 0 40 0x1300 75 53.3 59.5 102.3 255.2 308.8 450.5 200017.2 200000 20 1557.62 4256803 471960266 7996604 311807984 300798864921 312731903112 477901253414 670527539 0 0 0 0 261386 5122479 +ebbrt_tuned 0 10 0x1500 135 48.7 52.0 85.0 222.3 267.8 385.2 200062.3 200000 20 1687.35 4228875 470347971 7998920 311907078 300301016682 317211531933 439403168223 670644783 0 0 0 0 284763 6652220 +ebbrt_tuned 0 10 0x1500 75 48.2 51.5 83.3 220.7 265.5 388.5 200071.3 200000 20 1688.84 4230869 470496493 7999233 311917882 301031204655 320730453002 444249990539 684265282 0 0 0 0 283011 6643585 +ebbrt_tuned 0 10 0x1500 55 48.8 52.2 86.3 224.8 272.0 392.0 199859.1 200000 20 1688.71 4226145 469981457 7990796 311587824 301264814951 319128996779 441989240033 672861502 0 0 0 0 282850 6630756 +ebbrt_tuned 0 20 0x1500 135 49.5 53.3 87.3 225.5 273.6 393.6 200023.0 200000 20 1690.04 4232201 470549931 7997175 311838044 301393560627 319209804707 442141682419 679583519 0 0 0 0 280374 6345849 +ebbrt_tuned 0 20 0x1500 75 49.1 52.9 86.1 222.6 269.9 392.0 200176.2 200000 20 1693.21 4235072 470878548 8003412 312079166 301743230653 320295526511 443667572548 684297527 0 0 0 0 278597 6342132 +ebbrt_tuned 0 20 0x1500 55 49.2 53.1 86.7 223.8 270.6 392.2 199821.9 200000 20 1688.82 4229015 470126703 7989269 311528256 301266574855 319072288136 441961313439 685685362 0 0 0 0 279837 6343466 +ebbrt_tuned 0 30 0x1500 75 48.8 53.2 83.9 204.8 244.0 352.4 199904.5 200000 20 1661.87 4222560 469840903 7991251 311591628 294835055511 298083339267 413805778127 604912686 0 0 0 0 291285 5857562 +ebbrt_tuned 0 30 0x1500 55 48.5 53.0 84.5 206.9 247.0 354.4 200000.7 200000 20 1673.84 4227778 470242449 7996280 311799428 296750028300 304465114273 422370628354 631122711 0 0 0 0 288361 5843930 +ebbrt_tuned 0 40 0x1500 135 50.5 56.0 91.5 217.3 258.4 368.3 200089.3 200000 20 1672.7 4235851 470769491 7999809 311939380 297841276064 305577036688 423878216438 638201903 0 0 0 0 289775 5205458 +ebbrt_tuned 0 40 0x1500 75 51.4 56.8 94.2 222.7 266.7 376.7 200184.8 200000 20 1674.53 4240232 471203219 8003851 312097862 298002177274 307957190997 427062136559 644070137 0 0 0 0 287829 5206913 +ebbrt_tuned 0 40 0x1500 55 51.2 56.9 94.0 221.2 263.4 368.1 200076.0 200000 20 1675.94 4237847 470927466 7999253 311917492 298586228402 307278184101 426110989542 641629453 0 0 0 0 288747 5196371 +ebbrt_tuned 0 10 0x1700 135 48.0 51.5 74.2 142.0 183.6 232.8 50101.1 50000 20 1408.43 1602960 150417289 2003907 78150874 75378908140 99655120637 148339170867 159931836 0 0 0 0 427493 2485901 +ebbrt_tuned 0 10 0x1700 75 48.1 51.6 74.2 144.5 187.4 236.8 49954.6 50000 20 1362.23 1529547 143473205 1912062 74569444 72305606460 95704221247 142196290672 156059701 0 0 0 0 406525 2370756 +ebbrt_tuned 0 10 0x1700 55 48.0 51.5 74.2 145.9 187.4 236.3 50013.4 50000 20 1415.59 1600337 150146713 2000383 78013878 75799097014 100793025748 149458496917 164973827 0 0 0 0 426687 2481589 +ebbrt_tuned 0 10 0x1700 135 46.5 49.0 71.5 158.3 192.7 247.9 99899.8 100000 20 1574.23 2302418 246338071 3995369 155810602 148909822973 172468015221 234041009792 316657611 0 0 0 0 371679 3955003 +ebbrt_tuned 0 10 0x1700 75 47.6 50.4 73.0 161.8 196.8 256.0 99942.1 100000 20 1575.11 2304398 246515896 3996935 155870304 149684718028 173313680080 234763308732 316657299 0 0 0 0 371025 3954786 +ebbrt_tuned 0 10 0x1700 55 47.5 50.4 72.2 156.6 194.4 249.9 100036.3 100000 20 1517.63 2206698 236498161 3847069 150027898 143921662287 167165576842 226252684416 308258731 0 0 0 0 356055 3795056 +ebbrt_tuned 0 20 0x1700 135 49.0 52.6 76.1 148.0 193.5 244.5 49935.2 50000 20 1418.19 1598005 149951457 1997241 77890836 76027559777 101900214105 150812660385 170502632 0 0 0 0 420870 2456984 +ebbrt_tuned 0 20 0x1700 75 47.8 51.4 75.3 147.7 193.9 245.0 49989.7 50000 20 1418.94 1595160 149828064 1999418 77975528 76062041207 102225276308 151171866449 170160467 0 0 0 0 420945 2455545 +ebbrt_tuned 0 20 0x1700 55 48.1 51.9 75.5 145.5 192.0 244.1 50009.0 50000 20 1418.05 1594013 149780327 2000266 78009024 76187204277 102100058137 150896522208 168196829 0 0 0 0 420320 2455226 +ebbrt_tuned 0 20 0x1700 135 47.5 50.6 73.8 162.1 198.6 258.3 99928.5 100000 20 1578.51 2298202 246120319 3996375 155849610 149738462064 174600398249 236251440745 322754892 0 0 0 0 368890 3884083 +ebbrt_tuned 0 20 0x1700 75 47.3 50.3 73.6 162.1 198.4 254.3 99914.7 100000 20 1577.2 2300425 246220983 3995975 155834010 149592094699 174461352677 236060050402 322205173 0 0 0 0 367726 3883272 +ebbrt_tuned 0 20 0x1700 55 46.9 49.8 72.8 161.9 199.0 256.5 100031.5 100000 20 1580.14 2303182 246510571 4000580 156013322 150334701958 175318728548 237060905391 324681695 0 0 0 0 369242 3888508 +ebbrt_tuned 0 30 0x1700 135 49.1 52.9 77.4 148.4 195.0 247.6 50028.1 50000 20 1420.61 1590444 149626662 2000733 78023278 76206569272 102678114401 151800234766 172082394 0 0 0 0 417785 2413545 +ebbrt_tuned 0 30 0x1700 75 48.3 52.3 76.7 146.5 192.6 245.8 49974.7 50000 20 1421.38 1587160 149346741 1998794 77951672 76127593452 102819288957 151997103383 173733260 0 0 0 0 417016 2411045 +ebbrt_tuned 0 30 0x1700 55 48.4 52.4 77.0 148.0 194.3 248.9 49940.7 50000 20 1419.81 1588106 149359730 1997442 77898966 76121020564 102176528262 151098807677 171691876 0 0 0 0 415298 2406496 +ebbrt_tuned 0 30 0x1700 135 47.7 51.2 76.2 164.8 202.0 262.0 99901.9 100000 20 1581.14 2292867 245742846 3995375 155810124 149773379768 174361093509 235897570130 324082734 0 0 0 0 362622 3734553 +ebbrt_tuned 0 30 0x1700 75 47.9 51.4 76.3 168.0 203.8 265.7 100022.2 100000 20 1581.31 2295032 246028973 3999966 155986974 150203499674 175661966647 237532361842 329047175 0 0 0 0 363973 3741804 +ebbrt_tuned 0 30 0x1700 55 47.6 51.0 75.7 165.2 202.5 260.8 100082.5 100000 20 1583.36 2292627 245914728 4002533 156087936 150296417871 176421150735 238381794752 331825259 0 0 0 0 364013 3742217 +ebbrt_tuned 0 40 0x1700 135 49.3 53.6 79.5 153.3 199.3 250.4 49940.7 50000 20 1420.52 1580433 148925815 1997438 77898122 76323964562 102462578674 151707211292 173232009 0 0 0 0 411239 2337843 +ebbrt_tuned 0 40 0x1700 75 49.4 53.6 79.6 151.3 196.9 250.6 49999.2 50000 20 1420.59 1582561 149084638 1999790 77990022 76269443936 102530397989 151743191534 173504883 0 0 0 0 413724 2344876 +ebbrt_tuned 0 40 0x1700 55 48.7 53.3 79.1 152.2 197.0 249.7 50044.2 50000 20 1421.7 1581151 149040014 2001638 78062090 76416869128 102858596821 152206706451 174701351 0 0 0 0 412749 2345648 +ebbrt_tuned 0 40 0x1700 135 48.7 52.7 79.6 170.0 205.2 267.7 100159.7 100000 20 1581.14 2284459 245512169 4005707 156213968 150686781780 175573228388 237457908431 330033560 0 0 0 0 362873 3555548 +ebbrt_tuned 0 40 0x1700 75 48.0 52.0 79.1 171.4 207.6 267.7 99994.2 100000 20 1582.62 2279224 245012702 3999090 155956358 150600375464 175637582388 237479107344 330078979 0 0 0 0 362267 3548384 +ebbrt_tuned 0 40 0x1700 55 48.3 52.3 79.7 169.6 207.0 267.5 100083.8 100000 20 1583.76 2286253 245565765 4002723 156097242 150905505154 175964357786 237823625053 331384289 0 0 0 0 363680 3558898 +ebbrt_tuned 0 10 0x1900 135 45.3 47.6 70.4 167.7 198.2 275.4 199797.7 200000 20 1957.67 4199752 468391448 7988238 311487914 293547594290 310471876775 365953003291 600306407 0 0 0 0 318590 6913475 +ebbrt_tuned 0 10 0x1900 75 45.8 48.1 71.6 172.3 201.9 283.4 199940.8 200000 20 1982.87 4204408 468771673 7993939 311709104 295381206974 316814626581 372715070345 625172360 0 0 0 0 315935 6894649 +ebbrt_tuned 0 10 0x1900 55 45.8 48.2 72.3 176.1 206.3 290.2 199942.5 200000 20 1984.4 4205460 468808813 7993935 311709978 295890146060 319374335105 375448243234 634084951 0 0 0 0 314359 6883503 +ebbrt_tuned 0 20 0x1900 135 46.2 49.2 74.9 179.0 211.5 301.1 200222.9 200000 20 1986.23 4212886 469628603 8005152 312148388 296405735897 320424670284 376593592405 639199669 0 0 0 0 309716 6525373 +ebbrt_tuned 0 20 0x1900 75 46.3 49.4 74.4 176.3 206.3 294.7 200155.1 200000 20 1989.21 4215174 469648397 8002488 312042028 297225073025 320856760872 377053334277 639968420 0 0 0 0 310777 6524800 +ebbrt_tuned 0 20 0x1900 55 47.2 50.5 76.6 181.9 212.9 296.5 199976.1 200000 20 1988.61 4210455 469159242 7995103 311754966 296809820408 323332991751 379808097289 645192872 0 0 0 0 309684 6517375 +ebbrt_tuned 0 30 0x1900 135 46.9 50.9 76.9 175.0 203.9 281.8 200317.1 200000 20 1960.89 4212270 469662047 8008937 312295800 294565143982 309336607764 364905585264 606091999 0 0 0 0 313039 5941413 +ebbrt_tuned 0 30 0x1900 75 46.8 50.8 77.5 177.6 207.1 294.7 199998.5 200000 20 1980.78 4213827 469390262 7996104 311797346 295757080694 315516946100 371479180419 634735054 0 0 0 0 311067 5931907 +ebbrt_tuned 0 30 0x1900 55 48.0 52.1 79.2 181.8 212.9 296.1 200197.9 200000 20 1983.86 4217824 469865579 8004304 312116520 296824899023 318583493250 374683510479 639440428 0 0 0 0 311607 5937104 +ebbrt_tuned 0 40 0x1900 135 48.3 53.5 83.3 185.1 218.4 301.5 199814.8 200000 20 1981.56 4214615 469229569 7989024 311519526 296814376153 317473258887 373580576505 639421296 0 0 0 0 312891 5250111 +ebbrt_tuned 0 40 0x1900 75 48.6 53.8 84.7 186.7 220.0 301.9 199934.1 200000 20 1910.4 4070285 453131964 7717665 300939756 287346149785 308739360522 363004822169 620127565 0 0 0 0 302722 5077593 +ebbrt_tuned 0 40 0x1900 55 49.0 54.2 84.7 186.4 220.2 304.8 200047.0 200000 20 1982.27 4217802 469710411 7998173 311874464 297744080966 319885225413 376268587491 649849852 0 0 0 0 313772 5253168 ebbrt_tuned 0 50 0x1400 135 54.2 61.6 107.3 253.2 307.1 438.2 199698.0 200000 20 1619.3 4250429 471256757 7984396 311340852 302030453410 314969126265 457689613369 696894457 0 0 0 0 277782 4554590 ebbrt_tuned 1 50 0x1400 135 55.3 62.8 109.6 260.3 316.3 447.8 199927.3 200000 20 1622.94 4255879 471825328 7993643 311702152 302473730307 316554774632 460032548338 706208077 0 0 0 0 275635 4552256 ebbrt_tuned 2 50 0x1400 135 55.3 62.7 107.5 255.0 305.1 437.9 200097.0 200000 20 1620.46 4259124 472212160 8000045 311951240 302389039933 316067020927 459250092465 696329976 0 0 0 0 277255 4555408 @@ -112,7 +325,6 @@ ebbrt_tuned 0 50 0x1500 55 54.7 61.6 105.3 242.2 290.1 401.8 199987.3 200000 20 ebbrt_tuned 1 50 0x1500 55 54.0 60.9 103.5 238.7 285.5 403.8 199945.3 200000 20 1626.41 4013294 445474399 7569771 295165194 285995573041 300607863129 416516690210 653678532 0 0 0 0 272158 4334080 ebbrt_tuned 2 50 0x1500 55 52.4 58.8 100.1 232.4 276.2 388.6 199936.5 200000 20 1682.51 4244770 471156137 7993853 311707836 302269104205 316074519682 437979838363 686387960 0 0 0 0 289644 4576855 ebbrt_tuned 0 100 0x1500 135 62.8 73.9 135.0 266.6 314.8 436.9 199980.5 200000 20 1681.23 4275777 473125333 7995585 311776698 301215644343 310866150479 431221097899 678535739 0 0 0 0 355047 2754212 -ebbrt_tuned 1 100 0x1500 135 61.8 73.1 134.1 265.3 313.0 434.7 199789.8 200000 20 1554.26 3963266 438433386 7407632 288850250 279268874635 287460861359 398805352561 632221155 0 0 0 0 330484 2552469 ebbrt_tuned 2 100 0x1500 135 62.9 74.5 136.2 270.2 319.6 431.6 199897.3 200000 20 1680.45 4276688 473069571 7992169 311640074 301660400353 312217800117 433007624774 682366457 0 0 0 0 357190 2754595 ebbrt_tuned 0 100 0x1500 55 62.9 74.3 136.5 272.5 322.4 441.8 199847.4 200000 20 1680.29 4274401 472898912 7990270 311567738 301136105869 310989831745 431415796881 687093798 0 0 0 0 357120 2754154 ebbrt_tuned 1 100 0x1500 55 63.2 74.7 136.9 271.6 322.1 440.0 199883.6 200000 20 1681.38 4273165 472848795 7991652 311621262 301097377329 312089257245 432942795941 690497687 0 0 0 0 354054 2754458 @@ -128,6 +340,15 @@ ebbrt_tuned 2 100 0x1700 135 59.9 69.8 126.4 236.5 277.7 369.3 199800.8 200000 2 ebbrt_tuned 0 100 0x1700 55 60.5 70.7 128.5 241.7 282.7 377.0 200092.8 200000 20 1816.85 4264768 472588147 7999964 311948032 299945673510 310647213077 395670802214 655574339 0 0 0 0 382642 2785880 ebbrt_tuned 1 100 0x1700 55 59.6 69.5 127.4 240.0 280.6 380.1 200012.1 200000 20 1819.82 4262863 472384127 7996744 311819730 300406700675 311925256668 397362712094 662611483 0 0 0 0 379798 2782851 ebbrt_tuned 2 100 0x1700 55 60.6 71.2 129.1 242.2 283.7 383.8 199976.9 200000 20 1819.31 4264197 472414333 7995350 311767310 299874842516 310518720881 395469508864 653451604 0 0 0 0 381614 2784731 +ebbrt_tuned 0 2 0x1700 135 45.0 47.6 75.6 191.9 226.4 324.5 200114.7 200000 20 1836.36 4215210 469611454 8000725 311975356 300551442168 324104670944 411056672319 676958340 0 0 0 0 6003315 286629 +ebbrt_tuned 0 4 0x1700 135 44.5 47.4 75.6 192.5 228.4 331.2 200069.8 200000 20 1836.45 4214935 469553201 7999051 311910054 299995018993 325210025241 412489646535 680871865 0 0 0 0 6082998 286614 +ebbrt_tuned 0 10 0x1700 135 47.5 50.6 77.7 195.1 229.9 328.9 200045.7 200000 20 1834.94 4217175 469638024 7997985 311866718 299127744041 323546251444 410584206175 675641534 0 0 0 0 6012340 286815 +ebbrt_tuned 0 20 0x1700 135 48.5 52.0 79.6 198.2 234.9 336.0 200033.9 200000 20 1833.41 4218262 469667574 7997640 311856668 299756716561 323193886714 410201252301 673899157 0 0 0 0 5784093 287128 +ebbrt_tuned 0 30 0x1700 135 49.6 53.8 83.7 201.7 240.3 337.4 200014.1 200000 20 1831.65 4221730 469854490 7996688 311818148 300285765701 322152373374 408883393643 675892714 0 0 0 0 5498533 287447 +ebbrt_tuned 0 40 0x1700 135 51.3 56.2 89.5 204.5 244.5 342.5 200060.4 200000 20 1828.48 4227011 470247665 7998897 311908068 300705252874 319559153801 405700267658 671965946 0 0 0 0 5125083 287481 +ebbrt_tuned 0 50 0x1700 135 52.5 58.7 96.4 212.8 253.1 354.0 199963.1 200000 20 1826.82 4233937 470538408 7994926 311752022 301047307897 320389930074 406896305001 683035253 0 0 0 0 4835000 287194 +ebbrt_tuned 0 100 0x1700 135 58.9 68.9 126.4 240.2 282.1 375.3 199989.8 200000 20 1820.02 4263075 472329102 7995788 311785164 300630655366 314654066642 400585930724 680857026 0 0 0 0 3668487 285659 +ebbrt_tuned 0 200 0x1700 135 71.9 87.4 186.0 305.0 354.1 449.3 199748.0 200000 20 1814.34 4320653 475545723 7986186 311410472 299132887177 309869856743 395944751787 677071274 0 0 0 0 2829696 284818 ebbrt_tuned 1 50 0x1300 135 57.2 65.0 113.4 270.2 330.2 470.1 200057.5 200000 20 1546.9 4263536 472474618 7998551 311888380 300108024879 307429402867 469959022254 665665503 0 0 0 0 268889 4536209 ebbrt_tuned 3 50 0x1300 135 56.5 64.3 113.7 270.6 329.7 470.9 199809.6 200000 20 1555.19 4257463 471829882 7988764 311508756 300612406180 309570723681 473181388487 671442731 0 0 0 0 268819 4543952 ebbrt_tuned 0 50 0x1300 95 56.4 64.1 112.0 268.6 326.4 467.2 200001.1 200000 20 1549.87 4259523 472148307 7996261 311800864 300857633686 308508800318 471558788665 668927629 0 0 0 0 270639 4539987 @@ -145,191 +366,117 @@ ebbrt_tuned 1 50 0x1300 55 56.8 64.8 114.6 275.6 337.6 482.0 199992.0 200000 20 ebbrt_tuned 2 50 0x1300 55 57.4 65.6 116.2 278.2 340.4 489.5 200055.1 200000 20 1558.79 4266304 472614391 7998252 311875702 302381648226 314270938320 480232636088 693005269 0 0 0 0 266312 4535899 ebbrt_tuned 3 50 0x1300 55 57.2 65.6 115.9 276.2 338.5 483.9 199783.9 200000 20 1560.11 4264601 472227955 7987758 311469462 301559858222 315186044908 481665074674 697867896 0 0 0 0 264467 4531693 ebbrt_tuned 4 50 0x1300 55 57.5 66.0 116.7 282.9 347.3 494.0 200083.9 200000 20 1563.8 4269647 472838248 7999704 311937514 302629666788 316623254445 483817186424 696523188 0 0 0 0 262629 4534292 -ebbrt_tuned 0 300 0x1800 95 65.3 83.5 215.4 343.7 365.5 430.3 50072.4 50000 20 1720.77 1381728 137123773 2002717 78104416 75479103250 87268858621 109720786629 151953489 0 0 0 0 496071 932960 -ebbrt_tuned 0 300 0x1800 75 64.8 82.4 214.6 344.4 366.4 433.3 50037.7 50000 20 1729.42 1379117 136920680 2001361 78051474 75853635752 88015288080 110587480484 156185629 0 0 0 0 498355 933823 -ebbrt_tuned 0 300 0x1800 55 65.2 82.0 213.6 344.2 365.7 431.4 49943.9 50000 20 1733.16 1379833 136861735 1997570 77903440 75902335360 88803626874 111458316003 160263934 0 0 0 0 497575 932901 -ebbrt_tuned 0 300 0x1800 135 73.0 92.0 221.5 345.5 369.5 441.5 100108.5 100000 20 1869.57 2208663 240911473 4003627 156132056 150346536259 160451110086 189252547448 308357724 0 0 0 0 571896 972673 -ebbrt_tuned 0 300 0x1800 95 73.1 91.3 221.7 347.9 373.7 445.9 100058.4 100000 20 1867.84 2206348 240704976 4001567 156051296 151036604330 161981103998 190893986772 309271218 0 0 0 0 570702 972717 -ebbrt_tuned 0 300 0x1800 75 74.1 93.9 222.5 348.3 373.0 444.5 99978.1 100000 20 1866.65 2203084 240457440 3998446 155930184 150735144833 162563721736 191638260346 313118814 0 0 0 0 575775 972671 -ebbrt_tuned 0 300 0x1800 55 73.0 92.3 222.2 347.6 373.3 444.5 99976.8 100000 20 1748.33 2046009 223228288 3710848 144712784 140135363382 151126816147 179235686390 291415900 0 0 0 0 531839 902548 ebbrt_tuned 0 300 0x1800 135 76.8 98.0 229.9 360.6 399.2 482.2 200124.4 200000 20 2113.43 4371818 479020998 8001321 311999162 301171483200 305981978107 343311386865 633136753 0 0 0 0 685581 976510 ebbrt_tuned 0 300 0x1800 95 75.6 96.0 229.6 358.9 397.4 480.1 200123.7 200000 20 2117.98 4371874 479014385 8001039 311985540 301605391145 307890257002 345173041546 638400293 0 0 0 0 702626 976520 ebbrt_tuned 0 300 0x1800 75 76.2 97.4 227.5 358.0 395.7 480.9 199861.1 200000 20 2116.11 4365897 478394599 7990552 311578110 301179170847 308064669924 345502912921 644427878 0 0 0 0 689577 976514 ebbrt_tuned 0 300 0x1800 55 78.9 101.5 234.5 372.7 429.9 2132.4 200239.1 200000 20 1967.9 4391726 480366742 8005707 312170422 302059837288 307202887201 366094072225 647974508 0 0 0 0 661735 975141 -ebbrt_tuned 0 400 0x1800 135 76.9 101.7 273.4 436.8 468.8 531.1 50041.2 50000 20 1728.25 1336024 134329182 2001501 78056732 76643949703 90304303390 112503168835 167469119 0 0 0 0 560632 720440 -ebbrt_tuned 0 400 0x1800 95 76.0 100.8 271.4 435.9 467.6 529.9 50024.2 50000 20 1727.81 1333782 134189115 2000759 78028014 76529934394 90412971704 112764692169 167766845 0 0 0 0 564852 720469 -ebbrt_tuned 0 400 0x1800 75 76.3 100.6 269.7 435.4 467.6 531.9 50045.0 50000 20 1728.57 1334906 134290208 2001628 78061972 76620691802 90089911248 112210779322 166763252 0 0 0 0 553295 719969 -ebbrt_tuned 0 400 0x1800 55 75.9 100.4 272.2 435.1 466.7 531.1 50042.3 50000 20 1732.62 1333904 134217413 2001371 78051116 76617770008 90342719416 112576446818 167255768 0 0 0 0 554490 719935 -ebbrt_tuned 0 400 0x1800 135 79.4 104.2 274.9 436.6 470.3 537.6 99999.8 100000 20 1870.5 2204458 240525648 3999313 155964192 151882801525 164533610208 193317076262 325022566 0 0 0 0 669594 731852 -ebbrt_tuned 0 400 0x1800 95 82.7 109.0 279.1 438.0 473.0 545.9 100195.8 100000 20 1869.63 2206998 240884020 4006914 156257354 152509434455 165065407332 193867751520 326309363 0 0 0 0 667353 731860 -ebbrt_tuned 0 400 0x1800 75 82.4 108.2 278.6 438.1 472.9 540.6 99855.8 100000 20 1874.51 2201026 240199243 3993364 155731596 152124851570 165067025421 193966114590 326244777 0 0 0 0 675719 731846 -ebbrt_tuned 0 400 0x1800 55 82.0 108.0 275.8 436.3 469.8 536.8 99961.6 100000 20 1843.36 2203636 240452241 3997761 155904220 152385737840 165147851844 195339351822 323988643 0 0 0 0 658158 731866 ebbrt_tuned 0 400 0x1800 135 85.2 112.3 281.9 445.5 486.0 580.5 200128.4 200000 20 1972.11 4108805 447411770 7416454 289185738 282078835358 290574722343 325878723005 612075889 0 0 0 0 780012 679002 ebbrt_tuned 0 400 0x1800 95 85.9 113.9 285.2 447.9 487.0 584.9 199976.8 200000 20 2123.75 4431019 482409088 7995180 311758374 303929448821 312611600580 350495164002 659837968 0 0 0 0 804865 732411 ebbrt_tuned 0 400 0x1800 75 88.5 117.1 288.2 453.0 492.8 588.0 200012.4 200000 20 2121.5 4437100 482820633 7996630 311816916 304124537939 314018344174 356518030916 660824365 0 0 0 0 760942 732413 ebbrt_tuned 0 400 0x1800 55 88.2 116.5 291.9 469.8 531.5 3058.1 200007.0 200000 20 1956.46 4454674 483883728 7996449 311808098 304146270593 309362521906 373842526632 662155404 0 0 0 0 719199 731472 -ebbrt_tuned 0 50 0x1900 135 47.0 50.0 72.9 133.2 168.6 216.8 49988.4 50000 20 1773.92 1603960 150371822 1999412 77975388 76913054870 99046608975 121084721844 177797521 0 0 0 0 425388 2283760 -ebbrt_tuned 0 50 0x1900 95 86.3 116.8 333.3 533.2 569.6 633.6 50044.8 50000 20 1747.52 1302170 132320250 2001611 78061232 76057250161 89280069926 109388329701 166350633 0 0 0 0 724734 575243 -ebbrt_tuned 0 50 0x1900 75 47.4 50.4 73.5 134.6 170.4 219.8 50020.2 50000 20 1773.49 1613242 150949518 2000610 78022274 77016137700 99168123762 121244922977 178456698 0 0 0 0 422415 2276452 -ebbrt_tuned 0 50 0x1900 55 48.0 51.3 74.8 137.6 172.8 221.9 50050.8 50000 20 1772.12 1612705 150971030 2001895 78072038 77076912291 99709327031 121791031518 180393372 0 0 0 0 427911 2293541 -ebbrt_tuned 0 50 0x1900 135 47.0 50.4 77.4 148.8 180.1 232.1 99924.1 100000 20 1928.98 2294542 245838161 3996310 155846708 151055844343 174919732555 200062188727 332879076 0 0 0 0 378554 3374071 -ebbrt_tuned 0 50 0x1900 95 47.1 50.7 77.7 149.5 181.2 231.8 99969.3 100000 20 1931.17 2299947 246236006 3998093 155916728 151492853046 175942295363 201246562270 337313528 0 0 0 0 378845 3379475 -ebbrt_tuned 0 50 0x1900 75 47.3 50.7 77.8 150.2 181.9 235.3 99996.9 100000 20 1931.36 2299269 246241197 3999230 155960888 151170235901 175140242392 200339484668 334454396 0 0 0 0 377227 3374234 -ebbrt_tuned 0 50 0x1900 55 47.4 50.9 78.1 151.6 183.8 235.0 99988.0 100000 20 1899.43 2298605 246163018 3998733 155942086 151388456120 175621827419 203098610179 337646632 0 0 0 0 377013 3370397 -ebbrt_tuned 0 50 0x1900 135 49.3 55.0 87.7 179.8 210.9 287.5 199811.3 200000 20 2193.83 4213690 469189278 7988645 311502280 300786395065 325169564409 354191132164 671679648 0 0 0 0 332315 4680726 -ebbrt_tuned 0 50 0x1900 95 49.1 54.7 86.9 178.5 208.5 285.2 199879.4 200000 20 2193.67 4215948 469398886 7991562 311619104 301237092566 325509971597 354581524277 676584025 0 0 0 0 333741 4683925 -ebbrt_tuned 0 50 0x1900 75 49.4 55.1 87.8 180.2 211.7 287.8 200154.3 200000 20 2194.72 4219050 469886404 8002603 312049182 301273356050 325698686548 354753723609 673809531 0 0 0 0 333140 4682976 -ebbrt_tuned 0 50 0x1900 55 51.8 57.8 94.5 210.0 276.5 3911.1 200204.1 200000 20 2015.95 4250041 471836027 8003776 312088850 301597432676 322428665743 378858238539 673379059 0 0 0 0 311811 4613647 -ebbrt_tuned 0 100 0x1900 135 48.5 53.8 95.8 166.8 196.3 255.3 50047.9 50000 20 1775.92 1551969 147326654 2001716 78065182 77019823632 98373377390 120620492686 177859396 0 0 0 0 428067 1869108 -ebbrt_tuned 0 100 0x1900 95 49.1 54.3 96.3 168.0 195.3 254.8 50028.3 50000 20 1776.57 1550960 147244689 2001004 78037476 76815582525 98185755643 120501810766 179568033 0 0 0 0 427929 1870010 -ebbrt_tuned 0 100 0x1900 75 49.5 54.5 95.8 169.0 197.7 256.8 50036.6 50000 20 1644.72 1442152 136778394 1855982 72383148 71267180838 90954723578 111633627557 166345371 0 0 0 0 395048 1730935 -ebbrt_tuned 0 100 0x1900 55 48.8 54.1 96.2 168.0 196.5 254.7 50025.7 50000 20 1778.06 1552923 147329275 2000904 78033892 76939404951 97948899209 120201640321 179611733 0 0 0 0 428247 1865646 -ebbrt_tuned 0 100 0x1900 135 51.8 59.2 109.4 181.4 214.1 273.2 100135.4 100000 20 1928.95 2254242 243642130 4004644 156172364 151572125130 173321305515 198804640180 337366287 0 0 0 0 405258 2461104 -ebbrt_tuned 0 100 0x1900 95 52.0 58.8 109.1 180.2 212.8 272.8 99949.5 100000 20 1927.09 2252715 243398012 3997294 155884694 151124343431 173452585048 198910801060 333828244 0 0 0 0 403671 2459189 -ebbrt_tuned 0 100 0x1900 75 51.3 58.2 108.1 180.7 213.0 272.6 100089.5 100000 20 1847.69 2147236 231949027 3809069 148545232 144447211027 166275299020 190668693979 323401392 0 0 0 0 382790 2339204 -ebbrt_tuned 0 50 0x1a00 135 46.8 49.8 73.1 134.2 167.1 214.5 50025.2 50000 20 1806.44 1614385 151021916 2000866 78031892 77027301255 99277905262 119990698149 177053161 0 0 0 0 423763 2279336 -ebbrt_tuned 0 50 0x1a00 95 47.4 50.5 73.9 135.1 169.1 217.8 49983.4 50000 20 1805.01 1610244 150737028 1999193 77967294 76984579345 100055537621 120833491103 180787229 0 0 0 0 426591 2284396 -ebbrt_tuned 0 50 0x1a00 75 46.6 49.4 72.8 133.9 167.1 214.9 49930.7 50000 20 1802.24 1610587 150693719 1997113 77885952 76962564708 99453845448 120168139173 177761651 0 0 0 0 424664 2280805 -ebbrt_tuned 0 50 0x1a00 55 48.2 51.4 74.5 135.8 168.4 216.7 49960.7 50000 20 1805.62 1613078 150872282 1998249 77929976 76962800027 99571889904 120281532131 177916127 0 0 0 0 426465 2289116 -ebbrt_tuned 0 50 0x1a00 135 47.1 50.6 77.9 150.5 180.5 234.9 100035.6 100000 20 1962.73 2299136 246283381 4000739 156018996 151343096630 176250026098 198725882821 335758584 0 0 0 0 378028 3373166 -ebbrt_tuned 0 50 0x1a00 95 46.6 49.8 76.6 146.0 178.2 230.3 100140.4 100000 20 1964.5 2300235 246412824 4004943 156183426 151559880858 176286558174 198877848389 338098460 0 0 0 0 377702 3375396 -ebbrt_tuned 0 50 0x1a00 75 47.2 50.6 77.3 149.1 179.6 229.9 100006.6 100000 20 1962.67 2300701 246341303 3999478 155970816 151375847589 176316499211 198887659524 339084308 0 0 0 0 378882 3376454 -ebbrt_tuned 0 50 0x1a00 55 47.0 50.4 77.8 150.4 181.1 232.4 99997.0 100000 20 1927.63 2297530 246133179 3999247 155961792 151318112434 176332442652 201141169136 336275104 0 0 0 0 378026 3372702 +ebbrt_tuned 0 2 0x1900 135 42.3 44.5 66.7 165.7 195.2 274.0 200098.0 200000 20 1979.89 4207839 469164566 8000284 311961156 296265262538 320009087195 375866400092 640051470 0 0 0 0 6544031 286999 +ebbrt_tuned 0 4 0x1900 135 43.2 45.7 69.3 171.0 202.7 280.6 199838.9 200000 20 1980.8 4200617 468410697 7989583 311544878 296330747270 322882185195 379104145315 652538635 0 0 0 0 6591510 286981 +ebbrt_tuned 0 10 0x1900 135 46.0 48.4 71.2 173.6 203.7 283.4 200104.4 200000 20 1985.79 4210720 469300923 8000613 311974692 296276618273 323639935743 380103452632 652741459 0 0 0 0 6412803 287200 +ebbrt_tuned 0 20 0x1900 135 47.4 50.8 75.6 177.8 206.7 290.3 200200.0 200000 20 1987.21 4212891 469578597 8004242 312112278 297041298144 324081961347 380581814505 654916062 0 0 0 0 6194479 287570 +ebbrt_tuned 0 30 0x1900 135 48.2 52.4 79.4 182.4 213.3 294.7 200004.2 200000 20 1985.31 4212052 469321268 7996455 311810558 296874772841 322143590587 378475873568 653701612 0 0 0 0 5817055 287792 +ebbrt_tuned 0 40 0x1900 135 49.8 54.8 84.5 185.1 217.8 298.7 200167.2 200000 20 1984.56 4220383 469966159 8001862 312012576 298326205241 322139125415 378589313332 660801727 0 0 0 0 5451201 287826 +ebbrt_tuned 0 50 0x1900 135 50.3 55.9 90.7 191.6 225.3 312.9 200092.6 200000 20 1980.04 4225828 470205550 8000014 311949892 299172362459 321390503596 377972452157 661672922 0 0 0 0 5074075 287525 +ebbrt_tuned 0 100 0x1900 135 58.8 68.4 123.9 223.4 261.9 347.9 200068.3 200000 20 1975.68 4254444 471907083 7999310 311925098 299543558533 316391716501 373360355000 662546283 0 0 0 0 3883854 286107 +ebbrt_tuned 0 200 0x1900 135 70.4 84.9 180.9 291.3 332.4 419.1 199875.9 200000 20 1966.65 4313615 475212220 7991146 311606154 298551753573 312728036046 370915110148 666822246 0 0 0 0 2931807 285743 ebbrt_tuned 0 50 0x1a00 135 48.6 54.2 85.8 175.3 205.5 279.6 200004.8 200000 20 2234.88 4212997 469347069 7995824 311777610 301057008901 326072409029 348389307629 668563329 0 0 0 0 337778 4694979 ebbrt_tuned 0 50 0x1a00 95 49.9 55.6 88.6 177.8 206.9 280.4 199871.7 200000 20 2230.78 4212652 469222102 7991207 311602496 300746753191 325436868889 347669258538 661673197 0 0 0 0 337031 4692314 -ebbrt_tuned 0 50 0x1a00 75 49.4 55.2 87.6 176.9 206.4 278.5 199883.3 200000 20 2067.56 3911530 435474599 7414688 289125132 278943509933 301388153879 322058889782 619416710 0 0 0 0 312898 4345725 ebbrt_tuned 0 50 0x1a00 55 50.5 56.2 91.8 207.2 287.4 4086.2 199857.5 200000 20 2049.54 4245594 471176465 7990806 311589970 300725814066 323679715189 374299340171 680561763 0 0 0 0 315105 4625145 -ebbrt_tuned 0 100 0x1a00 135 48.9 54.5 96.9 168.1 195.8 253.9 49965.7 50000 20 1801.92 1552930 147262502 1998502 77939936 76846976840 98507788236 119551600420 179416803 0 0 0 0 428652 1867540 -ebbrt_tuned 0 100 0x1a00 95 49.2 54.5 96.3 168.1 195.0 253.7 49966.2 50000 20 1668.26 1437782 136381163 1851476 72206940 71286477375 91108844617 110467139271 165872551 0 0 0 0 393611 1721734 -ebbrt_tuned 0 100 0x1a00 75 48.9 53.9 96.0 167.7 195.8 253.8 49989.8 50000 20 1798.01 1547946 147018399 1999389 77973592 76728542976 98713425633 119768749578 180704939 0 0 0 0 429852 1870844 -ebbrt_tuned 0 100 0x1a00 55 49.2 54.3 96.7 168.3 197.2 257.1 50002.5 50000 20 1802.34 1554333 147407851 1999989 77998206 76827379544 98323930921 119272566277 178979536 0 0 0 0 426133 1863441 -ebbrt_tuned 0 100 0x1a00 135 52.0 59.1 109.6 181.0 211.7 272.5 100066.2 100000 20 1955.5 2255756 243682808 4001996 156068980 151329108722 173914456108 196687353167 335600185 0 0 0 0 403093 2459251 -ebbrt_tuned 0 100 0x1a00 95 51.4 58.2 108.3 178.9 209.9 269.0 100025.9 100000 20 1814.39 2103036 227195985 3730250 145471758 140987444434 162062923097 183470098799 314786162 0 0 0 0 376276 2293343 -ebbrt_tuned 0 100 0x1a00 75 51.4 58.3 108.4 179.5 210.7 270.0 100111.8 100000 20 1957.37 2258636 243934566 4003770 156137408 151460403556 174730348511 197562153131 338569506 0 0 0 0 403691 2463422 -ebbrt_tuned 0 100 0x1a00 55 51.4 58.4 107.2 178.3 209.5 268.4 99838.6 100000 20 1925.94 2249663 243074063 3992846 155711440 151396303261 174530222753 199450370043 338758031 0 0 0 0 404810 2457698 ebbrt_tuned 0 100 0x1a00 135 132.6 198.1 617.3 1008.6 1063.3 1186.1 199813.2 200000 20 2184.19 4734133 500386085 7988505 311498764 301633245949 309290791088 334650528484 657188607 0 0 0 0 1581671 292965 -ebbrt_tuned 0 300 0x1a00 135 63.5 80.9 212.8 339.7 362.8 421.8 50021.4 50000 20 1771.64 1384296 137206163 2000646 78023952 75432559213 88113794168 108228152221 153582336 0 0 0 0 498458 933140 -ebbrt_tuned 0 300 0x1a00 95 64.4 81.0 214.2 343.2 364.8 426.2 49951.5 50000 20 1772.99 1380856 136939467 1997893 77916398 75747200366 88785858861 108951832460 157119095 0 0 0 0 500144 933419 -ebbrt_tuned 0 300 0x1a00 75 63.8 80.2 214.4 342.5 364.4 427.5 50072.0 50000 20 1775.08 1380110 137032894 2002680 78102354 76090034963 89634964711 109742445975 158576335 0 0 0 0 500529 933526 -ebbrt_tuned 0 300 0x1a00 55 64.3 81.8 215.6 343.1 365.0 428.4 50070.2 50000 20 1775.89 1382287 137146257 2002624 78101044 75985846912 89923117920 110118549788 161563475 0 0 0 0 500442 933034 -ebbrt_tuned 0 300 0x1a00 135 72.7 92.5 220.2 344.8 367.6 436.6 100024.8 100000 20 1925.74 2204057 240547610 4000288 156002204 150444452718 162929936396 186483507011 310900472 0 0 0 0 570703 972445 -ebbrt_tuned 0 300 0x1a00 95 73.3 92.6 221.4 345.9 367.8 438.7 100083.6 100000 20 1789.29 2046925 223340988 3711996 144756796 140240585759 152018671983 173779320266 291638893 0 0 0 0 529136 902365 -ebbrt_tuned 0 300 0x1a00 75 71.6 90.0 218.8 345.1 367.9 437.5 100036.3 100000 20 1929.7 2205531 240675986 4000611 156013764 150939145155 164169541851 187555503698 315963666 0 0 0 0 568976 972566 -ebbrt_tuned 0 300 0x1a00 55 74.2 93.3 221.9 347.0 370.4 439.5 100249.0 100000 20 1903.11 2207546 241012614 4009246 156350402 151527040119 165046262840 190201466047 316191594 0 0 0 0 573184 972832 ebbrt_tuned 0 300 0x1a00 135 75.7 96.2 227.3 356.8 392.4 470.4 199818.4 200000 20 2035.75 4047297 443517086 7409304 288912400 278874185985 286981667390 310639098044 585690656 0 0 0 0 650254 905818 ebbrt_tuned 0 300 0x1a00 95 75.3 96.3 226.9 356.1 392.4 473.9 199822.7 200000 20 2196.75 4364468 478253032 7989051 311520058 301290006781 310938384641 335990807593 633750396 0 0 0 0 682603 976514 ebbrt_tuned 0 300 0x1a00 75 76.8 97.8 227.0 355.1 390.2 470.6 200014.0 200000 20 2199.55 4368021 478657410 7996375 311804726 301511044238 312527585546 337867586151 640560443 0 0 0 0 709004 976501 ebbrt_tuned 0 300 0x1a00 55 77.6 99.3 233.3 367.2 419.2 1941.0 200181.9 200000 20 2042.8 4389262 480133633 8003477 312082340 302184019092 309279058342 356197977897 642088459 0 0 0 0 675072 974875 -ebbrt_tuned 0 400 0x1a00 135 75.4 99.4 271.0 433.7 464.5 527.5 49981.0 50000 20 1778.55 1333667 134159681 1999109 77963736 76488458811 90810797344 110405445135 167137162 0 0 0 0 555617 720192 -ebbrt_tuned 0 400 0x1a00 95 76.2 101.2 273.6 436.3 467.4 527.3 49901.2 50000 20 1777.28 1331690 133911993 1995879 77837256 76380049513 90897625915 110627286290 167097858 0 0 0 0 562041 719818 -ebbrt_tuned 0 400 0x1a00 75 76.1 100.4 274.6 435.3 466.5 528.0 49910.0 50000 20 1775.73 1333687 134070646 1996266 77852544 76433694718 90942082983 110611188448 166771134 0 0 0 0 559131 720226 -ebbrt_tuned 0 400 0x1a00 55 75.2 99.9 271.3 435.4 466.7 525.8 49989.9 50000 20 1776.06 1332624 134085576 1999415 77975546 76594928498 90711477772 110182937241 166353473 0 0 0 0 551451 719776 -ebbrt_tuned 0 400 0x1a00 135 80.1 105.7 275.9 435.6 468.7 534.6 99994.1 100000 20 1929.55 2204198 240498890 3998944 155949980 152079872239 165411607117 188530240794 323403101 0 0 0 0 666038 731861 -ebbrt_tuned 0 400 0x1a00 95 81.3 106.8 273.8 437.3 471.0 534.4 100035.6 100000 20 1927.71 2205677 240643940 4000778 156022100 152274929339 165771088880 188713766656 322017455 0 0 0 0 674606 731856 -ebbrt_tuned 0 400 0x1a00 75 81.6 106.7 276.6 436.3 470.2 537.1 99970.0 100000 20 1927.95 2203667 240444288 3997886 155908100 152305565697 166530196160 189735788339 325115506 0 0 0 0 671312 731870 -ebbrt_tuned 0 400 0x1a00 55 82.2 107.4 277.5 436.4 470.1 536.2 99989.3 100000 20 1902.62 2204575 240563116 3998861 155947356 152455476506 166469397865 191271786766 324833810 0 0 0 0 669927 731909 ebbrt_tuned 0 400 0x1a00 135 86.2 113.5 282.8 444.6 484.3 573.0 199864.3 200000 20 2190.55 4429155 482195622 7990496 311572458 304118824536 312942258991 338228278291 652782287 0 0 0 0 787827 732415 ebbrt_tuned 0 400 0x1a00 95 84.1 111.7 280.8 442.4 480.8 568.2 199568.9 200000 20 2195.73 4421862 481472795 7978784 311116590 303251932122 314137447198 340303689420 657994006 0 0 0 0 822350 732415 ebbrt_tuned 0 400 0x1a00 75 83.2 109.5 278.5 441.2 480.7 568.8 200065.9 200000 20 2192.3 4433514 482646656 7998635 311892468 303866461543 312756895514 338899080394 650785569 0 0 0 0 774886 732414 ebbrt_tuned 0 400 0x1a00 55 85.7 113.3 288.2 464.2 527.5 3532.2 199739.0 200000 20 2035.31 4445013 482970113 7985440 311380264 303779541067 312280715231 361815388967 665230148 0 0 0 0 809635 731341 -ebbrt_tuned 0 300 0x1b00 95 63.9 81.4 214.1 342.0 364.4 423.9 50039.3 50000 20 1799.42 1383252 137193094 2001410 78053326 75409492008 88094551124 107057189525 152108107 0 0 0 0 496439 932769 -ebbrt_tuned 0 300 0x1b00 75 64.2 80.7 213.2 341.8 363.8 422.1 49989.4 50000 20 1806.99 1379113 136881013 1999358 77973748 75782208660 88511573480 107388371874 154272263 0 0 0 0 497935 932501 -ebbrt_tuned 0 300 0x1b00 55 64.8 82.4 214.0 341.4 364.1 425.3 49960.8 50000 20 1806.83 1380551 136919388 1998276 77931198 75870006403 89268247190 108176940860 156707505 0 0 0 0 495685 932294 -ebbrt_tuned 0 300 0x1b00 135 72.3 91.0 219.2 344.3 367.5 436.1 100193.6 100000 20 1821.46 2067215 225679655 3757418 146529576 141068287169 152958499341 172527145303 288129373 0 0 0 0 534782 911921 -ebbrt_tuned 0 300 0x1b00 95 73.7 92.1 221.3 346.7 368.8 436.7 99915.6 100000 20 1962.02 2202636 240335673 3995859 155829100 150842063673 164364745587 185649416058 314007140 0 0 0 0 573934 972421 -ebbrt_tuned 0 300 0x1b00 75 72.7 91.4 219.1 344.2 366.8 434.7 100122.6 100000 20 1959.84 2205503 240758689 4004121 156152400 150915256303 164675401106 185788860992 313490534 0 0 0 0 570286 972506 -ebbrt_tuned 0 300 0x1b00 55 73.2 92.1 220.5 345.8 368.3 437.7 100043.9 100000 20 1938.87 2205382 240676977 4000968 156028002 151162822741 165868164761 188443100898 315739901 0 0 0 0 568708 972588 +ebbrt_tuned 1 10 0x1b00 75 45.0 47.4 68.8 160.9 188.8 259.9 200005.4 200000 20 2148.54 4199176 468498527 7996600 311814934 295290690587 322444874356 353910917315 626006148 0 0 0 0 322834 6949490 +ebbrt_tuned 2 10 0x1b00 75 43.7 46.4 67.6 159.9 187.7 261.7 199921.1 200000 20 2152.86 4200841 468511797 7992968 311673358 295765047603 324242615252 355534159632 632187120 0 0 0 0 320002 6937906 +ebbrt_tuned 3 10 0x1b00 75 44.6 47.1 68.5 162.1 189.4 269.7 200050.0 200000 20 2157.9 4203274 468852414 7998264 311876894 296069430310 328299528500 359454612676 640628845 0 0 0 0 320794 6940973 +ebbrt_tuned 4 10 0x1b00 75 44.9 47.3 69.5 164.8 194.0 273.8 200286.4 200000 20 2158.39 4207339 469308193 8007711 312247604 296918403427 327872669690 359019700575 640357873 0 0 0 0 319059 6939735 +ebbrt_tuned 1 10 0x1b00 75 44.2 46.8 67.7 156.5 184.8 256.0 200102.0 200000 20 2147.94 4202046 468770538 8000539 311969512 295192929413 322498424064 353801276812 625795215 0 0 0 0 322057 6956082 +ebbrt_tuned 0 20 0x1b00 75 45.2 48.1 71.0 164.3 192.3 267.9 200066.4 200000 20 2150.22 4203595 468875134 7998382 311874356 295916723308 323613849310 354958907570 629964817 0 0 0 0 316027 6553658 +ebbrt_tuned 1 20 0x1b00 75 46.1 49.0 72.0 165.7 193.0 270.0 199952.2 200000 20 2154.98 4204147 468768110 7994066 311713352 296014205719 325684152293 357114233216 642499399 0 0 0 0 318016 6567921 +ebbrt_tuned 0 30 0x1b00 75 45.9 49.7 75.2 167.8 198.3 273.2 200008.9 200000 20 2156.92 4206809 468969431 7996189 311791080 296910469046 325599196241 357103936853 640445091 0 0 0 0 316951 5948148 +ebbrt_tuned 1 30 0x1b00 75 46.5 50.3 75.8 168.2 197.5 273.0 200076.3 200000 20 2075.94 4003352 446298521 7609682 296726238 282315335649 310013384423 339851342809 612415334 0 0 0 0 301234 5664419 +ebbrt_tuned 0 40 0x1b00 75 47.5 52.3 81.6 176.3 206.3 288.0 200108.5 200000 20 2157.37 4214088 469540085 8000652 311973924 298247960174 326450413527 358153733721 653630466 0 0 0 0 320041 5276573 +ebbrt_tuned 1 40 0x1b00 75 48.7 53.6 82.9 177.4 206.8 282.4 199893.1 200000 20 2154.28 4211146 469146490 7992229 311645436 297954236987 324831240704 356398792341 649740748 0 0 0 0 320445 5269084 ebbrt_tuned 0 300 0x1b00 135 74.6 94.6 224.5 353.7 387.6 462.3 200115.4 200000 20 2232.45 4366915 478696226 8000573 311965340 301220217573 311949682764 331298491506 637362354 0 0 0 0 715292 976517 ebbrt_tuned 0 300 0x1b00 95 75.6 96.2 228.2 356.3 391.9 470.2 200018.2 200000 20 2229.98 4366813 478607655 7996749 311816324 301471525398 312090670912 332710986531 636360644 0 0 0 0 679499 976515 ebbrt_tuned 0 300 0x1b00 75 75.1 95.5 225.4 353.5 388.8 468.2 200314.1 200000 20 2234.96 4371967 479243766 8008581 312279440 301928899079 312473124326 332072057429 641300380 0 0 0 0 706714 976515 ebbrt_tuned 0 300 0x1b00 55 79.3 100.3 232.9 371.4 432.7 3148.5 200083.2 200000 20 1919.82 4064742 444814614 7417186 289221348 279802067772 286912586909 326573773922 601097562 0 0 0 0 629254 903528 -ebbrt_tuned 0 400 0x1b00 135 75.1 98.5 269.6 434.0 464.9 525.4 50007.9 50000 20 1805.0 1333579 134169504 2000146 78004280 76580999144 91216346900 109759617919 167099396 0 0 0 0 560886 720178 -ebbrt_tuned 0 400 0x1b00 95 76.2 99.3 270.6 433.9 464.3 525.1 49999.8 50000 20 1802.75 1333285 134147691 1999818 77991110 76468364300 91368406635 109830637962 167256785 0 0 0 0 557419 720066 -ebbrt_tuned 0 400 0x1b00 75 76.1 100.3 272.2 434.4 465.5 526.4 50004.6 50000 20 1805.13 1333886 134170176 2000016 77999004 76610200264 91341873837 110016256667 168316308 0 0 0 0 559391 719839 -ebbrt_tuned 0 400 0x1b00 55 75.8 100.8 273.0 435.3 466.1 526.9 50047.4 50000 20 1806.6 1335356 134279071 2001690 78064510 76618755308 91686300993 110371929545 169448005 0 0 0 0 559216 720163 -ebbrt_tuned 0 400 0x1b00 135 81.0 106.6 276.4 435.9 469.0 533.6 100033.2 100000 20 1962.54 2204200 240558185 4000561 156012846 151934743135 166069303783 186860376047 322926863 0 0 0 0 672728 731850 -ebbrt_tuned 0 400 0x1b00 95 81.1 107.8 276.0 436.0 469.1 533.9 100021.4 100000 20 1963.74 2204073 240537989 3999936 155988130 152247081529 166625949421 187484208561 324376511 0 0 0 0 675337 731778 -ebbrt_tuned 0 400 0x1b00 75 80.4 106.2 275.2 435.0 467.2 531.4 99965.9 100000 20 1964.2 2202873 240436263 3997974 155912534 152236308898 166228746134 186333351797 323297950 0 0 0 0 657227 731828 -ebbrt_tuned 0 400 0x1b00 55 81.4 106.7 278.3 438.1 471.3 535.6 100019.8 100000 20 1936.69 2205764 240621256 3999719 155978186 152510358191 167077115093 189593957053 327088815 0 0 0 0 672313 731843 ebbrt_tuned 0 400 0x1b00 135 87.6 114.5 285.2 449.1 489.7 582.7 200220.6 200000 20 2231.78 4436224 482971379 8004865 312134634 304603525038 315833779868 338012328486 653343879 0 0 0 0 733565 732414 ebbrt_tuned 0 400 0x1b00 95 86.2 113.5 284.4 448.0 487.3 578.7 200155.9 200000 20 2228.59 4441559 483230551 8002703 312054822 304405498644 315425034840 335086235185 659505478 0 0 0 0 673714 732416 ebbrt_tuned 0 400 0x1b00 75 85.6 113.4 285.0 447.0 487.5 578.6 199893.7 200000 20 2066.44 4111663 447364975 7410516 288961630 281822375689 292160081670 311272238891 609253428 0 0 0 0 678052 679320 ebbrt_tuned 0 400 0x1b00 55 88.5 116.5 289.6 463.6 525.8 3376.0 200024.4 200000 20 2067.76 4450126 483619045 7997086 311832732 304138284990 312889799398 358994271200 661204052 0 0 0 0 824621 731279 -ebbrt_tuned 0 50 0x1d00 135 46.1 48.9 71.4 129.1 157.9 201.6 50021.0 50000 20 1883.77 1625754 151714546 2000664 78023904 75912342424 96650178705 114336339716 165376318 0 0 0 0 429010 2294758 -ebbrt_tuned 0 50 0x1d00 95 45.5 48.4 70.5 127.8 156.1 200.4 49987.0 50000 20 1882.3 1619897 151331877 1999356 77973234 75768510700 96992070751 114681101692 168029406 0 0 0 0 429254 2291654 -ebbrt_tuned 0 50 0x1d00 75 46.7 49.6 72.1 130.7 159.4 202.6 50000.8 50000 20 1884.72 1620324 151372440 1999813 77990210 75727874482 96846746396 114427866616 168087681 0 0 0 0 424403 2279177 -ebbrt_tuned 0 50 0x1d00 55 46.3 49.3 71.8 129.2 158.8 202.0 49970.3 50000 20 1884.17 1618058 151179857 1998673 77946380 75955260366 97497330376 115135526124 168859781 0 0 0 0 424529 2279063 -ebbrt_tuned 0 50 0x1d00 135 47.1 50.6 77.0 142.9 170.2 217.1 100072.0 100000 20 2056.56 2305687 246707003 4002035 156067208 149872811878 173154276504 189429191813 317773633 0 0 0 0 383167 3385263 -ebbrt_tuned 0 50 0x1d00 95 46.9 50.5 77.1 143.1 169.8 218.4 100068.4 100000 20 2059.29 2313925 247214199 4002117 156073878 149724721097 174256170817 190503130282 320621863 0 0 0 0 381683 3384887 -ebbrt_tuned 0 50 0x1d00 75 46.5 50.0 76.2 141.6 170.0 217.4 100104.1 100000 20 1909.26 2160685 231038202 3748577 146185414 140323886770 163732566061 178820430576 303993593 0 0 0 0 357567 3168779 -ebbrt_tuned 0 50 0x1d00 55 46.8 50.3 76.8 143.8 171.5 218.7 99940.4 100000 20 2026.51 2308412 246735169 3996908 155869122 149723094237 174703216657 193174053801 325258084 0 0 0 0 379367 3376832 +ebbrt_tuned 1 50 0x1b00 135 48.9 54.5 85.9 171.6 201.6 272.0 199908.3 200000 20 2266.96 4211949 469206802 7992590 311660334 301086562608 329036193849 345207472753 671387745 0 0 0 0 336764 4689886 +ebbrt_tuned 2 50 0x1b00 135 48.2 54.0 85.7 174.3 203.9 274.7 200089.5 200000 20 2268.44 4215707 469604587 7999864 311940606 302161488068 329500912437 345665931781 673684171 0 0 0 0 338161 4694106 +ebbrt_tuned 1 50 0x1b00 75 98.1 131.3 343.2 541.7 583.0 667.7 199977.9 200000 20 2199.91 4490222 485959619 7995145 311756194 296924270471 299148195863 319163801570 610767746 0 0 0 0 1091254 578985 +ebbrt_tuned 2 50 0x1b00 75 47.6 53.1 83.6 164.1 192.3 255.0 199940.1 200000 20 2249.77 4209387 469034820 7992795 311648754 297590888758 317583937096 334096225007 636697567 0 0 0 0 344022 4706338 +ebbrt_tuned 1 50 0x1b00 55 49.0 55.0 88.0 185.7 227.1 2300.2 200283.0 200000 20 1934.83 4021949 447368545 7618113 297058706 283494669513 299506807759 337572444886 605656357 0 0 0 0 310451 4427616 +ebbrt_tuned 2 50 0x1b00 55 49.2 55.1 89.8 194.4 247.6 3626.7 200117.6 200000 20 2084.65 4236332 470838540 8000881 311978642 298217154007 317724904691 359945069782 647949584 0 0 0 0 323613 4654727 +ebbrt_tuned 1 100 0x1b00 135 56.0 64.5 117.5 201.8 235.6 301.6 200051.2 200000 20 2257.68 4246348 471377327 7998070 311870400 300333195720 319432156674 336456004046 653739031 0 0 0 0 420823 2825563 +ebbrt_tuned 2 100 0x1b00 135 56.4 65.1 118.3 203.4 236.7 304.2 199808.5 200000 20 2259.68 4242259 470925991 7988787 311509894 300133125227 321546610830 338571709515 657137733 0 0 0 0 422258 2825926 +ebbrt_tuned 1 100 0x1b00 95 55.7 64.2 116.7 201.0 234.9 303.7 199962.8 200000 20 2258.33 4243032 471112519 7994758 311741904 300646618049 322088483653 339169574501 658521355 0 0 0 0 419763 2825244 +ebbrt_tuned 2 100 0x1b00 95 56.1 64.4 117.1 199.7 232.6 299.3 199926.0 200000 20 2258.91 4242533 471052545 7993089 311678454 300948460502 322212440285 339421929282 663975403 0 0 0 0 425407 2826232 +ebbrt_tuned 1 100 0x1b00 75 56.4 65.1 118.4 202.2 235.6 302.7 200072.8 200000 20 2256.09 4246194 471429181 7999351 311922392 300837397562 319848067226 336916351960 657427239 0 0 0 0 423705 2826080 +ebbrt_tuned 2 100 0x1b00 75 56.0 64.4 117.3 201.4 233.8 299.8 199704.1 200000 20 2257.39 4237271 470483892 7984131 311325526 300223212507 321346819989 338447783757 663719010 0 0 0 0 421682 2824880 +ebbrt_tuned 1 100 0x1b00 55 57.5 66.9 122.6 229.4 283.9 3353.3 200009.4 200000 20 2086.88 4268203 472703592 7996834 311827278 300835819053 317544711802 359341349986 663802338 0 0 0 0 402439 2799264 +ebbrt_tuned 2 100 0x1b00 55 58.3 67.6 123.9 231.4 289.4 3494.6 199826.8 200000 20 2083.32 4264842 472291287 7989305 311531604 300805225817 320070668452 364148397874 671145500 0 0 0 0 399184 2796284 +ebbrt_tuned 1 200 0x1b00 135 66.3 80.0 171.5 266.2 300.0 365.3 199870.0 200000 20 2223.14 4299003 474364837 7990489 311567266 294398109954 299626653477 318343295174 599205675 0 0 0 0 635316 1463542 +ebbrt_tuned 2 200 0x1b00 135 66.2 80.6 172.7 269.8 304.5 370.0 199976.6 200000 20 2233.69 4303466 474774885 7995401 311767150 296133928255 305751622901 324268145104 621861685 0 0 0 0 629565 1463565 +ebbrt_tuned 1 200 0x1b00 95 66.1 80.0 172.5 269.4 303.2 378.3 200154.0 200000 20 2236.33 4306701 475139550 8002547 312047104 296370164676 306496590768 324801976350 624717895 0 0 0 0 622525 1463610 +ebbrt_tuned 2 200 0x1b00 95 65.6 79.8 172.6 269.4 303.6 375.8 200012.4 200000 20 2240.77 4303036 474753171 7996890 311826984 296763245517 309081354298 327438876866 629652124 0 0 0 0 632471 1463575 +ebbrt_tuned 1 200 0x1b00 75 65.3 79.6 171.5 268.8 302.7 373.1 200116.5 200000 20 2078.81 3994794 440649354 7420407 289343274 275294968300 287086727651 304078978435 589127909 0 0 0 0 582365 1357406 +ebbrt_tuned 2 200 0x1b00 75 66.2 80.5 172.4 270.5 306.4 376.5 199782.7 200000 20 2245.11 4300030 474335259 7987455 311457222 296742216911 310865651036 329453862580 640242676 0 0 0 0 624163 1463504 +ebbrt_tuned 1 200 0x1b00 55 68.3 82.9 176.8 290.4 343.1 2613.5 199986.3 200000 20 2079.48 4323993 475992550 7995918 311788452 297274896237 307004859749 348354996946 636324647 0 0 0 0 591218 1459501 +ebbrt_tuned 2 200 0x1b00 55 67.5 82.6 178.4 293.0 349.0 3224.6 200151.8 200000 20 2079.65 4329191 476472144 8002029 312023600 298373607394 308888300386 351325632420 643436838 0 0 0 0 586969 1458395 +ebbrt_tuned 1 300 0x1b00 135 75.6 95.8 225.7 354.8 390.9 469.1 199971.4 200000 20 2240.9 4366235 478506897 7995025 311752008 303934174147 316769058706 336094969416 654804172 0 0 0 0 714770 976518 +ebbrt_tuned 2 300 0x1b00 135 75.9 97.0 226.7 358.3 396.2 475.7 199985.7 200000 20 2242.43 4367355 478604910 7995679 311781052 304171513464 316943345100 336368585663 655462309 0 0 0 0 697494 976511 +ebbrt_tuned 1 300 0x1b00 95 76.1 97.3 227.5 356.0 392.0 474.5 199991.8 200000 20 2245.3 4368242 478646408 7995701 311777546 304143579548 321229438301 341170567367 669933997 0 0 0 0 702960 976505 +ebbrt_tuned 2 300 0x1b00 95 76.1 97.1 228.2 357.3 393.3 474.9 199902.4 200000 20 2242.69 4367636 478515692 7992153 311639896 304406527688 319392027413 339384292096 663772894 0 0 0 0 693961 976510 +ebbrt_tuned 1 300 0x1b00 75 76.5 97.7 227.4 357.3 393.6 474.9 199982.2 200000 20 2239.42 4368098 478638233 7995548 311773116 303937909424 318939258762 338671438166 658655389 0 0 0 0 701272 976518 +ebbrt_tuned 2 300 0x1b00 75 76.4 97.3 228.0 357.0 393.7 473.5 199985.5 200000 20 2238.34 4367252 478575101 7995582 311775648 304292702889 319571039523 339033847019 665134992 0 0 0 0 689913 976512 +ebbrt_tuned 3 50 0x1b00 135 48.6 54.2 85.3 167.1 194.2 258.1 199957.1 200000 20 2253.93 4211181 469215710 7994565 311735200 297325274704 315696377798 332031326581 624656200 0 0 0 0 345138 4710093 +ebbrt_tuned 3 50 0x1b00 75 48.5 53.9 85.1 167.9 196.3 266.2 200121.9 200000 20 2259.21 4215264 469573491 8000908 311978512 298093071213 320580970777 336941333430 637653555 0 0 0 0 341873 4709473 +ebbrt_tuned 3 50 0x1b00 55 49.1 55.1 89.2 193.8 245.0 3325.2 199887.6 200000 20 2082.77 4229957 470249641 7991715 311623624 297863879806 316821738589 359784864038 646754836 0 0 0 0 324231 4645233 +ebbrt_tuned 3 100 0x1b00 135 56.4 64.9 118.0 202.5 236.4 301.9 200048.5 200000 20 2259.45 4244840 471285188 7998347 311884964 300287495567 320178037220 337318668264 653530397 0 0 0 0 424989 2826323 +ebbrt_tuned 3 100 0x1b00 95 55.9 64.1 117.2 201.7 234.2 302.1 200180.3 200000 20 2263.67 4249108 471723242 8003327 312072852 300866091967 321738713691 338822813265 657887841 0 0 0 0 424459 2826122 +ebbrt_tuned 3 100 0x1b00 75 55.1 63.3 115.4 198.3 231.6 298.7 200118.0 200000 20 2260.4 4245680 471415581 7997236 311787050 300774353724 320994105416 338016890157 659239144 0 0 0 0 423352 2826507 +ebbrt_tuned 3 100 0x1b00 55 145.5 210.0 643.1 1035.5 1122.7 2830.3 200079.9 200000 20 2044.75 4758868 502159285 7997441 311822154 301636512668 306848690655 353858527800 652129858 0 0 0 0 1416511 290055 +ebbrt_tuned 3 200 0x1b00 135 66.2 81.3 173.4 270.5 306.4 379.5 199975.8 200000 20 2250.63 4305204 474897672 7995053 311753494 299465061407 315396279937 333341904673 655982584 0 0 0 0 624566 1463504 +ebbrt_tuned 3 200 0x1b00 95 66.9 81.4 174.5 272.9 310.0 382.5 199799.6 200000 20 2250.79 4301584 474515753 7988317 311492046 298951276829 316486733755 334542086783 657145400 0 0 0 0 615868 1463535 +ebbrt_tuned 3 200 0x1b00 75 66.5 80.9 175.4 275.0 311.6 384.6 200082.3 200000 20 2087.46 3993945 440440504 7416018 289176736 278117293932 295282815647 312026545769 614447368 0 0 0 0 575159 1357030 +ebbrt_tuned 3 200 0x1b00 55 67.5 82.5 177.9 293.7 350.6 3086.0 199832.5 200000 20 2077.81 4326265 475924856 7989426 311535218 298922828403 312078929888 355571249530 658381216 0 0 0 0 583982 1458996 +ebbrt_tuned 3 300 0x1b00 135 76.5 97.2 227.6 356.6 393.0 474.1 199960.6 200000 20 2249.88 4366856 478545551 7994370 311727030 304776398109 320951378945 340372547339 667756289 0 0 0 0 710511 976505 +ebbrt_tuned 3 300 0x1b00 95 75.8 96.6 226.9 355.6 391.5 471.2 199896.6 200000 20 2248.76 4365984 478418647 7991660 311615170 304910126823 321501030575 340481050056 671796624 0 0 0 0 720639 976513 +ebbrt_tuned 3 300 0x1b00 75 74.6 94.8 225.5 353.4 388.2 470.0 200011.8 200000 20 2245.52 4369551 478768567 7996506 311806960 304928888970 321314718082 341013352248 669351231 0 0 0 0 687773 976511 +ebbrt_tuned 3 300 0x1b00 55 77.5 99.7 235.9 382.1 455.9 4073.3 200026.6 200000 20 2065.61 4397105 480414599 7996959 311824786 305382560356 318297834584 366240948409 676041780 0 0 0 0 676073 973490 +ebbrt_tuned 3 400 0x1b00 135 83.8 111.4 280.1 439.3 477.3 559.3 200124.9 200000 20 2227.32 4428312 482403189 8000549 311963298 301561958898 309568386728 329592502329 632726389 0 0 0 0 871163 732418 +ebbrt_tuned 3 400 0x1b00 95 85.9 113.4 285.1 443.6 482.9 568.8 200057.9 200000 20 2228.6 4431041 482471442 7998362 311882228 301894073107 311440830686 332607060594 634432397 0 0 0 0 828009 732416 +ebbrt_tuned 3 400 0x1b00 75 86.5 113.2 282.3 441.3 478.0 557.1 200003.3 200000 20 2228.75 4428718 482289858 7996110 311793578 301910864216 311792123938 333407044357 636978215 0 0 0 0 847141 732418 +ebbrt_tuned 3 400 0x1b00 55 87.0 114.6 287.4 459.0 511.9 2719.0 199972.9 200000 20 2065.29 4445895 483332134 7995101 311754768 302088344857 310072450404 356108031207 646727750 0 0 0 0 840165 731589 ebbrt_tuned 0 50 0x1d00 135 48.3 53.6 83.8 158.3 185.8 243.5 199981.2 200000 20 2319.33 4206782 468956633 7995418 311767140 294861307684 310107787720 317087229832 601385816 0 0 0 0 350584 4726507 ebbrt_tuned 0 50 0x1d00 95 48.2 53.6 84.3 162.6 189.0 251.2 199873.8 200000 20 2339.95 4206307 468820075 7991258 311604598 296423741958 316300500770 322686387026 623025002 0 0 0 0 347998 4719542 ebbrt_tuned 0 50 0x1d00 75 49.1 54.4 85.3 164.1 190.9 251.9 200084.2 200000 20 2346.05 4209830 469223986 7999580 311931186 297322833274 319281882690 325454367165 630935564 0 0 0 0 346425 4713583 ebbrt_tuned 0 50 0x1d00 55 50.3 56.0 90.9 191.3 243.9 2452.6 200049.3 200000 20 2090.01 4236069 470757277 7997925 311860246 297444417304 314146977272 359055027476 634006247 0 0 0 0 324760 4652522 -ebbrt_tuned 0 100 0x1d00 135 48.2 53.3 93.3 161.9 186.4 241.6 49924.3 50000 20 1884.03 1551913 147176739 1996765 77872308 76260776285 97174622884 114948948012 171050021 0 0 0 0 428393 1865213 -ebbrt_tuned 0 100 0x1d00 95 48.1 53.4 94.6 163.5 188.3 244.8 50093.1 50000 20 1887.43 1557268 147665511 2003585 78138038 76312704220 97769464707 115570403802 171721495 0 0 0 0 428613 1870340 -ebbrt_tuned 0 100 0x1d00 75 47.7 52.7 93.8 162.6 187.2 243.6 49964.7 50000 20 1887.29 1557986 147586255 1998427 77936938 76180534791 97114405991 114817151944 170208039 0 0 0 0 430140 1867672 -ebbrt_tuned 0 100 0x1d00 55 48.6 53.8 95.0 164.6 188.9 245.3 49976.2 50000 20 1884.8 1556596 147490789 1998826 77952430 76103649055 97524433577 115367318513 172703595 0 0 0 0 430597 1873056 -ebbrt_tuned 0 100 0x1d00 135 50.4 57.2 107.1 173.9 203.2 256.2 99944.1 100000 20 2055.91 2257067 243663946 3995988 155817546 149586712667 172712117823 189137311366 322614626 0 0 0 0 407859 2464578 -ebbrt_tuned 0 100 0x1d00 95 124.9 184.2 617.0 1009.4 1051.0 1147.0 99972.5 100000 20 1825.92 2096195 226107470 3705649 144513482 139055189147 145665104103 155410073421 292797889 0 0 0 0 1124137 268935 -ebbrt_tuned 0 100 0x1d00 75 52.5 59.1 108.8 176.7 205.5 262.1 99961.6 100000 20 2054.03 2261811 243958136 3997754 155903206 149764682676 172455642367 188955230951 323472532 0 0 0 0 406530 2462296 -ebbrt_tuned 0 100 0x1d00 55 51.3 58.1 107.9 175.5 204.4 262.0 99827.0 100000 20 2025.69 2252552 243240791 3992291 155690592 149838224818 173109709568 191825540084 326275023 0 0 0 0 408477 2464414 ebbrt_tuned 0 100 0x1d00 135 55.6 63.5 115.7 196.3 227.3 293.1 199723.1 200000 20 2345.04 4234364 470291149 7985242 311371564 298167963228 318685107712 325676863140 640735397 0 0 0 0 427449 2830874 ebbrt_tuned 0 100 0x1d00 95 55.8 63.8 116.4 197.8 230.4 299.8 199850.9 200000 20 2347.27 4238684 470746264 7990435 311573918 299386450496 319866533498 326603762648 645222925 0 0 0 0 428803 2830844 ebbrt_tuned 0 100 0x1d00 75 55.8 63.8 116.3 195.6 227.4 292.4 200038.3 200000 20 2350.31 4243257 471193838 7997926 311866044 300300266274 322081859946 328738197622 649187108 0 0 0 0 428350 2830681 ebbrt_tuned 0 100 0x1d00 55 57.3 66.4 122.8 233.8 299.1 3585.2 200064.6 200000 20 2084.02 4270972 472938490 7998879 311902800 300711758281 315766214423 363425158986 658957007 0 0 0 0 396886 2792261 -ebbrt_tuned 0 200 0x1d00 135 55.1 64.2 155.9 250.2 274.9 334.1 50019.7 50000 20 1743.91 1342736 130778107 1855555 72365628 70836320714 88697159504 104268040662 160186528 0 0 0 0 448170 1190696 -ebbrt_tuned 0 200 0x1d00 95 55.3 64.3 154.6 249.4 273.0 332.5 50018.2 50000 20 1879.38 1448963 141099469 2000544 78019744 76354490070 95544495213 112344092612 172725367 0 0 0 0 481787 1282295 -ebbrt_tuned 0 200 0x1d00 75 55.8 65.1 155.0 249.4 273.5 335.1 49939.6 50000 20 1878.09 1449829 141033276 1997426 77898042 76087921292 95375337900 112121185417 172380160 0 0 0 0 478653 1281376 -ebbrt_tuned 0 200 0x1d00 55 56.0 64.8 156.5 251.0 275.4 339.7 50012.1 50000 20 1876.88 1448320 141080295 2000305 78010468 76412744173 95329838088 112105516166 171003939 0 0 0 0 481621 1283245 -ebbrt_tuned 0 200 0x1d00 135 62.2 75.3 166.2 255.6 282.8 345.0 99999.1 100000 20 2048.39 2217558 241316172 3999239 155961956 149844483917 170453147847 186448075072 326493339 0 0 0 0 527892 1430676 -ebbrt_tuned 0 200 0x1d00 95 62.6 76.0 168.0 258.0 285.0 349.7 100188.7 100000 20 2048.58 2219483 241664367 4006732 156250938 150295006017 170338030269 186238577506 323991377 0 0 0 0 527111 1430544 -ebbrt_tuned 0 200 0x1d00 75 63.2 76.4 167.7 257.1 284.2 350.1 99939.7 100000 20 2050.71 2213569 241020434 3996842 155866938 150098980268 170491693924 186516222955 327145117 0 0 0 0 527610 1430597 -ebbrt_tuned 0 200 0x1d00 55 63.4 76.9 168.6 259.0 288.2 350.9 100015.5 100000 20 2014.42 2216336 241248495 3999957 155989076 150090708894 169513655732 187719766165 325547208 0 0 0 0 528294 1431138 ebbrt_tuned 0 200 0x1d00 135 65.8 80.1 171.3 268.2 301.3 367.3 199900.0 200000 20 2334.1 4301620 474574309 7992490 311655300 298980447331 315918446004 323874977256 645174512 0 0 0 0 625536 1463528 ebbrt_tuned 0 200 0x1d00 95 66.2 80.3 172.4 269.4 302.6 369.1 200120.6 200000 20 2340.43 4306130 475087275 8001295 311998170 299776159356 318638980594 326656398692 653522117 0 0 0 0 630099 1463569 ebbrt_tuned 0 200 0x1d00 75 66.6 81.2 174.0 271.2 305.4 374.9 200078.1 200000 20 2342.67 4305996 475017323 7998887 311901622 299568060529 319927777721 327703502533 655567236 0 0 0 0 630568 1463602 ebbrt_tuned 0 200 0x1d00 55 67.8 83.3 181.2 308.4 385.4 3854.9 199811.0 200000 20 2072.26 4332240 476322147 7988621 311500420 299515917077 313972746448 365721445048 664794151 0 0 0 0 570631 1456468 -ebbrt_tuned 0 300 0x1d00 135 65.4 82.4 215.7 344.3 365.9 429.2 49881.6 50000 20 1872.34 1377686 136668613 1995024 77803020 77136374709 95233858610 112061577309 173699806 0 0 0 0 496675 933005 -ebbrt_tuned 0 300 0x1d00 95 65.1 82.5 215.4 343.8 365.8 431.0 50003.3 50000 20 1870.41 1381614 137050271 1999987 77998310 77182201229 94681734493 111440108121 173004412 0 0 0 0 496586 932852 -ebbrt_tuned 0 300 0x1d00 75 65.1 82.7 215.9 345.6 366.4 431.7 49969.0 50000 20 1872.79 1376080 136669395 1998585 77943138 76977017926 95009599459 111888076109 175404340 0 0 0 0 499819 933589 -ebbrt_tuned 0 300 0x1d00 55 65.4 83.7 216.2 343.0 365.5 429.3 49924.9 50000 20 1871.24 1377925 136732768 1996739 77869746 77075522243 94641947369 111498038956 173095175 0 0 0 0 500166 933498 -ebbrt_tuned 0 300 0x1d00 135 72.8 91.6 221.9 346.6 368.8 437.5 99994.5 100000 20 2041.1 2203550 240484057 3998857 155943384 152622658462 171530317540 187934436896 332279605 0 0 0 0 573427 972763 -ebbrt_tuned 0 300 0x1d00 95 74.3 93.2 224.2 348.2 372.4 439.0 99999.6 100000 20 2044.03 2206327 240691710 3999323 155964658 152631700337 172311869405 188893679002 334167136 0 0 0 0 577290 972611 -ebbrt_tuned 0 300 0x1d00 75 73.8 93.2 221.8 347.5 369.8 439.8 100061.1 100000 20 2042.68 2205035 240635099 4001635 156054932 153020495307 171852163531 188256394287 332399877 0 0 0 0 576820 972666 -ebbrt_tuned 0 300 0x1d00 55 73.7 93.8 223.2 348.8 373.0 441.0 100032.4 100000 20 2012.06 2208309 240789033 4000631 156015474 152742817531 171451901631 190067595058 332820851 0 0 0 0 572601 972563 ebbrt_tuned 0 300 0x1d00 135 74.7 95.0 223.7 351.8 384.8 461.6 200114.1 200000 20 2342.24 4367192 478747295 7999755 311921116 304282509228 323328823767 331823410588 671387446 0 0 0 0 721297 976519 ebbrt_tuned 0 300 0x1d00 95 76.8 97.7 227.6 356.1 391.5 469.9 200016.1 200000 20 2338.69 4368652 478694514 7996326 311798132 304492496510 322462725455 331654555913 670049622 0 0 0 0 712134 976510 ebbrt_tuned 0 300 0x1d00 75 75.1 95.8 227.0 355.3 391.1 469.6 200022.3 200000 20 2332.79 4368756 478683892 7996918 311822982 305317338815 322748059123 333199787157 666964129 0 0 0 0 684480 976516 ebbrt_tuned 0 300 0x1d00 55 79.4 102.6 240.7 398.5 509.7 3994.0 200042.8 200000 20 2047.08 4403158 480787356 7998197 311877498 304742425907 315408205855 377210876284 673907031 0 0 0 0 649121 973467 -ebbrt_tuned 0 400 0x1d00 135 75.6 100.8 272.6 434.6 465.6 526.6 50088.2 50000 20 1867.58 1334295 134298739 2003328 78128188 77373795505 93787309022 109928927749 172473111 0 0 0 0 555214 720087 -ebbrt_tuned 0 400 0x1d00 95 75.4 100.4 271.8 435.1 466.2 526.3 49912.2 50000 20 1865.25 1335400 134172004 1996332 77855412 77055328300 93707965218 109973614864 174764154 0 0 0 0 556725 719973 -ebbrt_tuned 0 400 0x1d00 75 76.2 100.4 271.4 434.1 464.8 526.1 49957.5 50000 20 1864.0 1333216 134079232 1998029 77921618 77281270628 93984272286 110306270819 175541253 0 0 0 0 559327 720076 -ebbrt_tuned 0 400 0x1d00 55 75.4 99.6 272.0 434.7 465.1 525.6 50003.4 50000 20 1868.34 1331886 134065385 1999927 77995834 77374667701 93911905658 110148158701 174231155 0 0 0 0 553775 719973 -ebbrt_tuned 0 50 0x1800 135 46.0 48.7 71.0 130.9 162.4 207.6 50008.6 50000 20 1737.29 1618521 151257278 2000238 78007842 75305123694 93342913494 116823636105 158749441 0 0 0 0 427287 2286601 -ebbrt_tuned 1 50 0x1800 135 46.4 49.1 71.8 131.5 163.4 209.1 49910.2 50000 20 1741.59 1615300 150970798 1996238 77851794 75592771618 94240521196 117792194693 163588527 0 0 0 0 425494 2281185 -ebbrt_tuned 0 50 0x1800 95 47.1 50.1 72.7 134.4 166.2 213.3 50003.9 50000 20 1741.19 1614353 151007452 2000050 78000428 75866482165 95405671267 118919154935 169257747 0 0 0 0 427142 2284012 -ebbrt_tuned 1 50 0x1800 95 46.5 49.3 71.7 130.6 164.2 210.1 50093.9 50000 20 1745.81 1615479 151163389 2003559 78137406 75902713819 95537316596 119048987239 168331360 0 0 0 0 426915 2289631 -ebbrt_tuned 2 50 0x1800 95 46.7 49.4 72.6 134.3 167.3 215.7 50108.6 50000 20 1745.46 1615949 151208218 2004201 78162146 76177342569 95676589519 119171896982 168236035 0 0 0 0 427076 2290110 -ebbrt_tuned 0 50 0x1800 75 46.3 49.0 71.8 133.8 166.8 215.1 50062.7 50000 20 1746.31 1618531 151329498 2002352 78090084 76462007936 96022260337 119496288198 169918116 0 0 0 0 423068 2278042 -ebbrt_tuned 1 50 0x1800 75 47.1 50.1 72.9 134.5 167.6 217.4 50064.5 50000 20 1749.46 1610699 150845246 2002444 78093720 76239952546 96701207995 120331832457 173122025 0 0 0 0 426287 2287655 -ebbrt_tuned 2 50 0x1800 75 46.8 49.7 72.6 133.4 167.5 218.0 50109.4 50000 20 1746.38 1614713 151123446 2004193 78161974 76178740145 96472433198 120021689695 170719439 0 0 0 0 423602 2280897 -ebbrt_tuned 0 50 0x1800 55 47.3 50.3 73.1 133.5 167.8 217.8 49983.9 50000 20 1746.9 1617267 151148731 1999215 77967678 76264844380 96941006052 120509257415 172742950 0 0 0 0 426725 2288894 -ebbrt_tuned 1 50 0x1800 55 86.6 117.6 333.9 537.6 573.1 635.3 49905.6 50000 20 1721.99 1298123 131916354 1996059 77844614 75250364443 87020521153 108302841025 159796696 0 0 0 0 724721 568684 -ebbrt_tuned 2 50 0x1800 55 46.1 48.9 72.2 135.5 169.9 220.1 50069.5 50000 20 1746.31 1610332 150847830 2002605 78100504 76435547607 97055953785 120550328143 171395942 0 0 0 0 425008 2283415 -ebbrt_tuned 0 50 0x1800 135 46.8 50.1 77.3 148.8 181.2 231.4 99973.7 100000 20 1890.91 2297626 246134840 3998239 155921152 149816503852 171692410795 200019670928 322430845 0 0 0 0 379720 3379238 -ebbrt_tuned 1 50 0x1800 135 47.0 50.3 77.4 149.2 180.7 233.9 100069.9 100000 20 1893.2 2296854 246176971 4001940 156066520 149903026077 172048150770 200496249417 327917701 0 0 0 0 377436 3373842 -ebbrt_tuned 2 50 0x1800 135 89.6 119.6 332.2 531.5 569.6 638.5 99918.7 100000 20 1865.05 2208177 240675336 3996006 155835064 149372941455 160477741626 188878915595 311955147 0 0 0 0 893556 581187 -ebbrt_tuned 0 50 0x1800 95 46.9 50.2 77.1 148.2 179.6 230.7 100000.5 100000 20 1891.46 2299493 246233262 3999383 155967050 150002770398 171275694971 199549999920 324487830 0 0 0 0 379658 3375503 -ebbrt_tuned 1 50 0x1800 95 46.4 49.7 76.6 148.3 180.0 231.7 100045.3 100000 20 1891.28 2298811 246239360 4001001 156029340 150100400474 171843623118 200134374396 327673554 0 0 0 0 377673 3374792 -ebbrt_tuned 2 50 0x1800 95 46.7 49.9 76.9 148.3 180.1 234.8 100002.9 100000 20 1892.01 2299389 246253726 3999436 155968804 150117960595 172221159934 200532456004 328554811 0 0 0 0 378252 3374282 -ebbrt_tuned 0 50 0x1800 75 46.9 50.3 77.2 148.9 180.5 231.0 100032.9 100000 20 1890.95 2297664 246182848 4000626 156014658 150313162869 172545169629 200756524388 327724624 0 0 0 0 378689 3378292 -ebbrt_tuned 1 50 0x1800 75 47.3 50.8 77.7 150.2 181.2 235.0 100174.7 100000 20 1890.44 2302649 246650756 4006256 156235230 150745245616 173315240196 201677568158 330004777 0 0 0 0 377285 3378842 -ebbrt_tuned 2 50 0x1800 75 89.8 120.2 337.4 537.5 575.2 645.5 99868.4 100000 20 1863.92 2207200 240552483 3993921 155753002 149933305406 161414202964 189535002286 317586305 0 0 0 0 890068 572133 -ebbrt_tuned 0 50 0x1800 55 46.9 50.2 77.4 150.8 182.8 236.5 99887.6 100000 20 1860.67 2296306 245919040 3994744 155786128 150294461162 171631671210 201888950724 327565716 0 0 0 0 376635 3366577 -ebbrt_tuned 1 50 0x1800 55 46.9 50.2 77.5 150.6 183.0 235.3 100103.5 100000 20 1862.14 2298723 246323355 4003509 156127882 151019847549 172990971893 203391314980 330091727 0 0 0 0 376526 3375224 -ebbrt_tuned 2 50 0x1800 55 47.4 50.9 78.2 153.3 185.9 241.0 99833.0 100000 20 1862.6 2297250 245947450 3992541 155700190 150701843371 173703349061 204140813832 333465690 0 0 0 0 376611 3370354 ebbrt_tuned 0 50 0x1800 135 49.7 55.6 88.6 181.7 214.1 292.1 199964.0 200000 20 2149.15 4217910 469641558 7994981 311752136 300730054613 322642089226 358832688652 666430922 0 0 0 0 334371 4683721 ebbrt_tuned 1 50 0x1800 135 49.2 54.9 87.4 178.0 209.6 285.1 199982.0 200000 20 2150.44 4218958 469670123 7995388 311765124 301211615261 322124372329 358264834055 662317271 0 0 0 0 332413 4676293 ebbrt_tuned 2 50 0x1800 135 49.0 54.8 87.1 179.6 209.9 289.8 199944.6 200000 20 2150.3 4218772 469633306 7994153 311718162 300480603498 321319983964 357375268509 660539846 0 0 0 0 332277 4682948 @@ -342,188 +489,24 @@ ebbrt_tuned 2 50 0x1800 75 93.2 125.3 337.1 538.1 580.9 665.2 199879.5 200000 20 ebbrt_tuned 0 50 0x1800 55 49.7 55.7 91.3 206.0 273.1 3798.8 200085.4 200000 20 1972.91 4243436 471276793 7998640 311876746 301570552599 319955413266 382421186282 678028749 0 0 0 0 314076 4618625 ebbrt_tuned 1 50 0x1800 55 50.9 56.7 92.8 207.3 267.5 3605.7 200048.1 200000 20 1978.33 4244605 471309048 7998017 311870880 300999321802 320974661359 383197102191 677776403 0 0 0 0 313030 4624049 ebbrt_tuned 2 50 0x1800 55 50.9 57.0 94.1 212.7 278.2 4034.6 200061.0 200000 20 1975.7 4245577 471357893 7998628 311892532 301504742206 320837551216 383636997101 675286273 0 0 0 0 312786 4621559 -ebbrt_tuned 0 100 0x1800 135 48.2 53.2 95.7 167.3 197.0 257.5 50030.3 50000 20 1621.33 1443205 136870944 1857140 72428138 71539011090 90666728838 112655054276 165220274 0 0 0 0 395676 1726178 -ebbrt_tuned 1 100 0x1800 135 48.2 53.1 95.3 168.5 199.0 260.8 50040.3 50000 20 1548.07 1449593 137650918 1879174 73286346 72134943924 91735502017 113793253816 167543262 0 0 0 0 401718 1753270 -ebbrt_tuned 2 100 0x1800 135 48.3 53.5 95.6 168.2 198.2 261.6 50126.9 50000 20 1749.0 1556360 147670616 2004963 78192102 77011157813 97677167960 121342299090 177038592 0 0 0 0 427420 1873086 -ebbrt_tuned 0 100 0x1800 95 48.9 53.8 95.2 168.8 199.0 261.1 50066.4 50000 0 1746.83 891525 84588350 1102985 43015608 42459130590 53991806559 66673244542 98806554 0 0 0 0 236472 1047426 -ebbrt_tuned 1 50 0x1900 135 47.2 50.1 72.0 129.0 158.9 204.9 50097.2 50000 20 1758.15 1625392 151759274 2003709 78142146 75466966343 93886316953 116103361278 159435439 0 0 0 0 426908 2288942 -ebbrt_tuned 2 50 0x1900 135 45.8 48.5 71.0 129.4 161.0 206.7 50061.8 50000 20 1768.51 1617614 151253410 2002297 78087726 75847923503 95094122729 117316378440 165505391 0 0 0 0 427609 2286974 -ebbrt_tuned 1 50 0x1900 95 46.1 48.8 71.3 130.5 162.5 206.8 49931.6 50000 20 1769.26 1616006 151031454 1997060 77882670 75804913455 95045419475 117228173345 165467630 0 0 0 0 427462 2287023 -ebbrt_tuned 2 50 0x1900 95 45.8 48.5 71.0 131.6 164.0 208.9 49963.3 50000 20 1771.1 1613024 150887627 1998397 77935866 75744028515 95610863107 117743815008 167750345 0 0 0 0 425602 2283528 -ebbrt_tuned 1 50 0x1900 75 45.8 48.6 71.2 129.8 162.9 208.1 49930.9 50000 20 1770.37 1616401 151049182 1997100 77885288 75677882248 95478154553 117583676515 167685501 0 0 0 0 426788 2282653 -ebbrt_tuned 2 50 0x1900 75 46.6 49.5 72.3 133.1 164.5 209.8 50024.2 50000 20 1644.66 1503221 140430756 1855697 72372078 70488000018 88988361342 109568878626 156583049 0 0 0 0 394325 2117706 -ebbrt_tuned 1 50 0x1900 55 46.1 48.9 71.7 130.9 164.2 212.9 50043.6 50000 20 1771.83 1620335 151425011 2001531 78056936 76450925351 96793101962 119020091291 170397298 0 0 0 0 426881 2289206 -ebbrt_tuned 2 50 0x1900 55 47.5 50.6 73.3 133.5 166.6 214.0 50098.9 50000 20 1769.6 1614894 151131608 2003803 78146488 76238808652 96648172785 118792685036 171957048 0 0 0 0 424897 2282726 -ebbrt_tuned 1 50 0x1900 135 46.4 49.7 76.5 145.4 176.1 225.4 100049.2 100000 20 1920.5 2303621 246550756 4001208 156038742 149766638745 171315928520 196825220849 322855646 0 0 0 0 380907 3382096 -ebbrt_tuned 2 50 0x1900 135 47.0 50.4 77.4 147.7 177.9 226.4 99941.8 100000 20 1918.61 2303321 246413714 3996987 155873202 149845020523 172211777249 197664638348 322491912 0 0 0 0 381082 3380300 -ebbrt_tuned 1 50 0x1900 95 46.8 50.2 76.9 147.6 178.4 228.5 99854.5 100000 20 1917.95 2305197 246442834 3993526 155737952 149680224666 171660899000 197107043052 324142991 0 0 0 0 380827 3381933 -ebbrt_tuned 2 50 0x1900 95 46.3 49.6 76.5 146.7 178.6 227.7 100133.6 100000 20 1921.89 2300180 246441361 4004506 156164472 149961216800 172515352756 197954070006 326661631 0 0 0 0 380376 3384569 -ebbrt_tuned 1 50 0x1900 75 47.1 50.6 77.5 148.8 179.4 227.4 99917.6 100000 20 1921.3 2303876 246421146 3996082 155838694 150217376112 171943508205 197322831124 324518005 0 0 0 0 380280 3378085 -ebbrt_tuned 2 50 0x1900 75 46.7 50.1 76.8 147.8 178.5 228.5 100093.3 100000 20 1925.13 2303654 246588147 4002710 156094578 150126245799 172984383203 198439254738 326536680 0 0 0 0 378890 3378689 -ebbrt_tuned 1 50 0x1900 55 88.9 119.9 330.4 531.4 569.9 639.2 100042.1 100000 20 1866.46 2208821 240857738 4000943 156027764 149539774026 160729851299 187140349297 316203389 0 0 0 0 890451 578891 -ebbrt_tuned 2 50 0x1900 55 87.8 119.0 331.7 529.9 568.1 637.7 100088.5 100000 20 1866.19 2210367 240999433 4002673 156091872 149492517596 160469399937 186354936439 318042850 0 0 0 0 870771 581195 ebbrt_tuned 1 50 0x1900 135 94.0 126.3 337.8 534.9 578.0 665.4 199689.7 200000 20 2154.48 4482073 485163556 7983723 311312478 297367530555 303418043317 333356962517 637925682 0 0 0 0 1152511 581280 ebbrt_tuned 2 50 0x1900 135 49.2 55.1 87.4 177.5 207.0 283.9 200022.4 200000 20 2182.75 4215494 469549037 7997297 311842460 299195983275 319816771088 348591965603 657688045 0 0 0 0 335406 4685977 -ebbrt_tuned 1 50 0x1900 95 49.4 55.2 87.4 177.8 207.9 286.7 199986.5 200000 20 2026.93 3911879 435621200 7417924 289248188 278249050479 298177499614 324896717581 610291899 0 0 0 0 310057 4345749 ebbrt_tuned 2 50 0x1900 95 48.7 54.5 86.7 177.9 207.5 286.9 199941.1 200000 20 2182.94 4214062 469354484 7994083 311716888 300507651601 320854811262 349625826507 657537570 0 0 0 0 335055 4683954 ebbrt_tuned 1 50 0x1900 75 49.0 54.9 87.4 178.9 208.4 286.7 200121.6 200000 20 2186.98 4217241 469724981 8000860 311978768 301247655315 322736051719 351650104285 664942396 0 0 0 0 336084 4694572 ebbrt_tuned 2 50 0x1900 75 49.1 55.0 87.7 178.5 208.9 286.7 199977.6 200000 20 2191.27 4216729 469605570 7995188 311755266 300505855902 323593471862 352572346567 665706258 0 0 0 0 335394 4690811 ebbrt_tuned 1 50 0x1900 55 50.1 56.1 91.7 201.0 254.9 3414.3 199928.4 200000 20 2017.22 4236349 470692038 7992788 311654908 300366398023 319343611818 373740900714 662293926 0 0 0 0 317575 4631454 ebbrt_tuned 2 50 0x1900 55 49.5 55.7 91.2 205.5 272.3 4053.2 199841.7 200000 20 2016.23 4239102 470769414 7989605 311541732 300763456692 320811178390 376434349229 666558061 0 0 0 0 314326 4621541 -ebbrt_tuned 1 100 0x1900 135 48.1 53.4 95.3 166.5 196.1 253.0 50042.0 50000 20 1775.85 1553528 147384942 2001500 78056734 76914381137 97819651248 120145265539 178162170 0 0 0 0 429958 1872631 -ebbrt_tuned 2 100 0x1900 135 48.5 53.8 94.9 166.9 193.8 254.1 50002.5 50000 20 1645.38 1441631 136683482 1854495 72324740 71347024194 90338499344 111015142933 163185651 0 0 0 0 396767 1732189 -ebbrt_tuned 1 100 0x1900 95 48.4 53.5 94.9 166.9 196.8 258.0 49929.2 50000 20 1769.59 1551653 147154996 1997025 77882040 76676666577 97301522011 119584698584 177171387 0 0 0 0 426325 1862128 -ebbrt_tuned 2 100 0x1900 95 48.3 53.4 95.6 167.6 195.2 257.0 49993.3 50000 20 1770.99 1553262 147347930 1999594 77982336 76894687999 97461760672 119770294205 176452373 0 0 0 0 428733 1868681 -ebbrt_tuned 1 100 0x1900 75 48.4 53.4 94.7 166.6 194.9 257.8 50076.9 50000 20 1768.25 1552950 147418274 2002919 78112360 76960171452 97183178226 119486252487 176182442 0 0 0 0 425957 1864723 -ebbrt_tuned 2 100 0x1900 75 47.7 52.6 94.9 165.7 193.2 254.2 49889.9 50000 20 1771.28 1552776 147187722 1995430 77819928 76638429172 97058451477 119342984570 176966110 0 0 0 0 426014 1860786 -ebbrt_tuned 1 100 0x1900 55 48.2 53.2 94.9 166.0 192.4 251.5 50116.8 50000 20 1771.89 1556841 147665908 2004558 78176292 77111365284 97257413592 119488944876 173698064 0 0 0 0 426206 1866916 -ebbrt_tuned 2 100 0x1900 55 48.3 53.5 94.7 166.7 193.9 252.9 50080.0 50000 20 1771.38 1551392 147310752 2002973 78113452 76974848562 97223357475 119506558809 176580512 0 0 0 0 425681 1863666 -ebbrt_tuned 1 100 0x1900 135 50.5 57.7 108.1 180.3 211.4 271.5 100023.9 100000 20 1923.89 2254045 243522926 4000268 156000702 150986666308 172215821000 197807750912 332372306 0 0 0 0 407202 2463812 -ebbrt_tuned 2 100 0x1900 135 51.0 58.0 108.9 182.9 215.0 274.6 100028.5 100000 20 1921.89 2255800 243697453 4000478 156009170 151062102660 172221887434 197814110148 334499682 0 0 0 0 404996 2462290 -ebbrt_tuned 1 100 0x1900 95 50.9 58.2 108.9 182.6 214.1 273.0 100070.6 100000 20 1921.53 2258244 243861530 4001968 156065120 151276621410 172536488434 198123902822 331848384 0 0 0 0 405668 2462442 -ebbrt_tuned 2 100 0x1900 95 51.2 58.4 109.2 181.9 212.7 272.9 99979.6 100000 20 1921.95 2255008 243565846 3998185 155918030 151131777271 172408500447 197999022645 334832716 0 0 0 0 405189 2461166 -ebbrt_tuned 1 100 0x1900 75 51.2 58.3 108.9 182.1 214.4 272.3 100081.9 100000 20 1927.24 2258053 243877025 4002666 156095312 151487082753 172972092782 198573030259 335422063 0 0 0 0 404592 2463335 -ebbrt_tuned 2 100 0x1900 75 51.5 58.7 109.0 181.5 214.2 272.6 99877.3 100000 20 1925.73 2257921 243609605 3994413 155772252 150925329199 172679793848 198275722665 335917587 0 0 0 0 405581 2461996 -ebbrt_tuned 1 100 0x1900 55 50.7 57.7 107.3 180.1 213.4 272.5 100009.2 100000 20 1893.13 2251116 243371434 3999676 155978640 150970233296 172208361690 199721561890 331994537 0 0 0 0 402228 2459127 -ebbrt_tuned 2 100 0x1900 55 51.3 58.6 109.1 182.2 214.2 273.1 99962.4 100000 20 1894.68 2252838 243401074 3997789 155904106 150926705681 172172788294 199661898508 333765666 0 0 0 0 403003 2458572 ebbrt_tuned 1 100 0x1900 135 57.3 66.4 120.2 211.1 245.8 322.5 200054.2 200000 20 2183.15 4249223 471585201 7998404 311882624 301123614371 320111111988 349424384909 665504769 0 0 0 0 416215 2819073 ebbrt_tuned 2 100 0x1900 135 57.8 66.9 121.6 213.3 249.1 326.0 199993.7 200000 20 2187.03 4247099 471374443 7995774 311779230 300804917993 321125176755 350505916883 664757797 0 0 0 0 416400 2818402 ebbrt_tuned 1 100 0x1900 95 56.8 65.7 119.7 209.0 243.5 318.8 199919.7 200000 20 2185.32 4245617 471253144 7993069 311675054 301066959400 321060496470 350458062040 669527666 0 0 0 0 417904 2818585 ebbrt_tuned 2 100 0x1900 95 56.3 64.9 119.0 209.1 244.1 321.1 200021.8 200000 20 2187.61 4249477 471560922 7996419 311799608 301990737116 321648353054 351096764072 668995250 0 0 0 0 416927 2818172 -ebbrt_tuned 1 50 0x1b00 135 46.3 49.0 72.3 133.4 166.2 213.0 49886.8 50000 20 1828.22 1609773 150619683 1995343 77816788 76772812409 99800778618 119282292123 178765652 0 0 0 0 424991 2278641 -ebbrt_tuned 2 50 0x1b00 135 45.9 48.7 71.7 131.5 165.0 209.3 50008.1 50000 20 1825.07 1612649 150907902 2000177 78005466 77020986716 99927874398 119346721887 177912254 0 0 0 0 425976 2285971 -ebbrt_tuned 1 50 0x1b00 95 46.1 48.9 71.9 131.7 164.4 211.8 50013.8 50000 20 1823.77 1610716 150801337 2000396 78014236 76976281995 99609566001 118990261079 178406614 0 0 0 0 422157 2270945 -ebbrt_tuned 2 50 0x1b00 95 46.4 49.1 72.7 134.6 167.7 214.9 49928.3 50000 20 1823.68 1612825 150835072 1996813 77872816 76891605114 100407396160 119926401698 181409671 0 0 0 0 426394 2284692 -ebbrt_tuned 1 50 0x1b00 75 46.1 48.9 72.2 132.9 166.0 213.7 49905.2 50000 20 1827.01 1607940 150527955 1996064 77844970 76874995418 99853058719 119362229637 178651062 0 0 0 0 424114 2275295 -ebbrt_tuned 2 50 0x1b00 75 45.6 48.4 71.9 130.7 163.9 212.7 49990.5 50000 20 1829.53 1613191 150925879 1999499 77979130 76927343312 100326313042 119834035480 180590303 0 0 0 0 426211 2284743 -ebbrt_tuned 1 50 0x1b00 55 46.1 48.9 72.2 133.3 166.4 214.0 49893.5 50000 20 1828.56 1615567 150959990 1995580 77826644 76876340403 99846287710 119278551848 178685447 0 0 0 0 424528 2281093 -ebbrt_tuned 2 50 0x1b00 55 46.2 49.1 72.6 132.9 166.2 214.3 49957.3 50000 20 1827.57 1608942 150627539 1998147 77925822 76941442238 100384665841 119920781411 179602300 0 0 0 0 426251 2283297 -ebbrt_tuned 1 50 0x1b00 135 46.2 49.6 76.7 146.3 176.6 225.6 100020.0 100000 20 1992.84 2299207 246252038 3999980 155989462 151463712973 178276600830 198252645741 340651519 0 0 0 0 378951 3378658 -ebbrt_tuned 2 50 0x1b00 135 46.6 49.9 76.5 145.6 175.8 225.3 100116.8 100000 20 1993.86 2310379 247026477 4004033 156148716 151426844697 177573629553 197529719879 337188020 0 0 0 0 378141 3377941 -ebbrt_tuned 1 50 0x1b00 95 46.5 49.9 77.2 147.1 176.9 226.5 100048.9 100000 20 1995.04 2307295 246778651 4001312 156042484 151188378465 178351804378 198254699086 337507462 0 0 0 0 379261 3382924 -ebbrt_tuned 2 50 0x1b00 95 46.6 49.9 76.6 147.2 177.4 225.9 100060.8 100000 20 1990.39 2299571 246319395 4001711 156058638 151201843753 177286817078 197165256927 335130310 0 0 0 0 376849 3368885 -ebbrt_tuned 1 50 0x1b00 75 46.7 50.1 77.2 147.4 178.1 227.5 100010.2 100000 20 1993.1 2300685 246316919 3999767 155982132 151548279000 177900504148 197799771939 337245432 0 0 0 0 378752 3373273 -ebbrt_tuned 2 50 0x1b00 75 47.0 50.5 77.8 150.5 180.2 229.9 99992.2 100000 20 1989.63 2306086 246646987 3999016 155953064 151312788559 177993055232 197885096667 337773205 0 0 0 0 378290 3375969 -ebbrt_tuned 1 50 0x1b00 55 46.7 50.1 77.3 147.4 178.2 226.2 100143.5 100000 20 1962.57 2303778 246627214 4004969 156185584 151434598771 178117769574 200015479384 339342606 0 0 0 0 378292 3376927 -ebbrt_tuned 2 50 0x1b00 55 46.7 50.0 77.1 149.0 179.8 228.9 99941.9 100000 20 1962.27 2303576 246435497 3997035 155875186 151274853904 177415416871 199307514274 341161596 0 0 0 0 377779 3373109 -ebbrt_tuned 1 50 0x1b00 135 48.9 54.5 85.9 171.6 201.6 272.0 199908.3 200000 20 2266.96 4211949 469206802 7992590 311660334 301086562608 329036193849 345207472753 671387745 0 0 0 0 336764 4689886 -ebbrt_tuned 2 50 0x1b00 135 48.2 54.0 85.7 174.3 203.9 274.7 200089.5 200000 20 2268.44 4215707 469604587 7999864 311940606 302161488068 329500912437 345665931781 673684171 0 0 0 0 338161 4694106 -ebbrt_tuned 1 50 0x1b00 95 48.8 54.7 86.9 176.0 204.8 277.4 200030.6 200000 20 2267.46 4217247 469692367 7997342 311845462 301807628029 330242768125 346418372712 680125393 0 0 0 0 339838 4695604 -ebbrt_tuned 2 50 0x1b00 95 47.8 53.0 83.4 161.2 188.0 251.2 199823.2 200000 20 2228.18 4205272 468665319 7989013 311517664 295030605049 307091234600 323844769667 598801539 0 0 0 0 345965 4715284 -ebbrt_tuned 1 50 0x1b00 75 98.1 131.3 343.2 541.7 583.0 667.7 199977.9 200000 20 2199.91 4490222 485959619 7995145 311756194 296924270471 299148195863 319163801570 610767746 0 0 0 0 1091254 578985 -ebbrt_tuned 2 50 0x1b00 75 47.6 53.1 83.6 164.1 192.3 255.0 199940.1 200000 20 2249.77 4209387 469034820 7992795 311648754 297590888758 317583937096 334096225007 636697567 0 0 0 0 344022 4706338 -ebbrt_tuned 1 50 0x1b00 55 49.0 55.0 88.0 185.7 227.1 2300.2 200283.0 200000 20 1934.83 4021949 447368545 7618113 297058706 283494669513 299506807759 337572444886 605656357 0 0 0 0 310451 4427616 -ebbrt_tuned 2 50 0x1b00 55 49.2 55.1 89.8 194.4 247.6 3626.7 200117.6 200000 20 2084.65 4236332 470838540 8000881 311978642 298217154007 317724904691 359945069782 647949584 0 0 0 0 323613 4654727 -ebbrt_tuned 1 100 0x1b00 135 48.7 53.8 94.5 164.5 191.2 248.0 50026.9 50000 20 1689.43 1443256 136808858 1855398 72359634 70741419309 89822926176 108238016948 159304502 0 0 0 0 392904 1732356 -ebbrt_tuned 2 100 0x1b00 135 47.7 52.7 93.1 163.8 189.7 248.4 50095.4 50000 20 1824.99 1558974 147791754 2003655 78140762 76229286002 96632752080 116490819693 170495849 0 0 0 0 424578 1871114 -ebbrt_tuned 1 100 0x1b00 95 48.9 54.1 95.2 164.9 190.3 248.5 50059.7 50000 20 1825.16 1555091 147512818 2002263 78086532 76341207239 96804378154 116619524274 171710992 0 0 0 0 426027 1873350 -ebbrt_tuned 2 100 0x1b00 95 47.8 52.8 94.2 164.5 190.2 248.4 50022.9 50000 20 1823.99 1557113 147585907 2000770 78028126 76209259013 97121896347 117017158876 172960826 0 0 0 0 425958 1871639 -ebbrt_tuned 1 100 0x1b00 75 48.2 53.5 95.2 165.8 190.3 248.3 49929.7 50000 20 1825.25 1557175 147500174 1997013 77882134 76267373417 96972577007 116859539149 172938509 0 0 0 0 426681 1872489 -ebbrt_tuned 2 100 0x1b00 75 48.0 53.1 94.0 164.7 189.6 246.5 49985.2 50000 20 1822.49 1557671 147581730 1999261 77969394 76131713560 96859165385 116656893239 172492854 0 0 0 0 425684 1871378 -ebbrt_tuned 1 100 0x1b00 55 47.4 52.3 93.4 163.6 188.3 247.7 50054.1 50000 20 1825.16 1556030 147566316 2002012 78076746 76482259677 97011160772 116795321723 172740351 0 0 0 0 424780 1868299 -ebbrt_tuned 2 100 0x1b00 55 47.4 52.5 93.9 164.1 190.1 246.2 49954.4 50000 20 1823.03 1555417 147408338 1998036 77922142 76362616768 96765536408 116584138677 171789858 0 0 0 0 423427 1862959 -ebbrt_tuned 1 100 0x1b00 135 50.8 57.9 107.8 177.6 206.8 267.1 99903.7 100000 20 1978.75 2258784 243702620 3995279 155805848 149860791946 171471105512 192162008002 323530670 0 0 0 0 407408 2465148 -ebbrt_tuned 2 100 0x1b00 135 50.5 57.6 107.2 176.7 206.3 263.8 100126.5 100000 20 1979.82 2260277 244032443 4004344 156159976 150296484787 171428506073 192012600060 323779383 0 0 0 0 405473 2462863 -ebbrt_tuned 1 100 0x1b00 95 50.6 57.7 108.0 178.1 207.5 266.4 99874.2 100000 20 1980.2 2256306 243493042 3994268 155767290 150263312931 172008373700 192606891144 325508060 0 0 0 0 404679 2460834 -ebbrt_tuned 2 100 0x1b00 95 52.0 58.7 108.9 178.8 208.9 267.4 100016.7 100000 20 1982.04 2252786 243480597 3999730 155977016 150615310723 172415540405 192987894101 328759685 0 0 0 0 404867 2462829 -ebbrt_tuned 1 100 0x1b00 75 50.6 57.6 107.4 177.4 206.9 264.7 100096.7 100000 20 1980.91 2258889 243897427 4003008 156106652 150764335546 173140124075 193637862662 328953331 0 0 0 0 402290 2463665 -ebbrt_tuned 2 100 0x1b00 75 50.9 58.1 108.7 178.2 208.1 266.4 100044.2 100000 20 1984.04 2257799 243804324 4000977 156028238 150971656735 173249873843 193734700738 329486214 0 0 0 0 405839 2466292 -ebbrt_tuned 1 100 0x1b00 55 50.5 57.5 106.8 177.6 208.1 269.1 99981.2 100000 20 1951.54 2258284 243739691 3998569 155934862 150896086197 172524873876 194875765942 328574626 0 0 0 0 401714 2461641 -ebbrt_tuned 2 100 0x1b00 55 51.6 58.8 109.0 180.4 211.8 269.6 99979.5 100000 20 1952.67 2256228 243618568 3998570 155935636 150820668366 171598805781 193647780256 324953565 0 0 0 0 402387 2459338 -ebbrt_tuned 1 100 0x1b00 135 56.0 64.5 117.5 201.8 235.6 301.6 200051.2 200000 20 2257.68 4246348 471377327 7998070 311870400 300333195720 319432156674 336456004046 653739031 0 0 0 0 420823 2825563 -ebbrt_tuned 2 100 0x1b00 135 56.4 65.1 118.3 203.4 236.7 304.2 199808.5 200000 20 2259.68 4242259 470925991 7988787 311509894 300133125227 321546610830 338571709515 657137733 0 0 0 0 422258 2825926 -ebbrt_tuned 1 100 0x1b00 95 55.7 64.2 116.7 201.0 234.9 303.7 199962.8 200000 20 2258.33 4243032 471112519 7994758 311741904 300646618049 322088483653 339169574501 658521355 0 0 0 0 419763 2825244 -ebbrt_tuned 2 100 0x1b00 95 56.1 64.4 117.1 199.7 232.6 299.3 199926.0 200000 20 2258.91 4242533 471052545 7993089 311678454 300948460502 322212440285 339421929282 663975403 0 0 0 0 425407 2826232 -ebbrt_tuned 1 100 0x1b00 75 56.4 65.1 118.4 202.2 235.6 302.7 200072.8 200000 20 2256.09 4246194 471429181 7999351 311922392 300837397562 319848067226 336916351960 657427239 0 0 0 0 423705 2826080 -ebbrt_tuned 2 100 0x1b00 75 56.0 64.4 117.3 201.4 233.8 299.8 199704.1 200000 20 2257.39 4237271 470483892 7984131 311325526 300223212507 321346819989 338447783757 663719010 0 0 0 0 421682 2824880 -ebbrt_tuned 1 100 0x1b00 55 57.5 66.9 122.6 229.4 283.9 3353.3 200009.4 200000 20 2086.88 4268203 472703592 7996834 311827278 300835819053 317544711802 359341349986 663802338 0 0 0 0 402439 2799264 -ebbrt_tuned 2 100 0x1b00 55 58.3 67.6 123.9 231.4 289.4 3494.6 199826.8 200000 20 2083.32 4264842 472291287 7989305 311531604 300805225817 320070668452 364148397874 671145500 0 0 0 0 399184 2796284 -ebbrt_tuned 1 200 0x1b00 135 55.2 64.1 155.1 250.5 274.9 341.6 49867.8 50000 20 1819.25 1449021 140924396 1994551 77785652 76173206385 95515016288 114390824162 173863485 0 0 0 0 476214 1281279 -ebbrt_tuned 2 200 0x1b00 135 55.8 64.9 156.2 250.9 275.3 339.9 50105.9 50000 20 1817.2 1447757 141131802 2004025 78155440 76562412371 95156017798 113976892517 173927419 0 0 0 0 475108 1282744 -ebbrt_tuned 1 200 0x1b00 95 55.2 64.1 154.4 249.9 274.6 338.7 49973.7 50000 20 1819.71 1447496 140967314 1998769 77950200 76464946809 95222104397 114102940321 173472446 0 0 0 0 475550 1281807 -ebbrt_tuned 2 200 0x1b00 95 55.0 64.2 155.7 250.6 275.6 343.9 49995.0 50000 20 1820.95 1446657 140942103 1999667 77985138 76275610468 95431415484 114335064122 174976548 0 0 0 0 477280 1283436 -ebbrt_tuned 1 200 0x1b00 75 55.9 65.2 155.7 250.5 275.1 336.5 49944.0 50000 20 1686.86 1340793 130552291 1851777 72218722 70821279883 88199001312 105718281942 162108764 0 0 0 0 440009 1187498 -ebbrt_tuned 2 200 0x1b00 75 55.1 64.2 155.8 250.8 275.1 341.1 50030.1 50000 20 1818.51 1445882 140935508 2001050 78039084 76542006381 95317637852 114107010587 175462761 0 0 0 0 477013 1281942 -ebbrt_tuned 1 200 0x1b00 55 56.0 65.2 156.7 250.6 275.7 339.5 50014.3 50000 20 1817.91 1445246 140875072 2000447 78015888 76561927398 95757340904 114579255084 174458208 0 0 0 0 478626 1283310 -ebbrt_tuned 2 200 0x1b00 55 56.3 65.6 156.6 251.2 275.8 340.9 50025.1 50000 20 1822.27 1445704 140902840 2000788 78028266 76438254116 95728229814 114557709215 175036169 0 0 0 0 475434 1280173 -ebbrt_tuned 1 200 0x1b00 135 63.4 76.6 167.8 258.6 287.3 352.4 100021.8 100000 20 1979.41 2219194 241454078 4000202 155998834 150234193327 169916451203 190037686343 330074283 0 0 0 0 524593 1430027 -ebbrt_tuned 2 200 0x1b00 135 62.9 76.9 167.3 257.4 286.6 355.4 100099.5 100000 20 1978.24 2216209 241337939 4003258 156118856 150197032573 169905816226 189950255693 330811552 0 0 0 0 518993 1430425 -ebbrt_tuned 1 200 0x1b00 95 62.6 76.4 167.4 256.9 285.3 351.0 99959.2 100000 20 1977.29 2214932 241108131 3997686 155900750 150314339051 169829757118 189883778268 328456794 0 0 0 0 521543 1430729 -ebbrt_tuned 2 200 0x1b00 95 62.8 76.7 169.0 259.7 290.0 355.1 100100.8 100000 20 1977.46 2216997 241390186 4003239 156115682 150349890932 170407847647 190438752573 331785347 0 0 0 0 524260 1430982 -ebbrt_tuned 1 200 0x1b00 75 63.6 77.0 168.3 260.9 290.9 358.0 99869.8 100000 20 1976.25 2217424 241192007 3994116 155761074 150568803796 171028776893 191294563395 334737195 0 0 0 0 525815 1431217 -ebbrt_tuned 2 200 0x1b00 75 62.4 76.0 167.2 259.6 289.8 354.4 100091.8 100000 20 1979.56 2215136 241278294 4002971 156106322 150919711416 171269226379 191262034994 332903054 0 0 0 0 521729 1430756 -ebbrt_tuned 1 200 0x1b00 55 63.7 77.0 168.4 259.0 288.3 353.8 100014.6 100000 20 1949.31 2218019 241392172 3999701 155979634 150519640447 170326007772 192006367583 332089283 0 0 0 0 521877 1430493 -ebbrt_tuned 2 200 0x1b00 55 61.1 74.8 165.1 253.0 279.7 338.9 99975.1 100000 20 1934.64 2217162 241284811 3998392 155928836 147097199439 158383642938 180297603054 288648408 0 0 0 0 531321 1430413 -ebbrt_tuned 1 200 0x1b00 135 66.3 80.0 171.5 266.2 300.0 365.3 199870.0 200000 20 2223.14 4299003 474364837 7990489 311567266 294398109954 299626653477 318343295174 599205675 0 0 0 0 635316 1463542 -ebbrt_tuned 2 200 0x1b00 135 66.2 80.6 172.7 269.8 304.5 370.0 199976.6 200000 20 2233.69 4303466 474774885 7995401 311767150 296133928255 305751622901 324268145104 621861685 0 0 0 0 629565 1463565 -ebbrt_tuned 1 200 0x1b00 95 66.1 80.0 172.5 269.4 303.2 378.3 200154.0 200000 20 2236.33 4306701 475139550 8002547 312047104 296370164676 306496590768 324801976350 624717895 0 0 0 0 622525 1463610 -ebbrt_tuned 2 200 0x1b00 95 65.6 79.8 172.6 269.4 303.6 375.8 200012.4 200000 20 2240.77 4303036 474753171 7996890 311826984 296763245517 309081354298 327438876866 629652124 0 0 0 0 632471 1463575 -ebbrt_tuned 1 200 0x1b00 75 65.3 79.6 171.5 268.8 302.7 373.1 200116.5 200000 20 2078.81 3994794 440649354 7420407 289343274 275294968300 287086727651 304078978435 589127909 0 0 0 0 582365 1357406 -ebbrt_tuned 2 200 0x1b00 75 66.2 80.5 172.4 270.5 306.4 376.5 199782.7 200000 20 2245.11 4300030 474335259 7987455 311457222 296742216911 310865651036 329453862580 640242676 0 0 0 0 624163 1463504 -ebbrt_tuned 1 200 0x1b00 55 68.3 82.9 176.8 290.4 343.1 2613.5 199986.3 200000 20 2079.48 4323993 475992550 7995918 311788452 297274896237 307004859749 348354996946 636324647 0 0 0 0 591218 1459501 -ebbrt_tuned 2 200 0x1b00 55 67.5 82.6 178.4 293.0 349.0 3224.6 200151.8 200000 20 2079.65 4329191 476472144 8002029 312023600 298373607394 308888300386 351325632420 643436838 0 0 0 0 586969 1458395 -ebbrt_tuned 1 300 0x1b00 135 63.7 80.3 213.8 343.4 365.6 433.5 50085.6 50000 20 1811.38 1379936 137017797 2003227 78124208 76878835286 93085135779 111904196571 170096593 0 0 0 0 492650 932836 -ebbrt_tuned 2 300 0x1b00 135 63.3 80.2 213.0 343.5 365.3 433.1 49935.9 50000 20 1808.44 1382963 137049043 1997293 77892672 76833905652 93107810535 111860285119 169038117 0 0 0 0 491429 932540 -ebbrt_tuned 1 300 0x1b00 95 64.1 81.7 215.5 343.8 366.0 431.7 50004.7 50000 20 1812.19 1379059 136880545 1999991 77998064 76923982217 93172907122 112051122055 170369564 0 0 0 0 496633 932951 -ebbrt_tuned 2 300 0x1b00 95 64.9 82.1 215.3 344.7 366.0 433.1 49951.0 50000 20 1809.93 1374848 136564183 1997876 77915636 76864873460 93452358393 112386750481 172417195 0 0 0 0 496592 932586 -ebbrt_tuned 1 300 0x1b00 75 65.4 83.0 215.4 343.1 364.6 428.2 49926.7 50000 20 1810.97 1377780 136699928 1996846 77875312 76947638712 93713754385 112751615779 172235761 0 0 0 0 499023 933182 -ebbrt_tuned 2 300 0x1b00 75 64.8 82.4 215.8 344.1 365.9 431.9 50042.0 50000 20 1810.95 1382273 137114636 2001537 78058338 77093076961 93888969239 112894107395 171897664 0 0 0 0 498484 933984 -ebbrt_tuned 1 300 0x1b00 55 64.4 80.8 213.9 342.8 365.2 430.4 49927.4 50000 20 1807.48 1377749 136711802 1996874 77876450 76983521557 93121594893 112032145151 169962910 0 0 0 0 495276 932688 -ebbrt_tuned 2 300 0x1b00 55 64.0 80.9 215.4 344.2 366.0 429.4 49992.1 50000 20 1810.83 1379314 136873440 1999522 77979506 77004676723 93553294237 112413413487 171089211 0 0 0 0 492926 932572 -ebbrt_tuned 1 300 0x1b00 135 73.6 93.4 222.7 349.9 376.0 444.8 99926.5 100000 20 1965.23 2204741 240479643 3996386 155850220 152514099670 169111007675 190094148154 326152789 0 0 0 0 574264 972635 -ebbrt_tuned 2 300 0x1b00 135 73.1 90.8 220.8 345.5 368.2 438.5 99915.3 100000 20 1963.12 2204537 240448048 3995700 155822352 152296209502 169375381503 190322265953 330265142 0 0 0 0 566266 972587 -ebbrt_tuned 1 300 0x1b00 95 74.0 92.9 222.1 345.9 369.4 439.0 100025.5 100000 20 1964.98 2202971 240486599 4000313 156002660 152414679756 169256747148 190213702568 328370371 0 0 0 0 570366 972678 -ebbrt_tuned 2 300 0x1b00 95 72.9 91.2 219.2 345.1 369.4 439.2 99864.8 100000 20 1962.23 2204393 240415316 3993664 155743232 152040568003 168247436904 189054020669 325678073 0 0 0 0 570752 972528 -ebbrt_tuned 1 300 0x1b00 75 73.4 93.0 221.4 346.8 371.9 443.8 99928.2 100000 20 1967.74 2206094 240554059 3996468 155853222 152427804154 168774154618 189547067243 326297635 0 0 0 0 567807 972687 -ebbrt_tuned 2 300 0x1b00 75 72.6 91.9 220.7 347.7 371.4 442.7 100049.3 100000 20 1965.41 2203920 240532520 4001319 156042426 152691885978 169852073586 190938961944 329316940 0 0 0 0 572778 972523 -ebbrt_tuned 1 300 0x1b00 55 74.3 93.5 222.9 348.9 373.1 441.5 100036.9 100000 20 1939.41 2205736 240642837 4000663 156016858 152631995585 168545265360 191106332312 327405915 0 0 0 0 567707 972686 -ebbrt_tuned 2 300 0x1b00 55 73.0 92.3 222.5 348.2 374.6 443.4 99983.2 100000 20 1938.73 2203876 240470956 3998575 155934684 152665439948 168794710035 191446176294 328323555 0 0 0 0 569523 972578 -ebbrt_tuned 1 300 0x1b00 135 75.6 95.8 225.7 354.8 390.9 469.1 199971.4 200000 20 2240.9 4366235 478506897 7995025 311752008 303934174147 316769058706 336094969416 654804172 0 0 0 0 714770 976518 -ebbrt_tuned 2 300 0x1b00 135 75.9 97.0 226.7 358.3 396.2 475.7 199985.7 200000 20 2242.43 4367355 478604910 7995679 311781052 304171513464 316943345100 336368585663 655462309 0 0 0 0 697494 976511 -ebbrt_tuned 1 300 0x1b00 95 76.1 97.3 227.5 356.0 392.0 474.5 199991.8 200000 20 2245.3 4368242 478646408 7995701 311777546 304143579548 321229438301 341170567367 669933997 0 0 0 0 702960 976505 -ebbrt_tuned 2 300 0x1b00 95 76.1 97.1 228.2 357.3 393.3 474.9 199902.4 200000 20 2242.69 4367636 478515692 7992153 311639896 304406527688 319392027413 339384292096 663772894 0 0 0 0 693961 976510 -ebbrt_tuned 1 300 0x1b00 75 76.5 97.7 227.4 357.3 393.6 474.9 199982.2 200000 20 2239.42 4368098 478638233 7995548 311773116 303937909424 318939258762 338671438166 658655389 0 0 0 0 701272 976518 -ebbrt_tuned 2 300 0x1b00 75 76.4 97.3 228.0 357.0 393.7 473.5 199985.5 200000 20 2238.34 4367252 478575101 7995582 311775648 304292702889 319571039523 339033847019 665134992 0 0 0 0 689913 976512 -ebbrt_tuned 1 50 0x1c00 135 46.3 49.1 72.3 132.3 165.3 213.2 49999.8 50000 20 1859.56 1614189 150996787 1999876 77993574 77311936282 101181910614 119430570022 182013675 0 0 0 0 419393 2279115 -ebbrt_tuned 2 50 0x1c00 135 46.7 49.6 73.1 134.0 167.7 213.7 50021.1 50000 20 1862.72 1613016 150931427 2000646 78024306 77247924388 101375384161 119641518748 182185501 0 0 0 0 417943 2276347 -ebbrt_tuned 1 50 0x1c00 95 46.0 48.9 72.5 133.0 167.0 212.8 49938.8 50000 20 1860.21 1612209 150802315 1997364 77895722 77006757022 100852322999 119154279395 181296018 0 0 0 0 419877 2279889 -ebbrt_tuned 2 50 0x1c00 95 46.1 49.0 72.2 133.2 165.8 212.9 50047.2 50000 20 1860.43 1613209 150974341 2001686 78063154 77194666036 100298285896 118505726991 179453105 0 0 0 0 418971 2276943 -ebbrt_tuned 1 50 0x1c00 75 46.5 49.4 72.8 132.7 166.7 213.2 50062.8 50000 20 1858.9 1616101 151184405 2002309 78088334 77241914827 101255217635 119537776555 183296874 0 0 0 0 422388 2288939 -ebbrt_tuned 2 50 0x1c00 75 46.7 49.5 72.8 134.3 167.9 213.5 50003.8 50000 20 1859.45 1615368 151053660 2000012 77999006 77356734528 100943296269 119138105747 182885939 0 0 0 0 418822 2278330 -ebbrt_tuned 1 50 0x1c00 55 46.2 49.1 72.6 133.3 166.1 212.3 50012.8 50000 20 1860.54 1612164 150883594 2000326 78011070 77266262809 100976221745 119229622236 182201372 0 0 0 0 418695 2277539 -ebbrt_tuned 2 50 0x1c00 55 46.5 49.3 72.6 134.0 166.9 211.9 49984.2 50000 20 1859.29 1615221 151023235 1999209 77967648 77055811211 100801245900 119021384633 181952301 0 0 0 0 421739 2285626 -ebbrt_tuned 1 50 0x1c00 135 46.0 49.5 77.0 146.5 177.1 225.2 99964.9 100000 20 2032.14 2300709 246284003 3997967 155911646 151784612420 179555384040 197028693868 344624433 0 0 0 0 377844 3379146 -ebbrt_tuned 2 50 0x1c00 135 46.5 50.0 77.2 147.2 176.5 225.1 99808.2 100000 20 2033.86 2298655 245999376 3991446 155655284 151700334332 179303044051 196740169707 342045298 0 0 0 0 377490 3376897 -ebbrt_tuned 1 50 0x1c00 95 45.6 49.1 75.1 137.9 164.3 208.1 100021.9 100000 20 2002.39 2309094 246847947 4000246 156000594 148354661852 167733071636 186694713141 300057557 0 0 0 0 379996 3381191 -ebbrt_tuned 2 50 0x1c00 95 45.7 49.4 75.6 140.7 167.1 213.7 100196.2 100000 20 2019.03 2310020 247120547 4007054 156265988 149281672979 170543146889 189114211880 310469803 0 0 0 0 380853 3391491 -ebbrt_tuned 1 50 0x1c00 75 88.5 120.0 335.2 536.7 573.0 637.2 99922.5 100000 20 1979.24 2208568 240713071 3996172 155842206 148816269699 159459361967 178015318076 305228153 0 0 0 0 892855 569901 -ebbrt_tuned 2 50 0x1c00 75 46.1 49.6 76.6 143.1 170.7 218.9 99981.4 100000 20 2021.68 2308762 246807973 3998438 155930716 149676336127 172540699398 190846320661 319740212 0 0 0 0 380073 3383220 -ebbrt_tuned 1 50 0x1c00 55 45.9 49.2 75.9 141.0 170.1 220.5 99992.1 100000 20 1993.74 2306304 246655778 3999056 155954170 149652103254 173542706565 193971304372 321166930 0 0 0 0 379043 3382244 -ebbrt_tuned 2 50 0x1c00 55 46.6 50.0 76.8 143.7 173.4 223.3 100073.2 100000 20 1848.37 2179349 233217720 3790011 147802894 141979643603 164263874953 183371739267 304399984 0 0 0 0 357785 3197210 ebbrt_tuned 1 50 0x1c00 135 47.9 53.2 83.7 164.2 192.4 260.7 199827.8 200000 20 2307.77 4207392 468808207 7989415 311533148 297072116149 318924607170 329916219875 637570003 0 0 0 0 343343 4709970 ebbrt_tuned 2 50 0x1c00 135 48.6 54.4 85.7 167.3 194.4 261.1 199868.5 200000 20 2307.92 4209325 468992941 7991126 311600402 297873304223 320100379656 331008037395 640009257 0 0 0 0 344627 4711296 -ebbrt_tuned 1 50 0x1c00 95 48.2 53.9 85.2 166.9 195.8 262.3 200207.3 200000 20 2141.55 3908595 435423184 7418728 289282518 276369091494 298173731992 308276937873 597026557 0 0 0 0 316362 4363004 ebbrt_tuned 2 50 0x1c00 95 48.4 54.0 84.9 167.6 197.9 267.0 199778.6 200000 20 2309.91 4206988 468775265 7987511 311461678 298174745396 322400071005 333327443910 649657303 0 0 0 0 341546 4703220 ebbrt_tuned 1 50 0x1c00 75 47.9 53.5 85.0 167.1 195.4 262.6 200052.1 200000 20 2309.18 4211098 469309354 7998184 311871186 298698884652 322763016495 333624528262 651648341 0 0 0 0 342075 4708180 ebbrt_tuned 2 50 0x1c00 75 48.0 53.4 84.8 168.3 197.9 263.3 199939.8 200000 20 2319.24 4211440 469187318 7993875 311707322 299579744298 325472183259 336157577139 658417169 0 0 0 0 342148 4704627 ebbrt_tuned 1 50 0x1c00 55 49.8 55.8 90.3 199.9 268.4 3783.4 200007.7 200000 20 1935.52 3940950 437415348 7420798 289363820 278514720128 297623491425 340668890509 613073533 0 0 0 0 295626 4300009 ebbrt_tuned 2 50 0x1c00 55 96.3 131.0 350.1 568.7 633.1 3080.3 199918.8 200000 20 2069.37 4511926 487188412 7992792 311664532 301450935025 309941707058 359718580246 654141479 0 0 0 0 1132678 565140 -ebbrt_tuned 1 100 0x1c00 135 47.4 52.5 93.2 163.9 188.7 247.8 49996.5 50000 20 1861.52 1553349 147343517 1999710 77987244 76742607921 98178932082 116850151588 175346058 0 0 0 0 422520 1864195 -ebbrt_tuned 2 100 0x1c00 135 48.1 53.3 95.8 165.0 190.0 246.9 49976.1 50000 20 1859.59 1553506 147325645 1998863 77953242 76664439504 97894702704 116549465262 174670968 0 0 0 0 426405 1867656 -ebbrt_tuned 1 100 0x1c00 95 48.2 53.6 95.5 166.0 191.6 248.9 50003.5 50000 20 1858.03 1556928 147557695 1999939 77996068 76643355504 98124916894 116734595085 175424018 0 0 0 0 423612 1862985 -ebbrt_tuned 2 100 0x1c00 95 47.8 53.0 95.7 165.2 190.2 248.5 49996.0 50000 20 1858.59 1557355 147568320 1999709 77986896 76777464479 98602909036 117317727230 177359711 0 0 0 0 426611 1874196 -ebbrt_tuned 1 100 0x1c00 75 47.9 53.1 95.4 165.6 191.5 246.5 50050.9 50000 20 1854.16 1553825 147406370 2001898 78072410 76666876467 98311162987 116949075982 176309671 0 0 0 0 424558 1868658 -ebbrt_tuned 2 100 0x1c00 75 49.2 54.4 95.5 166.3 192.3 250.2 50027.0 50000 20 1853.56 1558353 147672145 2000928 78034100 76657298923 98091894142 116699728907 174872548 0 0 0 0 423946 1869177 -ebbrt_tuned 1 100 0x1c00 55 47.4 52.4 93.2 164.3 191.1 246.3 50017.3 50000 20 1855.26 1556542 147526416 2000526 78019512 76830803782 98200661602 116805479727 174483737 0 0 0 0 423738 1865639 -ebbrt_tuned 2 100 0x1c00 55 47.9 53.1 94.6 164.9 190.4 248.6 50100.7 50000 20 1855.58 1559321 147816103 2003806 78146712 76865980945 98490959956 117148075664 176335211 0 0 0 0 424784 1871282 -ebbrt_tuned 1 100 0x1c00 135 50.6 57.6 107.0 176.7 205.8 263.2 99983.5 100000 20 2025.78 2259153 243815565 3998400 155924424 150918583951 174620949354 192672492769 329845040 0 0 0 0 402558 2461436 -ebbrt_tuned 2 100 0x1c00 135 127.3 185.5 611.2 1007.6 1049.7 1143.0 99861.1 100000 20 1956.46 2257182 243549157 3993521 155738994 151080047827 161828808112 174047023761 325356654 0 0 0 0 1339650 291210 -ebbrt_tuned 1 100 0x1c00 95 50.1 57.2 107.0 175.5 205.7 263.7 100089.4 100000 20 2026.19 2259373 243954998 4002909 156103776 151177514849 174742288062 192912669725 334013427 0 0 0 0 405857 2462721 -ebbrt_tuned 2 100 0x1c00 95 51.7 58.6 108.6 178.3 208.5 267.1 100088.8 100000 20 2025.45 2258562 243905498 4002828 156101212 151103839121 174684950127 192978632197 334584058 0 0 0 0 403918 2463723 -ebbrt_tuned 1 100 0x1c00 75 51.1 58.0 107.7 177.9 207.6 268.1 99986.2 100000 20 2028.2 2257786 243757053 3998730 155941284 150957388901 175442615481 193735906239 337870115 0 0 0 0 403382 2461213 -ebbrt_tuned 2 100 0x1c00 75 121.7 182.1 607.1 1001.2 1043.2 1137.9 99928.7 100000 20 1973.04 2258825 243733999 3996295 155846736 150783584746 163162459834 181006171248 325996518 0 0 0 0 1362348 292380 -ebbrt_tuned 1 100 0x1c00 55 51.2 58.2 108.3 178.0 207.4 266.8 100077.5 100000 20 1992.45 2258381 243863977 4002328 156080768 150962749801 174114535160 194178856589 334698187 0 0 0 0 402446 2460902 -ebbrt_tuned 2 100 0x1c00 55 50.5 57.6 107.9 178.6 207.6 266.4 100103.6 100000 20 1991.38 2255243 243688848 4003405 156123606 151221700690 174512712677 194633997843 335302087 0 0 0 0 403773 2460845 ebbrt_tuned 1 100 0x1c00 135 55.4 63.6 116.2 199.2 233.1 299.2 199982.9 200000 20 2306.56 4242470 471107034 7995608 311774622 300933986037 322084792134 333706663135 659438752 0 0 0 0 425324 2827013 ebbrt_tuned 2 100 0x1c00 135 55.7 63.8 116.9 200.9 234.7 300.8 200196.1 200000 20 2307.64 4246869 471581428 8003962 312101382 301135607446 323019724777 334561948999 662604783 0 0 0 0 422833 2826290 ebbrt_tuned 1 100 0x1c00 95 55.3 63.8 117.1 200.6 234.0 301.7 199910.0 200000 20 2309.16 4240154 470905424 7992429 311647710 300385669748 323927362181 335428377156 660180802 0 0 0 0 424050 2825986 @@ -532,22 +515,6 @@ ebbrt_tuned 1 100 0x1c00 75 55.2 63.5 116.9 201.1 234.1 300.1 199858.3 200000 20 ebbrt_tuned 2 100 0x1c00 75 56.1 64.4 117.6 201.9 235.1 301.7 200080.5 200000 20 2146.21 3936502 436988220 7413571 289081064 279062600118 301648809302 312305561178 622745644 0 0 0 0 391313 2619500 ebbrt_tuned 1 100 0x1c00 55 57.6 66.8 123.4 234.8 299.3 3698.1 200198.8 200000 20 2088.3 4276426 473380646 8004205 312110770 302317448521 321830650275 368420263783 678729851 0 0 0 0 393422 2791117 ebbrt_tuned 2 100 0x1c00 55 57.6 67.0 124.5 243.4 327.0 4241.3 200178.6 200000 20 2080.8 4279773 473552131 8003002 312056696 301648201111 322065584435 372104876009 680660493 0 0 0 0 385333 2783719 -ebbrt_tuned 1 200 0x1c00 135 56.1 65.4 157.5 251.4 275.5 336.9 49909.2 50000 20 1716.17 1341217 130518010 1848767 72101032 70949156370 89197352089 105601606445 163182647 0 0 0 0 443840 1187835 -ebbrt_tuned 2 200 0x1c00 135 55.2 64.0 155.5 250.7 275.2 338.7 49960.6 50000 20 1848.81 1445623 140834673 1998269 77930820 76456680482 95768267759 113372754309 175532914 0 0 0 0 475606 1281692 -ebbrt_tuned 1 200 0x1c00 95 55.2 64.5 156.3 250.6 275.1 338.6 50045.1 50000 20 1845.91 1448509 141082194 2001550 78057436 76801201576 95963805389 113609146968 175010228 0 0 0 0 477265 1282492 -ebbrt_tuned 2 200 0x1c00 95 54.4 62.7 151.9 247.1 269.9 326.2 50016.3 50000 20 1828.39 1454521 141435948 2000354 78009644 74684126700 89224083242 107173580532 151900690 0 0 0 0 479534 1281854 -ebbrt_tuned 1 200 0x1c00 75 54.7 63.2 153.2 247.5 270.9 329.1 50116.3 50000 20 1833.96 1457341 141709718 2004496 78173484 75313905961 90501792743 108520482284 157655456 0 0 0 0 482516 1284530 -ebbrt_tuned 2 200 0x1c00 75 55.2 64.0 154.8 248.9 272.5 328.8 50008.5 50000 20 1835.74 1453266 141327956 2000124 78003284 75313649744 90942658636 108924919242 160261115 0 0 0 0 478345 1282388 -ebbrt_tuned 1 200 0x1c00 55 54.8 63.6 154.1 248.2 271.7 330.8 49959.7 50000 20 1835.63 1449929 141078732 1998230 77929276 75153087293 91264038362 109227078565 160851694 0 0 0 0 479648 1283373 -ebbrt_tuned 2 200 0x1c00 55 55.0 63.6 153.5 248.1 271.5 331.4 50068.2 50000 20 1836.32 1453227 141405272 2002499 78094764 75276863609 91585861010 109508036771 161868474 0 0 0 0 478390 1282316 -ebbrt_tuned 1 200 0x1c00 135 62.3 76.2 166.2 255.7 284.1 348.2 99923.2 100000 20 1997.77 2216022 241151484 3996147 155841166 148411376782 164022522552 182712513616 311391618 0 0 0 0 527826 1430814 -ebbrt_tuned 2 200 0x1c00 135 62.2 75.9 167.3 255.8 282.9 343.7 99860.4 100000 20 1998.4 2213261 240943729 3993564 155740168 148151538143 164254314501 182799909959 310512962 0 0 0 0 526268 1430663 -ebbrt_tuned 1 200 0x1c00 95 62.0 75.5 167.0 256.4 284.2 343.9 100084.8 100000 20 2002.03 2217165 241419148 4002734 156097266 148641369544 164658909950 183051810745 309824974 0 0 0 0 527004 1431054 -ebbrt_tuned 2 200 0x1c00 95 61.7 75.1 165.5 255.0 282.2 344.9 100172.6 100000 20 2004.95 2220450 241707241 4006105 156226868 148893051136 166222969229 184575217952 314618566 0 0 0 0 521699 1430478 -ebbrt_tuned 1 200 0x1c00 75 61.8 75.0 164.9 255.3 283.1 347.8 99939.0 100000 20 2004.95 2215616 241170535 3996701 155862148 148397884265 166153232503 184564028244 313987167 0 0 0 0 527535 1430723 -ebbrt_tuned 2 200 0x1c00 75 62.4 76.2 165.8 256.1 284.1 349.1 99917.2 100000 20 2006.08 2215472 241147997 3995843 155826336 148705716967 165677005590 184053674586 313864304 0 0 0 0 523504 1430405 -ebbrt_tuned 1 200 0x1c00 55 61.8 75.5 166.2 256.4 284.1 348.6 100220.2 100000 20 1980.98 2222202 241819951 4008033 156304708 148957735099 166091230322 185966874534 316603371 0 0 0 0 523960 1430276 -ebbrt_tuned 2 200 0x1c00 55 61.8 75.6 165.7 256.0 283.3 349.4 99910.6 100000 20 1980.31 2216529 241185723 3995592 155816456 148576040111 166507353029 186441855993 315750539 0 0 0 0 527362 1430915 ebbrt_tuned 1 200 0x1c00 135 64.9 79.1 170.6 266.1 300.2 367.9 199909.5 200000 20 2284.22 4303182 474654703 7992815 311667600 296625556772 310365496190 323657498658 632492350 0 0 0 0 628738 1463541 ebbrt_tuned 2 200 0x1c00 135 66.8 80.3 172.3 270.6 305.1 380.7 200085.8 200000 20 2288.09 4306707 475103438 7999838 311941018 297242652656 313179655386 326382728360 638098666 0 0 0 0 622368 1463480 ebbrt_tuned 1 200 0x1c00 95 65.3 79.4 172.2 270.0 303.5 375.6 200000.4 200000 20 2283.6 4301746 474710652 7995727 311775220 297387817381 311242962760 324502721928 634910585 0 0 0 0 624426 1463573 @@ -556,22 +523,6 @@ ebbrt_tuned 1 200 0x1c00 75 66.5 80.4 172.6 270.9 304.0 380.5 199869.7 200000 20 ebbrt_tuned 2 200 0x1c00 75 66.4 80.8 173.0 270.9 305.5 377.7 199970.2 200000 20 2295.44 4304859 474804934 7995127 311756606 298974101608 315941480521 328990854380 647813203 0 0 0 0 627885 1463594 ebbrt_tuned 1 200 0x1c00 55 68.7 83.3 180.4 301.6 367.9 3782.5 200134.0 200000 20 2075.16 4330526 476550117 8000769 311967246 298879684654 309817759819 357041261067 647918802 0 0 0 0 576402 1457655 ebbrt_tuned 2 200 0x1c00 55 68.2 84.0 180.7 298.5 359.9 3164.0 200155.0 200000 20 2079.79 4332728 476689277 8002435 312040412 299373116340 312968957334 360084448422 654802396 0 0 0 0 583288 1458493 -ebbrt_tuned 1 300 0x1c00 135 64.9 82.7 215.8 344.3 365.7 432.9 50063.3 50000 20 1704.19 1278970 126975693 1855567 72366384 71489841815 87261613453 103834640157 159185652 0 0 0 0 453738 863800 -ebbrt_tuned 2 300 0x1c00 135 65.2 83.0 215.3 345.0 366.4 432.7 50057.0 50000 20 1836.46 1381335 137100950 2002129 78081458 77190042088 94382929956 112115625885 172778367 0 0 0 0 490764 933739 -ebbrt_tuned 1 300 0x1c00 95 65.0 82.0 215.7 344.2 365.6 430.6 50028.0 50000 20 1836.59 1379499 136945757 2000982 78036648 77027232897 93722438251 111471865209 170702509 0 0 0 0 492960 933471 -ebbrt_tuned 2 300 0x1c00 95 65.3 83.1 215.7 345.0 366.4 431.9 50062.3 50000 20 1704.97 1278907 126981904 1856260 72393166 71562563483 87386184568 103822997991 158849249 0 0 0 0 455392 865143 -ebbrt_tuned 1 300 0x1c00 75 63.8 80.8 214.5 344.8 366.3 432.4 49990.9 50000 20 1840.67 1379592 136909395 1999465 77977292 77135412628 94010599180 111862256626 172732343 0 0 0 0 493374 933157 -ebbrt_tuned 2 300 0x1c00 75 64.8 82.4 215.2 343.7 366.1 433.9 50115.8 50000 20 1839.79 1379302 137031216 2004500 78173790 77173485513 94101536429 111812099763 171778247 0 0 0 0 487753 932760 -ebbrt_tuned 1 300 0x1c00 55 65.2 83.2 215.5 344.5 366.2 431.9 50032.0 50000 20 1838.49 1378605 136907708 2001099 78040822 77103677040 93879617973 111601010039 172129590 0 0 0 0 487155 933017 -ebbrt_tuned 2 300 0x1c00 55 65.6 83.3 217.8 344.4 365.8 431.3 49865.7 50000 20 1705.58 1277637 126746150 1850567 72171456 71312407834 86865189927 103320858757 159144679 0 0 0 0 453714 865015 -ebbrt_tuned 1 300 0x1c00 135 72.4 91.5 222.1 346.4 368.6 437.7 99927.2 100000 20 2002.12 2202226 240334965 3996436 155851862 152520047158 169587275408 188256845904 328512324 0 0 0 0 568162 972559 -ebbrt_tuned 2 300 0x1c00 135 73.1 91.9 221.8 347.3 370.3 440.7 100000.4 100000 20 2003.04 2203753 240512031 3999156 155958036 152552977879 170402502146 189173092204 330996034 0 0 0 0 567387 972584 -ebbrt_tuned 1 300 0x1c00 95 71.8 91.0 220.6 345.8 369.2 439.9 99999.1 100000 20 2000.77 2206052 240653523 3999174 155959366 152618308369 170299902558 188889892820 332940605 0 0 0 0 571481 972585 -ebbrt_tuned 2 300 0x1c00 95 72.8 91.7 221.7 346.8 369.8 438.8 100093.5 100000 20 2000.39 2204459 240652322 4002769 156096072 152795063618 170789315020 189269689551 333736706 0 0 0 0 567897 972629 -ebbrt_tuned 1 300 0x1c00 75 73.1 92.3 220.9 347.7 372.1 438.2 99958.1 100000 20 1999.16 2201718 240313062 3997642 155898828 152608535392 170373614058 188930213202 333221468 0 0 0 0 561271 972675 -ebbrt_tuned 2 300 0x1c00 75 72.6 91.4 222.1 347.9 373.1 440.9 99909.2 100000 20 2003.24 2203827 240390693 3995564 155816888 152627061782 170509148253 189179673812 331445352 0 0 0 0 569810 972648 -ebbrt_tuned 1 300 0x1c00 55 73.8 94.1 222.8 347.8 370.1 440.9 100078.2 100000 20 1975.09 2208292 240830699 4002335 156082062 152734972879 171098377600 191356577749 332827625 0 0 0 0 569557 972629 -ebbrt_tuned 2 300 0x1c00 55 73.6 92.8 222.4 348.2 371.5 440.5 100080.6 100000 20 1974.44 2205803 240726602 4002395 156084570 152765174910 170222493651 190533629118 331582893 0 0 0 0 565110 972725 ebbrt_tuned 1 300 0x1c00 135 76.0 96.4 227.8 355.4 390.2 467.8 200000.6 200000 20 2289.26 4366044 478510842 7996363 311806078 304097966110 320312912318 334061776835 666929265 0 0 0 0 718644 976521 ebbrt_tuned 2 300 0x1c00 135 76.5 97.6 227.5 356.0 392.2 471.1 199993.7 200000 20 2291.77 4366393 478553696 7996036 311793958 304558127728 321110448669 335072744965 665977650 0 0 0 0 700932 976520 ebbrt_tuned 1 300 0x1c00 95 74.8 95.2 225.3 354.3 389.2 471.0 200011.9 200000 20 2295.35 4366820 478570562 7996551 311812998 304316943054 323186192769 337207242724 677188534 0 0 0 0 719274 976504 @@ -580,22 +531,6 @@ ebbrt_tuned 1 300 0x1c00 75 76.3 97.5 229.0 356.4 392.3 472.7 200181.1 200000 20 ebbrt_tuned 2 300 0x1c00 75 75.2 95.6 226.9 355.6 390.7 468.9 199943.5 200000 20 2295.69 4365906 478463084 7993848 311707580 305113404777 322852799608 336535383716 680473729 0 0 0 0 710038 976513 ebbrt_tuned 1 300 0x1c00 55 74.9 94.9 226.9 359.9 403.1 1146.4 200073.5 200000 20 2072.38 4380323 479463742 7999372 311925184 298823714588 297252880479 339774325160 595587533 0 0 0 0 693687 975526 ebbrt_tuned 2 300 0x1c00 55 76.9 97.7 232.4 365.9 422.7 2543.9 200059.7 200000 20 2074.2 4384263 479674710 7998455 311886944 300319740777 303306374240 348137789411 625415172 0 0 0 0 693889 974898 -ebbrt_tuned 1 400 0x1c00 135 75.2 98.8 271.4 433.2 462.9 520.0 49994.2 50000 20 1828.47 1335608 134250071 1999537 77979754 76123391639 90373638152 107989562638 163744321 0 0 0 0 565658 720567 -ebbrt_tuned 2 400 0x1c00 135 73.9 98.0 269.7 432.3 461.6 520.4 50048.1 50000 20 1827.19 1333873 134215035 2001754 78066706 76325067559 90199972106 107682551035 162451312 0 0 0 0 557527 720088 -ebbrt_tuned 1 400 0x1c00 95 74.2 98.3 270.4 432.8 462.1 522.3 50055.1 50000 20 1823.5 1334977 134284829 2002049 78078246 76282920708 90246296833 107646480371 161566526 0 0 0 0 563988 719987 -ebbrt_tuned 2 400 0x1c00 95 75.2 100.2 271.3 434.6 465.0 524.9 49889.7 50000 20 1823.42 1331707 133903624 1995388 77818470 76187068685 90585933628 108171862235 163305226 0 0 0 0 559581 720107 -ebbrt_tuned 1 400 0x1c00 75 74.4 99.8 269.2 432.3 462.2 520.1 49998.4 50000 20 1825.7 1335122 134246189 1999794 77990226 76278147138 90880410537 108397376588 165008854 0 0 0 0 557747 720155 -ebbrt_tuned 2 400 0x1c00 75 74.4 98.6 271.1 433.2 463.0 521.7 50014.2 50000 20 1823.45 1334310 134203926 2000305 78010182 76185851762 90372961226 107656615349 162468936 0 0 0 0 557707 720192 -ebbrt_tuned 1 400 0x1c00 55 74.7 98.6 272.6 433.4 463.6 523.7 50101.7 50000 20 1825.53 1333881 134281865 2003886 78150026 76496311044 90885795096 108375717764 164125373 0 0 0 0 559985 720323 -ebbrt_tuned 2 400 0x1c00 55 74.6 99.8 271.4 434.3 464.8 524.8 50010.9 50000 20 1828.11 1336201 134324990 2000249 78007642 76193092693 90896467233 108324237602 164280861 0 0 0 0 562426 719492 -ebbrt_tuned 1 400 0x1c00 135 79.2 105.0 274.0 434.2 466.4 530.4 99903.4 100000 20 1866.2 2041731 222787248 3703587 144431444 140408045906 153210060449 170588152929 293704085 0 0 0 0 628509 678540 -ebbrt_tuned 2 400 0x1c00 135 79.8 106.3 275.1 435.1 467.4 531.6 100034.9 100000 20 1987.98 2205519 240635890 4000710 156018278 151340229633 165367528100 184075287068 317367128 0 0 0 0 670896 731846 -ebbrt_tuned 1 400 0x1c00 95 80.2 106.2 274.1 435.6 468.0 533.5 99949.5 100000 20 1993.42 2204721 240488311 3997066 155876202 151293715619 166020706713 184328897955 319290913 0 0 0 0 672992 731864 -ebbrt_tuned 2 400 0x1c00 95 79.9 105.4 273.7 434.7 466.8 530.4 99952.1 100000 20 1990.14 2204650 240516681 3997365 155887710 151359070107 165899589051 184365349128 317409803 0 0 0 0 671080 731868 -ebbrt_tuned 1 400 0x1c00 75 80.5 106.6 275.3 435.9 468.7 531.5 99973.5 100000 20 1848.37 2042970 222905597 3705872 144520860 140485778156 154531357137 172024313317 297440385 0 0 0 0 622337 678583 -ebbrt_tuned 2 400 0x1c00 75 79.6 104.7 274.2 434.7 466.9 530.5 99949.1 100000 20 1991.55 2203156 240407373 3997254 155883614 151712736828 166686689596 185566906476 321920029 0 0 0 0 676314 731826 -ebbrt_tuned 1 400 0x1c00 55 81.2 106.9 275.3 434.5 467.5 534.0 100031.9 100000 20 1964.91 2204403 240563961 4000443 156009412 151707279090 166154912796 186098624086 320164846 0 0 0 0 667135 731842 -ebbrt_tuned 2 400 0x1c00 55 80.2 106.0 274.4 435.1 467.8 534.1 100085.7 100000 20 1965.89 2206464 240726109 4002496 156088000 151975142020 167487130965 187563858627 323794934 0 0 0 0 668234 731830 ebbrt_tuned 1 400 0x1c00 135 85.1 111.7 282.6 442.1 481.6 566.2 199888.7 200000 20 2274.33 4424321 481894607 7991806 311627622 303104292846 315502251530 332247670235 650884915 0 0 0 0 813400 732416 ebbrt_tuned 2 400 0x1c00 135 82.8 109.8 280.0 439.8 478.1 557.7 200048.4 200000 20 2275.94 4428304 482310618 7998421 311887374 304044367930 317101033343 331496699053 656066720 0 0 0 0 847097 732418 ebbrt_tuned 1 400 0x1c00 95 85.2 111.8 281.7 442.6 480.5 561.0 200099.3 200000 20 2266.89 4431916 482638444 8000338 311960480 304621313782 316966964137 332688951287 654175689 0 0 0 0 789835 732419 @@ -604,22 +539,6 @@ ebbrt_tuned 1 400 0x1c00 75 86.2 113.4 283.7 443.4 483.2 572.4 200134.0 200000 2 ebbrt_tuned 2 400 0x1c00 75 80.9 107.5 276.2 436.4 473.3 549.4 200068.3 200000 20 2287.81 4426400 482224619 7998785 311896006 304408860886 318904372899 332591047316 664998307 0 0 0 0 895460 732418 ebbrt_tuned 1 400 0x1c00 55 90.6 120.0 297.1 480.3 563.7 3011.2 199882.2 200000 20 2029.53 4458427 483947747 7991330 311607858 304089378588 309809340190 369995948391 656351336 0 0 0 0 678201 731607 ebbrt_tuned 2 400 0x1c00 55 100.0 129.9 312.0 501.6 586.7 3009.2 199861.4 200000 20 2010.52 4471327 484712713 7990060 311554186 304082392288 310675112137 379412399017 656496196 0 0 0 0 516936 731560 -ebbrt_tuned 1 50 0x1d00 135 46.3 49.3 71.4 128.3 157.0 202.5 50049.8 50000 20 1875.32 1621382 151460692 2001822 78069578 75838807105 97209167139 114865627532 168272549 0 0 0 0 428438 2292782 -ebbrt_tuned 2 50 0x1d00 135 45.6 48.5 70.8 127.9 158.4 201.7 49988.5 50000 20 1884.97 1622205 151453678 1999427 77976274 76024054039 97982236507 115648886009 171889049 0 0 0 0 429897 2296562 -ebbrt_tuned 1 50 0x1d00 95 44.9 48.0 70.5 127.0 156.3 201.2 49964.5 50000 20 1884.66 1618188 151178935 1998386 77934612 76301455895 98026195217 115665828070 168969709 0 0 0 0 428818 2286498 -ebbrt_tuned 2 50 0x1d00 95 45.6 48.6 71.2 129.1 158.8 202.0 50076.0 50000 20 1883.07 1620295 151431333 2002893 78111140 76200243491 97960619303 115506490064 170076055 0 0 0 0 426303 2284313 -ebbrt_tuned 1 50 0x1d00 75 46.0 49.0 71.9 129.1 159.1 203.6 49996.4 50000 20 1880.87 1616350 151114939 1999753 77988936 76063264889 98349115129 115907515897 172306493 0 0 0 0 428347 2285175 -ebbrt_tuned 2 50 0x1d00 75 46.3 49.3 72.0 130.5 161.4 205.6 49999.3 50000 20 1881.11 1617671 151204307 1999804 77990354 76263572497 98859745104 116433104879 172602042 0 0 0 0 428140 2291345 -ebbrt_tuned 1 50 0x1d00 55 45.3 48.3 70.9 128.0 159.6 203.3 49926.4 50000 20 1878.88 1616633 151037692 1996909 77877886 76090561291 98340460979 115875891049 171637304 0 0 0 0 427066 2284063 -ebbrt_tuned 2 50 0x1d00 55 85.7 116.3 329.5 531.0 566.7 626.2 50139.3 50000 20 1848.12 1303890 132534490 2005364 78207164 75391134726 89060893447 104869025700 160909392 0 0 0 0 733530 575463 -ebbrt_tuned 1 50 0x1d00 135 45.8 49.5 76.2 141.1 169.1 218.3 99929.7 100000 20 2053.63 2306952 246642469 3996575 155857748 149608143611 174058873380 190190216889 319761555 0 0 0 0 381170 3376464 -ebbrt_tuned 2 50 0x1d00 135 45.9 49.4 75.8 142.0 169.9 218.3 100030.3 100000 20 2057.1 2306504 246680099 4000400 156006490 149718330279 175291693077 191399683987 324531932 0 0 0 0 380229 3380814 -ebbrt_tuned 1 50 0x1d00 95 46.1 49.5 76.1 140.9 170.2 218.4 100081.5 100000 20 2060.86 2308299 246894693 4002405 156082150 150263624068 175928888273 191928839331 325923132 0 0 0 0 381836 3385779 -ebbrt_tuned 2 50 0x1d00 95 45.9 49.4 76.3 141.5 169.2 217.9 99993.4 100000 20 2059.12 2310780 246934088 3999011 155951900 149941629880 175718313271 191702768323 323684485 0 0 0 0 382475 3384832 -ebbrt_tuned 1 50 0x1d00 75 46.3 49.9 77.1 143.2 171.2 219.2 100087.4 100000 20 2061.2 2306727 246790650 4002735 156096596 150117666966 176007672955 192001019867 325703305 0 0 0 0 380937 3384107 -ebbrt_tuned 2 50 0x1d00 75 45.8 49.4 76.1 142.9 170.3 218.2 100062.8 100000 20 2061.03 2310186 246930521 4001831 156061712 150259280217 177080152364 193033839990 328914926 0 0 0 0 381680 3385886 -ebbrt_tuned 1 50 0x1d00 55 89.9 120.7 332.0 529.8 567.0 633.4 100033.8 100000 20 1992.59 2209157 240851889 4000700 156018480 149541326610 163994259366 181836161137 317361502 0 0 0 0 892290 581199 -ebbrt_tuned 2 50 0x1d00 55 46.0 49.5 76.5 142.7 170.9 219.0 100078.6 100000 20 2026.56 2303101 246527231 4002478 156087772 150446434008 175661325584 193497635453 328269412 0 0 0 0 380345 3381029 ebbrt_tuned 1 50 0x1d00 135 48.2 53.7 85.0 167.1 195.6 260.2 200001.2 200000 20 2349.11 4210902 469182053 7996505 311811240 298565390095 323859088382 329532313844 648309104 0 0 0 0 344269 4711846 ebbrt_tuned 2 50 0x1d00 135 48.1 53.8 84.8 167.0 194.3 258.3 200252.8 200000 20 2347.5 4215812 469790790 8005845 312169674 299838187961 326670620663 332192708246 656264469 0 0 0 0 343903 4708421 ebbrt_tuned 1 50 0x1d00 95 47.5 52.9 84.2 164.6 192.7 258.8 200108.0 200000 20 2350.41 4213602 469522383 8000742 311976034 300352525340 327113793736 332585867945 658472773 0 0 0 0 343513 4708539 @@ -628,22 +547,6 @@ ebbrt_tuned 1 50 0x1d00 75 47.6 53.0 84.2 165.1 192.8 259.9 199915.0 200000 20 2 ebbrt_tuned 2 50 0x1d00 75 90.3 121.9 328.7 525.3 564.8 640.7 200034.5 200000 20 2324.35 4482391 485531125 7997521 311848494 298899609262 312322966001 322552737220 640688671 0 0 0 0 1169943 585930 ebbrt_tuned 1 50 0x1d00 55 49.8 55.6 90.5 203.1 277.1 3762.3 200078.6 200000 20 2087.76 4248386 471563721 7999290 311921786 300792364583 321893838482 370717910960 669717488 0 0 0 0 315139 4620121 ebbrt_tuned 2 50 0x1d00 55 50.3 56.4 93.4 219.2 343.3 4363.3 200065.8 200000 20 2074.3 4253182 471838584 7999050 311911780 300725674153 321913156424 375007409942 668661454 0 0 0 0 312801 4614194 -ebbrt_tuned 1 100 0x1d00 135 47.5 52.7 94.6 163.2 187.5 243.4 49954.1 50000 20 1884.25 1557849 147551139 1997984 77919784 76729691439 98278161588 115853530222 173837174 0 0 0 0 426731 1859982 -ebbrt_tuned 2 100 0x1d00 135 47.5 52.5 93.5 163.8 188.5 246.9 50061.2 50000 20 1884.12 1554024 147451742 2002327 78089126 76869277803 99256321030 116933056295 176913085 0 0 0 0 428902 1868043 -ebbrt_tuned 1 100 0x1d00 95 47.8 53.2 94.1 164.8 189.1 246.6 49940.7 50000 20 1880.74 1556084 147443307 1997508 77901282 76932279319 98536293434 116145475078 174983826 0 0 0 0 428580 1868762 -ebbrt_tuned 2 100 0x1d00 95 47.4 52.5 93.2 163.0 188.7 246.1 49985.3 50000 20 1881.9 1555441 147456171 1999243 77969164 76846597599 99084317601 116735798411 177360448 0 0 0 0 429429 1871209 -ebbrt_tuned 1 100 0x1d00 75 47.5 52.4 93.5 163.8 188.2 243.3 49941.3 50000 20 1881.79 1553788 147329752 1997523 77901838 76716125522 98799971038 116479665568 177684331 0 0 0 0 428835 1868768 -ebbrt_tuned 2 100 0x1d00 75 48.8 54.3 96.8 165.7 190.8 247.9 50074.7 50000 20 1743.76 1443695 136889164 1856986 72422368 71276968602 91972631336 108374788894 164321701 0 0 0 0 398073 1733558 -ebbrt_tuned 1 100 0x1d00 55 47.5 52.6 94.2 163.8 188.8 246.5 49950.8 50000 20 1884.85 1553932 147326150 1997842 77913974 76772734066 99385422313 117058796728 177830947 0 0 0 0 429032 1865821 -ebbrt_tuned 2 100 0x1d00 55 48.7 54.1 95.8 164.4 188.5 245.3 49936.8 50000 20 1883.14 1556040 147426695 1997293 77892860 76800009312 99135945005 116745857438 177257058 0 0 0 0 428907 1868860 -ebbrt_tuned 1 100 0x1d00 135 49.9 56.8 106.5 174.4 203.9 261.3 99914.1 100000 20 2055.07 2259900 243770042 3995850 155829040 150805842434 175703355929 191775888082 333318202 0 0 0 0 406906 2461645 -ebbrt_tuned 2 100 0x1d00 135 49.9 57.0 106.5 174.3 204.6 261.2 100041.6 100000 20 2056.77 2258328 243808846 4001026 156031360 151180473522 175912771947 191816947500 333894876 0 0 0 0 404054 2460177 -ebbrt_tuned 1 100 0x1d00 95 50.6 57.5 107.2 174.6 204.0 261.3 99980.6 100000 20 2058.01 2257480 243728590 3998593 155936142 151226039765 175558834320 191591319699 333136752 0 0 0 0 406963 2462562 -ebbrt_tuned 2 100 0x1d00 95 50.1 56.9 106.7 175.4 205.1 263.8 99997.6 100000 20 2061.29 2256554 243683659 3999260 155961840 150858985888 175280010422 191234267842 332460019 0 0 0 0 403337 2460333 -ebbrt_tuned 1 100 0x1d00 75 51.0 58.0 108.7 178.0 208.1 266.2 99983.8 100000 20 2060.96 2255281 243579648 3998657 155938212 150851549693 176145272097 192280058708 336538642 0 0 0 0 406715 2461742 -ebbrt_tuned 2 100 0x1d00 75 49.8 56.9 106.5 175.7 205.3 259.5 99866.9 100000 20 2060.51 2255429 243451841 3993913 155753522 150853003742 175641784591 191544352459 331941197 0 0 0 0 405896 2461123 -ebbrt_tuned 1 100 0x1d00 55 50.9 58.0 108.6 179.3 208.3 267.8 100059.1 100000 20 2027.7 2259085 243920608 4001707 156057138 151304363042 176237779718 194348384470 336239792 0 0 0 0 406544 2464955 -ebbrt_tuned 2 100 0x1d00 55 50.8 57.9 107.8 177.3 207.6 265.9 100000.2 100000 20 2024.98 2259572 243866374 3999332 155965584 151033957394 174732781577 192690144344 330526819 0 0 0 0 405531 2459243 ebbrt_tuned 1 100 0x1d00 135 55.5 63.6 116.0 197.5 229.6 297.0 199735.8 200000 20 2347.39 4236144 470447766 7985859 311396670 300204747801 324001097775 330447620863 661436890 0 0 0 0 427966 2827925 ebbrt_tuned 2 100 0x1d00 135 54.8 62.6 114.1 187.7 217.6 273.6 200021.2 200000 20 2309.33 4239216 470910974 7997271 311841844 295466621348 305230403638 313631304008 597970143 0 0 0 0 439049 2841015 ebbrt_tuned 1 100 0x1d00 95 55.0 62.8 114.7 192.5 223.8 286.5 200000.9 200000 20 2326.2 4236318 470761804 7996099 311792950 296973007791 310725745379 318578725439 622725772 0 0 0 0 433244 2835202 @@ -652,22 +555,6 @@ ebbrt_tuned 1 100 0x1d00 75 55.0 63.1 115.4 195.6 227.0 293.6 200032.8 200000 20 ebbrt_tuned 2 100 0x1d00 75 55.0 63.0 115.6 195.5 226.7 291.3 200053.0 200000 20 2332.41 4239823 470981695 7998549 311890686 298294407907 316555225448 323721506878 631633057 0 0 0 0 428105 2832595 ebbrt_tuned 1 100 0x1d00 55 57.3 66.5 122.2 229.1 292.5 3105.7 199981.4 200000 20 2079.78 4269356 472696095 7995316 311762836 298197189569 311929278053 359704002439 644201213 0 0 0 0 403081 2798265 ebbrt_tuned 2 100 0x1d00 55 56.6 65.3 121.2 225.9 283.1 3227.3 200156.2 200000 20 2084.33 4267474 472799259 8002643 312050946 299053368233 311730225471 357789501667 645174433 0 0 0 0 400102 2797483 -ebbrt_tuned 1 200 0x1d00 135 55.3 64.2 155.5 249.5 273.3 334.7 49948.9 50000 20 1870.52 1448927 141032135 1997824 77913584 75724510940 94049093619 110854776790 168396300 0 0 0 0 477607 1282780 -ebbrt_tuned 2 200 0x1d00 135 54.9 63.5 154.6 248.1 271.6 330.8 50003.8 50000 20 1871.68 1451550 141232185 2000015 77998848 75923352450 94040995310 110791914012 168903614 0 0 0 0 476904 1281706 -ebbrt_tuned 1 200 0x1d00 95 54.9 63.9 154.4 249.2 273.2 334.0 50073.2 50000 20 1875.5 1451112 141291297 2002797 78107238 76063222376 94492381112 111295898545 169146881 0 0 0 0 478465 1283809 -ebbrt_tuned 2 200 0x1d00 95 55.3 64.2 155.5 250.1 274.3 337.2 49977.1 50000 20 1873.93 1449350 141075140 1998873 77954490 75942811564 94399927152 111217398793 170390709 0 0 0 0 478309 1283450 -ebbrt_tuned 1 200 0x1d00 75 55.2 64.1 154.5 249.3 273.8 334.6 49983.0 50000 20 1876.55 1449226 141083044 1999182 77966448 75911962276 94333001542 111073210010 168853773 0 0 0 0 477896 1281849 -ebbrt_tuned 2 200 0x1d00 75 55.0 63.7 154.1 248.7 272.5 332.8 49922.5 50000 20 1871.08 1448632 140964410 1996727 77870820 76103483930 94116453583 110766049782 168603405 0 0 0 0 477826 1282641 -ebbrt_tuned 1 200 0x1d00 55 54.5 63.3 154.3 249.3 273.4 335.5 49953.4 50000 20 1872.98 1447191 140928302 1998008 77920888 75983624674 94697577073 111454711071 170921644 0 0 0 0 479800 1283286 -ebbrt_tuned 2 200 0x1d00 55 55.2 64.2 155.7 249.1 272.9 334.1 49977.8 50000 20 1873.73 1446271 140881174 1998878 77954458 76311202577 95493786185 112384208776 172330131 0 0 0 0 479494 1284097 -ebbrt_tuned 1 200 0x1d00 135 61.8 75.7 166.2 255.2 283.7 351.6 99990.2 100000 20 2039.71 2216230 241233107 3998766 155942276 149733385639 168463390530 184418704024 322170783 0 0 0 0 520081 1430081 -ebbrt_tuned 2 200 0x1d00 135 62.4 75.9 166.6 257.2 284.9 346.4 99924.4 100000 20 2040.48 2219460 241373503 3996295 155846274 149787789626 169143226842 185111610449 320706253 0 0 0 0 527790 1430838 -ebbrt_tuned 1 200 0x1d00 95 62.0 75.4 165.9 255.6 284.0 348.1 100024.7 100000 20 2041.57 2217962 241367307 4000194 156000002 150092630282 169225215751 185046461050 322325602 0 0 0 0 521684 1430213 -ebbrt_tuned 2 200 0x1d00 95 62.4 76.2 166.8 257.1 284.7 349.2 100027.4 100000 20 2045.49 2217489 241369097 4000461 156009408 150099029784 170255226409 186082745049 324732680 0 0 0 0 521494 1430685 -ebbrt_tuned 1 200 0x1d00 75 61.0 74.6 165.5 255.7 283.9 349.3 99957.2 100000 20 2044.96 2216394 241230345 3997526 155894836 149754619161 170026329778 186065096896 325761249 0 0 0 0 524541 1430715 -ebbrt_tuned 2 200 0x1d00 75 62.5 76.2 166.1 256.5 283.8 349.4 100076.5 100000 20 2045.11 2218537 241479883 4002402 156084514 150111358276 169347549203 185258321739 323488689 0 0 0 0 523025 1430991 -ebbrt_tuned 1 200 0x1d00 55 62.0 75.7 166.0 254.9 282.8 346.1 100003.6 100000 20 2014.56 2215089 241216197 3999316 155961594 149748678823 169941710158 187817469137 325661985 0 0 0 0 525990 1431363 -ebbrt_tuned 2 200 0x1d00 55 61.8 75.5 165.8 255.9 285.2 350.4 100126.7 100000 20 2015.3 2220488 241639184 4004317 156158622 150226683966 170124655142 188056647275 326118349 0 0 0 0 526762 1431382 ebbrt_tuned 1 200 0x1d00 135 64.9 79.6 172.1 269.0 303.8 375.5 200093.7 200000 20 2335.77 4306870 475093694 7999832 311939326 299497769695 317693828617 325660812365 652051510 0 0 0 0 625490 1463555 ebbrt_tuned 2 200 0x1d00 135 65.7 79.9 173.1 271.5 306.7 378.3 199859.6 200000 20 2332.22 4296056 474208812 7988169 311449178 299051781412 315700231472 323580550261 646510445 0 0 0 0 628219 1463553 ebbrt_tuned 1 200 0x1d00 95 64.8 79.2 170.1 267.1 300.6 368.2 200131.2 200000 20 2332.27 4304477 474968313 8001012 311979554 299290598466 316773679017 324880580299 654857565 0 0 0 0 623830 1463492 @@ -676,22 +563,6 @@ ebbrt_tuned 1 200 0x1d00 75 66.4 80.7 173.6 270.9 304.1 375.1 200157.4 200000 20 ebbrt_tuned 2 200 0x1d00 75 65.9 79.9 171.1 268.8 302.2 371.6 199975.7 200000 20 2336.9 4301409 474623873 7995108 311754472 299386314791 317950314612 325957173486 657790951 0 0 0 0 623308 1463589 ebbrt_tuned 1 200 0x1d00 55 68.8 84.2 181.4 307.7 381.0 3306.4 199977.7 200000 20 2069.14 4332567 476500397 7994887 311746710 299186627025 311345074413 363124438353 658548762 0 0 0 0 570410 1456751 ebbrt_tuned 2 200 0x1d00 55 68.8 84.3 181.4 310.6 392.2 3906.1 199971.8 200000 20 2066.6 4339727 476958848 7995016 311749172 299669415276 314034143224 366768406354 667960910 0 0 0 0 565646 1455758 -ebbrt_tuned 1 300 0x1d00 135 64.6 81.5 213.9 343.2 365.2 429.9 50057.0 50000 20 1862.83 1379241 136963477 2001959 78073164 77279797224 94991523325 111641140471 174022481 0 0 0 0 488462 932778 -ebbrt_tuned 2 300 0x1d00 135 64.1 81.7 215.3 343.0 364.7 430.3 50043.9 50000 20 1863.83 1378098 136871258 2001517 78058028 76990380394 94558986240 111229038697 173532119 0 0 0 0 489878 933287 -ebbrt_tuned 1 300 0x1d00 95 64.3 81.2 215.2 341.9 364.8 429.5 49957.0 50000 20 1866.01 1377140 136714115 1998150 77926140 76935257672 94529705619 111260384188 173106122 0 0 0 0 490775 932796 -ebbrt_tuned 2 300 0x1d00 95 64.2 82.0 214.2 343.1 365.5 429.7 50010.5 50000 20 1867.65 1378060 136859917 2000266 78008770 77161327644 94676441644 111367117789 174339227 0 0 0 0 487770 932704 -ebbrt_tuned 1 300 0x1d00 75 64.7 82.0 215.1 343.6 365.7 431.1 50052.2 50000 20 1867.73 1380689 137025685 2001922 78073130 77354620218 94619590258 111253536012 173212362 0 0 0 0 490175 932790 -ebbrt_tuned 2 300 0x1d00 75 64.4 81.9 213.3 344.3 366.1 431.5 50007.1 50000 20 1733.46 1277708 126861193 1853815 72298024 71400817417 87796397274 103344830940 161872262 0 0 0 0 455006 865326 -ebbrt_tuned 1 300 0x1d00 55 63.9 82.0 214.3 342.4 364.9 428.1 50076.8 50000 20 1869.04 1377805 136898188 2002894 78110978 77437646107 94858321584 111491482839 174350989 0 0 0 0 492685 933263 -ebbrt_tuned 2 300 0x1d00 55 64.8 81.8 214.9 343.0 365.4 432.2 50037.1 50000 20 1868.12 1379220 136931101 2001299 78049232 77025446945 94298215572 110833114515 172258043 0 0 0 0 487540 932579 -ebbrt_tuned 1 300 0x1d00 135 72.6 91.7 220.1 345.8 368.4 440.1 99957.2 100000 20 2039.26 2203967 240475777 3997436 155888958 153118700197 172274749196 188463190445 337134087 0 0 0 0 568424 972482 -ebbrt_tuned 2 300 0x1d00 135 72.4 91.9 221.1 346.6 370.1 440.0 99872.0 100000 20 2037.68 2202986 240335726 3994037 155757942 153046398553 172033966363 187984733278 333484762 0 0 0 0 561142 972672 -ebbrt_tuned 1 300 0x1d00 95 73.1 91.8 221.6 348.3 371.9 440.3 99924.2 100000 20 2035.72 2202361 240323172 3996179 155840858 152866610917 171771500387 187968431363 334912016 0 0 0 0 569722 972730 -ebbrt_tuned 2 300 0x1d00 95 71.1 90.3 221.1 346.1 368.6 438.7 100117.1 100000 20 2040.36 2206674 240829569 4003939 156144882 153423790389 173365547194 189615227799 340835372 0 0 0 0 568852 972690 -ebbrt_tuned 1 300 0x1d00 75 72.9 92.1 221.3 345.7 369.0 438.4 99988.6 100000 20 2041.25 2204697 240523490 3998915 155949180 153000663025 172672538312 188891591176 336495692 0 0 0 0 568455 972681 -ebbrt_tuned 2 300 0x1d00 75 70.1 89.5 217.6 341.6 364.7 429.2 100036.1 100000 20 2004.53 2206698 240703475 4000651 156016824 149725916696 160183143687 177821295432 290495303 0 0 0 0 578289 972759 -ebbrt_tuned 1 300 0x1d00 55 71.1 89.6 218.1 342.2 366.1 431.7 99957.3 100000 20 1997.66 2204104 240462638 3997583 155896790 150215332988 162237560844 180771255936 302339094 0 0 0 0 565763 972569 -ebbrt_tuned 2 300 0x1d00 55 71.9 90.6 218.9 343.9 366.5 432.3 99980.8 100000 20 1995.99 2202711 240395637 3998200 155918928 150351324286 163686126845 182295048002 308197281 0 0 0 0 571605 972730 ebbrt_tuned 1 300 0x1d00 135 74.9 95.7 225.2 351.7 382.8 450.3 199973.4 200000 20 2311.34 4363158 478327898 7994877 311742560 300958825440 311107347312 321334037421 630038408 0 0 0 0 717896 976520 ebbrt_tuned 2 300 0x1d00 135 73.6 94.2 223.9 351.4 383.0 457.8 199951.7 200000 20 2309.87 4365079 478416160 7994114 311715286 300969728714 311460579105 322096463574 630264123 0 0 0 0 688656 976511 ebbrt_tuned 1 300 0x1d00 95 73.1 92.7 220.9 348.9 381.4 452.4 199826.1 200000 20 2322.08 4359776 477955464 7989291 311528560 301240713930 314471300015 323871850360 637848858 0 0 0 0 729846 976515 @@ -700,22 +571,6 @@ ebbrt_tuned 1 300 0x1d00 75 75.4 96.2 225.1 351.9 384.1 459.9 199832.0 200000 20 ebbrt_tuned 2 300 0x1d00 75 76.1 97.2 227.9 355.9 390.5 466.2 199929.9 200000 20 2321.39 4362931 478262425 7993319 311685964 302151289948 315863237294 325858003926 647394225 0 0 0 0 706957 976509 ebbrt_tuned 1 300 0x1d00 55 78.9 102.1 238.0 390.2 470.0 3099.0 200085.1 200000 20 2049.35 4395549 480410858 7999847 311943372 302627004782 311057279489 369741370702 654231002 0 0 0 0 646751 974660 ebbrt_tuned 2 300 0x1d00 55 79.9 101.8 237.6 385.4 457.3 3193.0 199765.9 200000 20 2060.98 4386858 479520822 7986316 311405960 302903688122 313122606351 368437485607 665879215 0 0 0 0 659076 974460 -ebbrt_tuned 1 400 0x1d00 135 74.7 99.5 271.3 434.3 464.9 524.1 49894.2 50000 20 1862.29 1334357 134091941 1995343 77812242 76829057046 92715750046 108980147596 169150632 0 0 0 0 550979 720226 -ebbrt_tuned 2 400 0x1d00 135 74.9 99.1 271.5 433.3 463.5 525.1 50012.6 50000 20 1860.54 1333500 134149038 2000304 78010446 76937411046 92622067136 108942861687 170000393 0 0 0 0 551986 720001 -ebbrt_tuned 1 400 0x1d00 95 75.0 99.3 271.2 434.1 464.8 525.3 50004.0 50000 20 1860.03 1335036 134252489 1999950 77995650 77057442004 92528315510 108829667234 169893282 0 0 0 0 553983 720172 -ebbrt_tuned 2 400 0x1d00 95 74.7 99.0 269.2 433.1 462.8 520.7 50003.3 50000 20 1856.47 1334268 134179066 1999911 77994750 76967319025 92563115727 108990355364 169763041 0 0 0 0 554769 720255 -ebbrt_tuned 1 400 0x1d00 75 76.3 101.0 271.8 435.3 465.9 524.6 50042.6 50000 20 1859.43 1335975 134353978 2001510 78057284 77117861529 92868940204 109153403891 170160750 0 0 0 0 550041 719971 -ebbrt_tuned 2 400 0x1d00 75 76.9 102.1 274.4 435.2 465.9 525.6 49976.7 50000 20 1855.55 1335605 134245711 1998940 77957168 77033554569 93014984584 109480180821 171408063 0 0 0 0 555955 720212 -ebbrt_tuned 1 400 0x1d00 55 74.3 98.8 270.9 434.5 464.7 522.3 50010.1 50000 20 1856.66 1335240 134275408 2000156 78004640 76891872658 92174613030 108514316565 169061109 0 0 0 0 552443 720275 -ebbrt_tuned 2 400 0x1d00 55 76.7 101.0 271.9 434.8 465.4 526.7 50055.5 50000 20 1856.85 1333238 134190267 2002042 78077944 77020954272 92388843746 108735515718 168825512 0 0 0 0 557938 720319 -ebbrt_tuned 1 400 0x1d00 135 80.7 106.5 275.7 434.9 467.5 531.3 99975.7 100000 20 2025.37 2203723 240473982 3998271 155922204 152466047319 168541204397 185000238733 327362089 0 0 0 0 673331 731857 -ebbrt_tuned 2 400 0x1d00 135 80.1 106.1 273.6 435.2 468.2 533.7 100111.2 100000 20 2026.87 2204998 240695326 4003646 156132430 152479977669 169477171391 185980056310 327866066 0 0 0 0 674362 731836 -ebbrt_tuned 1 400 0x1d00 95 80.6 105.1 275.6 434.1 466.5 530.3 100000.0 100000 20 1879.36 2041993 222876337 3706870 144560494 141456540539 156552900688 171641179991 301100568 0 0 0 0 625037 678415 -ebbrt_tuned 2 400 0x1d00 95 80.5 106.4 275.5 435.6 468.3 531.6 99933.6 100000 20 2027.0 2201493 240284595 3996570 155856452 152670943117 169070548325 185324869605 328407209 0 0 0 0 674349 731865 -ebbrt_tuned 1 400 0x1d00 75 80.4 105.6 276.0 436.2 469.1 533.2 99868.4 100000 20 2030.95 2200893 240204886 3993930 155754244 152468248451 169044844983 185165027144 328452829 0 0 0 0 665613 731856 -ebbrt_tuned 2 400 0x1d00 75 80.3 105.9 274.6 434.2 466.3 530.8 100049.7 100000 20 1884.72 2047779 223467556 3714877 144871594 141896112152 156700284658 171749934573 303883177 0 0 0 0 613975 679749 -ebbrt_tuned 1 400 0x1d00 55 81.0 106.9 274.4 435.8 469.0 532.8 100061.9 100000 20 1999.64 2205077 240642019 4001608 156053546 152732840325 168764062232 186899789338 326371536 0 0 0 0 662091 731874 -ebbrt_tuned 2 400 0x1d00 55 80.2 105.4 274.0 434.1 466.5 533.0 99874.7 100000 20 1999.4 2201766 240222537 3994309 155768984 152402696571 168330296264 186486999655 328007583 0 0 0 0 667042 731886 ebbrt_tuned 1 400 0x1d00 135 84.3 112.0 281.6 441.3 480.8 566.6 199940.4 200000 20 2310.25 4426900 482136525 7993753 311701386 304471807602 318091875838 327216356415 659925718 0 0 0 0 810855 732418 ebbrt_tuned 2 400 0x1d00 135 82.6 109.6 278.4 438.0 475.5 551.1 199974.2 200000 20 2154.47 4111418 447802399 7424494 289504268 282840602514 296993352938 306417352661 618429386 0 0 0 0 798605 680151 ebbrt_tuned 1 400 0x1d00 95 87.1 113.8 285.3 448.6 487.0 576.5 200203.9 200000 20 2302.62 4440312 483227960 8004280 312113974 305191653505 319709433803 330366383830 660487800 0 0 0 0 715654 732412 @@ -724,150 +579,1806 @@ ebbrt_tuned 1 400 0x1d00 75 81.8 108.0 276.3 438.2 476.6 551.3 199820.2 200000 2 ebbrt_tuned 2 400 0x1d00 75 86.4 113.5 286.7 445.4 483.6 569.7 200074.2 200000 20 2306.86 4433466 482632448 7998762 311896836 304783918544 320048890568 333667523836 663319393 0 0 0 0 797336 732411 ebbrt_tuned 1 400 0x1d00 55 88.4 117.6 295.7 479.2 569.7 4074.4 199997.0 200000 20 2048.92 4456624 483924655 7996223 311800092 304359632989 315060157340 374127034090 675118597 0 0 0 0 790023 731003 ebbrt_tuned 2 400 0x1d00 55 90.9 119.0 300.0 488.4 590.2 3860.3 199927.3 200000 20 2039.31 4460424 484112500 7993336 311686518 304777142966 316074658303 380554790159 674061806 0 0 0 0 756051 731223 -ebbrt_tuned 1 50 0x1a00 135 45.3 48.1 70.0 128.3 157.8 200.8 49966.9 50000 20 1781.32 1619305 151259232 1998547 77941834 75262251639 94102706136 114976530202 159993981 0 0 0 0 425582 2281031 -ebbrt_tuned 1 50 0x1a00 95 45.8 48.5 70.5 126.6 157.0 202.8 50010.9 50000 20 1789.56 1618482 151242321 2000304 78010102 75733029372 95032586851 115996054898 162162902 0 0 0 0 428761 2293133 -ebbrt_tuned 1 50 0x1a00 75 83.4 112.8 325.4 527.9 562.5 620.8 50014.5 50000 20 1635.62 1208649 122736013 1855381 72359982 69357087431 79289705176 96777437928 142522159 0 0 0 0 670848 535876 -ebbrt_tuned 1 50 0x1a00 55 46.1 48.8 71.2 128.2 160.9 205.2 50078.9 50000 20 1793.91 1615336 151153401 2002943 78113104 75869980862 96063422456 116955150322 169055625 0 0 0 0 425184 2281807 -ebbrt_tuned 1 50 0x1a00 135 46.5 49.7 76.1 141.8 170.5 220.1 99971.1 100000 20 1950.55 2306069 246645583 3998164 155918842 149047270801 170095804575 193168471069 313285284 0 0 0 0 382166 3385658 -ebbrt_tuned 1 50 0x1a00 95 89.9 120.9 333.7 530.7 567.3 631.7 100008.9 100000 20 1918.02 2208301 240786836 3999465 155970048 149002589202 159677940828 182798652983 306053095 0 0 0 0 894413 581195 -ebbrt_tuned 1 50 0x1a00 75 47.0 50.5 77.1 146.8 175.3 224.9 100074.1 100000 20 1953.51 2303226 246537754 4002302 156080668 149773722161 172054692409 194953029917 321140947 0 0 0 0 380820 3385020 -ebbrt_tuned 1 50 0x1a00 55 46.6 50.0 76.9 146.8 176.6 227.3 99871.4 100000 20 1922.9 2304107 246394808 3994247 155766618 149636218769 171846892507 196525218546 319735180 0 0 0 0 380303 3378284 ebbrt_tuned 1 50 0x1a00 135 48.6 54.2 85.9 170.7 200.5 269.9 199828.5 200000 20 2221.86 4209383 468980691 7989450 311533620 297479972003 318177178347 340474341109 638822098 0 0 0 0 341484 4702530 ebbrt_tuned 1 50 0x1a00 95 48.5 54.0 85.9 171.3 200.6 271.7 199837.2 200000 20 2219.77 4208401 468895478 7989781 311548730 298162232069 317862355318 340123044911 639246830 0 0 0 0 340818 4700605 ebbrt_tuned 1 50 0x1a00 75 48.0 53.5 85.0 169.8 199.1 271.9 199859.3 200000 20 2219.27 4210949 469046417 7991011 311599824 298011192768 318823412135 341020080741 639655034 0 0 0 0 338748 4694843 ebbrt_tuned 1 50 0x1a00 55 50.9 56.4 91.0 193.3 237.9 2831.8 200036.4 200000 20 2057.27 4238016 470898218 7997912 311866338 298777458308 317983873969 363615604567 654462440 0 0 0 0 324114 4652440 -ebbrt_tuned 1 100 0x1a00 135 48.3 53.5 95.2 165.9 193.1 250.3 49948.3 50000 20 1795.05 1554697 147366814 1997705 77907594 76234963250 96815877652 117849548515 173530023 0 0 0 0 428217 1866446 -ebbrt_tuned 1 100 0x1a00 95 124.6 182.8 610.5 1013.7 1058.5 1143.2 49984.9 50000 20 1749.12 1230348 127920264 1999162 77965678 75473447474 85401294039 102452312237 160686623 0 0 0 0 1064992 289455 -ebbrt_tuned 1 100 0x1a00 75 48.2 53.4 94.9 165.4 192.0 247.3 50029.4 50000 20 1795.4 1555804 147516130 2001012 78038024 76411975296 96832690747 117884828523 172832769 0 0 0 0 427972 1866318 -ebbrt_tuned 1 100 0x1a00 55 47.3 52.3 93.8 164.7 191.6 248.5 50059.9 50000 20 1794.75 1557220 147645680 2002250 78085830 76440315700 96488926479 117423287230 171755290 0 0 0 0 425999 1863100 -ebbrt_tuned 1 100 0x1a00 135 51.1 57.8 107.6 177.6 208.0 266.6 100057.3 100000 20 1951.63 2256996 243755606 4001114 156027692 150571168081 172289491572 195230720780 327341271 0 0 0 0 406285 2463194 -ebbrt_tuned 1 100 0x1a00 95 52.6 59.5 109.4 180.7 212.0 270.0 99831.5 100000 20 1952.59 2256772 243517736 3992605 155702130 150305228339 171557856629 194439488985 324555081 0 0 0 0 406581 2461343 -ebbrt_tuned 1 100 0x1a00 75 50.7 57.7 108.2 178.1 209.3 267.9 99976.2 100000 20 1954.58 2252642 243404418 3998412 155929836 150773640560 172526502343 195474842577 329277471 0 0 0 0 404997 2460724 -ebbrt_tuned 1 100 0x1a00 55 50.9 58.1 108.9 180.8 211.3 270.8 99946.6 100000 20 1920.67 2256377 243609216 3997123 155877200 150681631352 171427008621 196183715938 326565733 0 0 0 0 404280 2461259 ebbrt_tuned 1 100 0x1a00 135 57.4 66.3 119.5 204.9 240.0 307.8 200009.6 200000 20 2129.95 3959095 439381957 7454816 290688770 280183671944 297389153454 318887154272 607913569 0 0 0 0 393315 2633359 ebbrt_tuned 1 100 0x1a00 95 56.5 65.3 118.9 205.9 240.3 312.3 199927.7 200000 20 2219.53 4244059 471076101 7993405 311688774 300313579682 319504848690 342521426923 658986991 0 0 0 0 422457 2823933 ebbrt_tuned 1 100 0x1a00 75 56.4 65.1 118.5 205.6 239.8 308.3 200021.8 200000 20 2217.98 4245534 471327511 7997143 311834698 300181302540 318106457992 341036867391 654704763 0 0 0 0 419859 2823063 ebbrt_tuned 1 100 0x1a00 55 57.0 66.0 122.0 231.1 290.1 3750.0 200233.7 200000 20 2050.01 4272738 473169548 8005425 312158400 301030048082 315386881611 362851304724 659580358 0 0 0 0 399352 2795654 -ebbrt_tuned 1 200 0x1a00 135 55.4 64.6 156.3 251.0 275.2 340.3 49915.2 50000 20 1784.84 1445855 140813630 1996474 77861056 76215566822 94483479752 114512483396 171215386 0 0 0 0 482541 1283558 -ebbrt_tuned 1 200 0x1a00 95 55.7 65.0 155.4 250.6 275.1 340.1 49939.5 50000 20 1783.74 1446058 140831026 1997374 77894894 76446964424 94390768381 114433739406 172293540 0 0 0 0 480279 1283646 -ebbrt_tuned 1 200 0x1a00 75 55.8 65.2 156.1 250.6 275.1 341.2 49899.2 50000 20 1784.75 1447010 140842655 1995793 77834312 76238880632 94392631572 114415477497 171901540 0 0 0 0 479598 1282071 -ebbrt_tuned 1 200 0x1a00 55 55.8 64.9 156.3 251.1 275.8 344.3 49886.4 50000 20 1784.85 1447447 140870295 1995310 77815426 76246869870 94424574690 114422467599 171916496 0 0 0 0 478854 1282143 -ebbrt_tuned 1 200 0x1a00 135 62.9 76.7 167.9 259.3 289.0 355.7 99914.1 100000 20 1939.72 2215111 241091803 3995732 155824890 149919159750 168520980494 191212222912 325917475 0 0 0 0 524774 1430949 -ebbrt_tuned 1 200 0x1a00 95 63.6 76.8 167.5 259.5 289.1 356.8 100067.9 100000 20 1942.97 2219249 241514490 4002067 156071388 150444448886 168355312041 190968038591 325952218 0 0 0 0 523834 1430836 -ebbrt_tuned 1 200 0x1a00 75 62.6 76.3 167.7 259.9 290.5 359.5 99929.4 100000 20 1943.74 2213504 241024006 3996533 155856036 150044318341 168389439004 191050997267 325775471 0 0 0 0 526357 1431223 -ebbrt_tuned 1 200 0x1a00 55 64.0 77.3 169.5 261.3 290.7 361.8 100002.4 100000 20 1914.19 2208151 240819653 3994582 155708772 150087000430 168032459986 192495516093 325535589 0 0 0 0 520614 1429321 ebbrt_tuned 1 200 0x1a00 135 67.2 81.8 174.4 273.7 312.7 387.3 200096.5 200000 20 2213.96 4309279 475245868 7999941 311942246 299747481098 316901398145 340673826280 658176550 0 0 0 0 612394 1463507 ebbrt_tuned 1 200 0x1a00 95 66.6 80.3 173.3 273.5 312.1 389.7 200011.4 200000 20 2053.51 4058036 447598402 7537484 293914200 282433642698 296861682508 318988948419 618109209 0 0 0 0 577497 1379721 ebbrt_tuned 1 200 0x1a00 75 66.0 80.2 173.3 273.7 312.0 388.4 200027.3 200000 20 2213.49 4311089 475289069 7997572 311853588 299245293891 316677479591 340397808427 657519365 0 0 0 0 611841 1463550 ebbrt_tuned 1 200 0x1a00 55 68.7 83.9 180.8 299.0 358.1 3275.3 200074.5 200000 20 2045.89 4333166 476656106 7998901 311902766 299799236437 312240194240 360876561759 662566731 0 0 0 0 575992 1458115 -ebbrt_tuned 1 300 0x1a00 135 64.5 81.5 214.3 342.6 365.5 432.0 50038.0 50000 20 1781.24 1381057 137023053 2001325 78049540 77259619439 93910295952 113811047375 172784331 0 0 0 0 493838 932694 -ebbrt_tuned 1 300 0x1a00 95 65.3 83.2 216.1 344.1 366.1 433.6 50009.4 50000 20 1780.66 1376844 136759694 2000228 78007062 76963918577 93580222697 113550605103 172815787 0 0 0 0 494923 932850 -ebbrt_tuned 1 300 0x1a00 75 64.4 81.9 214.6 343.9 366.1 431.0 50106.2 50000 20 1778.73 1378638 136955050 2003981 78154056 77172162057 93875676041 113846323062 173763799 0 0 0 0 495034 932837 -ebbrt_tuned 1 300 0x1a00 55 65.2 82.6 214.2 345.2 366.9 435.4 50084.9 50000 20 1781.24 1377853 136915104 2002740 78098450 77252440008 94253734539 114318234784 173497820 0 0 0 0 496882 932837 -ebbrt_tuned 1 300 0x1a00 135 73.2 92.6 222.7 349.1 375.4 445.1 100091.4 100000 20 1792.84 2045935 223219988 3710597 144702994 141737139310 158063347113 179642512977 310673580 0 0 0 0 526974 901785 -ebbrt_tuned 1 300 0x1a00 95 73.9 93.2 223.9 348.7 374.0 444.7 100010.6 100000 20 1934.12 2204473 240590952 3999756 155981072 152745631826 170927518229 194373777421 335774563 0 0 0 0 574909 972822 -ebbrt_tuned 1 300 0x1a00 75 73.1 92.1 221.5 347.9 374.4 444.1 100001.4 100000 20 1935.71 2205889 240624835 3999131 155953210 153155421293 170423354597 193708244212 336573275 0 0 0 0 568974 972730 -ebbrt_tuned 1 300 0x1a00 55 71.3 89.7 218.8 343.3 365.9 434.0 99910.8 100000 20 1893.43 2202870 240342227 3995727 155824286 149502440011 158069570781 182576891404 290733705 0 0 0 0 574216 972544 ebbrt_tuned 1 300 0x1a00 135 76.0 96.8 224.7 352.6 386.8 463.2 200137.9 200000 20 2181.3 4366174 478676708 8001594 312005572 299686422241 302057604956 327239073826 612020408 0 0 0 0 715889 976522 ebbrt_tuned 1 300 0x1a00 95 74.9 95.5 225.9 355.5 391.9 471.1 199841.2 200000 20 2182.93 4366251 478418556 7989591 311539266 300763955307 306500680261 332876177027 627152067 0 0 0 0 682708 976509 ebbrt_tuned 1 300 0x1a00 75 74.3 94.9 224.3 354.4 389.6 468.5 199904.2 200000 20 2189.74 4365384 478376501 7991418 311603754 300868293825 308629344833 334249519384 631028219 0 0 0 0 705224 976502 ebbrt_tuned 1 300 0x1a00 55 77.5 99.1 231.6 366.9 420.7 1967.2 200027.7 200000 20 2044.41 4382197 479511922 7996984 311822496 301335376017 306792265647 352087993671 637877320 0 0 0 0 679128 975176 -ebbrt_tuned 1 400 0x1a00 135 74.6 99.9 273.2 435.6 467.2 528.1 50008.1 50000 20 1773.99 1331776 134055635 1999755 77982870 76389158511 90724912042 110446405315 165147939 0 0 0 0 560213 720125 -ebbrt_tuned 1 400 0x1a00 95 76.2 100.7 272.2 434.7 465.6 527.9 50081.3 50000 20 1772.38 1334684 134316813 2002924 78111328 76430129495 90563372255 110190950202 165055671 0 0 0 0 555933 719942 -ebbrt_tuned 1 400 0x1a00 75 75.2 100.2 272.0 434.3 464.3 525.8 50070.7 50000 20 1776.09 1331775 134128193 2002669 78102420 76519642671 90912868635 110593072786 166252761 0 0 0 0 560219 720227 -ebbrt_tuned 1 400 0x1a00 55 77.0 102.4 274.7 435.0 466.3 527.7 50016.6 50000 20 1776.64 1331878 134057477 2000392 78011508 76353630168 90863785560 110545758126 166341967 0 0 0 0 559592 720065 -ebbrt_tuned 1 400 0x1a00 135 81.7 107.7 276.0 435.2 468.1 532.7 100172.0 100000 20 1930.59 2207155 240918312 4006153 156231008 151632464693 165448397316 188611357883 319757016 0 0 0 0 673912 731872 -ebbrt_tuned 1 400 0x1a00 95 79.7 104.2 275.3 435.2 468.8 534.1 100142.0 100000 20 1926.81 2204993 240744926 4004956 156184150 151716418045 164822042167 188053628636 319241134 0 0 0 0 669276 731858 -ebbrt_tuned 1 400 0x1a00 75 80.8 105.4 275.3 435.7 469.0 534.4 99834.7 100000 20 1924.82 2199097 240041829 3991325 155635046 151549220875 165273609509 188821454241 321754589 0 0 0 0 675919 731834 -ebbrt_tuned 1 400 0x1a00 55 81.5 107.7 277.5 436.9 470.1 534.7 99987.2 100000 20 1902.22 2201829 240358939 3998318 155918728 151674895536 165343482087 190088404008 323595613 0 0 0 0 673403 731845 ebbrt_tuned 1 400 0x1a00 135 87.2 114.1 285.1 449.6 490.9 586.5 200023.3 200000 20 2186.12 4360713 478777053 7931671 308297934 303083567725 311317066050 337527039267 647717720 0 0 0 0 752538 732413 ebbrt_tuned 1 400 0x1a00 95 86.8 114.2 284.8 447.1 487.6 578.3 199996.1 200000 20 2194.64 4431382 482443038 7995539 311769504 303582655413 312628368043 338380820901 655291773 0 0 0 0 779429 732414 ebbrt_tuned 1 400 0x1a00 75 86.3 113.9 283.4 446.9 488.2 579.6 200079.0 200000 20 2190.37 4434943 482726563 7999145 311909288 304313733302 313582911570 339256136514 652182122 0 0 0 0 764873 732416 ebbrt_tuned 1 400 0x1a00 55 87.3 116.2 288.6 464.2 526.6 3209.3 199934.9 200000 20 1883.16 4121177 447802667 7402986 288662208 281454217625 289736257613 336478065216 616061644 0 0 0 0 690765 677537 -ebbrt_tuned 3 50 0x1b00 135 46.2 49.0 72.2 133.3 167.0 215.2 49976.9 50000 20 1830.37 1614203 150959271 1998910 77955994 76799992587 99768167467 119262046527 177275337 0 0 0 0 425583 2282442 -ebbrt_tuned 3 50 0x1b00 95 46.5 49.4 72.5 131.2 164.2 210.2 50089.5 50000 20 1833.61 1615078 151140369 2003415 78132000 77136253595 100264593111 119776038989 178704369 0 0 0 0 427227 2291455 -ebbrt_tuned 3 50 0x1b00 75 46.5 49.4 72.3 131.9 165.3 213.4 49988.9 50000 20 1832.76 1614733 150999028 1999441 77976722 76646276028 99514186140 119024412610 175277544 0 0 0 0 425323 2282032 -ebbrt_tuned 3 50 0x1b00 55 86.6 117.0 336.3 540.7 575.1 637.2 50009.5 50000 20 1799.96 1299653 132128871 2000235 78007590 76036042762 89698048645 107478367719 165600414 0 0 0 0 727102 568774 -ebbrt_tuned 3 50 0x1b00 135 46.3 49.7 76.9 147.4 176.9 226.0 100077.5 100000 20 1991.91 2302721 246518401 4002283 156080062 151284902522 176971038012 196990793217 332265192 0 0 0 0 379498 3377435 -ebbrt_tuned 3 50 0x1b00 95 46.2 49.5 75.6 139.6 166.2 210.8 99995.4 100000 20 1969.71 2306071 246668589 3997647 155878224 148323580753 166852820158 187864150205 300000203 0 0 0 0 380894 3376449 -ebbrt_tuned 3 50 0x1b00 75 45.8 49.1 75.3 140.4 168.4 215.5 100027.4 100000 20 1977.7 2304553 246574820 3999698 155967530 149040943621 169973000275 190755234967 312907465 0 0 0 0 381367 3379094 -ebbrt_tuned 3 50 0x1b00 55 45.8 49.2 75.6 143.4 171.0 220.3 100152.0 100000 20 1952.01 2307973 246928157 4005377 156200978 149394723930 171709449109 194117026298 318478589 0 0 0 0 382707 3391429 -ebbrt_tuned 3 50 0x1b00 135 48.6 54.2 85.3 167.1 194.2 258.1 199957.1 200000 20 2253.93 4211181 469215710 7994565 311735200 297325274704 315696377798 332031326581 624656200 0 0 0 0 345138 4710093 -ebbrt_tuned 3 50 0x1b00 95 48.7 54.4 85.6 168.8 197.2 267.6 200032.2 200000 20 2257.71 4212633 469337494 7997696 311859130 297588909087 318899444116 335341624874 636537584 0 0 0 0 343436 4709013 -ebbrt_tuned 3 50 0x1b00 75 48.5 53.9 85.1 167.9 196.3 266.2 200121.9 200000 20 2259.21 4215264 469573491 8000908 311978512 298093071213 320580970777 336941333430 637653555 0 0 0 0 341873 4709473 -ebbrt_tuned 3 50 0x1b00 55 49.1 55.1 89.2 193.8 245.0 3325.2 199887.6 200000 20 2082.77 4229957 470249641 7991715 311623624 297863879806 316821738589 359784864038 646754836 0 0 0 0 324231 4645233 -ebbrt_tuned 3 100 0x1b00 135 47.4 52.2 93.8 163.5 188.6 246.4 49988.2 50000 20 1828.78 1554504 147402406 1999364 77973330 76176098083 97151131265 117076862162 172685006 0 0 0 0 428952 1869595 -ebbrt_tuned 3 100 0x1b00 95 47.9 53.0 93.9 163.8 190.3 246.5 50054.6 50000 20 1826.11 1557724 147641941 2002027 78077674 76325071730 97029065380 116905784463 172509288 0 0 0 0 431078 1874565 -ebbrt_tuned 3 100 0x1b00 75 48.2 53.4 94.1 164.3 190.5 249.4 49951.8 50000 20 1826.63 1555994 147452297 1997880 77916214 76235528554 96785955026 116650399501 170471457 0 0 0 0 429232 1869760 -ebbrt_tuned 3 100 0x1b00 55 48.0 53.2 94.4 165.7 191.6 248.5 49970.2 50000 20 1824.21 1556351 147478866 1998647 77945708 76148627945 97183663230 117122051731 174427239 0 0 0 0 430782 1872012 -ebbrt_tuned 3 100 0x1b00 135 50.0 57.1 107.6 176.2 206.5 260.9 100002.9 100000 20 1979.94 2255138 243546674 3999376 155965480 150109204665 171461393580 192262425548 324281582 0 0 0 0 407023 2462053 -ebbrt_tuned 3 100 0x1b00 95 50.8 58.0 107.8 176.8 206.1 265.3 100064.8 100000 20 1985.86 2256123 243725607 4001746 156057494 150097394358 171957782292 192662552990 325841428 0 0 0 0 407286 2465058 -ebbrt_tuned 3 100 0x1b00 75 50.4 57.5 107.8 177.2 207.6 265.9 100108.8 100000 20 1981.28 2261284 244043679 4003594 156129968 150337459214 172717404412 193440181958 327852808 0 0 0 0 407783 2464939 -ebbrt_tuned 3 100 0x1b00 55 50.9 58.1 108.5 179.8 211.3 269.3 100033.3 100000 20 1953.38 2255346 243616167 4000743 156020412 150532103105 172672755875 195230283634 328827791 0 0 0 0 407555 2465669 -ebbrt_tuned 3 100 0x1b00 135 56.4 64.9 118.0 202.5 236.4 301.9 200048.5 200000 20 2259.45 4244840 471285188 7998347 311884964 300287495567 320178037220 337318668264 653530397 0 0 0 0 424989 2826323 -ebbrt_tuned 3 100 0x1b00 95 55.9 64.1 117.2 201.7 234.2 302.1 200180.3 200000 20 2263.67 4249108 471723242 8003327 312072852 300866091967 321738713691 338822813265 657887841 0 0 0 0 424459 2826122 -ebbrt_tuned 3 100 0x1b00 75 55.1 63.3 115.4 198.3 231.6 298.7 200118.0 200000 20 2260.4 4245680 471415581 7997236 311787050 300774353724 320994105416 338016890157 659239144 0 0 0 0 423352 2826507 -ebbrt_tuned 3 100 0x1b00 55 145.5 210.0 643.1 1035.5 1122.7 2830.3 200079.9 200000 20 2044.75 4758868 502159285 7997441 311822154 301636512668 306848690655 353858527800 652129858 0 0 0 0 1416511 290055 -ebbrt_tuned 3 200 0x1b00 135 55.4 64.6 155.7 250.1 274.8 338.4 50041.6 50000 20 1818.13 1447703 141031000 2001515 78057520 76452592949 94761524415 113583436989 172098188 0 0 0 0 478102 1282004 -ebbrt_tuned 3 200 0x1b00 95 55.4 64.5 156.1 250.6 275.4 338.7 50056.7 50000 20 1818.98 1445932 140952674 2002093 78080398 76329206691 94612718351 113502554916 171254501 0 0 0 0 479795 1282507 -ebbrt_tuned 3 200 0x1b00 75 55.4 64.6 154.2 249.5 273.5 338.8 49956.6 50000 20 1818.19 1448331 140994692 1998120 77924956 76369159537 94809551021 113678214151 171638877 0 0 0 0 479318 1280503 -ebbrt_tuned 3 200 0x1b00 55 55.2 64.2 154.8 249.8 274.0 338.5 49971.6 50000 20 1686.05 1342857 130712276 1851913 72222810 70774486761 87949134213 105433560609 159018677 0 0 0 0 445577 1188854 -ebbrt_tuned 3 200 0x1b00 135 61.8 75.8 167.4 259.1 288.4 353.1 100018.0 100000 20 1973.16 2217850 241373477 3999936 155987834 150196784135 168920499766 189041462364 324694545 0 0 0 0 527515 1430745 -ebbrt_tuned 3 200 0x1b00 95 62.3 76.0 167.4 257.8 286.5 351.6 100021.2 100000 20 1977.25 2214529 241156244 4000055 155993190 150158110448 169222137021 189557110813 328051927 0 0 0 0 529459 1430638 -ebbrt_tuned 3 200 0x1b00 75 62.5 76.2 167.7 258.4 288.3 354.0 100141.0 100000 20 1975.69 2217024 241440486 4005001 156186054 150322173482 168861557789 188972516459 325481547 0 0 0 0 521355 1430675 -ebbrt_tuned 3 200 0x1b00 55 62.6 76.1 168.6 259.3 289.0 354.4 99905.0 100000 20 1948.38 2211702 240867634 3995590 155819220 149909112439 169243974012 191382500211 329226628 0 0 0 0 527207 1431352 -ebbrt_tuned 3 200 0x1b00 135 66.2 81.3 173.4 270.5 306.4 379.5 199975.8 200000 20 2250.63 4305204 474897672 7995053 311753494 299465061407 315396279937 333341904673 655982584 0 0 0 0 624566 1463504 -ebbrt_tuned 3 200 0x1b00 95 66.9 81.4 174.5 272.9 310.0 382.5 199799.6 200000 20 2250.79 4301584 474515753 7988317 311492046 298951276829 316486733755 334542086783 657145400 0 0 0 0 615868 1463535 -ebbrt_tuned 3 200 0x1b00 75 66.5 80.9 175.4 275.0 311.6 384.6 200082.3 200000 20 2087.46 3993945 440440504 7416018 289176736 278117293932 295282815647 312026545769 614447368 0 0 0 0 575159 1357030 -ebbrt_tuned 3 200 0x1b00 55 67.5 82.5 177.9 293.7 350.6 3086.0 199832.5 200000 20 2077.81 4326265 475924856 7989426 311535218 298922828403 312078929888 355571249530 658381216 0 0 0 0 583982 1458996 -ebbrt_tuned 3 300 0x1b00 135 64.1 81.4 213.4 343.3 365.9 434.3 49993.1 50000 20 1813.88 1378325 136832787 1999556 77980854 77131978587 94178532611 113039078772 172227575 0 0 0 0 493152 932811 -ebbrt_tuned 3 300 0x1b00 95 66.6 84.1 217.4 345.0 366.7 434.2 49999.0 50000 20 1814.6 1376604 136739652 1999731 77987676 76988704027 93957872309 112830998175 172898922 0 0 0 0 494459 932924 -ebbrt_tuned 3 300 0x1b00 75 64.8 82.3 215.5 344.1 365.5 430.7 49979.3 50000 20 1813.34 1377286 136739330 1998992 77958890 77133377285 94266577777 113084082654 173069132 0 0 0 0 494395 932727 -ebbrt_tuned 3 300 0x1b00 55 64.8 82.0 215.3 345.2 366.4 435.1 50093.9 50000 20 1815.2 1380739 137079968 2003585 78137910 77402635964 94488697772 113329383794 172768007 0 0 0 0 497230 934042 -ebbrt_tuned 3 300 0x1b00 135 73.5 92.4 221.3 347.4 371.3 442.5 100010.2 100000 20 1970.6 2204534 240551601 3999566 155971052 152727473930 170477218574 191058076747 333279663 0 0 0 0 570317 972572 -ebbrt_tuned 3 300 0x1b00 95 73.3 92.6 221.0 347.7 371.8 441.7 100127.2 100000 20 1975.17 2207220 240853079 4004420 156163960 152837630184 170869554439 191505076947 331989372 0 0 0 0 569135 972699 -ebbrt_tuned 3 300 0x1b00 75 72.1 90.9 221.2 348.1 372.5 440.4 99919.9 100000 20 1976.23 2202801 240375566 3996063 155836528 152482827647 170398409893 190937666920 331836074 0 0 0 0 567057 972569 -ebbrt_tuned 3 300 0x1b00 55 74.1 94.4 223.9 349.3 376.1 446.1 100142.1 100000 20 1944.49 2206197 240819509 4004741 156172240 152999955881 170902087829 193807911209 334195591 0 0 0 0 570258 972754 -ebbrt_tuned 3 300 0x1b00 135 76.5 97.2 227.6 356.6 393.0 474.1 199960.6 200000 20 2249.88 4366856 478545551 7994370 311727030 304776398109 320951378945 340372547339 667756289 0 0 0 0 710511 976505 -ebbrt_tuned 3 300 0x1b00 95 75.8 96.6 226.9 355.6 391.5 471.2 199896.6 200000 20 2248.76 4365984 478418647 7991660 311615170 304910126823 321501030575 340481050056 671796624 0 0 0 0 720639 976513 -ebbrt_tuned 3 300 0x1b00 75 74.6 94.8 225.5 353.4 388.2 470.0 200011.8 200000 20 2245.52 4369551 478768567 7996506 311806960 304928888970 321314718082 341013352248 669351231 0 0 0 0 687773 976511 -ebbrt_tuned 3 300 0x1b00 55 77.5 99.7 235.9 382.1 455.9 4073.3 200026.6 200000 20 2065.61 4397105 480414599 7996959 311824786 305382560356 318297834584 366240948409 676041780 0 0 0 0 676073 973490 -ebbrt_tuned 3 400 0x1b00 135 75.7 100.8 273.6 435.9 467.3 529.0 49953.8 50000 20 1811.39 1333025 134076797 1997984 77919992 77260259183 93674663483 112244056822 175293119 0 0 0 0 557641 720207 -ebbrt_tuned 3 400 0x1b00 95 73.2 97.7 269.7 432.3 461.4 519.7 49962.9 50000 20 1786.97 1336034 134251047 1998288 77931166 75296600718 86447765496 104909762290 150077148 0 0 0 0 556948 720046 -ebbrt_tuned 3 400 0x1b00 75 74.6 98.6 271.1 432.9 461.7 518.6 50052.2 50000 20 1798.39 1337727 134481111 2001919 78072726 75786153137 87619682800 106121707844 155592980 0 0 0 0 558906 720144 -ebbrt_tuned 3 400 0x1b00 55 74.8 99.3 269.4 432.1 461.0 519.0 49901.5 50000 20 1799.58 1333685 134061907 1995829 77836088 75751403296 87990678176 106556108907 156825150 0 0 0 0 559574 720100 -ebbrt_tuned 3 400 0x1b00 135 80.8 106.7 274.7 433.4 465.6 530.4 100012.1 100000 20 1952.47 2201833 240408575 3999842 155985588 150249987595 160394050876 181242281969 304843144 0 0 0 0 668255 731846 -ebbrt_tuned 3 400 0x1b00 95 79.2 103.9 272.8 433.7 465.3 528.8 100012.3 100000 20 1954.91 2203853 240536390 3999667 155977596 151077869307 162346524797 183501225862 310725516 0 0 0 0 680189 731857 -ebbrt_tuned 3 400 0x1b00 75 80.5 106.3 273.8 434.3 466.3 529.8 100032.3 100000 20 1956.23 2204195 240560453 4000536 156012932 150922547764 162768216927 183749119515 310294964 0 0 0 0 680140 731883 -ebbrt_tuned 3 400 0x1b00 55 80.9 107.0 276.3 435.1 466.9 530.6 99951.9 100000 20 1933.48 2203071 240414332 3997266 155882934 151067512027 163163536481 185680749021 312218503 0 0 0 0 680500 731856 -ebbrt_tuned 3 400 0x1b00 135 83.8 111.4 280.1 439.3 477.3 559.3 200124.9 200000 20 2227.32 4428312 482403189 8000549 311963298 301561958898 309568386728 329592502329 632726389 0 0 0 0 871163 732418 -ebbrt_tuned 3 400 0x1b00 95 85.9 113.4 285.1 443.6 482.9 568.8 200057.9 200000 20 2228.6 4431041 482471442 7998362 311882228 301894073107 311440830686 332607060594 634432397 0 0 0 0 828009 732416 -ebbrt_tuned 3 400 0x1b00 75 86.5 113.2 282.3 441.3 478.0 557.1 200003.3 200000 20 2228.75 4428718 482289858 7996110 311793578 301910864216 311792123938 333407044357 636978215 0 0 0 0 847141 732418 -ebbrt_tuned 3 400 0x1b00 55 87.0 114.6 287.4 459.0 511.9 2719.0 199972.9 200000 20 2065.29 4445895 483332134 7995101 311754768 302088344857 310072450404 356108031207 646727750 0 0 0 0 840165 731589 -linux_tuned 0 50 0x1d00 135 55.6 57.4 75.1 127.9 155.6 193.8 49961.0 50000 20 2037.38 1612211 166459337 3003164 90111690 86519065351 142068189617 145108270659 199736041 472571 723545 163384 0 836613 2080311 -linux_tuned 1 50 0x1d00 135 55.8 57.6 75.7 128.5 156.1 193.4 49998.2 50000 20 2017.75 1606946 166131533 3005379 90178056 87170391043 142835839884 145240503264 207658233 523687 815320 175051 0 839431 2122632 -linux_tuned 2 50 0x1d00 135 55.9 57.8 75.3 127.5 155.8 195.1 50028.7 50000 20 2038.49 1607559 166217006 3007113 90229704 86989658370 142678663071 145382834017 207315086 484246 729515 165107 0 833342 2085514 -linux_tuned 0 50 0x1d00 95 55.5 57.3 75.1 128.1 154.7 193.2 49977.8 50000 20 2051.63 1605431 166038460 3004136 90140706 86853272687 142977001875 145557042005 204335540 482401 734483 167573 0 840120 2088986 -linux_tuned 1 50 0x1d00 95 56.2 58.3 75.8 129.6 158.4 196.2 49915.5 50000 20 2050.23 1608593 166172780 3000396 90028548 87256780044 143506470833 145987160406 212584428 487051 739911 169651 0 843315 2098038 -linux_tuned 2 50 0x1d00 95 56.1 58.1 75.8 129.9 159.1 199.2 50024.5 50000 20 2048.01 1606678 166153730 3006941 90224892 87457784439 144110490747 146746536049 213144695 482985 724725 169113 0 838581 2091304 -linux_tuned 0 50 0x1d00 75 55.7 57.6 75.2 128.3 155.4 194.9 49966.5 50000 20 2054.09 1604437 165983041 3003432 90119400 87018258140 143236029569 145591232715 207370621 485666 757059 168813 0 837202 2105740 -linux_tuned 1 50 0x1d00 75 55.8 57.6 75.2 128.2 157.3 196.6 50105.6 50000 20 2054.33 1607013 166302973 3011860 90372768 87766985514 144640577599 147174888205 214604492 481076 717160 166111 0 831597 2087413 -linux_tuned 2 50 0x1d00 75 55.9 57.8 75.8 128.9 159.3 199.7 50003.2 50000 20 2054.29 1608341 166264714 3005670 90186714 87654327903 144759908107 147352854564 214707662 486806 732413 169562 0 844734 2098934 -linux_tuned 0 50 0x1d00 55 56.1 58.1 75.5 127.6 156.9 196.5 49957.6 50000 20 2045.82 1607583 166174079 3003057 90108774 87099489976 143478858564 146460063435 209076462 487556 732449 167280 0 836304 2093787 -linux_tuned 1 50 0x1d00 55 56.6 58.7 76.7 130.5 160.1 200.2 49968.2 50000 20 2042.23 1607254 166169088 3003566 90123756 87763276483 145308019689 148460529516 216522720 486600 728023 169601 0 838242 2100169 -linux_tuned 2 50 0x1d00 55 56.2 58.2 76.1 130.3 160.6 201.3 50010.8 50000 20 2040.96 1602808 165896798 3006191 90202668 87512679276 144931062006 147978380997 215560071 490152 735240 169140 0 841247 2100951 -linux_tuned 0 50 0x1d00 135 56.0 58.3 80.7 144.5 170.4 213.4 99986.7 100000 20 2250.0 2298338 271884388 6018060 180599706 162573297018 256290482393 256505036540 385631073 1116985 1029308 137988 0 790663 3238275 -linux_tuned 1 50 0x1d00 135 56.3 58.6 79.9 143.6 170.6 215.8 99993.8 100000 20 2250.8 2287392 271187135 6018516 180613686 163333685693 257632033219 257808954144 397641191 1112142 1003485 138185 0 790963 3233093 -linux_tuned 2 50 0x1d00 135 56.4 58.8 81.2 143.4 170.5 216.8 99979.9 100000 20 2253.16 2293765 271613504 6017387 180579216 163625332170 258295782521 258496393141 400775876 1131777 1035272 142795 0 789070 3255121 -linux_tuned 0 50 0x1d00 95 56.3 58.7 80.3 144.4 170.9 217.1 99952.0 100000 20 2258.46 2300558 272031817 6015708 180527988 163118559813 258509286684 258728169754 392515372 1124815 1014610 139101 0 785930 3238403 -linux_tuned 1 50 0x1d00 95 56.3 58.7 80.9 145.7 172.2 221.1 100102.6 100000 20 2257.95 2292053 271650873 6025139 180812862 164560289764 260368525608 260675521465 407889062 1120125 1001023 138690 0 787815 3239277 -linux_tuned 2 50 0x1d00 95 56.4 58.9 81.0 145.9 173.4 220.0 100142.3 100000 20 2261.55 2285631 271265660 6027837 180894480 165153453423 261371644266 261643192015 409990145 1122145 996950 138899 0 788170 3236834 -linux_tuned 0 50 0x1d00 75 56.6 59.1 80.8 145.5 171.7 217.8 99985.5 100000 20 2264.41 2290033 271343865 6017958 180596700 163352033042 259586788505 259779705665 396790767 1132135 1003796 138943 0 790100 3240646 -linux_tuned 1 50 0x1d00 75 56.7 59.3 81.5 145.2 173.1 220.7 100063.7 100000 20 2261.72 2290364 271480646 6022802 180743094 164705016378 261048329628 261193781770 411385372 1131586 1008483 139142 0 786887 3236626 -linux_tuned 2 50 0x1d00 75 56.6 59.2 82.5 146.7 173.7 223.6 100034.1 100000 20 2262.77 2288180 271316240 6021155 180693072 164604112250 260920408715 261063928122 411903673 1133540 1004406 138464 0 786781 3241216 -linux_tuned 0 50 0x1d00 55 57.3 60.4 84.2 152.3 181.9 231.7 100100.8 100000 20 2128.95 2283489 271059652 6027154 180879972 163604497298 258791507630 273525053397 401644386 1160241 921585 141783 0 777632 3249567 -linux_tuned 1 50 0x1d00 55 57.2 60.2 83.9 149.1 177.8 226.1 99990.1 100000 20 2125.77 2291838 271448523 6019605 180650862 161685698980 255375083056 267969323993 374407050 1169123 963096 141251 0 784941 3255524 -linux_tuned 2 50 0x1d00 55 57.3 60.4 83.7 151.7 181.4 242.2 99938.4 100000 20 2123.53 2286049 271037563 6018957 180640236 162156974781 258008149100 272410613644 383729661 1167971 942021 142136 0 783967 3260152 +ebbrt_tuned 6 50 0xf00 95 56.2 59.6 92.8 204.2 264.4 333.2 50022.7 50000 20 1066.4 1536776 146362994 2000709 78025548 76225243423 92126023640 184416979278 167863895 0 0 0 0 399677 2240761 +ebbrt_tuned 7 50 0xf00 95 56.2 59.7 93.4 206.1 268.4 334.5 50031.6 50000 20 1066.16 1532864 146167465 2001154 78043612 75918644563 91832970461 183822776093 165936874 0 0 0 0 396626 2233534 +ebbrt_tuned 4 50 0xf00 55 56.1 59.4 91.7 200.3 255.7 323.6 49993.5 50000 20 1056.78 1541845 146664306 1999599 77982674 75127851918 89489482184 179609451063 156844398 0 0 0 0 397654 2235863 +ebbrt_tuned 6 50 0xf00 55 56.1 59.5 92.3 202.3 260.8 329.2 50059.7 50000 20 1064.13 1543321 146793733 2002231 78085702 75753862045 90646631175 181715308654 162794821 0 0 0 0 396837 2238187 +ebbrt_tuned 4 50 0x1300 95 53.6 58.3 92.8 207.2 248.2 333.1 100140.1 100000 20 1354.36 2263558 244264589 4004795 156177640 151116473475 169254808941 266570855978 332699139 0 0 0 0 346619 3326236 +ebbrt_tuned 5 50 0x1300 95 52.8 57.4 92.1 205.6 247.5 327.1 100120.1 100000 20 1355.16 2258977 243958346 4004038 156147854 151328974139 170186337662 268058841831 338067893 0 0 0 0 346638 3325574 +ebbrt_tuned 6 50 0x1300 95 52.8 57.4 91.8 203.5 245.7 324.8 100045.7 100000 20 1305.99 2229435 240457898 3941551 153709754 149039407366 167555043309 263825354261 331408377 0 0 0 0 344221 3282060 +ebbrt_tuned 7 50 0x1300 95 52.5 56.8 91.7 206.8 247.5 326.7 99967.9 100000 20 1353.22 2262009 243990359 3997986 155910834 151233522463 169298860879 266606984207 333435659 0 0 0 0 348884 3329896 +ebbrt_tuned 4 200 0x1300 75 71.0 85.8 181.6 295.6 344.1 437.7 99985.7 100000 20 1343.57 2208966 240793215 3998739 155942078 150491050563 163002972465 257969837879 327004072 0 0 0 0 466055 1429102 +ebbrt_tuned 5 200 0x1300 75 72.3 86.5 183.5 297.0 347.7 436.8 100088.4 100000 20 1342.71 2212427 241123856 4002867 156102476 150195982498 162660359545 257713395577 327739321 0 0 0 0 474064 1429998 +ebbrt_tuned 6 200 0x1300 75 72.8 88.4 184.8 299.7 351.6 444.8 100013.3 100000 20 1344.26 2208534 240768144 3999745 155981762 149957006389 163018877784 258113817398 326086348 0 0 0 0 466625 1429723 +ebbrt_tuned 7 200 0x1300 75 72.3 86.7 184.9 298.7 349.9 440.5 99901.1 100000 20 1344.79 2207879 240674234 3995116 155796618 149932085114 163305054896 258668266310 329556700 0 0 0 0 470813 1429893 +ebbrt_tuned 4 100 0x1400 135 62.8 74.0 135.1 269.3 321.5 442.4 199899.0 200000 20 1599.99 4276849 473122588 7992401 311650734 297416094467 298274331174 434205352324 641850414 0 0 0 0 351954 2753481 +ebbrt_tuned 5 100 0x1400 135 62.9 74.0 134.8 268.4 318.0 435.6 199857.7 200000 20 1600.12 4275555 472965296 7990612 311583138 297251475665 297988235716 433804131669 644860072 0 0 0 0 354761 2752314 +ebbrt_tuned 6 100 0x1400 135 63.3 74.9 137.2 275.1 325.5 445.3 200117.4 200000 20 1601.69 4282399 473640997 8000934 311981376 298329221670 300266894543 437026370215 651069250 0 0 0 0 355854 2751714 +ebbrt_tuned 7 100 0x1400 135 62.8 74.3 136.0 270.6 321.4 440.6 200144.6 200000 20 1599.98 4282985 473688073 8001918 312022190 298432992084 299299017906 435599299123 648626887 0 0 0 0 354750 2752499 +ebbrt_tuned 4 50 0x1500 55 51.8 55.9 85.4 166.0 212.9 271.1 50006.7 50000 20 1320.17 1568980 148276620 2000129 78003126 76188852492 99963693747 156899056468 173194778 0 0 0 0 402891 2253893 +ebbrt_tuned 5 50 0x1500 55 51.5 55.7 84.7 164.5 211.4 271.4 49976.7 50000 20 1322.25 1566420 148117693 1998865 77954064 76070122880 100022169495 157078661501 173306519 0 0 0 0 403873 2257759 +ebbrt_tuned 6 50 0x1500 55 53.3 57.8 87.3 171.1 218.0 275.3 49981.0 50000 20 1323.06 1568745 148238840 1999066 77961332 76241944719 99615652385 156415730694 171408074 0 0 0 0 404572 2259935 +ebbrt_tuned 4 50 0x1500 75 51.1 55.2 87.2 187.6 227.6 297.5 99897.2 100000 20 1466.83 2265646 244102477 3995189 155802336 151062167346 172621606015 250610185660 334747517 0 0 0 0 356020 3336925 +ebbrt_tuned 6 50 0x1500 75 51.3 55.4 87.7 186.6 227.2 293.9 99950.6 100000 20 1464.63 2269447 244408784 3997192 155881054 150884429468 171531929198 248982354122 331676976 0 0 0 0 355982 3341130 +ebbrt_tuned 7 50 0x1500 75 51.7 55.8 87.9 187.9 228.5 296.1 100114.3 100000 20 1468.46 2270368 244616289 4003758 156135200 151491750905 172350803200 250076924902 333790207 0 0 0 0 355399 3340006 +ebbrt_tuned 4 50 0x1500 55 51.1 55.3 87.5 187.8 227.3 298.9 99974.4 100000 20 1465.82 2274552 244708676 3998184 155917776 150687447111 171500505368 249181850081 331575248 0 0 0 0 355867 3340480 +ebbrt_tuned 5 50 0x1500 55 51.4 55.6 87.5 185.7 226.6 294.5 99894.1 100000 20 1467.25 2268433 244228770 3995041 155797536 150866625249 172300449317 250088049940 333150958 0 0 0 0 358138 3344801 +ebbrt_tuned 6 50 0x1500 55 51.1 55.3 87.0 185.6 226.3 298.6 99839.2 100000 20 1467.01 2274093 244553499 3992927 155714756 150895521366 171989172663 249886651900 334422821 0 0 0 0 356371 3338907 +ebbrt_tuned 7 50 0x1500 55 51.7 56.0 88.6 188.9 227.6 298.1 99994.6 100000 20 1466.95 2269636 244465279 3999057 155954648 151134822978 172854876439 250759276661 333017079 0 0 0 0 357390 3346516 +ebbrt_tuned 4 50 0x1500 55 54.0 60.9 102.3 232.4 275.8 389.1 200005.5 200000 20 1682.68 4245231 471303912 7996676 311818622 300250619591 311647311763 432077128802 666069785 0 0 0 0 296056 4600816 +ebbrt_tuned 5 50 0x1500 55 54.4 61.2 103.6 234.5 281.4 394.3 200112.1 200000 20 1681.7 4244550 471347688 8000399 311960898 300692675499 310940719068 431062195444 660789074 0 0 0 0 292974 4591915 +ebbrt_tuned 6 50 0x1500 55 53.3 60.0 101.1 230.4 273.3 389.5 200033.8 200000 20 1678.13 4243860 471242390 7997837 311863518 301185281432 312316705852 432956150760 667762553 0 0 0 0 291643 4589207 +ebbrt_tuned 7 50 0x1500 55 53.7 60.4 102.5 234.4 276.8 393.8 200090.7 200000 20 1681.51 4247517 471538280 8000033 311947662 300812909848 311661836627 432053006196 666453392 0 0 0 0 292339 4592072 +ebbrt_tuned 4 100 0x1500 135 62.3 73.1 131.6 247.3 291.5 393.8 199772.0 200000 20 1650.32 4261411 472066654 7987190 311447724 294599012670 289891250597 403497354607 603099598 0 0 0 0 377404 2780692 +ebbrt_tuned 5 100 0x1500 135 62.0 73.5 132.7 254.5 298.8 401.7 199941.8 200000 20 1664.06 4265019 472432218 7992195 311614208 296244872448 296030428703 411550073157 625761020 0 0 0 0 372623 2774015 +ebbrt_tuned 7 100 0x1500 135 60.4 70.9 129.2 251.2 295.7 400.7 200052.2 200000 20 1667.9 4271432 472929103 7998057 311868970 297075613868 299340348503 415907041248 637473602 0 0 0 0 367998 2768591 +ebbrt_tuned 4 50 0x1700 55 50.0 54.4 85.9 176.2 213.0 274.5 99982.1 100000 20 1582.01 2278987 244974644 3998451 155929908 150977875072 175438396842 237398541719 331443773 0 0 0 0 361104 3348983 +ebbrt_tuned 5 50 0x1700 55 49.5 54.0 84.7 175.4 212.4 275.0 100042.9 100000 20 1581.54 2277346 244958885 4001009 156030438 151034048572 175134702398 236950238839 328723110 0 0 0 0 358310 3336760 +ebbrt_tuned 6 50 0x1700 55 50.7 55.2 86.6 178.3 214.9 275.4 100108.3 100000 20 1580.92 2276626 244977552 4003740 156137160 150894169829 175485262910 237476940841 332070992 0 0 0 0 361438 3350183 +ebbrt_tuned 7 50 0x1700 55 50.1 54.4 85.6 177.1 213.4 276.8 99958.0 100000 20 1584.04 2277790 244893232 3997686 155901424 150837926376 175648380106 237596516570 330729089 0 0 0 0 358901 3341441 +ebbrt_tuned 5 50 0x1800 95 51.8 57.7 95.2 206.4 243.5 335.8 199916.3 200000 20 1899.16 4227049 470134098 7992740 311658960 300942834095 319673745406 390179891725 662424776 0 0 0 0 316162 4641283 +ebbrt_tuned 6 50 0x1800 95 50.1 56.3 91.9 200.5 237.3 330.7 199928.5 200000 20 1904.52 4227917 470198680 7993484 311692132 300807229528 321145284751 392028502467 665937376 0 0 0 0 310581 4627805 +ebbrt_tuned 5 50 0x1900 95 52.1 58.2 94.4 199.0 234.6 320.7 199919.9 200000 20 1988.93 4222350 469811982 7993382 311691356 300796626824 324442864723 381325670972 673773527 0 0 0 0 318834 4647936 +ebbrt_tuned 6 50 0x1900 95 50.5 56.6 92.4 196.9 231.4 318.1 200074.2 200000 20 1991.33 4228147 470353606 7999442 311926338 301176962107 324842308512 381537677357 666085311 0 0 0 0 318529 4649263 +ebbrt_tuned 7 50 0x1900 95 50.4 56.5 92.0 196.9 230.7 322.2 200089.5 200000 20 1992.15 4226426 470260886 7999630 311932532 300822471279 323959414639 380629295741 667580191 0 0 0 0 319275 4648491 +ebbrt_tuned 4 100 0x1900 135 54.9 62.1 114.6 194.6 227.9 292.5 100186.9 100000 20 1701.29 2249840 243474179 4006858 156259034 149418036052 171977144156 221523413890 314186720 0 0 0 0 390059 2455258 +ebbrt_tuned 5 100 0x1900 135 54.2 61.5 113.8 193.6 228.7 293.1 100125.2 100000 20 1700.3 2246485 243175541 4003945 156140370 149203981439 172915579313 222565978071 318042295 0 0 0 0 386998 2449326 +ebbrt_tuned 6 100 0x1900 135 54.1 61.8 115.1 196.1 231.4 296.7 100118.9 100000 20 1700.88 2249309 243355362 4004019 156146994 149306966227 173335414064 222773490094 318029284 0 0 0 0 388240 2449420 +ebbrt_tuned 7 100 0x1900 135 53.6 61.2 113.3 193.7 227.7 293.7 100010.7 100000 20 1701.94 2254005 243492919 3999750 155981468 149256017812 173006127496 222508827626 317760545 0 0 0 0 388897 2451602 +ebbrt_tuned 4 100 0x1900 95 54.8 62.3 115.0 195.9 230.3 296.8 99991.3 100000 20 1704.79 2245417 242991357 3998943 155950202 149108922284 174162744694 223614821245 319930314 0 0 0 0 388042 2450159 +ebbrt_tuned 5 100 0x1900 95 54.3 61.6 113.4 193.1 229.2 293.4 99921.1 100000 20 1700.84 2252788 243346267 3996114 155839316 149467267408 173239746999 222636578484 319126256 0 0 0 0 388405 2450022 +ebbrt_tuned 6 100 0x1900 95 54.6 61.9 114.2 195.9 230.6 295.4 99928.1 100000 20 1702.47 2250869 243228624 3996507 155854848 149258017646 173853371783 223145881646 320821637 0 0 0 0 385885 2448965 +ebbrt_tuned 7 100 0x1900 95 55.6 63.3 116.0 199.0 234.4 296.7 99932.3 100000 20 1698.52 2251191 243257866 3996462 155852298 149124128552 173448094945 222747527322 320047735 0 0 0 0 388560 2450848 +ebbrt_tuned 4 50 0x1a00 95 49.3 54.0 84.4 164.8 198.1 254.9 100095.1 100000 20 1785.95 2290355 245815102 4002875 156100276 150009138359 180171868740 223652996207 328709598 0 0 0 0 366061 3359888 +ebbrt_tuned 5 50 0x1a00 95 48.4 53.1 83.2 162.4 195.4 250.5 99959.8 100000 20 1780.1 2282684 245189464 3997699 155901460 150050911250 179386383937 222763238855 326763794 0 0 0 0 367031 3356105 +ebbrt_tuned 6 50 0x1a00 95 49.5 54.1 83.8 162.0 195.7 250.9 99888.2 100000 20 1776.47 2286216 245343559 3994919 155793156 149781892423 178783183940 221986746658 324207414 0 0 0 0 366705 3355875 +ebbrt_tuned 7 50 0x1a00 95 49.2 54.0 84.2 164.9 197.4 256.5 100001.4 100000 20 1782.48 2284779 245356000 3999250 155961634 150229684876 179979344072 223351919657 329034462 0 0 0 0 365223 3354228 +ebbrt_tuned 4 50 0x1a00 75 50.1 56.2 90.8 189.8 222.9 303.0 200112.7 200000 20 2073.35 4225202 470189988 8000678 311971680 301345499374 328941900547 372868467614 678142565 0 0 0 0 322558 4660433 +ebbrt_tuned 5 50 0x1a00 75 50.6 56.6 91.1 192.2 226.1 306.7 199981.3 200000 20 2071.51 4220981 469828564 7995534 311772796 300621771819 328042875171 371922464269 676154420 0 0 0 0 322555 4654733 +ebbrt_tuned 6 50 0x1a00 75 49.9 55.7 90.1 190.9 223.7 304.5 199907.9 200000 20 2072.51 4221694 469771628 7992492 311653198 300840400930 328329526587 371935679888 666022330 0 0 0 0 324472 4660145 +ebbrt_tuned 7 50 0x1a00 75 50.9 56.7 91.4 191.3 226.1 309.1 199894.0 200000 20 2077.76 4219806 469642835 7991913 311630356 301790622760 331010884226 375007223994 679503175 0 0 0 0 323035 4658424 +ebbrt_tuned 4 300 0x1a00 135 79.0 101.4 233.7 364.6 404.3 490.1 199919.1 200000 20 2023.74 4373193 478892873 7992921 311668322 300646970677 309167699223 358497925702 632983299 0 0 0 0 672278 976522 +ebbrt_tuned 5 300 0x1a00 135 77.3 98.8 230.7 361.4 400.7 484.5 200022.9 200000 20 2028.92 4375706 479130973 7997306 311841984 300822607276 311447289839 360393142083 638560360 0 0 0 0 674077 976522 +ebbrt_tuned 6 300 0x1a00 135 77.6 99.3 231.3 363.4 402.9 489.7 199950.9 200000 20 2028.06 4371748 478812152 7993950 311705694 301311314650 311657705939 360188457763 642652417 0 0 0 0 666836 976514 +ebbrt_tuned 7 300 0x1a00 135 79.4 101.3 234.6 365.7 406.7 491.2 200107.4 200000 20 2030.69 4376562 479258697 8000529 311968900 301507285126 312683767782 360513192513 646543423 0 0 0 0 673823 976513 +ebbrt_tuned 4 50 0x1b00 75 49.3 54.1 84.2 161.5 193.7 248.7 100108.3 100000 20 1857.88 2290169 245820655 4003603 156130078 150628239188 182634823361 220641763894 331568592 0 0 0 0 366509 3361027 +ebbrt_tuned 5 50 0x1b00 75 48.2 53.0 83.0 160.8 194.4 254.0 100083.0 100000 20 1858.48 2286723 245566614 4002602 156093046 150692390612 182867262060 220741046322 330558066 0 0 0 0 365543 3355757 +ebbrt_tuned 6 50 0x1b00 75 49.2 54.2 84.1 163.8 197.0 250.0 100024.3 100000 20 1856.45 2289948 245728510 4000201 155996664 150624157982 182274075828 219948327697 328627376 0 0 0 0 367627 3360687 +ebbrt_tuned 7 50 0x1b00 75 47.8 52.4 82.2 158.7 190.8 245.7 100168.8 100000 20 1789.28 2178455 233799252 3810726 148610664 143729172528 174422775098 210401242917 316929614 0 0 0 0 347689 3194343 +ebbrt_tuned 4 50 0x1b00 95 50.1 56.2 90.5 186.7 220.1 297.5 200164.0 200000 20 2167.17 4224094 470169679 8002835 312055958 301762097006 333049138895 364735845915 685847314 0 0 0 0 326224 4667557 +ebbrt_tuned 5 50 0x1b00 95 50.1 55.9 90.2 187.0 220.7 298.7 199941.0 200000 20 2164.92 4219719 469683675 7994098 311719958 301904951327 333778285931 365442632639 682821736 0 0 0 0 325675 4660676 +ebbrt_tuned 6 50 0x1b00 95 49.5 55.4 88.9 183.6 215.4 296.7 200134.1 200000 20 2168.77 4226449 470346905 8001780 312017198 301874605224 334381939609 366076852605 688825727 0 0 0 0 324862 4662928 +ebbrt_tuned 7 50 0x1b00 95 50.4 56.2 90.7 186.9 219.2 298.9 200123.4 200000 20 2009.71 3955586 440252873 7498554 292396750 283229856055 313587292815 343240096484 644427148 0 0 0 0 305731 4370557 +ebbrt_tuned 4 100 0x1b00 95 52.1 58.6 104.2 179.2 207.8 271.9 50019.4 50000 20 1646.08 1536012 146331587 2000653 78023930 75669018587 103021658208 141135510924 167667044 0 0 0 0 414704 1855303 +ebbrt_tuned 5 100 0x1b00 95 51.8 58.0 103.4 178.9 206.8 271.3 50044.8 50000 20 1643.51 1531385 146058641 2001576 78060062 75880265204 103137810402 141199463435 169205539 0 0 0 0 415634 1858906 +ebbrt_tuned 6 100 0x1b00 95 51.0 57.3 102.1 177.0 204.3 268.5 49975.3 50000 20 1644.48 1538069 146395757 1998889 77955066 75711341245 103053606535 140937859829 167425092 0 0 0 0 415001 1857769 +ebbrt_tuned 7 100 0x1b00 95 51.6 58.1 103.1 179.6 207.5 270.1 49947.4 50000 20 1646.62 1529422 145840734 1997747 77910516 75762909357 103341531681 141250140326 167971466 0 0 0 0 412480 1853046 +ebbrt_tuned 4 200 0x1b00 75 65.9 79.8 172.7 267.9 298.9 368.2 100139.7 100000 20 1833.96 2216398 241418015 4004982 156185808 150056341386 174832998363 212250729488 325819749 0 0 0 0 499568 1430633 +ebbrt_tuned 5 200 0x1b00 75 66.5 80.5 173.0 268.9 301.2 371.8 100009.8 100000 20 1836.86 2215154 241203534 3999691 155978402 149797982365 175113538354 212493996945 327166309 0 0 0 0 497894 1430085 +ebbrt_tuned 6 200 0x1b00 75 65.4 79.1 172.3 268.3 299.9 369.6 100012.4 100000 20 1838.47 2214534 241200339 3999654 155977208 149830460222 175363317308 213054958677 328388760 0 0 0 0 501251 1430848 +ebbrt_tuned 7 200 0x1b00 75 66.1 80.2 173.6 269.4 301.2 368.8 99968.3 100000 20 1834.73 2214553 241098052 3998046 155915008 149688080933 174794016922 212318444285 328658653 0 0 0 0 498792 1429789 +ebbrt_tuned 4 50 0x1c00 95 49.8 55.8 89.3 181.9 212.3 287.9 200007.1 200000 20 2261.21 4218189 469664618 7996215 311796592 301260189572 335153293160 355460227467 678831844 0 0 0 0 330997 4672849 +ebbrt_tuned 5 50 0x1c00 95 50.3 56.1 89.8 182.0 213.4 286.5 200094.5 200000 20 2258.17 4219042 469828159 8000229 311957212 301576835078 333102658469 353245528729 670116805 0 0 0 0 331400 4674646 +ebbrt_tuned 6 50 0x1c00 95 50.1 56.0 89.6 182.9 214.0 289.9 199889.0 200000 20 2260.17 4214631 469333310 7991909 311630594 301525863499 334063309466 354238478434 675510869 0 0 0 0 331545 4673169 +ebbrt_tuned 7 50 0x1c00 95 49.2 55.0 88.7 180.8 211.3 285.8 199927.4 200000 20 2256.66 4214319 469353039 7993255 311685130 301482790805 333852942743 354051201596 675163273 0 0 0 0 330471 4671489 +ebbrt_tuned 4 50 0x1d00 95 47.2 51.8 81.6 153.8 185.1 240.3 100000.1 100000 20 2020.39 2289347 245629730 3999308 155963354 151047300389 188047094794 215763069476 335718903 0 0 0 0 367670 3356499 +ebbrt_tuned 5 50 0x1d00 95 48.4 53.3 82.7 157.2 188.7 242.7 100030.5 100000 20 2018.39 2290915 245738834 4000328 156002130 151016221425 187447762378 215025036006 334198679 0 0 0 0 367524 3358018 +ebbrt_tuned 6 50 0x1d00 95 49.3 54.3 83.9 158.2 190.3 245.2 99982.6 100000 20 2019.37 2290890 245710571 3998676 155939432 151373524824 188045437873 215812536603 339416549 0 0 0 0 367232 3355144 +ebbrt_tuned 4 50 0x1d00 135 48.5 54.5 87.0 177.0 206.7 276.4 200131.9 200000 20 2361.02 4218426 469807702 8001529 312005610 302385935560 339720938775 349011194214 682678065 0 0 0 0 333254 4681500 +ebbrt_tuned 5 50 0x1d00 135 49.3 55.2 88.0 178.6 207.5 280.6 200046.5 200000 20 2360.81 4217452 469700708 7998177 311877046 301413340164 339593417921 348966998040 684780104 0 0 0 0 333393 4684985 +ebbrt_tuned 6 50 0x1d00 135 48.0 53.4 84.7 163.7 192.7 255.4 199979.4 200000 20 2303.69 4211308 469246701 7995424 311766014 294335831394 316348680537 329139692907 603190543 0 0 0 0 341968 4704643 +ebbrt_tuned 4 100 0x1d00 55 53.7 61.1 113.4 190.5 223.8 293.3 99920.4 100000 20 1969.23 2249545 243148696 3996164 155841246 150758322123 182735113007 215488228878 333563542 0 0 0 0 389777 2449631 +ebbrt_tuned 5 100 0x1d00 55 52.9 60.3 111.3 183.3 210.4 267.1 100024.6 100000 20 1948.54 2252363 243417285 4000036 155990320 147676059014 173251198522 206410140711 296968784 0 0 0 0 394805 2457529 +ebbrt_tuned 6 100 0x1d00 55 52.7 60.0 111.9 184.2 214.1 276.1 99915.7 100000 20 1959.19 2255769 243543796 3995914 155831852 148172001944 175415173333 209868298607 308956050 0 0 0 0 393480 2457737 +ebbrt_tuned 7 100 0x1d00 55 53.1 60.3 112.5 186.0 217.2 285.8 100052.9 100000 20 1956.75 2250632 243396857 4001349 156043716 148413593130 175805555343 210463955098 310981393 0 0 0 0 392740 2457658 +ebbrt_tuned 0 50 0xd00 135 58.5 62.6 99.8 233.9 305.3 392.3 50163.1 50000 20 991.07 1512803 145089284 2006336 78245712 76316937892 90081808850 202929675145 172201165 0 0 0 0 385532 2212335 +ebbrt_tuned 1 50 0xd00 135 59.0 63.2 100.7 239.5 308.2 388.8 49902.1 50000 20 991.55 1507504 144485166 1995954 77840670 75996355942 90081185662 202924645733 171680709 0 0 0 0 389385 2214034 +ebbrt_tuned 2 50 0xd00 135 57.7 61.7 99.0 234.4 306.2 390.9 50003.1 50000 20 992.18 1513241 144928224 2000000 77998554 76280421491 89977266129 202697408084 171824026 0 0 0 0 386825 2209159 +ebbrt_tuned 0 50 0xd00 95 58.2 62.2 99.8 236.5 307.8 393.5 50007.7 50000 20 992.01 1510864 144818324 2000126 78003276 76103461050 90130218783 203052615483 173482303 0 0 0 0 386159 2207110 +ebbrt_tuned 1 50 0xd00 95 58.9 63.0 100.9 238.1 309.5 395.0 49950.6 50000 20 992.56 1511758 144795330 1997893 77916170 76224315458 90196665837 203169624770 171674216 0 0 0 0 387940 2212653 +ebbrt_tuned 2 50 0xd00 95 58.8 62.9 100.8 238.2 309.9 395.5 49966.1 50000 20 992.58 1510334 144703747 1998478 77938914 76263893794 90352413494 203510794139 171959650 0 0 0 0 388435 2215628 +ebbrt_tuned 0 50 0xd00 75 58.3 62.3 99.6 236.1 305.3 391.4 49961.1 50000 20 992.4 1508480 144595020 1998323 77933324 76138931890 90403568587 203642314446 173946377 0 0 0 0 388422 2213279 +ebbrt_tuned 1 50 0xd00 75 58.3 62.3 99.4 230.6 302.9 391.7 50028.1 50000 20 993.66 1507338 144607079 2000981 78036482 76335408126 90535005189 203919341673 172714213 0 0 0 0 388610 2214405 +ebbrt_tuned 2 50 0xd00 75 59.5 63.5 101.4 238.3 308.4 392.0 50002.6 50000 20 992.34 1508991 144668192 1999909 77995290 76237183338 90097771731 202948190804 171828182 0 0 0 0 385592 2207902 +ebbrt_tuned 0 50 0xd00 55 58.2 62.4 100.1 237.6 310.6 394.8 49988.0 50000 20 993.18 1506721 144543023 1999373 77973726 76351015937 90611921542 204094372826 173272511 0 0 0 0 387135 2208762 +ebbrt_tuned 1 50 0xd00 55 57.9 62.0 99.4 232.7 306.0 392.1 49974.4 50000 20 993.89 1508518 144633061 1998807 77952090 76338729834 90611422112 204094058611 173704804 0 0 0 0 389300 2214618 +ebbrt_tuned 2 50 0xd00 55 58.5 62.6 100.7 237.4 309.3 395.0 49976.4 50000 20 992.11 1509676 144696200 1998795 77950404 76491827021 90051588829 202832393543 171655629 0 0 0 0 385885 2203789 +ebbrt_tuned 0 100 0xd00 135 62.0 68.7 121.6 257.7 328.2 426.4 49942.0 50000 20 992.86 1470162 142279928 1997540 77902366 76153606718 89446069649 201568007985 172578703 0 0 0 0 385787 1822646 +ebbrt_tuned 1 100 0xd00 135 60.7 66.7 119.7 255.2 326.5 422.2 49989.5 50000 20 992.41 1468432 142240526 1999430 77976374 76414822754 89557782588 201818096590 172988042 0 0 0 0 386820 1823807 +ebbrt_tuned 2 100 0xd00 135 60.4 67.1 120.5 261.4 329.2 426.9 49976.3 50000 20 992.36 1469811 142279069 1998923 77956272 76403171991 89724675183 202192057419 173856858 0 0 0 0 387625 1829077 +ebbrt_tuned 0 100 0xd00 95 60.8 67.2 120.3 256.3 325.7 425.8 49909.7 50000 20 991.39 1466918 142044442 1996169 77849146 76290444509 89502105125 201688306046 173479575 0 0 0 0 385705 1824248 +ebbrt_tuned 2 100 0xd00 95 61.7 68.7 123.0 265.1 330.5 431.8 49932.8 50000 20 990.53 1470834 142333337 1997141 77887164 76419882087 89571125235 201862124332 174007461 0 0 0 0 388728 1829782 +ebbrt_tuned 1 100 0xd00 55 60.4 66.6 118.1 248.3 306.8 396.1 50003.6 50000 20 985.95 1484079 143181569 1999991 77997942 74971766463 85466275595 192896514898 154122018 0 0 0 0 392790 1834902 +ebbrt_tuned 2 100 0xd00 55 59.6 66.0 117.0 243.9 306.0 396.0 49863.3 50000 20 987.47 1480389 142785610 1994422 77781078 75043972275 85707303405 193385232642 155700035 0 0 0 0 391080 1828795 +ebbrt_tuned 0 200 0xd00 135 69.6 83.4 182.4 310.8 375.0 484.8 50123.3 50000 20 986.65 1402999 138466274 2004714 78182234 75294218654 84043194852 189637053808 155928137 0 0 0 0 432938 1272948 +ebbrt_tuned 1 200 0xd00 135 68.5 82.7 182.2 308.7 374.0 483.1 50043.9 50000 20 987.05 1400514 138226399 2001598 78060706 75150450702 84621603210 190934610204 160026048 0 0 0 0 432143 1271194 +ebbrt_tuned 2 200 0xd00 135 70.5 84.6 184.3 315.7 378.6 488.3 50136.1 50000 20 987.52 1404202 138523466 2005259 78203168 75211589505 84832309311 191391206736 160828279 0 0 0 0 434377 1273449 +ebbrt_tuned 0 200 0xd00 95 70.5 83.9 183.8 315.9 380.9 489.6 50089.5 50000 20 987.3 1401676 138335381 2003402 78130962 75437368783 84690240853 191051978801 160048556 0 0 0 0 430764 1271972 +ebbrt_tuned 1 200 0xd00 95 69.6 83.2 180.8 304.2 368.2 479.7 50080.1 50000 20 983.42 1404503 138489082 2003069 78117966 74615320674 82835964058 187001331649 150261379 0 0 0 0 430237 1273745 +ebbrt_tuned 2 200 0xd00 95 70.2 84.2 182.7 309.9 374.1 485.1 49916.6 50000 20 984.68 1406704 138457871 1996509 77862164 74852002158 83515617521 188503937968 155644115 0 0 0 0 431518 1274026 +ebbrt_tuned 0 200 0xd00 75 70.9 84.9 184.0 313.0 378.5 484.8 49885.8 50000 20 985.4 1400642 138055883 1995258 77813302 74952420292 83876153674 189277894434 156229970 0 0 0 0 428742 1271501 +ebbrt_tuned 1 200 0xd00 75 70.6 84.4 184.8 314.8 380.4 487.8 49975.3 50000 20 986.44 1401384 138195071 1998808 77951882 75076882483 84379155005 190383305559 158905200 0 0 0 0 430260 1272627 +ebbrt_tuned 2 200 0xd00 75 69.2 82.1 182.6 310.5 377.6 485.9 50038.7 50000 20 987.0 1402155 138303101 2001386 78052610 75075580923 84522426599 190693963158 159358173 0 0 0 0 429189 1271919 +ebbrt_tuned 0 200 0xd00 55 70.7 84.3 184.2 315.8 379.5 488.2 50051.5 50000 20 987.5 1404293 138449634 2001916 78072910 75264974965 84902078501 191540566538 161826790 0 0 0 0 428112 1272843 +ebbrt_tuned 2 200 0xd00 55 69.6 83.3 184.2 315.7 382.7 488.9 50006.1 50000 20 987.81 1400766 138173692 2000052 78000766 75379735484 85263675143 192310634365 162118066 0 0 0 0 426788 1271115 +ebbrt_tuned 0 50 0xf00 135 55.9 59.2 92.8 205.6 269.9 340.8 49855.4 50000 20 1062.53 1525403 145503570 1994018 77764944 75936536949 92634673748 185324684643 171808983 0 0 0 0 393435 2229256 +ebbrt_tuned 1 50 0xf00 135 56.5 60.0 94.3 208.4 272.0 340.2 49967.6 50000 20 1065.89 1530337 145910573 1998498 77940062 76018130446 92754821594 185503882864 169320274 0 0 0 0 392247 2229951 +ebbrt_tuned 1 50 0xf00 95 56.2 59.7 93.5 207.1 271.5 345.8 50024.7 50000 20 1065.25 1531660 146067547 2000855 78031590 76089565363 92984173429 185991041307 173287260 0 0 0 0 393171 2235033 +ebbrt_tuned 2 50 0xf00 95 56.3 59.9 94.3 207.7 272.3 347.6 50001.9 50000 20 1064.69 1531842 146059889 1999864 77993598 76170352669 92730191231 185492706293 171403920 0 0 0 0 392559 2230895 +ebbrt_tuned 0 50 0xf00 75 56.7 60.3 94.7 208.7 274.1 350.8 49989.6 50000 20 1049.89 1510727 144039249 1972300 76917698 75076261392 91552312811 183106345361 169090482 0 0 0 0 385379 2199140 +ebbrt_tuned 1 50 0xf00 75 56.3 59.7 94.1 210.1 273.0 344.4 50012.5 50000 20 1067.04 1528533 145884837 2000369 78012666 76349343616 93209802274 186371634233 173164754 0 0 0 0 393073 2233288 +ebbrt_tuned 2 50 0xf00 75 56.7 60.2 95.1 207.9 273.2 345.3 49984.4 50000 20 1067.96 1528989 145836430 1999246 77969140 76083145068 93037390210 186062248864 172534600 0 0 0 0 391725 2229632 +ebbrt_tuned 0 50 0xf00 55 56.0 59.4 93.6 209.9 273.0 345.5 49954.2 50000 20 1065.44 1533999 146129484 1998019 77921138 76272083337 93063276683 186105841664 173208737 0 0 0 0 391888 2231320 +ebbrt_tuned 2 50 0xf00 55 56.2 59.7 94.0 208.1 273.0 344.1 50076.2 50000 20 1066.88 1531665 146121442 2002860 78110190 76294075578 93306323747 186572815092 173733231 0 0 0 0 390296 2228170 +ebbrt_tuned 0 100 0xf00 135 58.8 65.3 115.6 228.1 292.4 376.8 49931.2 50000 20 1064.05 1487739 143338365 1997035 77881834 75972935756 91769257245 183863614473 171223314 0 0 0 0 390334 1829775 +ebbrt_tuned 1 100 0xf00 135 58.1 64.4 115.0 229.0 293.9 379.5 49870.2 50000 20 1062.83 1485557 143145620 1994626 77788950 75993235153 91545040375 183447494575 171740468 0 0 0 0 392187 1830902 +ebbrt_tuned 2 100 0xf00 135 58.7 65.5 115.8 230.5 292.6 379.0 49932.9 50000 20 1063.87 1489408 143413613 1997150 77887364 76180933958 91740435256 183722592548 170037246 0 0 0 0 393522 1839292 +ebbrt_tuned 0 100 0xf00 95 58.5 64.9 115.2 235.7 295.8 384.9 49963.3 50000 20 1066.37 1487955 143369068 1998388 77935430 76162844845 91849221217 184002176444 172420666 0 0 0 0 390454 1830640 +ebbrt_tuned 1 100 0xf00 95 58.8 65.3 115.9 233.6 294.6 384.8 50045.5 50000 20 1066.37 1486636 143377725 2001651 78062516 76482371714 92158103407 184604416109 175076706 0 0 0 0 390529 1833714 +ebbrt_tuned 2 100 0xf00 95 58.3 64.7 114.9 228.2 292.7 381.1 50021.5 50000 20 1066.77 1487083 143399788 2000725 78026588 76346097564 91854624955 183943245399 172729728 0 0 0 0 390338 1833420 +ebbrt_tuned 0 100 0xf00 75 58.9 65.5 116.5 235.4 295.1 383.3 50016.9 50000 20 1067.44 1489384 143512735 2000554 78020174 76502197914 92248432181 184690198370 172634987 0 0 0 0 392357 1838188 +ebbrt_tuned 1 100 0xf00 75 58.5 65.0 115.4 230.7 293.2 382.8 50024.3 50000 20 1066.75 1487891 143438616 2000812 78029644 76495462577 92562806145 185305083891 174603912 0 0 0 0 392647 1838748 +ebbrt_tuned 2 100 0xf00 75 58.2 64.4 114.8 228.8 291.6 380.3 50015.6 50000 20 988.79 1379837 133038869 1856077 72385494 70933034967 85361916818 170922189951 161002979 0 0 0 0 360760 1697097 +ebbrt_tuned 0 100 0xf00 55 58.3 64.8 116.1 230.2 292.0 380.0 49912.7 50000 20 1065.02 1482975 142993494 1996368 77856710 76431864079 92400812869 185047279633 176030346 0 0 0 0 390713 1833900 +ebbrt_tuned 1 100 0xf00 55 59.2 65.4 115.6 236.7 295.3 381.2 50030.1 50000 20 1065.44 1488758 143500017 2001029 78038438 76556035864 92034210139 184253412811 171697481 0 0 0 0 390101 1831853 +ebbrt_tuned 2 100 0xf00 55 59.6 66.3 117.2 235.3 295.8 386.4 49997.4 50000 20 1065.35 1488729 143461370 1999729 77987762 76593253147 92135237106 184494667117 174358566 0 0 0 0 388890 1828239 +ebbrt_tuned 0 200 0xf00 135 67.6 81.1 179.7 299.0 357.3 464.6 50055.7 50000 20 1062.38 1412853 138973703 2002018 78076816 76233000273 89651776278 179574001492 170635608 0 0 0 0 434339 1275440 +ebbrt_tuned 1 200 0xf00 135 68.3 81.9 181.0 302.1 361.5 470.5 50036.9 50000 20 1063.3 1409353 138764238 2001345 78050850 76359350822 90414895533 181120739054 173363095 0 0 0 0 435365 1276461 +ebbrt_tuned 2 200 0xf00 135 65.5 78.1 174.4 287.9 339.9 437.3 50038.0 50000 20 1052.7 1411934 138879546 2001356 78050966 74562248195 85301420847 171577445647 151230291 0 0 0 0 442168 1276487 +ebbrt_tuned 0 200 0xf00 95 66.1 79.3 176.6 292.2 346.4 443.0 49971.8 50000 20 1057.33 1412114 138849155 1998677 77947100 74869074718 86152002642 173219502526 155597608 0 0 0 0 444799 1278211 +ebbrt_tuned 1 200 0xf00 95 67.4 81.1 178.4 295.1 347.8 446.1 50006.8 50000 20 1058.57 1408917 138673003 2000154 78004544 75125427603 86793493217 174403374775 159104639 0 0 0 0 441402 1275932 +ebbrt_tuned 2 200 0xf00 95 66.9 80.3 178.0 293.9 349.6 450.8 50040.4 50000 20 1060.72 1408262 138705124 2001428 78054022 75085131689 86968821844 174711168131 160124208 0 0 0 0 442634 1275375 +ebbrt_tuned 0 200 0xf00 75 66.9 79.9 176.8 293.7 349.0 450.5 49918.0 50000 20 1059.76 1412041 138758859 1996567 77865036 74840751567 86653191248 174037082590 158258206 0 0 0 0 437754 1273159 +ebbrt_tuned 1 200 0xf00 75 66.6 79.5 175.8 294.2 349.3 453.8 49999.2 50000 20 1058.26 1407749 138610859 1999827 77991506 75232517596 87447689296 175590080991 162974976 0 0 0 0 439140 1274384 +ebbrt_tuned 2 200 0xf00 75 67.1 80.5 179.0 297.3 353.0 453.1 49973.0 50000 20 1060.82 1409850 138698498 1998752 77949544 75621435605 88082469184 176751323496 162603435 0 0 0 0 441614 1275886 +ebbrt_tuned 0 200 0xf00 55 67.1 80.4 176.9 293.8 348.5 452.6 49938.1 50000 20 1060.54 1408955 138612479 1997301 77893642 75286976180 88035915880 176693245602 164016277 0 0 0 0 440930 1276137 +ebbrt_tuned 1 200 0xf00 55 68.1 81.1 179.1 296.4 349.6 451.3 49924.4 50000 20 1060.4 1412158 138795304 1996792 77872458 75206811741 88017093184 176638566392 164493353 0 0 0 0 442813 1276331 +ebbrt_tuned 2 200 0xf00 55 68.1 81.8 181.0 298.6 355.4 460.0 50071.7 50000 20 1060.67 1410004 138812011 2002666 78102798 75637080592 88400366311 177324969741 164322986 0 0 0 0 443488 1276418 +ebbrt_tuned 0 50 0x1100 135 54.4 58.1 89.5 189.3 249.3 316.2 49980.7 50000 20 1146.76 1545054 146813708 1999068 77962322 76785724552 96522935934 175037104496 174633279 0 0 0 0 400416 2240713 +ebbrt_tuned 1 50 0x1100 135 55.1 58.7 90.5 187.8 245.3 316.2 50042.6 50000 20 1146.31 1546143 146950507 2001573 78059562 76787771973 96127571002 174341620783 171132478 0 0 0 0 399942 2238266 +ebbrt_tuned 2 50 0x1100 135 55.0 58.6 90.8 191.3 248.6 316.2 49974.1 50000 20 1149.95 1542549 146670904 1998823 77952208 76699518410 96662425895 175331806295 175627540 0 0 0 0 399016 2236454 +ebbrt_tuned 0 50 0x1100 95 53.8 57.6 89.4 191.0 248.3 316.3 50081.5 50000 20 1147.17 1551021 147299281 2003135 78120630 76894041204 96585177659 175142290106 174457951 0 0 0 0 400333 2245464 +ebbrt_tuned 1 50 0x1100 95 55.4 59.1 91.8 194.0 251.4 322.6 49963.1 50000 20 1147.07 1544870 146779450 1998323 77933122 76600405658 96619003143 175270183586 175866865 0 0 0 0 400749 2241586 +ebbrt_tuned 2 50 0x1100 95 53.5 57.5 88.2 184.2 233.8 294.7 50010.0 50000 20 1134.87 1553896 147370206 2000259 78008152 75186661978 92433179334 168842879192 157712887 0 0 0 0 405636 2256006 +ebbrt_tuned 0 50 0x1100 75 53.8 57.6 87.6 182.7 234.9 294.9 50092.7 50000 20 1140.17 1557797 147722579 2003547 78136716 75626378376 93055059385 169742661235 161309446 0 0 0 0 403431 2253283 +ebbrt_tuned 1 50 0x1100 75 54.4 58.2 88.5 185.9 238.1 299.8 49908.1 50000 20 1144.2 1549712 147019724 1996192 77849778 75583954719 93282747971 170095518097 163201123 0 0 0 0 402095 2242522 +ebbrt_tuned 2 50 0x1100 75 54.5 58.3 88.7 186.3 241.1 303.5 49987.7 50000 20 1142.26 1554594 147402415 1999316 77970926 75625923993 93769957967 170905089115 166035013 0 0 0 0 403078 2248354 +ebbrt_tuned 0 50 0x1100 55 54.4 58.1 89.3 186.2 241.2 306.3 50052.8 50000 20 1143.86 1550904 147248512 2001970 78075258 75650502418 94237503239 171660982467 167003271 0 0 0 0 401479 2245353 +ebbrt_tuned 1 50 0x1100 55 54.2 58.0 89.0 187.6 242.9 303.9 50039.5 50000 20 1145.07 1549512 147161542 2001442 78054378 75839389478 94132885136 171395322631 166365336 0 0 0 0 400331 2241364 +ebbrt_tuned 2 50 0x1100 55 53.9 57.7 88.6 186.4 241.7 304.1 49996.4 50000 20 1144.38 1549210 147090253 1999650 77984412 76183286580 94720942012 172362020869 168251665 0 0 0 0 401472 2244190 +ebbrt_tuned 0 50 0x1100 135 54.0 58.3 94.8 223.2 269.3 364.6 99856.3 100000 20 1252.03 2251959 243283978 3993425 155734670 150995996742 164693411005 286008825671 332510681 0 0 0 0 342156 3310107 +ebbrt_tuned 1 50 0x1100 135 54.3 58.8 95.5 227.7 272.1 367.3 100030.4 100000 20 1255.75 2251081 243375971 4000468 156008684 151208043574 166239953962 288579602997 334777737 0 0 0 0 343210 3321176 +ebbrt_tuned 2 50 0x1100 135 54.9 59.1 96.1 225.3 270.3 362.1 99851.9 100000 20 1256.46 2250948 243150454 3993242 155726854 150910310372 165538598826 287425575837 334512991 0 0 0 0 343160 3314874 +ebbrt_tuned 0 50 0x1100 95 54.6 58.8 95.9 224.6 270.6 361.1 99919.4 100000 20 1256.1 2249818 243182914 3996056 155836382 151155875975 165126401922 286746157689 334932416 0 0 0 0 343428 3316941 +ebbrt_tuned 1 50 0x1100 95 54.6 58.9 96.3 227.7 272.1 364.1 99975.4 100000 20 1256.33 2253206 243430645 3998257 155922662 151056476934 165514022569 287371181581 335215085 0 0 0 0 342561 3316277 +ebbrt_tuned 2 50 0x1100 95 54.8 58.8 96.3 226.1 272.2 364.2 100109.8 100000 20 1255.57 2255180 243707505 4003645 156132808 151374562666 165486972863 287312598710 335976646 0 0 0 0 342457 3318550 +ebbrt_tuned 0 50 0x1100 75 54.8 59.0 96.3 226.9 273.2 365.4 99915.1 100000 20 1255.17 2251339 243281044 3995790 155823960 150914133514 164341080988 285374888049 330431463 0 0 0 0 340274 3304709 +ebbrt_tuned 1 50 0x1100 75 55.1 59.2 96.8 228.1 273.0 362.1 99889.9 100000 20 1254.63 2249419 243128576 3994869 155791348 151048771580 165177878226 286728027324 332927424 0 0 0 0 342326 3312830 +ebbrt_tuned 2 50 0x1100 75 54.6 58.8 95.7 226.8 271.4 362.9 99913.8 100000 20 1254.82 2249916 243200921 3995803 155827940 151252971354 165204721927 286841499539 333345838 0 0 0 0 341216 3310024 +ebbrt_tuned 1 50 0x1100 55 55.1 59.6 96.7 228.9 273.8 365.8 100035.8 100000 20 1252.88 2253784 243561071 4000423 156004162 151125430466 165626285292 287563399497 336200583 0 0 0 0 342359 3314025 +ebbrt_tuned 2 50 0x1100 55 54.1 58.5 95.8 224.7 271.2 366.1 99958.9 100000 20 1256.58 2249609 243223893 3997714 155902892 151096920347 166190227677 288461729887 337391236 0 0 0 0 342121 3314606 +ebbrt_tuned 0 100 0x1100 135 56.9 62.7 110.6 208.8 263.2 338.3 50069.4 50000 20 1142.33 1505126 144539211 2002622 78100882 75806848711 93397410790 170473202062 166561522 0 0 0 0 404288 1843621 +ebbrt_tuned 1 100 0x1100 135 57.2 63.4 112.5 211.4 264.5 343.7 49986.7 50000 20 1142.74 1507042 144545417 1999269 77970060 75702444856 93265392187 170225188668 165702745 0 0 0 0 403245 1844577 +ebbrt_tuned 2 100 0x1100 135 56.7 62.8 111.1 213.0 265.3 342.6 50032.4 50000 20 1144.82 1503380 144391851 2001101 78041282 75963674831 94081422993 171610038834 169434077 0 0 0 0 403407 1847137 +ebbrt_tuned 0 100 0x1100 95 56.7 62.3 109.6 206.8 262.5 341.8 50023.4 50000 20 1143.19 1503929 144396545 2000782 78028534 75820276724 93399907736 170393455542 166308330 0 0 0 0 401363 1840548 +ebbrt_tuned 2 100 0x1100 95 57.0 63.1 112.5 215.1 267.1 345.4 50051.3 50000 20 1140.55 1502405 144336068 2001894 78072264 75904097627 93664024018 170883083364 168132784 0 0 0 0 402379 1841763 +ebbrt_tuned 0 100 0x1100 75 56.9 62.8 111.6 213.3 266.4 347.6 50020.3 50000 20 1142.14 1504854 144448105 2000578 78021264 75820419875 93441174163 170447102526 166914152 0 0 0 0 398696 1833156 +ebbrt_tuned 1 100 0x1100 75 57.0 63.1 111.9 214.2 267.0 350.0 50065.0 50000 20 1143.72 1505398 144543079 2002450 78093864 75946198909 94080783702 171509255366 168886738 0 0 0 0 402195 1844703 +ebbrt_tuned 2 100 0x1100 75 57.1 63.1 111.1 212.7 268.1 348.7 50010.0 50000 20 1142.94 1507114 144591421 2000291 78009894 75796526478 93870507918 171128974074 168549522 0 0 0 0 400590 1837812 +ebbrt_tuned 0 100 0x1100 55 56.2 62.2 110.9 211.6 266.3 347.0 49916.5 50000 20 1063.63 1443802 138540033 1919326 74853008 73137663323 90175431490 164432303615 162287068 0 0 0 0 386845 1768716 +ebbrt_tuned 1 100 0x1100 55 56.4 62.5 110.8 210.1 263.2 339.6 49943.1 50000 20 1142.07 1501866 144189315 1997578 77904142 75910000493 93888400760 171187616105 168978116 0 0 0 0 401899 1840917 +ebbrt_tuned 2 100 0x1100 55 57.1 63.2 111.9 212.1 266.1 349.0 50099.3 50000 20 1059.37 1397867 134197951 1858486 72480914 70531797395 87451045480 159363052488 157295598 0 0 0 0 370658 1708114 +ebbrt_tuned 0 100 0x1100 135 59.0 68.7 126.3 254.1 301.2 397.3 99755.8 100000 20 1252.49 2224027 241438158 3989460 155580904 150664075017 163336710539 284017067652 334819712 0 0 0 0 361472 2408209 +ebbrt_tuned 0 100 0x1100 95 59.3 69.3 126.7 254.5 303.6 401.2 100208.2 100000 20 1252.64 2232508 242439249 4007683 156290788 151077542706 162847550130 283127155063 332239098 0 0 0 0 363525 2413482 +ebbrt_tuned 2 100 0x1100 95 58.6 67.8 122.9 238.2 285.1 371.8 100067.8 100000 20 1242.42 2234080 242413924 4001967 156067236 148639151569 155769014946 271973814078 305658268 0 0 0 0 366492 2422603 +ebbrt_tuned 0 100 0x1100 75 58.7 67.7 123.5 238.2 285.2 370.6 100051.6 100000 20 1242.63 2232557 242283843 4001385 156044858 148654312680 156541609182 273192331583 310535095 0 0 0 0 366326 2419643 +ebbrt_tuned 0 100 0x1100 55 58.5 68.0 124.0 244.7 293.6 383.5 100106.7 100000 20 1247.79 2230225 242181810 4003518 156127770 149540045141 159478354248 277868943184 318886834 0 0 0 0 362352 2415889 +ebbrt_tuned 1 100 0x1100 55 59.9 69.6 126.2 248.7 296.9 392.2 99855.1 100000 20 1247.45 2231548 242012787 3993517 155738472 149075525620 159194943490 277356578463 318090495 0 0 0 0 361788 2415083 +ebbrt_tuned 2 100 0x1100 55 59.4 69.3 125.7 247.1 294.6 391.9 99996.3 100000 20 1248.4 2229910 242046073 3999209 155959994 149323956049 159790396772 278420766175 321710730 0 0 0 0 365693 2419547 +ebbrt_tuned 0 200 0x1100 135 64.8 78.2 173.9 284.6 331.6 427.5 49991.5 50000 20 1138.71 1418539 139258503 1999513 77979140 75509226308 90942844286 165806514609 165671748 0 0 0 0 447926 1279225 +ebbrt_tuned 2 200 0x1100 135 65.9 79.0 176.2 287.6 334.8 432.6 50055.7 50000 20 1135.3 1417932 139262357 2002062 78078680 75606504255 91475286969 166750272397 168051886 0 0 0 0 445208 1278467 +ebbrt_tuned 0 200 0x1100 95 65.2 78.2 175.0 285.7 334.3 434.5 49987.6 50000 20 1138.23 1413880 138954280 1999309 77970682 75592703043 91530928927 166838260514 168530593 0 0 0 0 445111 1277499 +ebbrt_tuned 1 200 0x1100 95 64.3 77.4 172.8 284.0 332.4 431.9 50042.5 50000 20 1136.61 1418092 139250541 2001532 78058222 75553869645 90737149421 165370034333 164350548 0 0 0 0 444115 1277843 +ebbrt_tuned 2 200 0x1100 95 64.5 77.6 173.5 284.8 331.8 429.6 50107.7 50000 20 1139.39 1418179 139344485 2004137 78159982 75895718672 91276088235 166272160591 166029635 0 0 0 0 446950 1279681 +ebbrt_tuned 0 200 0x1100 75 65.8 78.9 174.7 287.2 335.3 432.6 50047.3 50000 20 1136.44 1415213 139098663 2001773 78067700 75599190081 91172196994 166151948573 166789922 0 0 0 0 447087 1279476 +ebbrt_tuned 1 200 0x1100 75 64.5 77.0 172.5 278.1 322.4 412.2 50037.7 50000 20 1130.09 1420159 139403499 2001322 78050216 74484623495 87494674492 160384806791 151547615 0 0 0 0 445543 1280323 +ebbrt_tuned 2 200 0x1100 75 64.4 77.4 173.8 282.5 327.8 420.8 49947.3 50000 20 1133.15 1417226 139126698 1997689 77908400 74854209506 88234249431 161594313416 156254252 0 0 0 0 444870 1277521 +ebbrt_tuned 0 200 0x1100 55 64.8 77.7 172.8 281.9 327.9 423.8 50090.8 50000 20 1135.22 1421379 139538760 2003412 78130272 75217407074 88802826561 162419792935 157584580 0 0 0 0 440661 1277541 +ebbrt_tuned 1 200 0x1100 55 64.8 77.2 172.6 281.5 325.7 419.1 50015.2 50000 20 1136.03 1416861 139176426 2000438 78015534 75022801728 89075350390 162878117258 159716409 0 0 0 0 443668 1279136 +ebbrt_tuned 2 200 0x1100 55 64.0 76.6 171.9 279.5 323.6 417.5 49969.3 50000 20 1136.83 1419591 139292467 1998625 77944682 74913996908 89377199792 163343088729 160458113 0 0 0 0 442384 1277642 +ebbrt_tuned 0 200 0x1100 135 73.7 88.8 187.8 310.9 367.2 475.8 100111.4 100000 20 1244.37 2209981 240982516 4003687 156135124 149345515774 156926190418 274002361932 321423700 0 0 0 0 458125 1428105 +ebbrt_tuned 1 200 0x1100 135 73.2 88.5 186.4 309.5 364.8 461.2 100135.6 100000 20 1243.15 2211337 241131857 4004789 156177674 149010476559 156418043389 273136571543 318388439 0 0 0 0 465684 1428959 +ebbrt_tuned 2 200 0x1100 135 74.4 90.3 188.5 313.6 366.5 465.9 99989.0 100000 20 1241.15 2206792 240674349 3998904 155948850 148778386115 156198659372 272659298852 317944135 0 0 0 0 463274 1427637 +ebbrt_tuned 0 200 0x1100 95 74.0 89.1 188.3 309.7 364.1 466.6 100010.7 100000 20 1241.68 2207886 240763995 3999673 155976636 149042326633 156996600876 274066167326 319415618 0 0 0 0 461816 1427797 +ebbrt_tuned 1 200 0x1100 95 73.5 88.2 188.6 311.7 366.4 466.3 100125.5 100000 20 1244.16 2210968 241055726 4004181 156154814 149261577312 156845031328 273745015004 318913686 0 0 0 0 462188 1428127 +ebbrt_tuned 2 200 0x1100 95 74.4 90.5 189.2 311.6 366.8 466.7 100063.7 100000 20 1242.87 2208482 240852541 4001716 156058340 149406709415 157910456817 275615705476 324584085 0 0 0 0 462073 1428149 +ebbrt_tuned 0 200 0x1100 75 74.7 89.8 187.6 312.7 365.6 464.6 99980.6 100000 20 1244.12 2208234 240760421 3998470 155930108 149224138231 157873592195 275477506556 322792763 0 0 0 0 459747 1427941 +ebbrt_tuned 1 200 0x1100 75 74.1 89.7 187.6 310.9 364.8 469.1 100002.5 100000 20 1243.27 2206502 240683596 3999332 155965862 149398123453 156741204947 273568872020 319735189 0 0 0 0 463946 1427720 +ebbrt_tuned 2 200 0x1100 75 75.1 91.0 188.9 313.1 368.3 473.6 100040.4 100000 20 1247.17 2207048 240751756 4000930 156027000 149671484681 157916812955 275439227658 324407153 0 0 0 0 458513 1427828 +ebbrt_tuned 0 200 0x1100 55 74.0 89.3 187.3 312.5 366.2 469.4 100050.2 100000 20 1244.94 2211102 241010022 4001239 156039252 149770918483 157911113921 275502792294 326450811 0 0 0 0 460681 1427683 +ebbrt_tuned 1 200 0x1100 55 73.7 89.1 188.9 315.1 371.0 476.9 99980.9 100000 20 1244.94 2206411 240631958 3998454 155929666 149843410679 157705623065 275029824421 322901698 0 0 0 0 457353 1427557 +ebbrt_tuned 2 200 0x1100 55 73.5 88.7 187.2 312.0 368.3 469.7 99881.1 100000 20 1247.84 2205452 240470280 3994618 155781530 149932519067 158347529455 276137981469 324989008 0 0 0 0 458307 1427706 +ebbrt_tuned 0 50 0x1300 135 52.9 57.1 87.7 177.7 230.3 296.3 50057.3 50000 20 1228.79 1557862 147667883 2002142 78081912 76698112966 98864826234 165407362466 173656846 0 0 0 0 400039 2252224 +ebbrt_tuned 1 50 0x1300 135 52.6 56.6 86.9 176.5 229.9 293.9 49963.7 50000 20 1227.92 1557889 147583118 1998364 77934680 76615821829 98638159703 165068870812 173384642 0 0 0 0 398590 2246929 +ebbrt_tuned 2 50 0x1300 135 53.4 57.5 88.0 179.9 230.2 294.7 50047.6 50000 20 1228.79 1554486 147449916 2001731 78065948 76729823885 99197158149 165991851840 175631460 0 0 0 0 400612 2252619 +ebbrt_tuned 0 50 0x1300 95 53.3 57.5 87.9 180.6 232.4 294.6 50175.6 50000 20 1228.76 1558976 147873155 2006823 78263638 77040344620 99147435820 165878971225 175787637 0 0 0 0 400126 2256053 +ebbrt_tuned 1 50 0x1300 95 53.5 57.7 88.6 180.4 231.2 294.1 49990.0 50000 20 1228.71 1555176 147461002 1999401 77975352 76628280202 99120414858 165807262200 175223175 0 0 0 0 400385 2252762 +ebbrt_tuned 2 50 0x1300 95 53.9 57.9 88.4 178.3 231.8 293.7 49984.8 50000 20 1228.82 1557308 147568838 1999203 77966638 76747318711 99246462751 166055659032 176595146 0 0 0 0 399692 2250860 +ebbrt_tuned 0 50 0x1300 75 52.9 57.1 87.8 179.1 231.9 294.4 50017.0 50000 20 1228.68 1558659 147678855 2000472 78016890 76747707581 98997247903 165661913881 175411908 0 0 0 0 399144 2248381 +ebbrt_tuned 1 50 0x1300 75 52.9 56.9 87.3 179.4 232.2 295.5 49982.4 50000 20 1228.18 1555878 147461806 1999153 77965424 76623250001 98661858194 165118455128 174534185 0 0 0 0 398615 2246194 +ebbrt_tuned 2 50 0x1300 75 51.6 55.7 84.1 166.1 213.6 270.1 49884.9 50000 20 1216.67 1564835 147908752 1995274 77814224 74943240267 94346953994 159539140546 156072687 0 0 0 0 407135 2254250 +ebbrt_tuned 0 50 0x1300 55 53.2 57.4 86.7 173.7 220.4 275.9 50068.6 50000 20 1222.27 1566598 148231205 2002550 78096908 75590474580 95269209642 160741092903 159630215 0 0 0 0 408029 2258465 +ebbrt_tuned 2 50 0x1300 55 52.7 56.8 86.1 173.3 221.8 279.2 49940.9 50000 20 1220.01 1559895 147672556 1997462 77899614 75510365423 95808523388 161385004669 163610017 0 0 0 0 406559 2252342 +ebbrt_tuned 0 50 0x1300 135 52.9 57.4 91.7 203.5 245.6 324.3 100003.0 100000 20 1352.7 2264859 244188882 3999469 155970036 151031010880 169271874132 266645247880 332321612 0 0 0 0 351097 3332032 +ebbrt_tuned 1 50 0x1300 135 53.3 57.9 92.5 204.9 246.9 324.9 99982.3 100000 20 1351.62 2259333 243795498 3998630 155937076 150686141044 169123681528 266504190981 333045329 0 0 0 0 350773 3330394 +ebbrt_tuned 2 50 0x1300 135 52.8 57.3 91.6 206.3 246.9 325.0 100053.2 100000 20 1353.82 2259307 243892285 4001391 156045064 151198382395 169684800474 267323492224 335017980 0 0 0 0 353026 3333928 +ebbrt_tuned 1 50 0x1300 95 52.3 56.5 90.4 202.5 245.4 320.0 99967.7 100000 20 1353.91 2261143 243924561 3998055 155914830 151069838537 169580141135 267119296264 335831535 0 0 0 0 350980 3331924 +ebbrt_tuned 2 50 0x1300 95 52.8 57.1 90.9 202.5 245.5 322.9 99994.8 100000 20 1353.42 2262205 244001353 3999049 155953592 151220131821 169183318539 266485360498 333003351 0 0 0 0 350901 3328729 +ebbrt_tuned 0 50 0x1300 75 52.4 56.8 91.2 205.2 246.9 328.7 99903.3 100000 20 1353.13 2261543 243844889 3995488 155814672 150901623586 168992235428 266190065794 329596039 0 0 0 0 349674 3325063 +ebbrt_tuned 1 50 0x1300 75 53.0 57.5 92.4 206.6 247.7 328.9 100088.8 100000 20 1351.75 2262614 244097724 4002844 156102938 151186976662 168636727775 265639560853 328865143 0 0 0 0 352077 3333036 +ebbrt_tuned 2 50 0x1300 75 52.4 56.8 90.9 202.8 245.9 322.3 100054.8 100000 20 1354.09 2262432 244082108 4001562 156051708 151335976248 169365175510 266696211552 331168919 0 0 0 0 349764 3327692 +ebbrt_tuned 0 50 0x1300 55 53.0 57.5 91.9 204.6 247.1 327.2 99813.6 100000 20 1353.02 2257283 243495057 3991821 155671616 150777237857 169288130165 266570850352 330311384 0 0 0 0 351080 3326573 +ebbrt_tuned 1 50 0x1300 55 52.5 56.9 91.2 204.9 247.1 325.2 100085.7 100000 20 1354.96 2262868 244148089 4002778 156099088 151235529278 170008884288 267715769511 334781667 0 0 0 0 349207 3329103 +ebbrt_tuned 2 50 0x1300 55 53.2 57.6 91.6 202.2 246.7 326.6 100090.3 100000 20 1354.1 2262280 244118530 4002846 156102242 151278963363 169934214081 267548597416 333374512 0 0 0 0 349847 3330096 +ebbrt_tuned 0 100 0x1300 135 55.3 61.2 107.6 199.4 245.5 322.7 50021.7 50000 20 1221.64 1512811 144945098 2000749 78027758 75347604737 94532074750 159925219837 162382332 0 0 0 0 407748 1848603 +ebbrt_tuned 1 100 0x1300 135 55.5 61.5 108.1 199.0 243.5 316.6 50114.0 50000 20 1221.63 1512310 144995466 2004386 78168932 75729677542 94781249670 160271328892 163766907 0 0 0 0 406275 1846435 +ebbrt_tuned 0 100 0x1300 95 55.2 61.2 108.0 199.3 245.5 321.4 50166.5 50000 20 1224.44 1516222 145287766 2006452 78249608 75963767870 95434891429 161163919921 165609887 0 0 0 0 406802 1851664 +ebbrt_tuned 1 100 0x1300 95 55.1 61.2 107.3 200.2 246.4 321.5 49940.5 50000 20 1224.67 1512488 144807842 1997444 77898880 75538092257 95465248718 161122969978 165332407 0 0 0 0 404094 1839754 +ebbrt_tuned 2 100 0x1300 95 55.3 61.3 108.4 201.7 247.6 319.7 49953.0 50000 20 1224.89 1509670 144669242 1997993 77920152 75779883881 95614711613 161379407843 166945062 0 0 0 0 406967 1846955 +ebbrt_tuned 0 100 0x1300 75 55.3 61.6 109.6 203.3 248.2 323.8 49989.9 50000 20 1224.74 1513246 144924241 1999408 77975276 75819745977 96192580285 162291073361 168727998 0 0 0 0 406059 1845921 +ebbrt_tuned 1 100 0x1300 75 55.7 61.9 109.3 201.6 248.6 324.8 50054.6 50000 20 1224.85 1512876 144968212 2002069 78079342 76021097054 96122296110 162081578579 167805931 0 0 0 0 407422 1850675 +ebbrt_tuned 2 100 0x1300 75 55.1 61.1 108.9 201.4 247.1 323.1 49984.4 50000 20 1224.74 1511325 144797016 1999230 77968444 75868093912 96180356270 162247866174 168965591 0 0 0 0 407081 1849982 +ebbrt_tuned 0 100 0x1300 55 54.9 60.9 108.4 200.1 247.8 324.6 50083.8 50000 20 1225.59 1517634 145301372 2003165 78121126 75932714225 96156360941 162144857594 168493348 0 0 0 0 405950 1846844 +ebbrt_tuned 1 100 0x1300 55 55.2 61.3 109.0 201.7 248.5 324.3 50110.9 50000 20 1223.81 1511663 144946401 2004187 78161422 76046271994 96395886900 162464503395 168764593 0 0 0 0 407114 1852435 +ebbrt_tuned 2 100 0x1300 55 55.7 62.0 109.2 202.4 250.4 326.5 49988.5 50000 20 1224.7 1513512 144941800 1999347 77973074 75735670212 96161897680 162183149745 169674687 0 0 0 0 406468 1847751 +ebbrt_tuned 0 100 0x1300 135 57.6 66.7 122.4 232.4 278.0 360.6 99912.3 100000 20 1350.07 2236783 242389291 3995753 155825644 150843082412 167191987422 264041499178 334137259 0 0 0 0 373969 2429781 +ebbrt_tuned 1 100 0x1300 135 57.6 66.7 122.5 232.2 276.3 363.2 99881.4 100000 20 1349.76 2233414 242156801 3994377 155767984 150848288393 166976920190 263664707949 333688201 0 0 0 0 367245 2419474 +ebbrt_tuned 2 100 0x1300 135 57.7 66.6 121.3 228.7 274.4 359.8 100072.1 100000 20 1350.29 2234079 242401166 4002058 156071032 150770656098 167206481689 264107569994 335331266 0 0 0 0 371582 2425087 +ebbrt_tuned 0 100 0x1300 95 57.8 66.7 122.3 234.8 280.1 361.1 99988.2 100000 20 1350.81 2236185 242453616 3998859 155945954 150695472547 167682157457 264753264448 334308325 0 0 0 0 371464 2426679 +ebbrt_tuned 1 100 0x1300 95 57.9 67.1 122.9 233.6 279.4 365.3 99855.3 100000 20 1351.3 2235181 242236020 3993356 155730118 150632191260 167553021645 264495520191 332494708 0 0 0 0 369624 2421618 +ebbrt_tuned 0 100 0x1300 75 57.5 66.8 122.2 233.5 279.9 363.2 100059.3 100000 20 1250.35 2095785 227303954 3750978 146277622 141560684036 156414440464 246961604774 311423381 0 0 0 0 348646 2273139 +ebbrt_tuned 1 100 0x1300 75 58.2 67.0 121.8 230.5 275.5 359.2 100135.1 100000 20 1351.96 2236992 242630977 4004715 156174128 151027003940 167667250068 264566992315 333352251 0 0 0 0 372104 2427211 +ebbrt_tuned 2 100 0x1300 75 56.7 65.4 118.6 219.2 260.7 335.8 99945.5 100000 20 1328.21 2239485 242607352 3997179 155881088 147779176642 157355979378 250468476174 293470961 0 0 0 0 378607 2434130 +ebbrt_tuned 0 100 0x1300 55 58.2 66.6 121.1 223.2 266.6 342.2 100087.0 100000 20 1338.19 2240817 242846892 4002811 156101076 148643202257 160154165801 254495234518 308288808 0 0 0 0 378008 2435740 +ebbrt_tuned 1 100 0x1300 55 57.1 65.6 120.2 224.6 267.1 344.7 100009.1 100000 20 1339.66 2238798 242615698 3999688 155978502 148691627278 161524249522 256481981627 314237462 0 0 0 0 380075 2436304 +ebbrt_tuned 2 100 0x1300 55 57.1 65.7 120.6 225.0 268.9 348.7 99909.7 100000 20 1341.41 2235123 242301228 3995644 155821476 149188996846 161863891293 256733892157 314852515 0 0 0 0 376046 2430346 +ebbrt_tuned 0 200 0x1300 135 63.8 76.3 171.7 279.1 322.0 411.1 49943.7 50000 20 1218.38 1420980 139330312 1997553 77902554 75615479316 93042464944 156740867587 164986055 0 0 0 0 449470 1279343 +ebbrt_tuned 1 200 0x1300 135 63.5 75.9 170.1 276.0 318.3 409.7 50032.7 50000 20 1219.38 1423871 139632417 2001140 78042960 75616440061 92934858927 156487331723 164108636 0 0 0 0 451160 1279335 +ebbrt_tuned 2 200 0x1300 135 64.6 77.1 172.2 277.7 320.6 409.2 50060.5 50000 20 1219.49 1422349 139550986 2002241 78085202 75571709560 93351597331 157202117720 166026674 0 0 0 0 452108 1280239 +ebbrt_tuned 0 200 0x1300 95 64.4 76.6 170.5 275.5 317.0 405.6 49936.5 50000 20 1217.74 1422792 139427118 1997221 77888916 75338924738 93106841252 156797251214 164959057 0 0 0 0 452172 1280199 +ebbrt_tuned 1 200 0x1300 95 63.0 75.6 170.6 276.5 316.4 407.1 50049.2 50000 20 1218.47 1425129 139693044 2001765 78067168 75605972637 92998600690 156570923179 165408532 0 0 0 0 449694 1279658 +ebbrt_tuned 2 200 0x1300 95 64.4 76.7 172.8 276.9 318.9 409.0 50018.3 50000 20 1218.29 1423152 139542071 2000567 78019968 75528385230 93330407088 157159747531 166841889 0 0 0 0 451253 1280057 +ebbrt_tuned 0 200 0x1300 75 65.1 77.3 171.8 278.6 320.8 408.6 50125.9 50000 20 1219.38 1423311 139658490 2004814 78186140 75783863638 93920131525 158102976127 168573650 0 0 0 0 451719 1281030 +ebbrt_tuned 1 200 0x1300 75 65.1 78.0 173.6 278.2 320.2 412.0 49933.3 50000 20 1217.23 1422648 139412393 1997142 77886956 75476419733 93488770891 157453885744 167414174 0 0 0 0 452820 1280308 +ebbrt_tuned 2 200 0x1300 75 63.7 76.2 170.3 275.6 317.5 408.4 49882.8 50000 20 1217.91 1419776 139193588 1995144 77808576 75524112028 93269586566 157122618280 167156720 0 0 0 0 451569 1278574 +ebbrt_tuned 0 200 0x1300 55 62.6 75.0 167.6 274.8 315.4 404.6 49973.5 50000 20 1218.24 1419687 139296909 1998690 77946700 75519005348 93511392829 157356999557 167607001 0 0 0 0 448057 1276689 +ebbrt_tuned 1 200 0x1300 55 63.8 76.4 171.4 276.0 317.3 405.0 50020.4 50000 20 1219.13 1424502 139627209 2000646 78023494 75741598827 93492021798 157314207795 166421918 0 0 0 0 450437 1278330 +ebbrt_tuned 2 200 0x1300 55 64.3 77.0 172.4 276.9 320.0 407.4 49994.4 50000 20 1220.15 1420190 139339786 1999581 77982462 75712125385 93593051270 157533324110 167357889 0 0 0 0 453571 1280489 +ebbrt_tuned 0 200 0x1300 135 70.0 84.2 181.2 291.7 340.0 431.8 100053.3 100000 20 1335.48 2210024 240974429 4001451 156047380 148533361558 158380440722 251804325910 312396419 0 0 0 0 476082 1430112 +ebbrt_tuned 1 200 0x1300 135 70.7 85.5 183.0 293.5 339.9 431.3 100080.4 100000 20 1336.97 2209653 240949766 4002416 156085954 148752856025 159529890432 253512594632 315451904 0 0 0 0 477222 1430818 +ebbrt_tuned 2 200 0x1300 135 71.3 85.9 182.5 293.8 340.2 429.7 100142.5 100000 20 1339.02 2213471 241235730 4004910 156182206 148769716197 159595343606 253456226752 313944891 0 0 0 0 479684 1430871 +ebbrt_tuned 0 200 0x1300 95 71.0 85.3 181.5 293.1 338.7 430.0 99959.1 100000 20 1339.62 2209882 240812569 3997716 155902510 148441531115 159776458354 253650159728 315276818 0 0 0 0 476222 1430144 +ebbrt_tuned 1 200 0x1300 95 71.6 86.4 183.3 295.8 346.0 437.6 100099.2 100000 20 1339.53 2213695 241229695 4003161 156115304 149213335691 160093263348 254216180858 316056103 0 0 0 0 477295 1430032 +ebbrt_tuned 2 200 0x1300 95 70.4 85.0 180.8 292.0 341.1 429.5 99939.9 100000 20 1340.74 2207715 240678178 3996889 155868726 148571043455 159652990022 253526934291 314502689 0 0 0 0 477506 1430359 +ebbrt_tuned 0 200 0x1300 75 72.1 86.8 182.5 294.4 343.2 434.3 100033.4 100000 20 1340.58 2209025 240870603 4000511 156008274 148718990865 159610748613 253434489570 317009790 0 0 0 0 475678 1429843 +ebbrt_tuned 1 200 0x1300 75 71.7 85.9 183.6 295.3 344.4 433.7 99922.3 100000 20 1339.59 2209883 240781950 3996207 155843134 148831708698 160288171803 254493010073 318491231 0 0 0 0 477747 1429640 +ebbrt_tuned 0 200 0x1300 55 71.9 85.9 183.0 295.5 343.0 431.8 100016.5 100000 20 1338.56 2210380 240927111 3999948 155987750 149287555622 160573040560 254938776265 321034761 0 0 0 0 475565 1430185 +ebbrt_tuned 1 200 0x1300 55 71.1 85.6 182.3 294.1 341.9 434.3 100133.6 100000 20 1338.71 2210952 241071637 4004624 156171182 149300209442 160729949881 255004873935 318572185 0 0 0 0 475925 1430238 +ebbrt_tuned 2 200 0x1300 55 72.1 86.2 182.8 295.8 345.7 435.8 100159.9 100000 20 1338.79 2212685 241207596 4005700 156212438 149475785981 160331389470 254293002003 318330058 0 0 0 0 475181 1430146 +ebbrt_tuned 0 300 0x1300 75 76.3 95.5 232.8 365.5 401.8 499.5 50027.0 50000 20 1210.67 1359626 135753415 2000879 78032070 76658346144 93126978241 156906011654 169449129 0 0 0 0 469292 931330 +ebbrt_tuned 1 300 0x1300 75 76.4 95.8 232.1 364.2 401.1 495.6 49967.2 50000 20 1217.72 1360837 135741417 1998555 77941920 76753789233 93736624338 157831922668 170176935 0 0 0 0 472731 932561 +ebbrt_tuned 2 300 0x1300 75 75.5 94.8 233.2 364.9 401.9 499.7 50086.0 50000 20 1129.01 1265280 126167140 1856886 72417806 71253115019 86293966244 145305586833 156584152 0 0 0 0 433251 863491 +ebbrt_tuned 0 300 0x1300 55 75.1 94.1 232.1 364.2 401.5 497.9 49911.5 50000 20 1217.76 1360210 135641383 1996263 77852340 76593002590 93265748865 157069961037 169618949 0 0 0 0 467787 931333 +ebbrt_tuned 2 300 0x1300 55 75.8 94.5 232.3 364.7 402.3 494.9 50046.5 50000 20 1213.41 1362328 135933941 2001696 78064730 76910290768 93412488367 157339124943 169617987 0 0 0 0 473326 932511 +ebbrt_tuned 0 50 0x1500 135 52.1 56.2 85.8 165.2 214.1 272.9 49888.4 50000 20 1320.04 1562256 147754028 1995369 77817224 76131841903 99979227357 156882934266 172292068 0 0 0 0 408541 2252703 +ebbrt_tuned 1 50 0x1500 135 51.8 56.0 85.1 167.3 216.4 274.4 50072.6 50000 20 1322.45 1566175 148183970 2002673 78102676 76694132251 100064624377 156923164400 172701932 0 0 0 0 405063 2246736 +ebbrt_tuned 2 50 0x1500 135 52.5 56.7 86.3 166.1 215.4 274.5 49991.1 50000 20 1319.22 1566120 148105924 1999506 77979078 76454108734 100378788398 157365478635 173473157 0 0 0 0 407578 2253992 +ebbrt_tuned 1 50 0x1500 95 50.8 55.1 84.0 165.5 213.2 270.9 50035.0 50000 20 1318.88 1568466 148291404 2001300 78049440 76628392406 100724955601 157851512458 175912348 0 0 0 0 407816 2257917 +ebbrt_tuned 2 50 0x1500 95 51.9 56.1 85.6 165.0 213.5 271.7 50046.5 50000 20 1322.13 1567397 148241517 2001669 78063406 76659721040 100959567686 158179780017 176412742 0 0 0 0 407283 2257702 +ebbrt_tuned 0 50 0x1500 75 51.9 56.1 85.4 166.6 214.6 274.0 50038.3 50000 20 1321.68 1568012 148272041 2001386 78052798 76692888700 100647351132 157652123248 174672188 0 0 0 0 404907 2250356 +ebbrt_tuned 1 50 0x1500 75 51.2 55.4 85.2 168.3 215.6 273.5 49990.6 50000 20 1322.15 1564960 148035429 1999483 77978278 76635567594 100859398983 157979895284 175216861 0 0 0 0 408453 2260009 +ebbrt_tuned 2 50 0x1500 75 51.7 56.0 85.7 168.9 215.0 273.9 49954.5 50000 20 1224.71 1483745 140231721 1890653 73734582 72590545935 95275166129 149330211937 166643079 0 0 0 0 384410 2133162 +ebbrt_tuned 0 50 0x1500 55 51.6 55.8 85.0 165.6 214.2 273.8 50023.7 50000 20 1322.64 1566184 148125391 2000796 78029352 76720668120 101189502794 158562168304 177999670 0 0 0 0 408832 2259544 +ebbrt_tuned 1 50 0x1500 55 51.5 55.8 85.4 167.5 215.7 273.0 50032.2 50000 20 1323.56 1567061 148187663 2001144 78043190 76834252578 100877898493 158033342052 175382766 0 0 0 0 407025 2255148 +ebbrt_tuned 1 50 0x1500 135 50.9 55.2 88.1 191.3 230.1 299.7 100068.9 100000 20 1468.92 2273030 244742370 4002059 156071058 150981499582 172705211776 250402383115 332511322 0 0 0 0 357232 3340181 +ebbrt_tuned 1 50 0x1500 95 51.1 55.3 87.7 188.4 227.9 298.1 99980.9 100000 20 1359.37 2104743 226615354 3706067 144529356 140147951297 160268211640 232447940902 313199886 0 0 0 0 332474 3096889 +ebbrt_tuned 2 50 0x1500 95 51.1 55.2 88.1 191.7 232.3 303.6 99824.0 100000 20 1471.07 2265448 243993332 3992269 155689646 150868607371 173918250945 252174436061 340329973 0 0 0 0 356362 3334811 +ebbrt_tuned 1 50 0x1500 75 50.0 54.2 84.7 174.7 211.3 273.6 99856.8 100000 20 1442.99 2275958 244674086 3993535 155739602 147893658852 163225848425 239364761033 297560887 0 0 0 0 359178 3344041 +ebbrt_tuned 2 50 0x1500 75 50.8 55.1 86.1 183.0 218.7 280.4 99900.4 100000 20 1458.0 2272840 244546906 3995402 155811686 148626856464 165295944614 241724386662 308507430 0 0 0 0 358331 3341341 +ebbrt_tuned 0 50 0x1500 55 51.6 55.8 87.6 185.0 222.6 291.5 100055.2 100000 20 1459.0 2274529 244824298 4001528 156050856 148980077753 166418832199 243115010438 312792587 0 0 0 0 357948 3343039 +ebbrt_tuned 1 50 0x1500 55 51.3 55.5 87.1 182.2 220.3 285.3 100002.4 100000 20 1461.05 2273236 244693775 3999495 155971740 149657988846 167941476247 244869696821 318066091 0 0 0 0 358181 3346883 +ebbrt_tuned 2 50 0x1500 55 50.4 54.6 85.9 181.0 220.0 284.9 99897.3 100000 20 1462.77 2270761 244378004 3995029 155796858 149301646855 167749412017 244539794764 316514282 0 0 0 0 358241 3338752 +ebbrt_tuned 0 100 0x1500 135 54.2 60.3 106.2 189.7 226.7 296.2 50000.8 50000 20 1313.16 1523855 145571356 1999786 77990150 75256503801 95321255035 151764687784 159835795 0 0 0 0 408798 1856258 +ebbrt_tuned 1 100 0x1500 135 54.3 60.3 106.6 191.3 229.6 298.2 50007.8 50000 20 1314.12 1519136 145273112 2000112 78002724 75519361720 95970641362 152591367758 163445928 0 0 0 0 405755 1851701 +ebbrt_tuned 2 100 0x1500 135 53.3 59.4 105.2 189.6 229.4 298.5 49960.5 50000 20 1314.17 1521162 145375315 1998251 77929584 75319731584 95806948916 152229665577 162977083 0 0 0 0 406975 1851237 +ebbrt_tuned 0 100 0x1500 95 54.1 60.3 106.2 193.0 233.1 300.6 49975.5 50000 20 1316.91 1519101 145269045 1998870 77954344 75235551943 96369995075 152997509075 164967959 0 0 0 0 404816 1847725 +ebbrt_tuned 1 100 0x1500 95 54.8 60.9 106.7 192.1 232.8 301.9 50025.9 50000 20 1316.36 1518982 145309370 2000891 78033092 75584825540 96836351314 153578296686 166653635 0 0 0 0 406197 1854275 +ebbrt_tuned 2 100 0x1500 95 54.3 60.5 107.1 191.1 230.5 301.5 50095.5 50000 20 1316.99 1524593 145717879 2003612 78138984 76093885187 96998722156 153611849483 165775273 0 0 0 0 405489 1853663 +ebbrt_tuned 0 100 0x1500 75 53.8 60.1 105.6 190.9 231.0 299.4 50017.9 50000 20 1316.73 1523972 145587229 2000595 78021638 75748609415 96874164209 153430143516 166362565 0 0 0 0 405845 1853079 +ebbrt_tuned 1 100 0x1500 75 54.1 60.3 106.3 190.4 231.3 302.1 49955.5 50000 20 1317.42 1519559 145268632 1998106 77924762 75604006629 97173843811 153903392892 167670261 0 0 0 0 405253 1849951 +ebbrt_tuned 2 100 0x1500 75 53.9 60.1 107.3 193.3 235.0 303.0 50007.6 50000 20 1319.14 1519562 145319353 2000194 78006096 75852333172 97489427170 154321129511 169540084 0 0 0 0 405103 1850330 +ebbrt_tuned 0 100 0x1500 55 54.2 60.4 106.7 191.8 231.7 300.7 50070.5 50000 20 1319.62 1518246 145294976 2002675 78102798 75928468234 98155279543 155244844239 171054958 0 0 0 0 406219 1854989 +ebbrt_tuned 0 100 0x1500 135 55.3 63.4 117.1 209.8 252.5 325.3 99999.9 100000 20 1459.22 2240957 242747227 3999140 155956314 149270579903 166328631672 243197648461 318226931 0 0 0 0 381123 2440648 +ebbrt_tuned 1 100 0x1500 135 55.9 64.6 118.3 213.0 254.0 328.6 100021.4 100000 20 1458.22 2241624 242816490 4000048 155990240 149221488932 166141197514 242965631235 318182274 0 0 0 0 381621 2442415 +ebbrt_tuned 2 100 0x1500 135 56.8 65.2 118.3 214.4 255.0 328.7 100030.1 100000 20 1459.56 2241432 242780173 4000428 156007572 149291644774 167027289670 243975944777 319128100 0 0 0 0 380527 2442537 +ebbrt_tuned 0 100 0x1500 95 55.7 63.8 117.0 205.2 244.8 315.0 100079.3 100000 20 1437.22 2247365 243221362 4002499 156088418 147896757125 160390520302 236247488973 296375178 0 0 0 0 385828 2445227 +ebbrt_tuned 1 100 0x1500 95 55.8 63.9 117.2 207.4 248.5 319.5 100079.4 100000 20 1450.62 2241603 242880090 4002466 156086702 148589679637 162507374255 238689430203 306316751 0 0 0 0 382313 2438894 +ebbrt_tuned 2 100 0x1500 95 56.5 64.7 118.0 209.9 250.1 321.4 100054.2 100000 20 1451.88 2245942 243094581 4001398 156044144 148641091837 163670447046 240115657134 311170110 0 0 0 0 383212 2442108 +ebbrt_tuned 0 100 0x1500 75 56.5 64.6 118.3 210.3 251.4 322.7 100151.4 100000 20 1454.17 2244989 243137880 4005267 156193998 149496390125 164833962293 241395958907 313902988 0 0 0 0 383482 2441326 +ebbrt_tuned 1 100 0x1500 75 55.6 63.8 117.2 211.3 251.6 327.7 99952.0 100000 20 1348.7 2080355 225183364 3706199 144534906 138092985098 152558651094 223462590994 291350721 0 0 0 0 354331 2261639 +ebbrt_tuned 2 100 0x1500 75 55.9 64.4 118.6 214.5 255.3 330.3 99949.4 100000 20 1457.39 2235309 242357513 3997161 155878112 149242655436 166109550617 243085578541 319254206 0 0 0 0 382625 2439818 +ebbrt_tuned 0 100 0x1500 55 55.7 64.1 117.6 212.7 252.5 325.9 100008.7 100000 20 1457.02 2238868 242607661 3999376 155961542 149284810447 166108513198 242869931699 316931251 0 0 0 0 383969 2441422 +ebbrt_tuned 1 100 0x1500 55 55.9 63.9 117.7 211.3 252.6 326.1 100006.4 100000 20 1457.36 2239688 242651894 3999411 155967662 149216581329 166377772645 243230227641 318264097 0 0 0 0 383339 2438467 +ebbrt_tuned 0 200 0x1500 135 63.0 75.0 168.3 271.5 304.6 392.7 49935.0 50000 20 1303.15 1423891 139470397 1997166 77888166 75256395271 94218707633 148836237711 166399349 0 0 0 0 451484 1279678 +ebbrt_tuned 1 200 0x1500 135 62.7 75.2 170.0 271.6 306.1 390.6 50042.9 50000 20 1307.33 1427943 139851523 2001515 78057418 75470668478 94510707427 149093338400 166134001 0 0 0 0 450324 1278740 +ebbrt_tuned 0 200 0x1500 95 61.8 73.6 166.9 270.3 304.4 391.3 50017.7 50000 20 1309.45 1427596 139798039 2000554 78019882 75748870852 94912323871 149662383737 166592115 0 0 0 0 454545 1282852 +ebbrt_tuned 1 200 0x1500 95 62.3 74.7 169.1 271.8 305.9 392.6 50000.6 50000 20 1308.15 1423177 139538059 1999861 77992878 75498198225 94465935861 149090104726 166230688 0 0 0 0 454912 1282581 +ebbrt_tuned 2 200 0x1500 95 63.4 75.5 168.9 272.4 305.0 392.5 49965.5 50000 20 1309.04 1427253 139741857 1998474 77938844 75403537615 94749946079 149454134186 166382019 0 0 0 0 453458 1280263 +ebbrt_tuned 0 200 0x1500 75 61.2 73.2 166.3 269.9 302.9 392.7 49976.0 50000 20 1308.45 1427566 139753008 1998859 77954084 75337657422 94521507517 149107106063 167471187 0 0 0 0 449604 1279294 +ebbrt_tuned 1 200 0x1500 75 62.7 74.9 168.9 270.8 303.5 389.8 50025.5 50000 20 1309.19 1429620 139944442 2000899 78033576 75550420041 94797074420 149540858628 168814819 0 0 0 0 449782 1278243 +ebbrt_tuned 2 200 0x1500 75 62.5 74.4 168.9 271.6 303.2 389.4 49979.8 50000 20 1309.5 1427543 139740278 1999005 77959112 75362490574 94836503167 149613535383 168332447 0 0 0 0 449821 1279811 +ebbrt_tuned 0 200 0x1500 55 62.1 74.3 168.4 270.6 302.8 387.5 49957.8 50000 20 1309.35 1426381 139674742 1998173 77927218 75456546642 94533674961 149077679009 167237476 0 0 0 0 450142 1277800 +ebbrt_tuned 1 200 0x1500 55 61.9 74.2 167.6 271.8 307.0 396.4 49972.8 50000 20 1308.99 1427914 139795127 1998717 77948766 75453147253 94413722852 148899340580 166607237 0 0 0 0 450600 1278808 +ebbrt_tuned 2 200 0x1500 55 61.4 73.7 166.4 269.5 303.2 388.1 49943.4 50000 20 1309.44 1427936 139767817 1997563 77903310 75564828228 94790188307 149449530378 167587824 0 0 0 0 449534 1278632 +ebbrt_tuned 0 200 0x1500 135 69.0 83.5 178.5 281.1 325.2 409.2 100108.6 100000 20 1444.33 2214307 241232644 4003687 156134448 148812672396 161883489923 237243707848 315665797 0 0 0 0 486680 1430662 +ebbrt_tuned 1 200 0x1500 135 70.0 83.8 179.1 277.6 319.2 394.4 100036.0 100000 20 1429.46 2213362 241101223 4000637 156016754 147007723123 156001675483 230207891350 292596122 0 0 0 0 493272 1431391 +ebbrt_tuned 2 200 0x1500 135 67.6 81.6 177.2 277.7 320.5 398.1 100076.5 100000 20 1442.29 2212672 241116858 4002344 156081668 147690172213 157907441584 232554728501 299288940 0 0 0 0 492382 1431142 +ebbrt_tuned 0 200 0x1500 95 68.5 82.6 177.2 279.3 321.4 400.7 100047.6 100000 20 1443.23 2212849 241118361 4001219 156039004 147710866127 158610876080 233280418353 303011602 0 0 0 0 489142 1431123 +ebbrt_tuned 1 200 0x1500 95 68.6 82.9 177.7 280.9 324.5 404.1 99954.2 100000 20 1445.25 2210402 240858751 3997507 155893496 148340888975 160120050893 235191794752 308188568 0 0 0 0 488315 1430492 +ebbrt_tuned 2 200 0x1500 95 69.8 83.6 178.3 283.5 326.5 404.8 100054.3 100000 20 1446.33 2211624 241018065 4001351 156043940 148387080082 160770195945 235999318312 310924627 0 0 0 0 486887 1430525 +ebbrt_tuned 0 200 0x1500 75 68.6 83.0 178.8 281.8 325.3 404.1 99832.7 100000 20 1446.32 2209425 240635966 3992544 155700766 148133001162 161139191782 236271519852 310432315 0 0 0 0 486598 1430554 +ebbrt_tuned 1 200 0x1500 75 70.4 84.7 180.5 286.3 329.7 410.3 100067.0 100000 20 1446.86 2212792 241133959 4001797 156058250 148551430261 161621417888 237045007243 314939384 0 0 0 0 487206 1430380 +ebbrt_tuned 2 200 0x1500 75 69.2 83.9 179.1 283.2 326.2 405.5 100009.9 100000 20 1446.9 2210011 240885959 3999545 155973050 148475459484 162213633543 238077505785 317133749 0 0 0 0 489884 1430970 +ebbrt_tuned 0 200 0x1500 55 68.8 82.8 178.0 282.8 326.4 407.6 100006.2 100000 20 1450.0 2210945 240942578 3999421 155966254 148964536624 162715647457 238374502644 314798218 0 0 0 0 485007 1430467 +ebbrt_tuned 1 200 0x1500 55 68.4 82.5 179.1 281.7 325.6 407.8 100006.8 100000 20 1449.42 2211449 241002775 3999440 155968154 148607273742 162535466039 238105972794 315512723 0 0 0 0 483343 1430208 +ebbrt_tuned 2 200 0x1500 55 68.9 83.4 180.2 286.2 329.3 411.2 100004.7 100000 20 1449.72 2214938 241163644 3999515 155971952 148701142173 162225103107 237680407683 314952332 0 0 0 0 485575 1429924 +ebbrt_tuned 0 300 0x1500 135 74.5 93.0 229.8 360.4 394.5 482.5 50019.7 50000 20 1303.81 1367837 136230706 2000589 78020376 76326027268 94203800895 148865218397 167722274 0 0 0 0 473120 933115 +ebbrt_tuned 1 300 0x1500 135 73.4 92.0 228.7 358.9 392.4 480.4 50072.7 50000 20 1303.93 1366071 136186756 2002671 78102478 76574007107 94264929392 148888016051 168011703 0 0 0 0 467610 932405 +ebbrt_tuned 2 300 0x1500 135 74.1 92.2 227.7 359.5 393.6 482.5 49989.9 50000 20 1304.4 1364961 136018480 1999485 77978670 76465621037 93944094276 148473924529 167401860 0 0 0 0 469938 932161 +ebbrt_tuned 0 300 0x1500 95 74.0 92.5 229.1 360.7 394.9 480.6 50035.9 50000 20 1304.78 1366681 136185674 2001184 78043832 76466367363 94373074613 149156785702 170388852 0 0 0 0 470278 932619 +ebbrt_tuned 1 300 0x1500 95 73.8 91.7 227.5 359.7 393.4 480.1 50086.8 50000 20 1304.28 1368067 136329158 2003333 78128398 76602782031 94307231743 148989293360 168889698 0 0 0 0 470082 932345 +ebbrt_tuned 2 300 0x1500 95 74.7 93.4 228.4 359.8 392.7 476.5 50057.0 50000 20 1251.94 1266292 126205388 1855844 72377578 70908612513 87698216788 138510262985 157688768 0 0 0 0 433798 863655 +ebbrt_tuned 0 300 0x1500 75 74.9 93.6 228.9 360.6 395.3 483.0 50063.6 50000 20 1304.2 1366311 136166338 2002314 78088824 76647247460 94373923436 149104675544 169467106 0 0 0 0 469395 933032 +ebbrt_tuned 1 300 0x1500 75 74.5 92.7 227.3 359.9 394.3 483.6 49947.6 50000 20 1305.66 1364639 135951738 1997743 77910496 76491214459 94801499250 149641078017 170443314 0 0 0 0 468131 931910 +ebbrt_tuned 2 300 0x1500 75 73.7 91.6 227.3 360.7 396.0 484.5 49922.0 50000 20 1304.99 1364028 135877223 1996585 77865244 76635866225 94635319876 149423639990 170686548 0 0 0 0 468477 931755 +ebbrt_tuned 0 300 0x1500 55 75.0 93.6 230.8 361.1 396.4 482.6 50109.2 50000 20 1306.15 1366788 136266905 2004197 78162102 76755469239 94624434476 149284277764 169444908 0 0 0 0 467067 931921 +ebbrt_tuned 1 300 0x1500 55 75.6 94.4 229.3 360.7 394.9 482.9 49986.3 50000 20 1305.31 1366781 136132543 1999234 77968966 76726895349 94625458612 149219392178 169698142 0 0 0 0 464286 931440 +ebbrt_tuned 2 300 0x1500 55 74.6 93.3 229.1 361.5 396.3 483.1 50016.6 50000 20 1305.77 1364136 136001208 2000473 78016874 76761626680 94707300489 149379097585 169106794 0 0 0 0 466754 931451 +ebbrt_tuned 2 300 0x1500 135 79.7 101.0 234.4 364.6 404.8 497.0 99919.7 100000 20 1440.88 2202175 240308629 3995983 155834600 151316895329 163564704101 240461703694 323554688 0 0 0 0 539278 972507 +ebbrt_tuned 0 300 0x1500 95 79.3 100.6 232.2 364.8 404.3 497.0 99972.2 100000 20 1444.78 2203941 240488370 3998128 155918170 151264795646 164464574558 241454533020 324255409 0 0 0 0 535185 972592 +ebbrt_tuned 1 300 0x1500 95 79.4 100.6 233.1 365.0 404.9 494.4 99987.5 100000 20 1444.22 2205767 240611598 3998690 155939660 151457818700 163553397015 240204596828 321744120 0 0 0 0 535982 972539 +ebbrt_tuned 2 300 0x1500 95 78.6 99.5 233.1 363.9 403.7 495.0 100023.1 100000 20 1444.31 2203884 240496636 4000296 156002942 151792155940 164061676690 240830188018 323505900 0 0 0 0 533935 972579 +ebbrt_tuned 0 300 0x1500 75 79.3 100.5 234.6 365.0 404.7 496.4 99901.3 100000 20 1445.55 2202753 240326804 3995199 155803042 151667242122 164522329275 241370820054 325802906 0 0 0 0 533995 972519 +ebbrt_tuned 1 300 0x1500 75 80.6 102.1 234.0 364.8 405.0 496.4 100128.1 100000 20 1446.89 2205849 240772092 4004304 156157164 152188024401 165468799638 242658702282 329236007 0 0 0 0 536207 972648 +ebbrt_tuned 2 300 0x1500 75 79.4 100.3 234.5 364.0 404.0 493.9 99917.1 100000 20 1445.5 2203194 240384302 3995825 155828156 152073802786 164945040216 241890654537 325840517 0 0 0 0 533332 972510 +ebbrt_tuned 0 300 0x1500 55 80.0 100.9 233.7 364.6 404.3 499.9 99995.0 100000 20 1444.9 2205298 240584216 3998990 155949540 152254736821 164755921019 241625875054 326502162 0 0 0 0 535268 972307 +ebbrt_tuned 2 300 0x1500 55 80.4 101.6 234.9 366.4 408.4 500.3 100094.5 100000 20 1441.78 2205675 240723547 4002918 156105430 152192250834 165384067817 242637518449 332135254 0 0 0 0 531566 972519 +ebbrt_tuned 0 50 0x1700 135 50.9 55.2 83.8 156.8 200.6 253.2 50031.6 50000 20 1415.98 1572274 148521731 2001141 78042834 76066700799 101619538083 151052329377 171944605 0 0 0 0 407549 2252022 +ebbrt_tuned 1 50 0x1700 135 51.9 56.1 84.3 158.2 202.4 256.1 50043.9 50000 20 1421.27 1572936 148542655 2001625 78062058 75979502015 102108724086 151642778860 172214272 0 0 0 0 411437 2264502 +ebbrt_tuned 2 50 0x1700 135 51.1 55.5 84.1 158.8 202.4 258.0 49926.0 50000 20 1419.89 1570977 148318977 1996936 77879208 76227881213 101487235362 150678167839 169127888 0 0 0 0 409846 2255260 +ebbrt_tuned 0 50 0x1700 95 50.3 54.9 82.6 155.0 200.0 258.0 50006.6 50000 20 1419.23 1576141 148690946 2000116 78002732 76074578971 101788981721 151133852089 171083277 0 0 0 0 411128 2263945 +ebbrt_tuned 1 50 0x1700 95 50.5 55.0 83.3 155.8 199.5 255.0 49936.4 50000 20 1416.02 1577072 148690663 1997347 77895124 76010048788 101278801340 150411977564 168103519 0 0 0 0 408879 2256721 +ebbrt_tuned 2 50 0x1700 95 50.4 54.9 83.5 156.1 200.4 257.2 49929.1 50000 20 1419.41 1573342 148464262 1996981 77880012 76009334137 102135064081 151603612011 171687471 0 0 0 0 412205 2261991 +ebbrt_tuned 0 50 0x1700 75 50.8 55.3 83.7 158.8 201.6 259.4 49956.2 50000 20 1419.08 1572655 148450165 1998136 77925894 76124565588 101944922397 151282531957 172325314 0 0 0 0 409578 2257639 +ebbrt_tuned 1 50 0x1700 75 50.6 55.1 83.4 157.2 201.7 260.3 50146.5 50000 20 1417.69 1577549 148937097 2005719 78221234 76250414117 101751238280 151034224851 171299963 0 0 0 0 410031 2263389 +ebbrt_tuned 2 50 0x1700 75 51.5 55.8 84.5 156.1 201.0 260.3 50002.4 50000 20 1417.97 1577980 148826000 1999935 77996252 76159090721 101904157651 151250882604 171997136 0 0 0 0 410617 2261436 +ebbrt_tuned 0 50 0x1700 55 50.6 55.1 83.2 158.5 202.6 258.5 49914.9 50000 20 1418.61 1573643 148468102 1996418 77858640 76039700593 101859198038 151161513478 171836530 0 0 0 0 409571 2258079 +ebbrt_tuned 1 50 0x1700 55 50.4 55.0 83.7 156.4 201.7 259.9 50055.8 50000 20 1418.95 1573090 148593323 2002040 78077296 76348282354 102055407700 151413824585 171623928 0 0 0 0 409587 2259654 +ebbrt_tuned 2 50 0x1700 55 50.5 55.0 83.9 155.7 201.7 258.3 49954.5 50000 20 1420.04 1570130 148307295 1998008 77920680 76029089345 102326742747 151860298377 174035207 0 0 0 0 410399 2259465 +ebbrt_tuned 0 50 0x1700 135 50.6 54.9 86.6 178.7 215.5 282.8 100094.3 100000 20 1580.57 2278594 245081340 4003133 156112872 151272775174 177216889214 239288513972 335865930 0 0 0 0 361677 3347187 +ebbrt_tuned 2 50 0x1700 135 49.9 54.4 85.5 176.9 215.1 277.5 99960.7 100000 20 1580.17 2272716 244612162 3997647 155899626 151197384987 176645878290 238786769138 337049917 0 0 0 0 362449 3344738 +ebbrt_tuned 0 50 0x1700 95 50.5 55.0 86.4 177.9 214.8 277.0 99962.8 100000 20 1581.21 2276187 244795351 3997649 155897254 151359361632 176786109748 238940687769 337879479 0 0 0 0 363351 3347882 +ebbrt_tuned 1 50 0x1700 95 50.0 54.6 86.0 177.0 214.5 278.2 100074.3 100000 20 1580.72 2278648 245081202 4002346 156082562 151182406027 177313577962 239554957301 337646284 0 0 0 0 361722 3351325 +ebbrt_tuned 2 50 0x1700 95 50.1 54.6 85.4 175.0 212.5 276.9 100123.4 100000 20 1580.36 2277732 245083890 4004108 156149236 151231743850 176894238798 239051922576 336612647 0 0 0 0 360892 3344568 +ebbrt_tuned 0 50 0x1700 75 49.9 54.3 86.1 180.2 217.0 280.5 100039.4 100000 20 1582.3 2274418 244794637 4000982 156029658 151139286100 177196725186 239379133520 337924117 0 0 0 0 361087 3343139 +ebbrt_tuned 1 50 0x1700 75 50.3 54.5 85.9 177.7 215.9 278.7 100126.1 100000 20 1584.22 2278738 245110261 4004272 156157096 151591867830 178244730834 240602197587 339339274 0 0 0 0 361604 3350444 +ebbrt_tuned 2 50 0x1700 75 50.5 54.7 85.6 177.1 214.2 278.8 100080.7 100000 20 1583.92 2281440 245244849 4002556 156089934 151274799061 177292966978 239415735841 337140221 0 0 0 0 361488 3346571 +ebbrt_tuned 0 50 0x1700 55 50.0 54.4 85.6 176.9 214.7 277.8 100089.6 100000 20 1581.47 2280522 245198910 4002777 156098440 151145016623 176596807299 238534621881 336029065 0 0 0 0 362435 3345555 +ebbrt_tuned 1 50 0x1700 55 49.9 54.3 85.4 176.6 213.9 277.4 100025.1 100000 20 1583.89 2273057 244674517 4000347 156004068 151094584347 176839040911 238801629898 336427537 0 0 0 0 363323 3347493 +ebbrt_tuned 0 100 0x1700 135 53.3 59.6 106.0 186.4 222.4 292.4 49931.4 50000 20 1415.72 1526218 145652125 1997055 77883252 75973870544 100209253468 149696852789 170360050 0 0 0 0 411673 1852061 +ebbrt_tuned 2 100 0x1700 135 53.7 60.1 106.2 187.5 225.1 294.7 49933.2 50000 20 1418.47 1520490 145327038 1997180 77888250 75921741402 100595643304 150282506948 172827954 0 0 0 0 410984 1850980 +ebbrt_tuned 0 100 0x1700 95 54.4 60.9 106.5 188.7 227.0 297.2 50014.0 50000 20 1417.01 1528167 145857823 2000432 78015258 76152101902 100282888032 149718737204 170197473 0 0 0 0 409089 1848871 +ebbrt_tuned 1 100 0x1700 95 52.9 58.9 104.0 182.0 213.5 278.0 49924.8 50000 20 1399.46 1526853 145646329 1996825 77873956 74799153622 96496971287 145944410578 156258747 0 0 0 0 416003 1858956 +ebbrt_tuned 2 100 0x1700 95 53.4 59.5 105.1 184.3 217.3 283.0 50032.8 50000 20 1408.46 1526294 145761329 2001175 78044646 75343243522 97393396376 146819714214 159225770 0 0 0 0 415639 1859216 +ebbrt_tuned 0 100 0x1700 75 53.1 59.1 104.0 183.3 216.5 278.4 50011.2 50000 20 1407.8 1531588 146057717 2000335 78011550 75439307652 97445006078 146734093686 159839897 0 0 0 0 413225 1854987 +ebbrt_tuned 1 100 0x1700 75 52.8 58.8 103.8 183.9 218.5 286.0 49947.0 50000 20 1307.64 1417995 135253072 1853736 72295368 69876998035 90968066776 136805405185 151488600 0 0 0 0 384062 1724684 +ebbrt_tuned 2 100 0x1700 75 53.5 59.5 105.3 185.7 220.7 287.6 50033.3 50000 20 1409.26 1530969 146030457 2001178 78044588 75331631633 98284155003 147668988117 162538304 0 0 0 0 413679 1858061 +ebbrt_tuned 0 100 0x1700 55 53.4 59.3 104.8 185.2 218.4 287.1 50010.6 50000 20 1410.64 1526373 145732866 2000242 78007808 75574090821 98617279404 148083956918 164261599 0 0 0 0 410730 1849328 +ebbrt_tuned 1 100 0x1700 55 53.7 59.7 105.9 187.7 222.7 289.4 49992.9 50000 20 1414.58 1526834 145747523 1999582 77981982 75909003666 99150159180 148751532887 166630406 0 0 0 0 412309 1855099 +ebbrt_tuned 2 100 0x1700 55 53.0 59.2 104.8 185.7 220.0 285.8 50145.0 50000 20 1311.63 1415062 135257964 1859240 72510104 70331575955 92386018609 138458013888 155713643 0 0 0 0 382500 1724827 +ebbrt_tuned 0 100 0x1700 135 54.2 61.7 114.2 196.2 233.2 298.8 99870.3 100000 20 1561.39 2244004 242754778 3994138 155762346 148314942659 166303085132 228327830688 308253202 0 0 0 0 389756 2448205 +ebbrt_tuned 1 100 0x1700 135 54.1 61.8 114.4 198.5 236.4 302.3 100073.3 100000 20 1567.03 2247388 243205696 4002041 156069642 148610695506 167635375687 229610959903 311297851 0 0 0 0 389467 2450807 +ebbrt_tuned 2 100 0x1700 135 54.3 62.1 114.9 200.8 239.7 308.9 100069.5 100000 20 1567.81 2244945 243027322 4002062 156071038 149335888343 168289000673 230046189902 311723777 0 0 0 0 387759 2446517 +ebbrt_tuned 0 100 0x1700 95 54.8 62.7 115.4 202.5 240.7 304.0 99928.8 100000 20 1454.71 2080448 225129260 3704985 144486758 138077293159 156114257175 213390082213 291340107 0 0 0 0 360144 2266307 +ebbrt_tuned 1 100 0x1700 95 54.9 62.4 115.1 202.3 240.7 304.8 99980.0 100000 20 1570.05 2244749 242947842 3998544 155933526 149198782147 169671100080 231654583204 317694133 0 0 0 0 387817 2448251 +ebbrt_tuned 2 100 0x1700 95 54.9 62.6 115.0 200.8 239.8 308.6 100009.1 100000 20 1570.36 2244916 242978871 3999675 155978446 149214267486 169256885212 231144766539 317902206 0 0 0 0 385856 2444603 +ebbrt_tuned 0 100 0x1700 75 54.4 62.1 114.9 201.9 240.3 308.7 99943.3 100000 20 1568.8 2241318 242687095 3997032 155874656 148964572653 169801908865 231740180996 318860980 0 0 0 0 385911 2444557 +ebbrt_tuned 1 100 0x1700 75 54.6 62.5 115.6 201.9 242.2 312.9 100048.4 100000 20 1568.83 2249629 243287151 4001198 156038300 149652139096 169949236351 231817432675 320383708 0 0 0 0 386347 2448227 +ebbrt_tuned 2 100 0x1700 75 55.5 63.2 116.7 203.9 242.7 308.9 100057.0 100000 20 1569.11 2244968 243033746 4001624 156053894 149425106380 170618161378 232741315371 321718130 0 0 0 0 386823 2446966 +ebbrt_tuned 0 100 0x1700 55 55.0 62.6 115.7 203.7 243.1 311.8 99896.3 100000 20 1569.42 2242864 242742481 3995150 155801176 149172732206 170771110303 232921529143 324330496 0 0 0 0 385044 2443662 +ebbrt_tuned 1 100 0x1700 55 55.2 62.9 116.4 205.5 244.1 315.2 100058.4 100000 20 1570.5 2245163 243060576 4001618 156052790 149622295170 170716761077 232623483412 321930066 0 0 0 0 388236 2448653 +ebbrt_tuned 2 100 0x1700 55 54.3 62.1 114.8 202.8 241.9 307.2 100019.3 100000 20 1569.96 2241630 242792001 4000014 155992138 149671682689 170603305491 232563867822 322923498 0 0 0 0 386066 2443491 +ebbrt_tuned 0 200 0x1700 135 61.3 72.9 165.4 267.6 298.0 376.5 49984.0 50000 20 1401.73 1430331 139950508 1999115 77963836 75081878881 95286483013 142565520992 162343040 0 0 0 0 460185 1279882 +ebbrt_tuned 1 200 0x1700 135 61.3 73.4 165.3 267.7 298.9 378.7 50052.9 50000 20 1403.37 1427775 139886033 2001925 78072480 75422973021 95969494917 143344696066 164159807 0 0 0 0 456229 1278049 +ebbrt_tuned 0 200 0x1700 95 61.3 72.9 166.1 267.9 298.1 374.2 50090.1 50000 20 1404.34 1432920 140215736 2003443 78132608 75558531025 96358373772 143742344385 165201722 0 0 0 0 456395 1279210 +ebbrt_tuned 1 200 0x1700 95 61.4 73.6 166.1 267.7 298.9 379.3 50018.9 50000 20 1405.31 1429204 139908099 2000483 78016380 75430305856 96482351440 144090159858 166161435 0 0 0 0 461063 1282303 +ebbrt_tuned 2 200 0x1700 95 61.4 73.2 165.1 267.7 298.3 377.8 49988.1 50000 20 1405.75 1435331 140244907 1999376 77974226 75356702989 96485709868 143984639269 165419737 0 0 0 0 457127 1279441 +ebbrt_tuned 0 200 0x1700 75 61.1 72.8 164.9 267.9 298.6 374.4 49978.1 50000 20 1406.58 1429459 139885504 1998958 77957172 75290577096 96653314768 144207136520 166684407 0 0 0 0 456710 1277365 +ebbrt_tuned 1 200 0x1700 75 61.6 73.5 166.0 266.5 296.7 377.9 50001.4 50000 20 1404.82 1430455 139971787 1999903 77994644 75278302187 96063001671 143398171312 164618156 0 0 0 0 459023 1280953 +ebbrt_tuned 2 200 0x1700 75 61.7 73.7 166.6 268.3 298.5 378.7 50157.1 50000 20 1404.93 1434023 140381086 2006098 78236540 75876314288 96561743903 143991960377 166297953 0 0 0 0 458728 1281738 +ebbrt_tuned 0 200 0x1700 55 60.8 72.3 165.0 266.9 297.8 378.7 50013.9 50000 20 1405.52 1430957 140043310 2000401 78013946 75450605526 96160017662 143489354011 165410828 0 0 0 0 459382 1279985 +ebbrt_tuned 1 200 0x1700 55 60.9 72.1 165.3 268.0 299.4 378.2 50053.7 50000 20 1404.84 1428360 139906712 2001954 78074728 75519832450 96762774366 144320983705 166678558 0 0 0 0 459283 1280400 +ebbrt_tuned 2 200 0x1700 55 62.0 74.0 165.6 268.7 300.3 380.0 50065.5 50000 20 1405.66 1434373 140286410 2002462 78094204 75496572400 96498834103 143967534779 166513821 0 0 0 0 458498 1281140 +ebbrt_tuned 0 200 0x1700 135 67.2 81.5 174.5 274.6 313.4 392.5 99871.9 100000 20 1560.5 2211568 240845823 3994204 155765446 148868382612 165453351555 226123036668 315567076 0 0 0 0 495341 1430183 +ebbrt_tuned 1 200 0x1700 135 67.3 81.0 175.5 274.1 313.4 391.3 99924.9 100000 20 1562.51 2211321 240892793 3996282 155845054 148855817649 166376046830 227417508885 318574668 0 0 0 0 496550 1430318 +ebbrt_tuned 2 200 0x1700 135 67.7 81.4 175.3 274.5 315.4 393.5 99953.4 100000 20 1562.12 2212186 240950109 3997390 155890196 148929084387 165823278134 226561165217 318477708 0 0 0 0 493902 1430536 +ebbrt_tuned 0 200 0x1700 95 68.0 82.3 176.8 276.5 318.9 396.7 99998.1 100000 20 1562.16 2213165 241069731 3999240 155960850 149324490664 166245994651 226964183843 319938678 0 0 0 0 492200 1429530 +ebbrt_tuned 1 200 0x1700 95 69.1 83.1 176.8 276.1 317.6 393.2 99965.5 100000 20 1562.53 2214514 241110502 3997840 155906308 149408789956 167353709839 228385901825 323714449 0 0 0 0 493487 1430432 +ebbrt_tuned 2 200 0x1700 95 67.6 81.6 175.9 275.1 314.9 395.5 100082.7 100000 20 1564.34 2213241 241159981 4002671 156095342 149701886343 167788887380 228954576310 326490549 0 0 0 0 490680 1429957 +ebbrt_tuned 0 200 0x1700 75 67.9 81.9 177.2 279.3 319.0 396.4 100015.0 100000 20 1561.22 2211705 241006169 3999728 155980576 149727217045 167377240919 228416744485 323445146 0 0 0 0 490760 1430362 +ebbrt_tuned 1 200 0x1700 75 66.4 81.0 174.2 273.6 311.7 392.2 99844.4 100000 20 1560.79 2208730 240614788 3992859 155710862 149694601466 167156581679 228055515207 323485865 0 0 0 0 492869 1430394 +ebbrt_tuned 2 200 0x1700 75 66.9 81.4 176.2 274.5 312.8 394.5 100016.2 100000 20 1561.4 2212793 241042137 3999928 155988450 149953016976 167790537892 229027449018 325552143 0 0 0 0 494002 1430849 +ebbrt_tuned 0 200 0x1700 55 68.1 82.1 176.6 276.4 317.5 394.9 99971.4 100000 20 1560.74 2212417 240959433 3997896 155907274 149624632858 166927126402 227755478626 321634567 0 0 0 0 495128 1430996 +ebbrt_tuned 1 200 0x1700 55 67.6 82.3 177.2 278.4 319.8 396.6 100039.2 100000 20 1560.27 2210334 240957952 4000913 156026728 149763528038 167643999760 228674485900 325809733 0 0 0 0 491898 1430905 +ebbrt_tuned 2 200 0x1700 55 67.3 81.4 174.6 274.0 313.3 395.5 100072.3 100000 20 1561.73 2214003 241202379 4002096 156073308 149536956049 167687403035 228580832256 324073621 0 0 0 0 488747 1430509 +ebbrt_tuned 0 300 0x1700 135 73.1 91.0 224.8 357.0 388.4 469.2 50013.8 50000 20 1398.3 1369307 136307095 2000377 78013194 76333981512 95416284486 142670925900 165920316 0 0 0 0 475484 931689 +ebbrt_tuned 1 300 0x1700 135 71.8 90.1 225.1 356.2 386.5 466.7 49963.5 50000 20 1400.41 1367985 136190383 1998383 77935468 76213562590 95506178827 142712822548 166266361 0 0 0 0 474588 931821 +ebbrt_tuned 2 300 0x1700 135 72.6 90.9 224.8 357.0 387.7 467.3 49915.5 50000 20 1399.36 1367678 136100073 1996485 77861326 76172840529 95352080564 142564994700 166120295 0 0 0 0 476118 931437 +ebbrt_tuned 0 300 0x1700 95 73.3 91.7 225.8 357.3 388.7 470.0 49953.5 50000 20 1398.43 1369296 136258450 1997980 77919624 76245404198 95549162244 143000582617 166789919 0 0 0 0 479591 931936 +ebbrt_tuned 1 300 0x1700 95 73.2 91.7 226.5 357.5 388.7 469.7 49978.5 50000 20 1400.14 1364965 136039112 1998986 77958716 76414377921 95915208541 143471780823 167603976 0 0 0 0 481088 932625 +ebbrt_tuned 2 300 0x1700 95 73.3 91.8 225.4 356.6 388.1 468.2 50002.3 50000 20 1400.61 1369134 136294516 1999869 77993650 76332758506 96373393997 144017626885 169626405 0 0 0 0 477321 932155 +ebbrt_tuned 0 300 0x1700 75 73.5 92.3 226.1 357.8 390.4 471.2 49997.5 50000 20 1397.82 1368647 136254673 1999747 77988236 76464393463 95756888241 143118241069 166888836 0 0 0 0 477132 932516 +ebbrt_tuned 1 300 0x1700 75 73.3 91.7 225.6 357.7 388.9 467.8 50113.8 50000 20 1400.92 1369684 136444484 2004400 78170260 76680551314 96318015824 143813771124 168702642 0 0 0 0 476557 933024 +ebbrt_tuned 2 300 0x1700 75 72.5 90.8 226.2 357.1 388.4 469.3 49941.5 50000 20 1399.66 1366265 136034663 1997472 77900100 76261433432 96168777749 143641435086 168534219 0 0 0 0 472017 931125 +ebbrt_tuned 0 300 0x1700 55 73.1 92.3 226.8 356.4 387.3 469.8 50031.0 50000 20 1399.18 1367636 136243313 2001076 78039782 76502179331 95873963817 143138338214 167447966 0 0 0 0 474497 931727 +ebbrt_tuned 1 300 0x1700 55 73.6 92.2 227.0 356.9 387.2 465.8 50043.9 50000 20 1401.06 1368316 136298579 2001624 78061694 76569759743 96192785920 143729832625 168695666 0 0 0 0 479683 932319 +ebbrt_tuned 2 300 0x1700 55 72.3 90.9 225.7 357.7 389.6 468.5 49982.7 50000 20 1398.93 1367103 136154159 1999136 77964408 76509431974 95750323814 143044073859 166767004 0 0 0 0 478681 932283 +ebbrt_tuned 0 300 0x1700 135 77.8 99.0 230.7 360.4 397.1 480.8 100109.4 100000 20 1553.91 2205910 240766921 4003645 156132924 152541684812 169297103604 231794182737 329883526 0 0 0 0 544824 972708 +ebbrt_tuned 1 300 0x1700 135 78.7 99.4 232.4 360.7 398.0 485.2 100017.7 100000 20 1552.4 2205088 240594217 3999966 155989120 152354768594 168279285290 230324802153 326723876 0 0 0 0 543324 972477 +ebbrt_tuned 2 300 0x1700 135 77.0 97.5 230.5 360.0 395.6 479.5 100168.8 100000 20 1553.57 2206706 240857567 4005947 156222834 152642273937 168643053344 230938860043 326871704 0 0 0 0 545889 972613 +ebbrt_tuned 0 300 0x1700 95 78.8 99.7 230.0 360.9 398.0 480.7 99991.0 100000 20 1554.04 2203564 240463290 3998844 155946086 152358263358 168179790454 230011528348 327781599 0 0 0 0 534640 972570 +ebbrt_tuned 1 300 0x1700 95 79.2 100.4 233.6 362.8 401.0 486.2 99969.8 100000 20 1556.14 2204641 240508994 3998106 155917172 152254057719 168964058387 231169146723 328564138 0 0 0 0 542373 972757 +ebbrt_tuned 2 300 0x1700 95 77.1 98.1 229.7 359.6 395.5 481.2 100062.2 100000 20 1557.64 2205925 240689149 4001526 156048814 152383930931 169294152911 231703646071 332198845 0 0 0 0 540440 972563 +ebbrt_tuned 0 300 0x1700 75 78.1 98.5 230.7 360.5 397.0 480.1 100045.6 100000 20 1557.3 2207237 240746462 4001178 156037332 152564220541 168826137172 230887734116 328992817 0 0 0 0 541359 972648 +ebbrt_tuned 1 300 0x1700 75 77.1 97.7 230.0 360.0 396.0 478.5 99879.3 100000 20 1556.46 2200783 240181486 3994265 155766468 152133778837 169195700484 231382229472 332217563 0 0 0 0 537661 972525 +ebbrt_tuned 2 300 0x1700 75 77.6 98.2 229.5 359.7 396.5 484.5 99844.5 100000 20 1555.51 2201288 240173114 3993002 155717294 152184977157 169186926873 231335061639 330488246 0 0 0 0 540519 972582 +ebbrt_tuned 0 300 0x1700 55 80.0 101.3 233.3 362.4 399.3 484.8 100072.7 100000 20 1442.6 2090528 228173183 3790569 147820154 144385534979 159956985492 218672432764 312621100 0 0 0 0 511951 921477 +ebbrt_tuned 1 300 0x1700 55 80.3 101.3 233.3 362.2 399.1 488.8 99994.8 100000 20 1554.22 2202678 240430795 3999056 155954196 152526129846 169472079949 231646011761 330799480 0 0 0 0 538433 972479 +ebbrt_tuned 2 300 0x1700 55 77.9 98.8 231.6 360.7 397.2 483.5 99984.8 100000 20 1555.08 2203012 240414499 3998635 155937536 152581760389 169332029116 231394224365 329564436 0 0 0 0 540882 972552 +ebbrt_tuned 0 50 0x1900 135 49.6 54.6 82.6 152.8 193.4 248.7 50025.9 50000 20 1530.56 1580220 148993367 2000880 78032582 76636484339 104742941131 147542210876 175090510 0 0 0 0 411653 2264885 +ebbrt_tuned 1 50 0x1900 135 48.6 53.3 80.7 146.0 182.7 230.1 50010.9 50000 20 1507.79 1585874 149304755 2000265 78008698 75110494343 100116223448 143306138652 156979390 0 0 0 0 414732 2273215 +ebbrt_tuned 2 50 0x1900 135 49.2 54.2 80.9 149.8 186.4 236.1 50090.5 50000 20 1518.8 1589232 149585870 2003478 78134174 75559763005 101191877999 144489367478 162540274 0 0 0 0 414503 2275542 +ebbrt_tuned 1 50 0x1900 95 48.9 53.9 80.6 148.5 186.6 239.5 49990.2 50000 20 1517.89 1586598 149340478 1999478 77977962 75526155693 101418976216 144439150092 163564346 0 0 0 0 413370 2267627 +ebbrt_tuned 2 50 0x1900 95 49.0 53.7 80.8 148.6 187.6 242.5 50064.3 50000 20 1521.25 1586388 149393520 2002378 78091160 75556082077 101626000136 144584576450 164412522 0 0 0 0 412085 2265535 +ebbrt_tuned 0 50 0x1900 75 48.6 53.5 80.6 149.3 187.5 238.3 50043.0 50000 20 1525.93 1585382 149315745 2001571 78059342 75772259390 102365116388 145482071611 167406866 0 0 0 0 413439 2269047 +ebbrt_tuned 1 50 0x1900 75 50.8 55.2 83.0 151.9 190.3 243.5 49902.7 50000 20 1527.71 1580239 148865863 1995981 77841844 75946065597 102674176679 145713513390 168237056 0 0 0 0 413218 2261314 +ebbrt_tuned 2 50 0x1900 75 49.5 54.4 82.0 152.1 190.6 246.2 49992.1 50000 20 1527.04 1582745 149086966 1999467 77977996 75850751095 102964007153 146047855405 170410491 0 0 0 0 412545 2264751 +ebbrt_tuned 0 50 0x1900 55 49.4 54.3 81.4 148.6 188.6 242.7 49912.9 50000 20 1526.5 1580225 148851096 1996418 77858990 75699759562 102651511554 145608970507 167627655 0 0 0 0 414115 2264777 +ebbrt_tuned 1 50 0x1900 55 49.8 54.7 82.7 151.8 190.9 245.2 50004.8 50000 20 1414.57 1465051 138106313 1854490 72324434 70497124328 95646095165 135507373595 155947795 0 0 0 0 383240 2103001 +ebbrt_tuned 2 50 0x1900 55 49.3 54.3 82.1 150.9 190.3 243.2 49997.2 50000 20 1527.53 1581261 149001581 1999737 77987794 75872805565 103298288461 146258977377 168836576 0 0 0 0 412067 2262540 +ebbrt_tuned 0 50 0x1900 135 48.5 53.4 83.0 165.6 200.4 257.6 99980.1 100000 20 1718.28 2282282 245183322 3998424 155929010 151092351279 180488484810 229396618525 333819980 0 0 0 0 366025 3353457 +ebbrt_tuned 1 50 0x1900 135 48.6 53.3 83.5 167.0 202.6 261.3 99911.9 100000 20 1714.25 2282951 245182846 3995591 155815916 150797304223 180337204209 229320305054 336278542 0 0 0 0 366936 3355178 +ebbrt_tuned 2 50 0x1900 135 48.9 53.8 84.3 166.9 202.7 263.3 100167.6 100000 20 1717.95 2285376 245585337 4006067 156227238 151391126081 181257610704 230228102291 337672525 0 0 0 0 365966 3356003 +ebbrt_tuned 1 50 0x1900 95 48.6 53.4 84.1 168.5 202.6 262.6 99933.3 100000 20 1711.66 2278349 244885231 3996533 155855728 151014511844 180163856719 229090181992 337748633 0 0 0 0 367345 3355415 +ebbrt_tuned 2 50 0x1900 95 48.4 53.1 82.8 163.6 200.3 259.6 100127.8 100000 20 1714.72 2282019 245327724 4004501 156166674 151342343877 180304033671 229117642382 335287813 0 0 0 0 364841 3348950 +ebbrt_tuned 0 50 0x1900 75 48.8 53.5 83.4 166.8 201.5 260.6 99989.1 100000 20 1718.03 2282853 245241326 3998646 155935682 151242883701 180785519817 229696380360 337161055 0 0 0 0 365563 3349874 +ebbrt_tuned 1 50 0x1900 75 49.2 54.2 84.5 167.9 202.9 259.8 99942.4 100000 20 1716.64 2278846 244960771 3996966 155871310 151235028019 180037893042 228681658499 332044348 0 0 0 0 365772 3347225 +ebbrt_tuned 2 50 0x1900 75 49.4 54.1 85.0 169.4 203.7 261.7 100090.1 100000 20 1717.68 2287084 245608638 4002826 156101448 151234615251 180604722669 229566044587 337408134 0 0 0 0 365037 3354548 +ebbrt_tuned 0 50 0x1900 55 49.4 54.3 84.7 169.8 205.6 265.7 99880.3 100000 20 1714.26 2282801 245100906 3994360 155768266 150762075310 179341772602 228092793286 332745714 0 0 0 0 364337 3345537 +ebbrt_tuned 1 50 0x1900 55 48.7 53.6 84.3 169.9 204.5 267.1 100039.3 100000 20 1717.11 2285605 245446112 4000941 156028256 151012789768 180229029050 229139440928 336407570 0 0 0 0 365060 3355960 +ebbrt_tuned 2 50 0x1900 55 48.6 53.5 84.0 166.8 202.5 265.9 100052.8 100000 20 1717.41 2282113 245280295 4001384 156044326 151277844834 180059274347 228740422736 333086842 0 0 0 0 365860 3352607 +ebbrt_tuned 0 100 0x1900 135 52.3 58.5 103.9 182.5 214.9 282.2 50004.9 50000 20 1522.92 1532095 146074579 2000047 77999952 75860769074 101756097880 145091276659 169441686 0 0 0 0 412900 1855855 +ebbrt_tuned 1 100 0x1900 135 53.0 59.2 104.7 184.4 216.2 279.2 50016.7 50000 20 1523.66 1528411 145869598 2000528 78018854 75866486026 101546159412 144743407913 167174791 0 0 0 0 413820 1856745 +ebbrt_tuned 2 100 0x1900 135 52.5 58.7 104.6 184.1 215.5 279.5 49937.6 50000 20 1524.31 1527951 145768768 1997363 77895744 75668121346 101852941665 145359536287 171591985 0 0 0 0 413342 1855802 +ebbrt_tuned 0 100 0x1900 95 52.7 59.0 105.1 183.7 215.0 277.1 50086.9 50000 20 1411.7 1421259 135608371 1858664 72486584 70506779371 94507018074 134722839524 157036147 0 0 0 0 385298 1726221 +ebbrt_tuned 1 100 0x1900 95 52.4 58.6 104.6 183.2 215.0 280.0 49973.0 50000 20 1524.13 1529506 145872704 1998690 77946942 75692605601 102154179170 145605101936 170166163 0 0 0 0 414336 1857587 +ebbrt_tuned 2 100 0x1900 95 52.5 58.7 104.6 183.5 214.4 277.9 50071.3 50000 20 1525.53 1528865 145956788 2002710 78103928 76223557493 102082316407 145304040495 170046323 0 0 0 0 414827 1858956 +ebbrt_tuned 0 100 0x1900 75 52.7 59.0 103.7 183.4 214.6 283.5 50031.1 50000 20 1523.36 1527185 145807517 2001085 78040774 75964515452 101627942699 144782955184 169135816 0 0 0 0 412624 1854880 +ebbrt_tuned 1 100 0x1900 75 52.9 59.2 105.1 183.5 215.1 280.6 49966.9 50000 20 1415.01 1419133 135326135 1853636 72292200 70343772379 94696542777 134890420026 157814244 0 0 0 0 385061 1724790 +ebbrt_tuned 2 100 0x1900 75 52.1 58.3 103.9 182.5 214.5 279.6 50035.2 50000 20 1522.6 1528061 145875705 2001262 78048022 75915915301 101657040507 144791768313 168531356 0 0 0 0 412617 1854396 +ebbrt_tuned 0 100 0x1900 55 52.8 59.1 104.5 182.9 215.3 279.9 49908.3 50000 20 1524.24 1528325 145731676 1996139 77846916 75808529078 102043132328 145330217374 171281513 0 0 0 0 414939 1859348 +ebbrt_tuned 1 100 0x1900 55 52.6 58.9 104.5 182.6 214.3 281.6 49992.1 50000 20 1522.4 1532171 146077562 1999552 77980908 75797175764 101771177672 144910750296 168934465 0 0 0 0 413963 1855323 +ebbrt_tuned 2 100 0x1900 55 52.3 58.7 104.3 183.3 214.4 280.6 50050.4 50000 20 1522.23 1532084 146113028 2001792 78068246 76036329618 102430913363 145795717080 171064909 0 0 0 0 414356 1861765 +ebbrt_tuned 0 100 0x1900 135 55.0 62.7 115.6 199.0 235.7 301.2 99843.2 100000 20 1709.27 2248058 242999079 3993046 155718962 150609527677 177010306276 226122414473 331720704 0 0 0 0 390065 2449839 +ebbrt_tuned 0 100 0x1900 95 53.9 61.2 112.5 188.5 222.9 283.3 100096.7 100000 20 1550.76 2090410 225892145 3711281 144732462 137008772046 154642213380 200857187564 271279194 0 0 0 0 363930 2274598 +ebbrt_tuned 1 100 0x1900 95 53.4 60.9 113.3 192.0 225.6 288.9 100054.2 100000 20 1691.69 2251401 243417718 4001498 156049530 148481797692 170026123969 219974974550 308079638 0 0 0 0 391495 2453692 +ebbrt_tuned 2 100 0x1900 95 54.0 61.3 113.7 191.4 226.1 291.1 99863.6 100000 20 1694.08 2247406 242971790 3993788 155749686 148210027212 170173338542 219753048222 309890335 0 0 0 0 388864 2448362 +ebbrt_tuned 0 100 0x1900 75 54.2 61.8 114.2 195.0 228.3 295.7 100042.5 100000 20 1699.3 2249839 243311915 4001042 156031302 149237056194 171868697566 221492550321 314542903 0 0 0 0 389613 2451789 +ebbrt_tuned 1 100 0x1900 75 54.0 61.5 113.5 193.3 228.7 296.7 99952.5 100000 20 1577.88 2086297 225458369 3704331 144462156 137999689459 160060815074 205969640774 292615040 0 0 0 0 362696 2269497 +ebbrt_tuned 2 100 0x1900 75 54.5 62.0 113.9 194.5 228.9 292.6 99945.3 100000 20 1702.57 2247006 243045616 3997180 155880976 149097712248 173341921324 222816971534 316661221 0 0 0 0 389637 2448094 +ebbrt_tuned 0 100 0x1900 55 54.4 62.0 114.4 195.6 229.7 297.7 100026.3 100000 20 1702.92 2246640 243130714 4000233 155999478 149175231424 173091394084 222647936450 318436800 0 0 0 0 392310 2452546 +ebbrt_tuned 1 100 0x1900 55 54.4 61.6 114.9 196.8 230.8 296.9 100062.3 100000 20 1704.49 2249330 243316714 4001764 156058912 149211563580 173377967733 222950428374 319712611 0 0 0 0 392304 2453908 +ebbrt_tuned 0 200 0x1900 135 60.9 72.6 165.1 266.2 295.3 364.4 49963.1 50000 20 1508.9 1433713 140099884 1998371 77934928 75398645894 97985806663 139181850640 166119049 0 0 0 0 460379 1279023 +ebbrt_tuned 1 200 0x1900 135 60.6 71.9 164.1 265.4 294.0 366.4 49965.3 50000 20 1511.85 1430011 139896684 1998426 77937048 75624389470 98624763345 140046422565 167576972 0 0 0 0 461420 1279233 +ebbrt_tuned 2 200 0x1900 135 60.4 71.8 165.0 265.4 294.1 365.9 50081.2 50000 20 1511.74 1434904 140328564 2003118 78119862 75539191501 98398805207 139678984242 166581689 0 0 0 0 460458 1280258 +ebbrt_tuned 0 200 0x1900 95 62.5 74.0 167.4 266.9 296.4 367.2 49916.3 50000 20 1512.03 1433146 140033770 1996430 77859672 75507868471 98345331364 139608617076 166435243 0 0 0 0 461121 1279022 +ebbrt_tuned 1 200 0x1900 95 61.0 72.6 165.1 265.2 294.8 365.2 50007.1 50000 20 1513.66 1434167 140192959 2000080 78001016 75649749442 98753677855 140048341582 168277108 0 0 0 0 458627 1278855 +ebbrt_tuned 2 200 0x1900 95 60.7 71.9 163.4 264.2 293.9 363.6 49997.3 50000 20 1514.97 1430810 139994035 1999743 77988230 75538922114 98707024027 140159393428 169082814 0 0 0 0 462538 1281046 +ebbrt_tuned 0 200 0x1900 75 61.3 72.7 164.6 265.2 294.9 367.5 50010.1 50000 20 1406.06 1326382 129806958 1855299 72355986 70148961849 91690415168 130174852261 157703246 0 0 0 0 426539 1186475 +ebbrt_tuned 1 200 0x1900 75 60.8 72.2 165.0 266.3 295.6 368.8 50102.9 50000 20 1515.75 1433616 140265938 2003957 78152970 75820210945 99332634576 140913788872 169471914 0 0 0 0 463031 1282845 +ebbrt_tuned 2 200 0x1900 75 60.7 72.7 165.3 265.1 293.7 366.7 50000.5 50000 20 1516.66 1430411 139948511 1999866 77993298 75730080864 98726607822 140097530802 168192810 0 0 0 0 464325 1283108 +ebbrt_tuned 0 200 0x1900 55 61.1 72.7 165.2 266.2 296.5 369.9 50016.7 50000 20 1514.91 1433710 140183067 2000486 78017488 75691705943 98679171986 139908241625 167938832 0 0 0 0 460157 1279363 +ebbrt_tuned 1 200 0x1900 55 58.7 69.5 161.7 260.6 288.5 356.4 50040.5 50000 20 1493.35 1437753 140446373 2001490 78056596 74375262722 94316876360 135583277847 151679481 0 0 0 0 458593 1278093 +ebbrt_tuned 2 200 0x1900 55 60.7 71.9 162.4 262.5 291.2 358.7 49957.7 50000 20 1503.45 1432514 140040556 1998137 77925740 74754881464 95322396819 136827777003 156871858 0 0 0 0 460832 1278660 +ebbrt_tuned 0 200 0x1900 135 66.9 80.9 175.1 272.0 306.8 383.4 100042.8 100000 20 1683.33 2213736 241157480 4001056 156031930 148514158594 168646006426 217103921915 316917142 0 0 0 0 500134 1430435 +ebbrt_tuned 1 200 0x1900 135 66.7 81.0 175.1 271.3 304.9 383.3 99790.4 100000 20 1687.81 2210960 240707101 3990881 155635622 148196296524 168616425123 217182261935 315929224 0 0 0 0 499005 1430332 +ebbrt_tuned 2 200 0x1900 135 66.3 80.6 173.6 271.6 304.5 380.8 100023.4 100000 20 1686.65 2214515 241173174 4000232 156000004 148752912135 169204892226 217796780171 315745592 0 0 0 0 504377 1431069 +ebbrt_tuned 0 200 0x1900 95 67.0 81.0 175.2 271.8 306.9 384.9 99947.8 100000 20 1687.24 2212142 240937805 3997220 155882086 148760664398 169583665893 218279380220 318011721 0 0 0 0 505918 1431086 +ebbrt_tuned 1 200 0x1900 95 67.3 81.1 173.9 271.3 304.7 381.1 100065.6 100000 20 1689.39 2212504 241083977 4001702 156053652 149034323584 169332734508 217613005808 316016505 0 0 0 0 496818 1429816 +ebbrt_tuned 2 200 0x1900 95 66.6 80.7 174.8 272.4 305.6 381.4 100158.2 100000 20 1690.54 2214561 241316985 4005634 156210790 149063182619 170657919466 219439935309 321026899 0 0 0 0 503880 1431384 +ebbrt_tuned 0 200 0x1900 75 67.2 81.1 174.4 271.4 304.0 378.6 100104.4 100000 20 1690.11 2213542 241198892 4003263 156118388 149191021244 170257498842 219096179430 323186810 0 0 0 0 503723 1430885 +ebbrt_tuned 1 200 0x1900 75 67.1 81.4 175.6 271.9 304.0 381.5 100051.9 100000 20 1692.56 2212071 241059366 4001339 156042630 149240073804 170886263695 219756074662 324762010 0 0 0 0 504376 1431170 +ebbrt_tuned 2 200 0x1900 75 66.6 80.3 174.4 272.8 308.6 382.1 99921.2 100000 20 1694.29 2210093 240797868 3996156 155840792 149353886294 170850189397 219614097738 325123884 0 0 0 0 499926 1430576 +ebbrt_tuned 0 200 0x1900 55 67.2 80.9 174.7 270.6 303.6 380.5 100020.6 100000 20 1696.32 2214728 241181265 4000119 155996222 149565518342 170846019489 219331213178 323864451 0 0 0 0 500626 1430313 +ebbrt_tuned 1 200 0x1900 55 66.6 81.4 174.5 272.4 304.9 380.2 100024.8 100000 20 1574.53 2066278 224993716 3735135 145658918 139758211718 159379399788 204533053100 302570538 0 0 0 0 463964 1335173 +ebbrt_tuned 2 200 0x1900 55 67.0 81.3 175.0 271.6 305.6 380.2 99812.6 100000 20 1694.03 2212098 240800025 3991828 155671422 149538510567 171288737580 219736784964 324014536 0 0 0 0 496811 1429703 +ebbrt_tuned 0 300 0x1900 135 69.9 87.9 221.6 353.0 380.6 449.5 49888.3 50000 20 1496.27 1369739 136202416 1995365 77817982 75683132649 94549456446 135830060278 158083518 0 0 0 0 479016 932045 +ebbrt_tuned 1 300 0x1900 135 71.1 89.5 224.0 355.0 384.0 456.2 50011.5 50000 20 1498.31 1369247 136299802 2000344 78011976 75805001214 95227503822 136468360544 161208749 0 0 0 0 478850 932482 +ebbrt_tuned 2 300 0x1900 135 70.6 88.7 223.9 353.5 381.3 452.1 49962.4 50000 20 1499.58 1369648 136255922 1998245 77930466 75672961779 95435511584 136851696290 160924825 0 0 0 0 483434 932503 +ebbrt_tuned 0 300 0x1900 95 72.3 90.6 225.2 354.9 383.6 456.3 49925.9 50000 20 1498.76 1371852 136371356 1996896 77877612 75854520426 95543492917 136820997238 161393569 0 0 0 0 481882 932150 +ebbrt_tuned 1 300 0x1900 95 72.4 91.6 224.8 355.0 384.5 458.7 49983.5 50000 20 1500.82 1370965 136388096 1999130 77964288 76285082859 96153256259 137411974608 162527161 0 0 0 0 479989 931966 +ebbrt_tuned 2 300 0x1900 95 71.9 91.1 224.7 355.4 384.6 457.2 49890.0 50000 20 1499.46 1369682 136192188 1995367 77817460 75881519413 95558197749 136610051905 160696199 0 0 0 0 481467 932394 +ebbrt_tuned 0 300 0x1900 75 72.4 91.1 225.2 355.0 384.1 454.4 49987.2 50000 20 1500.31 1371540 136409717 1999346 77972890 76010832242 96508233594 137853799033 164457186 0 0 0 0 481159 932720 +ebbrt_tuned 1 300 0x1900 75 71.6 89.5 222.7 355.4 385.8 459.5 49954.3 50000 20 1498.54 1370179 136304305 1998002 77920008 76160103755 96707837621 138115272458 165300567 0 0 0 0 482645 932965 +ebbrt_tuned 2 300 0x1900 75 71.3 89.8 221.9 353.8 382.4 455.2 49947.6 50000 20 1498.69 1370577 136315556 1997685 77908210 76114434096 97032788097 138471927073 165757369 0 0 0 0 481246 932789 +ebbrt_tuned 0 300 0x1900 55 72.5 91.0 225.0 355.9 385.9 459.6 50011.1 50000 20 1500.94 1370552 136373629 2000254 78008274 76282921260 97127174304 138571045390 166355082 0 0 0 0 482521 932689 +ebbrt_tuned 1 300 0x1900 55 71.8 90.7 225.4 355.7 385.7 465.6 50050.4 50000 20 1391.58 1280940 127347234 1865964 72771722 71184069513 90983196009 129649173002 155420056 0 0 0 0 448056 869305 +ebbrt_tuned 2 300 0x1900 55 70.7 89.4 223.7 355.5 385.4 460.9 49921.5 50000 20 1498.07 1368825 136185668 1996655 77867606 76091474975 96645049900 137919363828 164099504 0 0 0 0 480351 932274 +ebbrt_tuned 0 300 0x1900 135 76.1 97.1 226.8 356.2 389.6 471.5 99996.5 100000 20 1682.95 2205159 240584111 3999159 155958270 152357128713 171899553621 221294042450 326694594 0 0 0 0 540659 972473 +ebbrt_tuned 1 300 0x1900 135 78.3 98.8 230.2 358.4 392.1 473.3 99967.7 100000 20 1684.46 2201962 240358699 3998085 155916660 152190796717 171917947574 221387349080 328697431 0 0 0 0 545271 972618 +ebbrt_tuned 2 300 0x1900 135 76.0 96.7 226.0 356.8 391.6 469.8 99975.7 100000 20 1681.71 2206461 240634836 3998248 155922982 152195239753 171729847261 221219799260 324662534 0 0 0 0 546486 972682 +ebbrt_tuned 0 300 0x1900 95 76.0 96.6 226.7 356.4 389.4 467.7 100059.0 100000 20 1680.59 2206350 240688916 4001664 156055446 152188616949 172295413256 221729247016 328811274 0 0 0 0 542373 972519 +ebbrt_tuned 1 300 0x1900 95 78.0 98.9 230.0 358.6 393.0 471.4 99960.0 100000 20 1680.66 2204103 240483435 3997575 155895580 152368360780 172763315973 222661756835 330390205 0 0 0 0 549614 972607 +ebbrt_tuned 2 300 0x1900 95 78.0 98.9 229.6 358.9 393.5 475.2 100074.6 100000 20 1681.32 2204787 240662700 4002254 156078690 152596538354 172667881841 222646756643 331117521 0 0 0 0 551829 972685 +ebbrt_tuned 0 300 0x1900 75 79.0 99.7 230.0 359.3 394.8 476.0 100008.3 100000 20 1684.94 2203035 240447872 3999528 155972594 152479431750 172903669137 222402059225 329569118 0 0 0 0 541002 972483 +ebbrt_tuned 1 300 0x1900 75 77.8 98.9 229.3 357.4 391.5 472.4 99945.3 100000 20 1683.99 2201581 240322655 3997000 155873730 152373159235 172183413557 221586300071 328845956 0 0 0 0 543738 972440 +ebbrt_tuned 2 300 0x1900 75 75.3 96.0 227.6 356.3 390.2 470.2 100022.6 100000 20 1686.46 2203194 240486692 4000192 155997776 152470124738 172101448419 221542346917 330254000 0 0 0 0 540568 972441 +ebbrt_tuned 0 300 0x1900 55 77.1 98.0 229.4 358.9 393.4 472.9 99994.8 100000 20 1686.78 2205165 240563021 3998885 155947272 152416691970 172570696977 222489225192 332001686 0 0 0 0 552727 972727 +ebbrt_tuned 1 300 0x1900 55 77.6 98.4 228.5 357.2 390.7 472.0 100083.5 100000 20 1687.21 2203998 240616390 4002702 156096588 152711927057 172871958747 222423168412 329217014 0 0 0 0 545394 972640 +ebbrt_tuned 2 300 0x1900 55 76.4 96.8 227.6 356.7 390.9 472.2 100008.9 100000 20 1685.93 2205187 240577086 3999654 155978734 152424999841 172324338126 222047689723 331399365 0 0 0 0 547364 972653 +ebbrt_tuned 0 50 0x1b00 135 47.9 52.9 80.8 148.4 184.8 236.7 50085.1 50000 20 1652.08 1583423 149213046 2003254 78124934 76909180873 106973283285 144224094966 175427737 0 0 0 0 411157 2274288 +ebbrt_tuned 1 50 0x1b00 135 48.2 53.2 81.0 149.0 186.0 238.2 50089.5 50000 20 1654.45 1586515 149404145 2003405 78131150 76722956828 107101241945 144420635680 176472922 0 0 0 0 408162 2269149 +ebbrt_tuned 2 50 0x1b00 135 48.7 53.6 81.2 149.6 186.9 239.5 50054.0 50000 20 1654.11 1584415 149255302 2002046 78078204 76792321701 107324855649 144680538668 177809737 0 0 0 0 407891 2266736 +ebbrt_tuned 0 50 0x1b00 95 49.2 54.3 81.8 148.9 187.1 238.8 49957.1 50000 20 1654.81 1585590 149201658 1998138 77925794 76690000899 106814694837 144102431672 177084587 0 0 0 0 410031 2268756 +ebbrt_tuned 1 50 0x1b00 95 47.5 52.3 79.0 141.1 174.6 222.5 49972.0 50000 20 1625.5 1592675 149673014 1998736 77948964 74966954379 101706634289 139756177573 156984647 0 0 0 0 415354 2271746 +ebbrt_tuned 2 50 0x1b00 95 48.4 53.4 80.1 144.6 176.5 224.4 50010.8 50000 20 1637.54 1594561 149808107 2000246 78008124 75388393826 102527059738 140512163290 160665596 0 0 0 0 415679 2274631 +ebbrt_tuned 0 50 0x1b00 75 48.6 53.6 80.9 146.9 179.7 227.8 50081.9 50000 20 1639.57 1590706 149660632 2003160 78121530 75725131876 103131466042 141054754247 162759030 0 0 0 0 416155 2275779 +ebbrt_tuned 1 50 0x1b00 75 48.1 53.2 80.1 146.4 181.0 228.3 49954.5 50000 20 1641.42 1587242 149329379 1997976 77919498 75429080043 103160696603 140870294817 163714569 0 0 0 0 414272 2267823 +ebbrt_tuned 2 50 0x1b00 75 48.6 53.8 81.1 149.4 183.8 234.2 50079.4 50000 20 1641.01 1587205 149472543 2002965 78113224 75590172366 104050501012 141918446399 166885396 0 0 0 0 414902 2273689 +ebbrt_tuned 0 50 0x1b00 55 48.5 53.5 80.4 145.7 181.5 230.8 49950.6 50000 20 1643.05 1588945 149429649 1997878 77915762 75663232640 104103926590 141827310506 167007215 0 0 0 0 415175 2269345 +ebbrt_tuned 1 50 0x1b00 55 49.3 54.3 81.2 147.1 182.9 233.7 50075.1 50000 20 1645.99 1590570 149633364 2002786 78106686 76165020949 104666970664 142323480148 168003065 0 0 0 0 415524 2273316 +ebbrt_tuned 2 50 0x1b00 55 48.7 53.8 81.1 148.0 182.7 233.7 50082.4 50000 20 1646.58 1589747 149614002 2003146 78121090 75928169786 104509114197 142111912968 167762954 0 0 0 0 413926 2268425 +ebbrt_tuned 0 50 0x1b00 135 48.8 53.7 83.4 160.7 194.6 250.3 100134.2 100000 20 1856.47 2289994 245823385 4004695 156174348 151079939258 183781783041 221441736985 332922069 0 0 0 0 368760 3361588 +ebbrt_tuned 2 50 0x1b00 135 48.3 53.2 82.7 159.7 191.8 247.5 99917.5 100000 20 1850.38 2284062 245241448 3996056 155836952 150946929039 182865905864 220592462995 332544652 0 0 0 0 368867 3351478 +ebbrt_tuned 0 50 0x1b00 95 47.9 52.8 82.7 161.6 194.5 249.7 100028.2 100000 20 1856.26 2287720 245570624 4000414 156006262 151095053271 183464445448 221149478407 332616054 0 0 0 0 368580 3355997 +ebbrt_tuned 1 50 0x1b00 95 48.7 53.4 83.4 161.0 194.3 249.5 99959.3 100000 20 1856.29 2287118 245441142 3997533 155891750 150930037220 182917432265 220676344103 331991975 0 0 0 0 368516 3357955 +ebbrt_tuned 2 50 0x1b00 95 47.9 52.8 82.7 161.9 194.2 249.3 100037.7 100000 20 1855.96 2287450 245560656 4000834 156023678 151026231578 182641713614 220323223196 330611208 0 0 0 0 367644 3352553 +ebbrt_tuned 0 50 0x1b00 75 47.4 52.1 81.8 159.8 193.9 249.5 100010.3 100000 20 1787.01 2190959 235394936 3843117 149873244 145176055504 176086901368 212346726332 321665566 0 0 0 0 352842 3219856 +ebbrt_tuned 1 50 0x1b00 75 49.5 54.4 84.8 163.5 195.3 251.3 99999.1 100000 20 1857.46 2285528 245422055 3999285 155963058 150909195680 183337650020 220972326339 333776649 0 0 0 0 368344 3352345 +ebbrt_tuned 2 50 0x1b00 75 48.4 53.5 83.8 161.8 195.3 251.2 99948.4 100000 20 1858.08 2285883 245378828 3997296 155885454 150870756373 183588492965 221185751346 333878770 0 0 0 0 368813 3352497 +ebbrt_tuned 1 50 0x1b00 55 48.3 53.2 83.3 161.9 195.3 249.4 99895.8 100000 20 1853.93 2284917 245274520 3994894 155789804 150938259700 183844252144 221593103326 337044903 0 0 0 0 369250 3357336 +ebbrt_tuned 2 50 0x1b00 55 48.0 52.8 83.0 159.5 193.2 248.9 99822.3 100000 20 1858.56 2284339 245136807 3992245 155689098 150988590985 183609385876 221197449946 332907013 0 0 0 0 368562 3351896 +ebbrt_tuned 1 100 0x1b00 135 51.8 58.1 102.9 179.3 206.7 269.1 50005.2 50000 20 1639.26 1537512 146391169 2000062 78000454 75797089833 102882198625 140916192566 168169562 0 0 0 0 414293 1855946 +ebbrt_tuned 2 100 0x1b00 135 51.3 57.8 102.6 177.9 205.3 268.4 49967.7 50000 20 1642.66 1531962 146003619 1998504 77940458 75700941280 103571984523 141735221035 169899521 0 0 0 0 414141 1856557 +ebbrt_tuned 0 100 0x1b00 95 51.8 58.4 104.0 181.7 209.6 271.0 49958.7 50000 20 1641.91 1533728 146113121 1998208 77928568 75724732623 103157062075 141102628027 167734530 0 0 0 0 413282 1851221 +ebbrt_tuned 2 100 0x1b00 95 51.2 57.6 102.8 179.3 207.1 271.5 50023.5 50000 20 1640.17 1533123 146145226 2000743 78027348 75770206258 103239970221 141119316338 169499344 0 0 0 0 414645 1855036 +ebbrt_tuned 0 100 0x1b00 75 51.7 58.2 103.6 180.2 207.8 271.1 50005.4 50000 20 1642.64 1530894 146000993 2000057 78000576 75837790575 103682387604 141685244552 170808695 0 0 0 0 416531 1863498 +ebbrt_tuned 1 100 0x1b00 75 51.5 57.9 103.5 179.0 206.7 270.0 50001.9 50000 20 1645.71 1531297 146022272 1999903 77994066 75657904105 103970283798 142060052384 171147281 0 0 0 0 416451 1862406 +ebbrt_tuned 2 100 0x1b00 75 51.5 58.1 103.9 180.3 208.3 272.6 50030.0 50000 20 1644.49 1534068 146224549 2001054 78039246 76121023576 103729059024 141589260236 168491856 0 0 0 0 416475 1860717 +ebbrt_tuned 0 100 0x1b00 55 51.5 57.8 103.1 178.5 206.7 271.0 49942.1 50000 20 1643.24 1528825 145808209 1997559 77903332 75799741866 103428624617 141448266383 169564044 0 0 0 0 416948 1860805 +ebbrt_tuned 1 100 0x1b00 55 52.7 58.9 104.4 181.1 210.9 272.6 49977.0 50000 20 1641.72 1533737 146134189 1998912 77956240 75754190759 103555455713 141445221905 168517518 0 0 0 0 415082 1859488 +ebbrt_tuned 2 100 0x1b00 55 51.9 58.4 103.1 179.2 207.4 270.9 50003.1 50000 20 1645.34 1533560 146146524 1999975 77997158 75892802035 103700558878 141600504696 169184528 0 0 0 0 415366 1857725 +ebbrt_tuned 0 100 0x1b00 135 54.1 61.2 113.7 191.4 225.1 287.5 99986.2 100000 20 1851.45 2255012 243532404 3998553 155932932 150635552880 180415471669 218607550397 332973486 0 0 0 0 393458 2454587 +ebbrt_tuned 1 100 0x1b00 135 54.1 61.4 113.6 192.4 226.1 293.2 100040.3 100000 20 1850.78 2248473 243236957 4000996 156029994 150603984441 180588033149 218965085481 334992680 0 0 0 0 390588 2452513 +ebbrt_tuned 2 100 0x1b00 135 53.8 61.4 113.8 193.1 226.0 290.1 100055.4 100000 20 1851.49 2249335 243276233 4001378 156045100 150682525526 180761693631 218956585088 333261802 0 0 0 0 390551 2452176 +ebbrt_tuned 0 100 0x1b00 95 54.0 61.6 114.0 192.4 225.9 291.1 99892.6 100000 20 1850.6 2248690 243098285 3995025 155796856 150778240258 180974356838 219241858465 334563485 0 0 0 0 391976 2452460 +ebbrt_tuned 1 100 0x1b00 95 54.1 61.7 114.8 193.5 227.1 290.1 100139.7 100000 20 1847.83 2250217 243435267 4004965 156184530 150953646056 181241524001 219396077739 334542288 0 0 0 0 392945 2455034 +ebbrt_tuned 2 100 0x1b00 95 54.7 62.3 115.2 193.7 227.7 292.8 99910.0 100000 20 1713.06 2105359 227569581 3742809 145961334 140991620241 169112864463 205101229568 313705040 0 0 0 0 369118 2298868 +ebbrt_tuned 0 100 0x1b00 75 53.7 61.3 113.8 190.3 224.3 291.0 99982.1 100000 20 1850.3 2246902 243071217 3998612 155936728 150565031518 180683139691 218948958581 334223826 0 0 0 0 392771 2451179 +ebbrt_tuned 1 100 0x1b00 75 52.9 60.2 111.6 184.8 214.9 272.2 100032.0 100000 20 1815.57 2255043 243619605 4000551 156012088 147697973975 170467014288 210464455551 295617408 0 0 0 0 394701 2455837 +ebbrt_tuned 2 100 0x1b00 75 54.6 61.7 113.7 188.9 220.9 279.5 100058.0 100000 20 1834.45 2248997 243299584 4001667 156055616 148524486724 173405455121 213348355708 309893869 0 0 0 0 397623 2458214 +ebbrt_tuned 0 100 0x1b00 55 53.0 60.4 112.0 186.5 217.9 280.5 100023.8 100000 20 1833.86 2253553 243527414 4000242 156000578 148424450675 173816986518 213305810098 310108731 0 0 0 0 395394 2456628 +ebbrt_tuned 2 100 0x1b00 55 53.7 61.1 112.9 190.1 223.2 283.3 99816.7 100000 20 1830.72 2247144 242919118 3992072 155682522 148656190290 175002959829 214093263454 315148597 0 0 0 0 394383 2454554 +ebbrt_tuned 0 200 0x1b00 135 61.2 72.8 166.2 265.4 294.1 360.9 50099.1 50000 20 1630.12 1435178 140341928 2003859 78149076 75570456512 100302450187 136533721875 167516341 0 0 0 0 463377 1281427 +ebbrt_tuned 1 200 0x1b00 135 60.1 71.0 162.8 262.8 290.3 357.4 49967.5 50000 20 1629.39 1435071 140215349 1998547 77941844 75291234653 100082402786 136382209780 167927492 0 0 0 0 460268 1278907 +ebbrt_tuned 2 200 0x1b00 135 60.6 71.9 163.5 264.1 292.9 361.1 49973.6 50000 20 1632.01 1437002 140315683 1998812 77952328 75442093374 100112903236 136354372361 167117002 0 0 0 0 465913 1281885 +ebbrt_tuned 0 200 0x1b00 95 60.4 71.8 164.7 263.0 290.3 358.6 50005.3 50000 20 1632.67 1433540 140145570 1999998 77997646 75456933504 100277368213 136643898907 169256338 0 0 0 0 462530 1280938 +ebbrt_tuned 1 200 0x1b00 95 59.6 70.6 162.4 262.9 290.9 359.4 50045.5 50000 20 1630.42 1438006 140484437 2001570 78058344 75679461694 100432221641 136648698117 168822688 0 0 0 0 463215 1281237 +ebbrt_tuned 2 200 0x1b00 95 60.5 71.6 163.7 264.3 292.1 361.2 50002.3 50000 20 1571.68 1394048 136116098 1942296 75748454 73293134304 97395567087 132546762290 162643546 0 0 0 0 451424 1243436 +ebbrt_tuned 0 200 0x1b00 75 61.8 73.3 165.6 265.2 293.6 360.7 50009.5 50000 20 1632.44 1438627 140450125 2000205 78006394 75600112049 100682902213 136983229386 170414948 0 0 0 0 464986 1281779 +ebbrt_tuned 1 200 0x1b00 75 60.6 72.1 164.7 264.1 292.5 361.9 49920.3 50000 20 1629.53 1432934 140018568 1996665 77868294 75494205710 100413205339 136678260166 169016473 0 0 0 0 462726 1279572 +ebbrt_tuned 2 200 0x1b00 75 59.9 71.2 162.7 262.5 290.2 358.3 49934.4 50000 20 1627.48 1435939 140227127 1997216 77889798 75401640329 100006497359 136164475866 167974417 0 0 0 0 460319 1278711 +ebbrt_tuned 0 200 0x1b00 55 61.1 72.6 164.1 264.1 292.2 362.2 49975.9 50000 20 1628.97 1435890 140255811 1998797 77950940 75547496167 100080028978 136207637001 167648473 0 0 0 0 464840 1281307 +ebbrt_tuned 1 200 0x1b00 55 60.8 72.2 165.1 264.7 293.1 361.0 50042.5 50000 20 1631.61 1436890 140403125 2001567 78059238 75658884145 100645285371 136820004510 169668449 0 0 0 0 462363 1280187 +ebbrt_tuned 2 200 0x1b00 55 60.2 71.8 164.1 263.3 291.1 359.4 49961.8 50000 20 1630.83 1433492 140099751 1998271 77931074 75659903096 100400428834 136539812948 169797594 0 0 0 0 461868 1279356 +ebbrt_tuned 0 200 0x1b00 135 65.4 79.1 171.9 267.0 297.9 364.0 100184.7 100000 20 1822.86 2214808 241372479 4006564 156246998 148582726853 171032919614 209283685983 311029821 0 0 0 0 506822 1430361 +ebbrt_tuned 1 200 0x1b00 135 65.6 79.5 171.9 267.6 297.8 366.3 99982.9 100000 20 1821.48 2214236 241124777 3998655 155938982 148242816036 171400723345 209482453694 311924459 0 0 0 0 505182 1430544 +ebbrt_tuned 2 200 0x1b00 135 65.9 80.2 173.4 269.4 301.1 371.7 100016.2 100000 20 1824.11 2211699 240995720 3999794 155983516 148244910885 171460296645 209952350235 315722380 0 0 0 0 506106 1430394 +ebbrt_tuned 0 200 0x1b00 95 65.5 79.1 172.1 268.6 301.4 367.1 99888.7 100000 20 1824.04 2213027 240932948 3994871 155790602 148579304682 171433905401 209556704337 314104068 0 0 0 0 503885 1430286 +ebbrt_tuned 1 200 0x1b00 95 66.2 80.1 172.3 267.7 298.2 365.4 99940.9 100000 20 1689.28 2052783 223528887 3705557 144508836 137398854872 159113705853 194605624127 292143773 0 0 0 0 465554 1326115 +ebbrt_tuned 2 200 0x1b00 95 66.0 80.5 172.7 268.9 301.1 369.3 100008.2 100000 20 1825.14 2214964 241181621 3999730 155981214 148405533078 171716782775 209797061963 316647909 0 0 0 0 503411 1430352 +ebbrt_tuned 0 200 0x1b00 75 66.0 79.5 172.6 268.7 300.3 369.1 99864.0 100000 20 1821.83 2211889 240842717 3993934 155755162 148422155281 171404610402 209325311104 313379447 0 0 0 0 506614 1430569 +ebbrt_tuned 1 200 0x1b00 75 66.1 79.7 172.8 268.0 298.4 365.3 99871.7 100000 20 1754.46 2157722 234873216 3896070 151938576 144907239042 167733388736 205007156468 309242757 0 0 0 0 498245 1396287 +ebbrt_tuned 2 200 0x1b00 75 66.2 80.6 174.3 268.9 300.2 367.3 99985.4 100000 20 1824.76 2209399 240834126 3998719 155940592 148927400855 172588114480 210854482235 320243819 0 0 0 0 507794 1430120 +ebbrt_tuned 0 200 0x1b00 55 65.7 79.6 172.0 267.4 299.4 369.7 99863.8 100000 20 1820.0 2213999 240992362 3993942 155754896 148591581868 172084742921 210109352436 316745983 0 0 0 0 503049 1429527 +ebbrt_tuned 1 200 0x1b00 55 64.9 78.8 171.5 267.4 299.4 367.3 100001.5 100000 20 1827.08 2214408 241115607 3999364 155966476 148860631074 172809032679 210667848733 320733770 0 0 0 0 498756 1430039 +ebbrt_tuned 2 200 0x1b00 55 65.6 79.6 172.4 267.7 298.5 368.1 100061.4 100000 20 1694.92 2057414 224029832 3715229 144885442 138684220888 160844293076 196192886347 300201343 0 0 0 0 470575 1328600 +ebbrt_tuned 0 300 0x1b00 135 71.0 89.9 222.0 352.9 379.7 446.1 50078.8 50000 20 1620.99 1373787 136633355 2002972 78114142 76693783967 99304460836 135176623404 168499507 0 0 0 0 479407 932899 +ebbrt_tuned 1 300 0x1b00 135 71.0 90.0 222.3 352.9 380.0 452.3 50004.1 50000 20 1622.75 1370846 136393033 1999890 77993322 76824214257 100206746970 136270831220 170931091 0 0 0 0 479372 932159 +ebbrt_tuned 2 300 0x1b00 135 71.2 89.9 224.3 354.5 382.1 449.1 50039.1 50000 20 1620.04 1374090 136620742 2001430 78054248 76714143728 99593326482 135547493993 169825908 0 0 0 0 480132 932535 +ebbrt_tuned 0 300 0x1b00 95 69.8 88.7 221.1 353.5 381.6 450.3 49848.0 50000 20 1621.79 1369238 136111576 1993648 77749646 76627596816 100150073431 136388936098 170109589 0 0 0 0 485516 933006 +ebbrt_tuned 1 300 0x1b00 95 72.4 91.4 224.7 355.0 383.6 453.9 49949.9 50000 20 1622.44 1370603 136312437 1997836 77913662 76739295844 100065765651 136046790502 170781115 0 0 0 0 479710 932244 +ebbrt_tuned 2 300 0x1b00 95 70.3 89.1 222.0 352.9 380.5 450.4 49981.0 50000 20 1620.15 1371410 136410328 1999072 77961926 76791046503 100072608121 136089971908 171190868 0 0 0 0 478379 932164 +ebbrt_tuned 0 300 0x1b00 75 70.3 89.1 223.0 353.8 381.9 449.7 49899.8 50000 20 1620.63 1370304 136234988 1995750 77833106 76792771837 100010232256 136047540732 171122488 0 0 0 0 481009 932634 +ebbrt_tuned 1 300 0x1b00 75 70.9 89.6 222.1 353.4 380.7 451.4 50042.1 50000 20 1621.1 1371336 136471244 2001507 78056808 76929798593 100069057392 136093575303 170428502 0 0 0 0 483040 932998 +ebbrt_tuned 2 300 0x1b00 75 71.2 90.3 223.3 353.6 381.3 450.1 49956.2 50000 20 1618.58 1369042 136238601 1998047 77922794 76920748575 99965023031 135847920102 169494042 0 0 0 0 482059 932564 +ebbrt_tuned 0 300 0x1b00 55 70.9 89.7 223.1 353.6 382.3 453.4 50043.5 50000 20 1621.59 1370319 136404299 2001588 78060290 76949662828 100039459566 135832158486 170718613 0 0 0 0 477303 932039 +ebbrt_tuned 1 300 0x1b00 55 71.7 90.7 223.1 354.3 383.2 455.2 49960.1 50000 20 1501.79 1270438 126394131 1853328 72279258 71323643284 92799951506 126103194435 157436035 0 0 0 0 446156 864984 +ebbrt_tuned 2 300 0x1b00 55 72.0 90.3 223.5 354.4 382.7 453.4 49996.8 50000 20 1620.25 1371390 136404104 1999668 77985520 77025421216 99895099972 135748817126 170310000 0 0 0 0 479064 932335 +ebbrt_tuned 0 300 0x1b00 135 76.5 97.4 227.4 355.0 386.6 463.0 100012.7 100000 20 1818.62 2203674 240501462 3999762 155981646 152000228652 174893880140 213744057141 326515190 0 0 0 0 548917 972576 +ebbrt_tuned 1 300 0x1b00 135 74.3 94.5 225.7 353.4 384.7 459.1 100052.0 100000 20 1814.78 2205327 240673911 4001334 156042434 152237462715 174672529106 213957486208 329564926 0 0 0 0 552694 972548 +ebbrt_tuned 2 300 0x1b00 135 76.3 96.7 227.4 355.1 387.2 463.3 99879.4 100000 20 1813.83 2201645 240257909 3994444 155773116 152154645516 174578384356 213650273084 328040931 0 0 0 0 552307 972556 +ebbrt_tuned 0 300 0x1b00 95 77.0 97.3 227.6 354.7 385.9 458.1 100094.4 100000 20 1813.7 2206127 240738933 4002963 156106038 152543242200 174510945076 213094708774 328031298 0 0 0 0 543864 972363 +ebbrt_tuned 1 300 0x1b00 95 76.0 97.2 227.8 355.4 387.8 464.1 100026.3 100000 20 1815.21 2204518 240586971 4000276 155999762 152493296927 175107641909 213874662730 326735540 0 0 0 0 550279 972674 +ebbrt_tuned 2 300 0x1b00 95 77.8 99.2 228.8 356.4 387.8 460.4 99815.7 100000 20 1815.71 2202294 240208214 3991724 155665766 151953345492 174558137489 213749891419 328952502 0 0 0 0 553625 972628 +ebbrt_tuned 0 300 0x1b00 75 75.1 95.2 226.0 354.8 386.7 460.6 99937.7 100000 20 1819.34 2204291 240464472 3996582 155854354 152238528108 175094268550 213911260498 327276069 0 0 0 0 549963 972611 +ebbrt_tuned 1 300 0x1b00 75 75.7 95.9 226.6 354.6 385.7 462.6 100002.1 100000 20 1814.87 2206406 240661822 3999305 155964338 152100026968 174749733617 213798039887 327962306 0 0 0 0 551368 972625 +ebbrt_tuned 2 300 0x1b00 75 75.9 96.5 227.5 354.8 386.3 459.7 100028.9 100000 20 1816.25 2203216 240501663 4000427 156006498 152431737317 175604951680 214639162267 328804383 0 0 0 0 551961 972555 +ebbrt_tuned 0 300 0x1b00 55 77.7 98.5 229.9 356.7 388.2 465.3 99784.1 100000 20 1818.3 2201661 240138026 3990507 155618584 152170581449 175716757365 214974010332 331343778 0 0 0 0 553292 972590 +ebbrt_tuned 1 300 0x1b00 55 75.7 96.4 225.2 354.1 385.5 458.5 100073.5 100000 20 1817.35 2206015 240717979 4002098 156073652 152606115082 175097252802 214039799344 328472878 0 0 0 0 550149 972597 +ebbrt_tuned 2 300 0x1b00 55 76.8 98.1 228.4 356.2 388.6 465.7 100165.5 100000 20 1821.59 2206271 240841594 4005903 156220962 152813648510 176202355681 215344191678 333893569 0 0 0 0 552443 972704 +ebbrt_tuned 0 50 0x1d00 135 48.7 53.8 81.4 144.7 179.8 230.8 49892.9 50000 20 1653.6 1469196 138204427 1849683 72136986 70925834361 101204913103 131228255888 165515240 0 0 0 0 384141 2099042 +ebbrt_tuned 1 50 0x1d00 135 47.9 52.9 80.5 145.9 180.4 229.9 49995.3 50000 20 1789.78 1584215 149191829 1999675 77985540 76844503119 110115567806 142485088535 180655363 0 0 0 0 415915 2270963 +ebbrt_tuned 0 50 0x1d00 95 49.1 53.9 81.6 146.7 181.6 232.2 49960.5 50000 20 1780.87 1588098 149373489 1998199 77928008 76433132883 109066014585 141483156457 178619840 0 0 0 0 414042 2265376 +ebbrt_tuned 1 50 0x1d00 95 48.2 53.2 80.9 146.9 180.0 228.5 49970.3 50000 20 1791.8 1583086 149097410 1998627 77944642 76701782179 109353015500 141653108840 178152768 0 0 0 0 413868 2266398 +ebbrt_tuned 2 50 0x1d00 95 48.0 53.1 80.6 147.0 182.1 232.4 50021.0 50000 20 1787.79 1584486 149224914 2000692 78025122 76956531185 108817578161 140948772935 176005968 0 0 0 0 413782 2265100 +ebbrt_tuned 0 50 0x1d00 75 48.2 53.3 80.6 146.1 180.1 233.8 49985.3 50000 20 1786.71 1586055 149284533 1999274 77969916 76650357913 109013810149 141301338579 178842773 0 0 0 0 413630 2263937 +ebbrt_tuned 1 50 0x1d00 75 48.1 53.1 80.7 147.7 182.6 233.1 50016.9 50000 20 1787.1 1590313 149583309 2000540 78019600 76914836594 109302397218 141529099243 177987463 0 0 0 0 413220 2267647 +ebbrt_tuned 2 50 0x1d00 75 47.8 52.7 79.3 140.2 170.3 220.0 50011.0 50000 20 1753.12 1596315 149930398 2000251 78007142 74941569880 102917396200 136250929825 155536239 0 0 0 0 413357 2274756 +ebbrt_tuned 0 50 0x1d00 55 48.1 53.0 79.7 141.0 171.3 219.3 50073.1 50000 20 1768.27 1592974 149786648 2002756 78105724 75565458602 104030454652 137397824029 160459010 0 0 0 0 410438 2267659 +ebbrt_tuned 1 50 0x1d00 55 47.2 52.0 79.4 141.0 172.6 220.8 50053.7 50000 20 1769.54 1593309 149797248 2001961 78074480 75649414596 104512501488 137811493398 162835186 0 0 0 0 411504 2271268 +ebbrt_tuned 2 50 0x1d00 55 47.8 52.7 79.8 144.1 176.7 225.4 50040.0 50000 20 1772.9 1589926 149576171 2001393 78052994 75561124296 105241310592 138688539441 166346542 0 0 0 0 410196 2269928 +ebbrt_tuned 0 50 0x1d00 135 47.7 52.4 81.8 154.0 184.7 239.1 100062.5 100000 20 1993.02 2296524 246161596 4001855 156063188 149690168847 183949492893 212690198852 324257189 0 0 0 0 368491 3361293 +ebbrt_tuned 1 50 0x1d00 135 47.3 52.0 80.9 152.7 183.1 236.9 100116.9 100000 20 2004.43 2293754 246010113 4003867 156141996 149955771741 184487504602 213163801071 325959212 0 0 0 0 370147 3369130 +ebbrt_tuned 2 50 0x1d00 135 47.6 52.4 82.3 154.2 185.6 239.1 100138.8 100000 20 2009.4 2292885 246008293 4004916 156182574 150040317096 184687625468 213317596336 324234611 0 0 0 0 368798 3362940 +ebbrt_tuned 1 50 0x1d00 95 47.6 52.3 81.3 153.4 184.3 238.1 100038.5 100000 20 2011.63 2296094 246070203 4000740 156018890 149820753681 184964907585 213595913454 327514704 0 0 0 0 367772 3362289 +ebbrt_tuned 2 50 0x1d00 95 47.9 52.6 82.3 154.2 185.1 239.8 100124.2 100000 20 2008.33 2290408 245845374 4004234 156155582 150291528865 185721088284 214285124728 330534294 0 0 0 0 368321 3360230 +ebbrt_tuned 0 50 0x1d00 75 47.3 52.1 81.2 153.5 184.5 240.2 100029.7 100000 20 2009.24 2291220 245786699 4000552 156012432 150385296596 185326135903 213637978476 327792317 0 0 0 0 366298 3354626 +ebbrt_tuned 1 50 0x1d00 75 47.4 52.1 81.4 153.1 184.5 236.7 99828.1 100000 20 2009.98 2287277 245303750 3992263 155687974 150153712947 185969708197 214232120268 331135114 0 0 0 0 366493 3348511 +ebbrt_tuned 2 50 0x1d00 75 47.9 52.5 81.4 153.8 185.4 239.0 99966.3 100000 20 2006.8 2292950 245825139 3998050 155915648 150492191309 186058363762 214317057121 331669508 0 0 0 0 367210 3356546 +ebbrt_tuned 0 50 0x1d00 55 48.1 53.1 83.5 160.0 190.8 253.0 100052.3 100000 20 1970.53 2286522 245536466 4001409 156047082 150843675035 185965764599 219021435004 332352640 0 0 0 0 368174 3365672 +ebbrt_tuned 2 50 0x1d00 55 48.0 53.0 83.0 158.7 191.7 253.1 100029.3 100000 20 1969.28 2290423 245727546 4000349 156003762 151061268104 185275622945 218281463087 332494906 0 0 0 0 365768 3354862 +ebbrt_tuned 0 100 0x1d00 135 51.0 57.5 102.9 176.6 202.2 260.3 50015.0 50000 20 1769.7 1537520 146419528 2000402 78013498 75299749628 103404928368 137185516421 164290598 0 0 0 0 414698 1861989 +ebbrt_tuned 1 100 0x1d00 135 51.4 58.0 103.0 175.3 201.7 261.5 50100.8 50000 20 1771.23 1538522 146558123 2003869 78149078 75658412003 103760066497 137354036871 165106117 0 0 0 0 412567 1862418 +ebbrt_tuned 2 100 0x1d00 135 51.1 57.4 102.8 176.5 203.5 260.9 50026.2 50000 20 1769.22 1537957 146445044 2000845 78030884 75919350864 104116570261 137613010844 166705572 0 0 0 0 412303 1860805 +ebbrt_tuned 0 100 0x1d00 95 50.5 57.1 102.4 174.4 201.1 261.1 49996.4 50000 20 1767.83 1534301 146178353 1999731 77987950 75623493792 104266427983 137800108781 168102553 0 0 0 0 411962 1855676 +ebbrt_tuned 1 100 0x1d00 95 50.7 57.3 102.5 175.8 202.6 263.8 50040.2 50000 20 1770.74 1537698 146423679 2001440 78054786 75668925825 104517177372 138051295239 168308340 0 0 0 0 413412 1864324 +ebbrt_tuned 2 100 0x1d00 95 51.0 57.4 102.5 176.9 203.8 263.6 50006.0 50000 20 1771.35 1537443 146371798 2000116 78002914 75703369329 104606374402 138086346437 168648708 0 0 0 0 411483 1857984 +ebbrt_tuned 1 100 0x1d00 75 51.2 57.5 102.2 176.7 203.9 266.7 49967.8 50000 20 1764.22 1536793 146337423 1998518 77940960 75780804674 104811778648 138156667232 168179041 0 0 0 0 413726 1864193 +ebbrt_tuned 2 100 0x1d00 75 51.0 57.5 102.6 176.0 203.0 261.9 50043.6 50000 20 1772.72 1537430 146409377 2001612 78060972 75798674203 104844916615 138168344314 168625328 0 0 0 0 411814 1859685 +ebbrt_tuned 0 100 0x1d00 55 52.6 59.1 104.1 178.1 205.4 265.7 49992.9 50000 20 1773.4 1539238 146492166 1999562 77981386 75718489836 104977057315 138259632428 168704652 0 0 0 0 410192 1857214 +ebbrt_tuned 1 100 0x1d00 55 51.4 57.9 103.6 177.5 204.4 265.1 50025.1 50000 20 1770.93 1541841 146673001 2000802 78030112 75848835419 105182968010 138741311698 170271011 0 0 0 0 412457 1861901 +ebbrt_tuned 2 100 0x1d00 55 50.8 57.3 102.6 175.3 203.2 265.8 49947.8 50000 20 1768.34 1534875 146177211 1997772 77911518 75516217175 104712994980 138254523063 168005909 0 0 0 0 412594 1860654 +ebbrt_tuned 0 100 0x1d00 135 52.3 59.7 112.4 186.4 217.5 274.7 100011.2 100000 20 2000.84 2246761 243086124 3999711 155978770 150289095167 182896791140 211830611882 328574226 0 0 0 0 392506 2456634 +ebbrt_tuned 1 100 0x1d00 135 54.2 61.6 113.5 187.6 219.0 279.3 99973.4 100000 20 2003.53 2249602 243212791 3998216 155920848 150299767864 182854528954 211738091500 328529064 0 0 0 0 392234 2456853 +ebbrt_tuned 2 100 0x1d00 135 52.3 59.6 110.7 184.7 216.7 274.2 99892.9 100000 20 2003.03 2254357 243455232 3995037 155797160 150173018801 182446351025 211544663907 328693661 0 0 0 0 395177 2456539 +ebbrt_tuned 0 100 0x1d00 95 52.6 59.9 112.1 186.5 218.6 277.0 100016.8 100000 20 2001.74 2248657 243201589 3999943 155988456 150487804255 182890601484 211695315630 328478677 0 0 0 0 392093 2454238 +ebbrt_tuned 1 100 0x1d00 95 52.7 60.1 112.7 187.2 217.7 275.4 100016.7 100000 20 2004.86 2252493 243416661 3999877 155987218 150756025865 183783569850 212797399295 332606519 0 0 0 0 393929 2456627 +ebbrt_tuned 2 100 0x1d00 95 53.0 60.5 111.8 185.3 216.1 275.8 99934.8 100000 20 2000.94 2251687 243299338 3996649 155859594 150449703248 182865495333 211744054248 330390423 0 0 0 0 391866 2454637 +ebbrt_tuned 0 100 0x1d00 75 52.0 59.5 110.8 185.6 216.7 276.1 99956.4 100000 20 2003.8 2246926 243007991 3997558 155895290 150594405599 183318125626 212341618939 331322340 0 0 0 0 389438 2452008 +ebbrt_tuned 2 100 0x1d00 75 52.2 59.8 111.2 185.1 215.3 273.0 99989.8 100000 20 1994.67 2253946 243486403 3998638 155935804 150553014265 182547999115 211402904831 329145924 0 0 0 0 392477 2457893 +ebbrt_tuned 0 100 0x1d00 55 54.5 61.9 114.5 190.4 223.5 290.8 99860.6 100000 20 1971.97 2247716 242986005 3993737 155746632 150579956823 183324442382 216363983464 333444367 0 0 0 0 391840 2454002 +ebbrt_tuned 1 100 0x1d00 55 54.2 61.5 113.2 189.2 223.4 290.8 100123.5 100000 20 1972.68 2250841 243483886 4004177 156153164 150641761173 183012007235 215417955527 332049913 0 0 0 0 390001 2452153 +ebbrt_tuned 2 100 0x1d00 55 53.0 60.4 111.7 182.4 209.5 268.8 100091.2 100000 20 1949.4 2258264 243855509 4002668 156091752 147764605710 172977603480 205581578086 294629142 0 0 0 0 397033 2457952 +ebbrt_tuned 0 200 0x1d00 135 60.2 71.3 162.6 261.8 287.0 352.1 49989.0 50000 20 1757.47 1433999 140167794 1999362 77973642 75591047621 101736181672 133606443025 168335145 0 0 0 0 462411 1281380 +ebbrt_tuned 1 200 0x1d00 135 59.0 69.9 160.7 261.7 288.5 353.7 49911.5 50000 20 1755.57 1435894 140178232 1996306 77853984 75218358413 100874718782 132473383880 165399749 0 0 0 0 459860 1280308 +ebbrt_tuned 2 200 0x1d00 135 60.0 71.2 163.4 262.0 288.2 356.1 50001.9 50000 20 1754.85 1439142 140480786 1999881 77994358 75345822421 100847446231 132462032323 164926834 0 0 0 0 458434 1279134 +ebbrt_tuned 0 200 0x1d00 95 61.2 72.2 163.4 263.4 290.6 356.8 49955.2 50000 20 1758.18 1438480 140371765 1998097 77924246 75245561156 101285474813 133005564098 167784864 0 0 0 0 460013 1279662 +ebbrt_tuned 1 200 0x1d00 95 59.3 70.2 162.1 261.5 287.4 351.4 49923.8 50000 20 1755.9 1436867 140270550 1996785 77873370 75328686018 101259933254 132971562729 168167722 0 0 0 0 459588 1279120 +ebbrt_tuned 2 200 0x1d00 95 60.8 71.7 162.8 261.4 288.1 353.0 49960.2 50000 20 1755.5 1439599 140471196 1998197 77927724 75271649795 101398888510 133083718402 166438023 0 0 0 0 460645 1280697 +ebbrt_tuned 0 200 0x1d00 75 59.5 70.6 162.5 261.6 288.0 352.8 50036.1 50000 20 1757.27 1441099 140637460 2001311 78049532 75545823947 101674553113 133400179539 167764243 0 0 0 0 463446 1282887 +ebbrt_tuned 1 200 0x1d00 75 58.9 69.8 160.9 259.6 285.7 352.1 49983.9 50000 20 1624.54 1331435 130108275 1855483 72362844 69916106672 93922579977 123317298560 155429240 0 0 0 0 425909 1187213 +ebbrt_tuned 2 200 0x1d00 75 59.1 70.3 160.7 260.7 286.4 355.3 50041.3 50000 20 1751.07 1435231 140299503 2001500 78056738 75657480407 101959660706 133687668429 168977706 0 0 0 0 463569 1282977 +ebbrt_tuned 0 200 0x1d00 55 59.9 70.6 163.7 262.6 289.4 352.6 49996.7 50000 20 1626.13 1333669 130249608 1855156 72350560 69921901408 94352428150 123887913609 155391538 0 0 0 0 429671 1190254 +ebbrt_tuned 1 200 0x1d00 55 59.4 70.4 162.6 261.3 288.2 353.9 49917.2 50000 20 1750.92 1434809 140127904 1996546 77863866 75429783854 101372697010 133060187512 165688239 0 0 0 0 460513 1280677 +ebbrt_tuned 2 200 0x1d00 55 60.0 71.0 161.8 262.1 289.3 354.6 50044.7 50000 20 1593.95 1285700 125547338 1788890 69762496 67685259022 90977212850 119460441646 151463769 0 0 0 0 413630 1146672 +ebbrt_tuned 0 200 0x1d00 135 64.4 78.8 169.2 263.1 291.4 355.3 100017.7 100000 20 1961.53 2213717 241106779 3999958 155988614 147399591490 170042503318 200545221949 302432117 0 0 0 0 511430 1430086 +ebbrt_tuned 1 200 0x1d00 135 65.4 79.7 171.2 265.2 294.4 360.0 100018.5 100000 20 1967.69 2218522 241411843 3999998 155990640 147527204795 171401171129 201581165923 307575385 0 0 0 0 513874 1430740 +ebbrt_tuned 2 200 0x1d00 135 65.5 79.4 170.4 264.7 293.8 358.3 99828.6 100000 20 1974.49 2210129 240715087 3992434 155695988 147825025588 172469647530 202151950187 310048728 0 0 0 0 507762 1429930 +ebbrt_tuned 0 200 0x1d00 95 65.1 78.9 170.5 264.4 293.6 357.7 100098.1 100000 20 1975.2 2213800 241202220 4003266 156118428 148152303059 173781998002 203533943882 313169966 0 0 0 0 509861 1430376 +ebbrt_tuned 1 200 0x1d00 95 64.3 78.6 171.3 265.1 294.2 358.9 100238.9 100000 20 1977.64 2219198 241695077 4008765 156333326 148560396862 174664502198 204100113173 313620332 0 0 0 0 509679 1430425 +ebbrt_tuned 2 200 0x1d00 95 65.1 79.0 170.4 264.0 293.3 359.6 100070.1 100000 20 1976.24 2215125 241228887 4002008 156068282 148283860736 174279759024 203584221066 313631573 0 0 0 0 510142 1430398 +ebbrt_tuned 0 200 0x1d00 75 65.0 80.0 171.9 266.4 295.9 361.1 100097.4 100000 20 1976.31 2214804 241252497 4003142 156114034 148269883483 175429204887 204766916988 317194029 0 0 0 0 509384 1431039 +ebbrt_tuned 1 200 0x1d00 75 65.1 78.9 170.0 265.4 295.4 361.2 100123.5 100000 20 1978.71 2214428 241278239 4004250 156156458 148811243288 174552741022 203817032457 315377017 0 0 0 0 506653 1430505 +ebbrt_tuned 2 200 0x1d00 75 64.9 79.5 171.2 265.8 295.8 361.1 100110.2 100000 20 1978.72 2220132 241625193 4003583 156131470 148481795616 175449715347 204721310109 315387429 0 0 0 0 510705 1430638 +ebbrt_tuned 0 200 0x1d00 55 64.3 78.5 170.7 265.3 295.6 364.1 100022.5 100000 20 1951.78 2216519 241284661 4000131 155995198 148539605429 174760192639 207700367125 318339257 0 0 0 0 512576 1429848 +ebbrt_tuned 1 200 0x1d00 55 64.5 78.5 170.3 266.2 298.0 366.2 99896.0 100000 20 1949.9 2212471 240914091 3995158 155802194 148382173133 174093078501 206673447661 316313264 0 0 0 0 504972 1429708 +ebbrt_tuned 2 200 0x1d00 55 67.1 81.3 173.8 269.1 299.6 372.0 100081.0 100000 20 1810.06 2054836 223788684 3711141 144726610 137957871800 162422356706 192905391711 296088272 0 0 0 0 474059 1326317 +ebbrt_tuned 0 300 0x1d00 135 70.6 89.2 222.4 352.1 377.6 443.2 50040.0 50000 20 1741.86 1371515 136454864 2001477 78056126 76455780000 100397270654 131949068984 167095037 0 0 0 0 479071 931931 +ebbrt_tuned 1 300 0x1d00 135 71.0 89.1 223.1 351.8 377.2 441.9 50069.4 50000 20 1744.94 1372394 136570650 2002605 78100160 76585583249 100740138174 132314646928 168763056 0 0 0 0 477748 932217 +ebbrt_tuned 2 300 0x1d00 135 70.4 89.8 222.5 351.4 377.8 442.6 50051.7 50000 20 1745.02 1373035 136575312 2001837 78069922 76544153030 100565737505 131978271578 167667773 0 0 0 0 475716 931946 +ebbrt_tuned 0 300 0x1d00 95 70.9 90.3 223.5 353.2 380.3 443.2 50028.6 50000 20 1748.92 1373148 136540373 2000965 78036048 76641320143 100864288825 132326268475 169266720 0 0 0 0 474914 932286 +ebbrt_tuned 1 300 0x1d00 95 70.0 88.5 221.0 351.6 379.1 443.8 50040.3 50000 20 1746.18 1372957 136549282 2001424 78053798 76574816077 100643929525 132052734675 168974985 0 0 0 0 474178 932361 +ebbrt_tuned 2 300 0x1d00 95 70.1 89.0 222.3 352.3 379.0 443.7 50092.3 50000 20 1744.69 1371493 136537352 2003419 78131094 76937140827 101162096377 132598493650 169732869 0 0 0 0 477567 933048 +ebbrt_tuned 0 300 0x1d00 75 70.7 89.6 222.1 352.5 379.2 443.9 50087.5 50000 20 1742.86 1372474 136600897 2003288 78125450 76767357758 101329590855 132882428155 170752618 0 0 0 0 479366 933049 +ebbrt_tuned 1 300 0x1d00 75 70.5 89.3 222.1 352.9 379.6 443.0 49987.5 50000 20 1744.99 1372104 136433500 1999320 77971876 76860593860 101361868123 132867323940 170973937 0 0 0 0 475565 931654 +ebbrt_tuned 2 300 0x1d00 75 70.3 88.2 220.4 350.9 377.2 442.3 49946.4 50000 20 1741.2 1370084 136277456 1997709 77909230 76689657363 101181842235 132607125761 170274136 0 0 0 0 476543 932505 +ebbrt_tuned 0 300 0x1d00 55 70.8 90.0 222.2 352.8 380.1 445.8 50136.9 50000 20 1745.66 1373487 136675186 2005296 78204836 77053375344 101560503109 132992661650 171782072 0 0 0 0 476055 932624 +ebbrt_tuned 1 300 0x1d00 55 72.0 90.8 224.8 353.0 379.7 443.7 49938.4 50000 20 1741.55 1371915 136341119 1997349 77895114 76817012489 101266617362 132813615012 169989870 0 0 0 0 479553 932727 +ebbrt_tuned 2 300 0x1d00 55 70.2 89.5 222.7 352.8 379.5 442.1 50058.0 50000 20 1741.16 1371365 136487921 2002173 78083070 76947380815 101229365131 132777199422 169196103 0 0 0 0 479595 932611 +ebbrt_tuned 0 300 0x1d00 135 75.7 96.8 226.6 353.8 383.2 449.3 99979.6 100000 20 1962.4 2204980 240528338 3998366 155927376 151531322999 176024111625 206101314810 318624710 0 0 0 0 557686 972569 +ebbrt_tuned 1 300 0x1d00 135 75.0 96.3 226.3 352.8 380.8 448.9 99914.0 100000 20 1963.87 2200858 240236251 3995901 155830434 151283109141 175906132978 205737491308 321100693 0 0 0 0 553173 972555 +ebbrt_tuned 2 300 0x1d00 135 74.5 94.5 224.4 352.1 381.1 450.6 100148.7 100000 20 1968.56 2205919 240784536 4005146 156191318 151927480733 177714698346 207636762585 326527359 0 0 0 0 555257 972650 +ebbrt_tuned 0 300 0x1d00 95 75.9 97.0 227.9 354.3 384.1 453.5 100076.9 100000 20 1964.02 2203260 240567203 4002450 156086730 152048894957 177101056853 207396030511 326363850 0 0 0 0 559086 972536 +ebbrt_tuned 1 300 0x1d00 95 75.6 95.4 226.6 352.8 382.0 449.2 100079.2 100000 20 1965.14 2204837 240658738 4002396 156084638 152132104166 176819431378 206700475371 324441805 0 0 0 0 555753 972537 +ebbrt_tuned 2 300 0x1d00 95 76.0 96.7 226.4 353.8 383.7 452.6 99912.2 100000 20 1963.31 2202628 240345897 3995761 155824674 152002197270 177247458261 206726291693 325117487 0 0 0 0 554503 972474 +ebbrt_tuned 0 300 0x1d00 75 74.4 95.4 225.1 351.4 379.5 446.3 99895.9 100000 20 1967.7 2203722 240400114 3995164 155802284 152196093275 177591738276 207367419633 326955762 0 0 0 0 554251 972500 +ebbrt_tuned 1 300 0x1d00 75 75.2 95.5 226.2 351.7 380.5 447.4 100019.3 100000 20 1963.11 2206409 240710964 4000004 155990490 152480102043 178086691841 207982906324 329661313 0 0 0 0 554009 972508 +ebbrt_tuned 2 300 0x1d00 75 74.3 94.8 224.6 352.2 381.4 450.4 100095.8 100000 20 1964.54 2205249 240699564 4003134 156113656 152620709484 177913837807 207647429336 329366974 0 0 0 0 553977 972590 +ebbrt_tuned 0 300 0x1d00 55 75.9 96.2 225.8 353.8 384.8 457.2 99984.5 100000 20 1939.42 2202728 240420381 3998658 155939450 152215769322 176611889271 209531486314 326966221 0 0 0 0 549692 972452 +ebbrt_tuned 1 300 0x1d00 55 74.3 94.9 225.2 353.5 384.0 457.5 99979.2 100000 20 1939.27 2204055 240511389 3998499 155933198 152201677676 176518865475 209315584504 323204335 0 0 0 0 550465 972490 +ebbrt_tuned 2 300 0x1d00 55 75.7 96.7 228.0 354.9 385.1 454.4 99973.8 100000 20 1796.47 2044506 223051502 3707544 144586742 140960054416 164127985707 195136968043 304707662 0 0 0 0 513412 901621 +linux_tuned 0 10 0x1800 135 57.8 60.6 75.5 137.7 172.7 206.7 50064.7 50000 20 1645.53 1606760 166240671 3008674 90274734 87693400899 140081478275 169708798979 205756148 939471 345518 189899 0 858866 2536862 +linux_tuned 0 10 0x1800 75 57.6 60.4 75.2 137.8 174.4 209.1 50114.9 50000 20 1645.35 1600942 165874709 3011634 90363324 88178261906 141695435736 171659701044 215462085 935211 343588 191113 0 864358 2532508 +linux_tuned 0 10 0x1800 55 57.4 60.2 75.1 139.5 173.9 207.7 49990.0 50000 20 1646.64 1602333 165843013 3004136 90138408 87724113346 140612710523 170385901535 209608719 934371 354748 195113 0 866411 2532866 +linux_tuned 0 10 0x1800 135 58.9 61.5 79.4 161.6 188.6 248.6 100036.3 100000 20 1815.76 2284202 271016963 6019800 180647358 164047229455 253844476787 306796833176 398236764 1444146 400257 181493 0 955753 4111997 +linux_tuned 0 10 0x1800 75 58.1 61.0 78.5 161.2 189.2 246.4 100121.2 100000 20 1816.09 2290779 271583244 6024575 180789540 164473386782 254406159536 307467127692 401759127 1442243 396664 179050 0 948081 4120386 +linux_tuned 0 10 0x1800 55 58.3 61.2 79.1 162.3 190.1 251.4 100115.5 100000 20 1819.11 2286923 271291682 6024240 180779502 164606512519 255171463262 308396574357 406768401 1442617 401362 185129 0 951404 4118723 +linux_tuned 0 10 0x1800 135 60.6 63.9 95.7 236.2 299.3 460.8 199972.9 200000 20 2117.32 4247278 520804264 12176895 365844426 310175631315 453602869674 548106412654 798958175 2126569 342808 115792 0 408368 7679478 +linux_tuned 0 10 0x1800 75 60.5 63.7 94.7 234.8 302.6 487.0 200107.6 200000 20 2120.1 4263112 522017223 12215241 367085598 310606071885 455701012227 550641582504 806804719 2013831 335971 114460 0 415939 7693578 +linux_tuned 0 10 0x1800 55 61.6 65.1 100.5 265.7 351.1 648.8 199846.0 200000 20 2106.13 4260114 521470356 12204217 366768750 310399260784 456199783805 556949254387 813823948 1990358 330309 112310 0 404528 7698192 +linux_tuned 0 20 0x1800 135 57.8 60.6 76.1 139.0 174.4 210.4 50009.7 50000 20 1766.31 1602705 165889822 3005275 90172422 87972014488 141381023720 171319785867 217949273 939638 348533 189864 0 845449 2490573 +linux_tuned 0 20 0x1800 75 57.7 60.6 75.9 137.1 174.3 208.6 50000.9 50000 20 1646.48 1597770 165554753 3004928 90162588 88022664729 140930409833 170776164221 212911130 936549 350438 189394 0 849192 2487088 +linux_tuned 0 20 0x1800 55 57.7 60.5 76.0 138.9 176.1 210.9 49975.6 50000 20 1645.95 1597110 165471319 3003210 90110520 88005930747 141404512248 171351101952 214081240 929194 359320 192350 0 856815 2487280 +linux_tuned 0 20 0x1800 135 58.8 61.6 80.5 165.1 194.5 257.2 100119.6 100000 20 1820.27 2284197 271118837 6024315 180781506 164872148944 255681349628 309001253125 409332677 1484668 413112 180904 0 900812 4029137 +linux_tuned 0 20 0x1800 75 60.0 62.3 81.4 167.5 196.0 258.6 100020.9 100000 20 1819.37 2288090 271253662 6018270 180599814 164998656590 256404504618 309879476858 413516203 1480036 407337 181647 0 901602 4031657 +linux_tuned 0 20 0x1800 55 59.1 61.8 80.4 165.7 194.9 254.1 99991.0 100000 20 1818.67 2284758 271027529 6016703 180553248 164835041457 255451682416 308714008589 413278199 1466986 402787 178936 0 898881 4027152 +linux_tuned 0 20 0x1800 135 61.4 64.9 97.7 238.6 301.7 469.0 199956.5 200000 20 2123.54 4255773 521360042 12183540 366066894 310807444290 457257459342 552522373386 817238558 2155284 333425 102208 0 332382 7225769 +linux_tuned 0 20 0x1800 75 61.6 65.6 99.9 245.2 312.7 482.4 199976.9 200000 20 2124.48 4251495 521102437 12175406 365794530 311350809629 457460899792 552768265198 820104360 2187595 337136 102921 0 322496 7233072 +linux_tuned 0 20 0x1800 55 62.6 67.1 106.6 286.3 378.8 660.8 199829.2 200000 20 2106.35 4253956 521054322 12186317 366181944 310921990661 455622439924 555505507947 822428475 2101602 331291 102208 0 331391 7217926 +linux_tuned 0 30 0x1800 135 58.1 60.9 77.6 139.9 177.7 217.0 50049.4 50000 20 1646.64 1595621 165482189 3007850 90250218 87934653431 141450334359 171496200436 214343629 907862 372413 185916 0 834146 2388210 +linux_tuned 0 30 0x1800 75 58.4 61.2 77.8 139.6 176.3 214.6 50030.0 50000 20 1647.63 1598047 165578000 3006557 90211026 87983100476 140827197805 170701648373 213475156 912148 372161 187972 0 839205 2392274 +linux_tuned 0 30 0x1800 55 58.1 60.9 77.7 140.5 177.8 217.9 49993.0 50000 20 1648.23 1591890 165147295 3004395 90146388 87966785556 141500987043 171488331257 218060734 908782 357192 184010 0 829259 2382462 +linux_tuned 0 30 0x1800 135 60.5 62.8 83.6 170.4 199.3 263.9 99996.6 100000 20 1819.89 2284031 271004001 6017885 180591456 164597394511 255220574669 308432741069 410442267 1472924 403470 169716 0 857709 3799462 +linux_tuned 0 30 0x1800 75 59.4 62.2 83.8 172.3 201.5 271.2 100151.5 100000 20 1820.0 2285588 271251004 6028608 180917580 164688340580 254619273791 307714801032 411892806 1459916 398206 160094 0 841427 3778513 +linux_tuned 0 30 0x1800 55 60.1 62.4 83.5 168.9 198.4 264.0 100066.7 100000 20 1820.38 2280958 270843182 6022381 180727116 164770596729 256288484149 309729697397 414801136 1474368 406015 166288 0 858444 3797725 +linux_tuned 0 30 0x1800 135 61.8 66.4 105.0 266.8 348.9 584.1 200148.2 200000 20 2122.47 4268140 522400859 12207769 366837882 310563079093 457149178468 552391474143 814260743 2102777 331837 87401 0 289370 6363888 +linux_tuned 0 30 0x1800 75 62.3 66.8 104.4 257.0 326.8 531.1 199999.6 200000 20 2125.75 4262300 521782234 12194570 366428586 310588749414 457367408165 552655250139 820852818 2103720 323929 88125 0 286150 6364127 +linux_tuned 0 30 0x1800 55 62.1 66.7 105.2 256.2 329.5 523.2 199889.1 200000 20 2106.86 4261167 521573610 12188272 366240204 310161078077 456005389273 555567923168 824626844 2091045 325049 87319 0 283106 6375162 +linux_tuned 0 40 0x1800 135 59.5 61.8 80.0 141.4 179.3 220.6 49893.3 50000 20 1648.17 1577477 164091036 2998852 89981424 87585953509 141210774314 171521663074 214437492 796536 488525 180057 0 817787 2267262 +linux_tuned 0 40 0x1800 75 58.6 61.4 80.1 145.1 182.2 223.8 50067.9 50000 20 1649.15 1589380 165063031 3009295 90294606 88000187242 142468431125 173022062058 216221057 801250 486089 175704 0 810778 2271546 +linux_tuned 0 40 0x1800 55 57.7 60.6 78.2 137.9 169.7 206.2 50069.4 50000 20 1640.69 1597011 165593849 3009377 90297042 86334435930 137192479702 166719839768 194353473 807046 490057 178837 0 830141 2279794 +linux_tuned 0 40 0x1800 135 59.9 62.5 86.0 171.9 202.5 272.0 99856.0 100000 20 1822.1 2269920 269885175 6011453 180405306 164309740368 254494772785 307578112345 410937113 1433182 481872 155703 0 812100 3501536 +linux_tuned 0 40 0x1800 75 60.4 62.8 86.6 173.1 201.7 261.2 100017.1 100000 20 1823.47 2273027 270294289 6020756 180682878 164631085272 257609707529 311319442858 415730807 1470285 511718 161509 0 808444 3537191 +linux_tuned 0 40 0x1800 55 59.2 62.1 84.5 163.8 193.7 249.7 100055.0 100000 20 1811.34 2284666 271131560 6022066 180719364 162678147184 249655953593 301714402535 381921085 1488763 499144 158494 0 820855 3519996 +linux_tuned 0 40 0x1800 135 62.6 67.6 108.8 254.1 321.1 509.9 199704.4 200000 20 2121.34 4263718 521560529 12184601 366165312 310161116387 455063212985 549870980091 814327427 2021799 459312 74808 0 254499 5482680 +linux_tuned 0 40 0x1800 55 61.8 66.3 104.5 232.6 292.1 458.9 199713.8 200000 20 2098.78 4255341 520974399 12167815 365610078 306827629873 444894040755 540172817172 760125259 2123764 488864 74933 0 258992 5484613 +linux_tuned 0 10 0x1900 135 57.1 59.6 73.8 131.0 165.8 201.1 50041.2 50000 20 1722.43 1606569 166193309 3007116 90227592 87800645565 141609090688 164770402464 206380765 949882 349884 193455 0 876466 2536530 +linux_tuned 0 10 0x1900 75 57.3 60.1 74.1 131.5 167.1 201.6 49946.5 50000 20 1720.36 1605878 166008535 3001433 90057024 87584185271 141491868697 164663700618 208290812 945660 348481 191412 0 867949 2535039 +linux_tuned 0 10 0x1900 55 56.8 59.2 73.5 130.8 166.7 201.6 50033.2 50000 20 1721.87 1601889 165869787 3006585 90211392 87773583801 142053511034 165277770514 210962825 944357 347484 190131 0 870632 2531023 +linux_tuned 0 10 0x1900 135 57.4 60.3 76.8 152.3 180.4 237.0 99962.3 100000 20 1899.05 2293944 271616303 6013783 180462024 164222094488 255924405140 296946967603 397941406 1463138 397535 182973 0 969001 4119284 +linux_tuned 0 10 0x1900 75 58.1 60.9 77.8 155.3 183.1 240.5 99986.4 100000 20 1898.55 2295511 271714470 6015208 180504828 164535435126 256882011866 298055197557 403277284 1486078 405169 183744 0 967270 4122639 +linux_tuned 0 10 0x1900 55 58.5 61.2 78.5 157.7 186.0 246.0 99944.9 100000 20 1899.2 2296884 271744941 6012992 180439284 164765404968 257398981252 298664784401 415375242 1477756 398626 180902 0 958904 4120238 +linux_tuned 0 10 0x1900 135 59.8 63.1 94.4 236.6 300.9 471.8 200041.2 200000 20 2213.0 4247456 520876431 12178117 365872524 311052632817 460410996157 534086770916 799290469 2155844 342280 116823 0 448064 7665399 +linux_tuned 0 10 0x1900 75 60.7 63.9 96.6 251.9 333.7 599.0 200162.9 200000 20 2215.04 4251584 521300559 12192233 366316020 311278461850 461001638433 534761039766 806371164 2134859 343345 116935 0 446285 7667171 +linux_tuned 0 10 0x1900 55 62.1 65.9 101.7 258.1 332.8 543.3 200108.1 200000 20 2124.23 4253326 521349026 12195028 366418476 311397243911 459991062992 558219771676 818763316 2053508 339418 114253 0 399996 7694739 +linux_tuned 0 20 0x1900 135 57.9 60.7 75.1 133.4 168.8 204.3 49983.4 50000 20 1721.62 1604032 165927976 3003551 90120324 87756120729 142178307518 165458873165 211237321 946292 355885 191293 0 867314 2490912 +linux_tuned 0 20 0x1900 75 57.7 60.5 74.9 132.8 170.4 205.3 50056.7 50000 20 1866.54 1606996 166224441 3007981 90253344 88456255298 143181702033 166643775697 217764256 955024 361849 192076 0 860733 2494704 +linux_tuned 0 20 0x1900 55 57.5 60.4 74.9 132.3 169.4 203.7 50049.0 50000 20 1724.26 1601038 165801249 3007563 90240804 88161483124 142834885358 166164787337 214306975 950118 346558 188954 0 854291 2489334 +linux_tuned 0 20 0x1900 135 57.9 60.8 78.1 156.2 184.3 239.1 100037.2 100000 20 1899.98 2295024 271745677 6018323 180598404 164825467446 257662703374 298970161854 409954743 1511953 404111 180291 0 915056 4033697 +linux_tuned 0 20 0x1900 75 58.8 61.5 79.0 157.4 184.7 240.5 99948.5 100000 20 1902.39 2292447 271470419 6012881 180434784 165210522710 258888698600 300359620787 415298137 1508975 411475 182350 0 918665 4033504 +linux_tuned 0 20 0x1900 55 58.6 61.4 79.2 160.5 187.7 248.7 99983.4 100000 20 1903.69 2294385 271613764 6015195 180504588 165275166436 258203743359 299570273209 413464317 1490803 398346 179191 0 913401 4025494 +linux_tuned 0 20 0x1900 135 61.3 65.0 96.7 232.6 292.5 452.9 200131.3 200000 20 2217.38 4245643 520855072 12165169 365429226 311783795388 462695466212 536734068309 817309024 2296502 343501 105322 0 349042 7208130 +linux_tuned 0 20 0x1900 75 61.2 64.9 98.0 236.9 297.3 453.8 200118.2 200000 20 2218.58 4245433 520856494 12166903 365488614 312332547601 464466417325 538780165919 824316499 2275793 343445 104767 0 347377 7204812 +linux_tuned 0 20 0x1900 55 63.4 67.8 104.0 263.7 335.4 549.6 200087.8 200000 20 2127.78 4259584 521812597 12193775 366381318 311572512319 460707816109 559659453853 828928061 2122915 330269 100273 0 321173 7239353 +linux_tuned 0 30 0x1900 135 58.2 61.0 76.7 132.4 171.5 206.1 50031.0 50000 20 1722.29 1597632 165587169 3006607 90212550 87933528342 142572339637 166026639979 214082115 915972 380868 183702 0 846615 2383619 +linux_tuned 0 30 0x1900 75 57.4 60.2 75.8 133.4 169.9 204.9 50096.5 50000 20 1724.71 1605797 166188946 3010592 90332292 88219729152 143110066860 166569787388 215537300 924164 373714 185605 0 844699 2394872 +linux_tuned 0 30 0x1900 55 57.3 60.0 76.0 135.2 172.0 206.3 50091.2 50000 20 1721.88 1604854 166122440 3010310 90323958 88164424819 142832815652 166256957428 215574600 926376 376150 182616 0 833580 2388729 +linux_tuned 0 30 0x1900 135 60.4 62.5 82.0 158.8 187.3 244.3 100116.8 100000 20 1902.89 2286549 271287407 6023189 180744834 165226062805 258905407093 300381136409 414484506 1530065 423655 174148 0 871848 3808096 +linux_tuned 0 30 0x1900 75 58.0 60.9 80.3 158.6 187.0 243.2 100025.1 100000 20 1903.24 2287716 271235014 6017856 180585210 165331469515 259375386361 300917878327 414520213 1518881 422693 176234 0 871715 3807062 +linux_tuned 0 30 0x1900 55 59.0 61.7 81.3 160.7 188.5 248.3 100060.1 100000 20 1997.51 2294800 271754212 6020261 180658326 165141419456 259287741103 300835188597 421075652 1517301 415392 170096 0 868932 3803165 +linux_tuned 0 30 0x1900 135 60.9 65.1 99.3 233.8 295.3 454.6 200220.5 200000 20 2216.47 4254444 521524702 12181330 365950926 311369168266 461013547529 534774875347 814844201 2251148 338363 90246 0 308440 6332507 +linux_tuned 0 30 0x1900 75 61.4 65.6 99.9 228.5 285.0 427.0 200013.9 200000 20 2215.61 4244335 520643673 12155921 365149476 311241305636 462612836234 536629956134 818825578 2325187 351587 90050 0 286589 6365982 +linux_tuned 0 30 0x1900 55 62.3 67.0 104.5 248.5 314.3 478.6 200116.4 200000 20 2127.57 4263345 522071341 12196961 366487146 310658827597 458377826644 555456397027 822485008 2099408 325226 87110 0 283951 6379162 +linux_tuned 0 40 0x1900 135 57.8 60.7 78.0 136.7 172.4 207.8 49883.6 50000 20 1719.15 1595594 165267988 2998222 89962416 87653539509 142626966471 166461354503 214900985 804768 497467 176982 0 820476 2273705 +linux_tuned 0 40 0x1900 75 58.0 60.8 78.3 139.4 175.5 213.0 50020.4 50000 20 1722.45 1592114 165204693 3006572 90213390 87988290802 143674155332 167594617634 216182562 805915 492942 178250 0 824112 2277317 +linux_tuned 0 40 0x1900 55 56.5 58.7 76.0 131.6 164.4 202.4 50015.0 50000 20 1708.77 1600338 165751884 3005976 90194604 86421049198 137418395391 160488505745 194047910 813642 510993 176083 0 830909 2279980 +linux_tuned 0 40 0x1900 135 59.0 61.9 84.2 162.1 191.8 247.6 100061.6 100000 20 1901.16 2282968 271013681 6022966 180747912 165105987543 259610143557 301192934772 415240981 1484764 508441 157488 0 821314 3519134 +linux_tuned 0 40 0x1900 75 59.1 61.9 84.3 163.3 193.8 250.4 100098.0 100000 20 1903.87 2283488 271067636 6025234 180816060 165031995299 260479290037 302204426410 417989034 1480889 505539 155567 0 824616 3517972 +linux_tuned 0 40 0x1900 55 57.4 60.4 81.5 155.7 183.9 237.3 100101.0 100000 20 1889.55 2285711 271191762 6024207 180781902 162978796543 251546431244 291870163426 381708065 1509384 529071 158360 0 833699 3515720 +linux_tuned 0 40 0x1900 135 62.5 67.0 104.1 224.6 273.2 396.4 200119.9 200000 20 2212.9 4258837 521733727 12183138 366039756 311744768017 463334102435 537475441854 817000213 2133707 486905 76798 0 260011 5480001 +linux_tuned 0 40 0x1900 55 61.6 66.7 105.0 231.7 288.1 435.3 199946.2 200000 20 2124.67 4251460 521042001 12162018 365371566 307825747123 448312468890 539664430729 766701301 2194847 515535 73812 0 236779 5517169 +linux_tuned 0 10 0x1a00 135 56.2 58.0 72.2 126.8 162.2 194.2 50012.2 50000 20 1797.53 1617224 166830256 3005305 90173034 87773793089 142375271907 159332504468 205653308 971073 344194 189152 0 874969 2547281 +linux_tuned 0 10 0x1a00 75 56.3 58.1 72.5 127.7 161.7 193.1 50055.4 50000 20 1797.35 1615239 166765332 3007862 90249540 87718990245 142636388301 159548881559 207950168 969301 329394 186281 0 866284 2545028 +linux_tuned 0 10 0x1a00 55 56.4 58.4 72.7 128.3 162.8 197.1 50032.8 50000 20 1798.67 1617124 166883234 3006523 90209436 88029124283 143158355338 160223785045 210793341 971522 362136 194367 0 881577 2545201 +linux_tuned 0 10 0x1a00 135 56.9 59.4 76.1 149.7 176.3 223.8 99925.1 100000 20 1981.5 2301554 272034304 6010890 180373368 164438175505 258456998318 288380148189 398554366 1514514 400698 181890 0 972497 4124594 +linux_tuned 0 10 0x1a00 75 57.1 59.6 76.2 149.8 177.0 228.0 99873.1 100000 20 1983.24 2299640 271866826 6007440 180268746 164408515936 259103235987 289083462134 405301165 1511813 404442 184610 0 975559 4123734 +linux_tuned 0 10 0x1a00 55 57.1 59.8 76.6 151.3 178.7 231.7 99939.4 100000 20 1983.16 2293635 271552127 6011595 180394026 164814091667 259748738343 289838631008 406410721 1492397 397708 180761 0 976223 4115765 +linux_tuned 0 10 0x1a00 135 58.8 62.3 92.5 224.6 284.8 434.7 199616.6 200000 20 2313.04 4227552 519050249 12127409 364275432 310680174932 464890377000 518533556195 793941230 2280497 353448 121324 0 462411 7636524 +linux_tuned 0 10 0x1a00 75 58.9 62.3 91.1 220.9 278.0 434.8 199876.3 200000 20 2315.18 4235215 519864267 12147882 364904136 311595414011 468697925792 522780475579 803867242 2271523 359043 121591 0 446928 7646738 +linux_tuned 0 10 0x1a00 55 61.8 65.7 105.0 303.7 436.3 3669.5 200058.3 200000 20 2131.0 4278767 523173625 12279873 369258534 310683259040 463415126139 564877968009 820268172 1895567 320422 110352 0 394150 7724771 +linux_tuned 0 20 0x1a00 135 56.5 58.6 73.3 129.3 163.5 197.3 50022.6 50000 20 1799.09 1607991 166258802 3005924 90191520 87884646803 142940617407 159966222618 211116049 960112 348232 189505 0 867992 2493959 +linux_tuned 0 20 0x1a00 75 56.6 58.7 73.2 130.2 164.5 197.9 50018.7 50000 20 1802.34 1617928 166903311 3005714 90185316 88019702419 143449333438 160509669945 213052151 966183 344526 188023 0 858955 2501313 +linux_tuned 0 20 0x1a00 55 56.6 58.8 73.7 129.7 164.8 199.4 50070.6 50000 20 1805.32 1613182 166669178 3008871 90280200 88210382525 143900303871 161010765527 214019864 960406 345022 187783 0 859880 2496369 +linux_tuned 0 20 0x1a00 135 57.2 60.0 77.3 152.9 180.2 230.9 100123.5 100000 20 1989.65 2297665 272035242 6022410 180717684 165175644927 260897825795 291064574398 409336028 1541365 410801 184700 0 926803 4034213 +linux_tuned 0 20 0x1a00 75 57.3 60.2 77.2 151.7 180.1 232.1 100019.6 100000 20 1987.65 2301635 272167074 6015917 180522264 165454248579 260831153160 290990838476 413862988 1532423 406776 183380 0 928873 4036533 +linux_tuned 0 20 0x1a00 55 57.6 60.5 77.7 152.9 181.7 235.1 99995.0 100000 20 1990.95 2300037 272031055 6014874 180492162 165368255378 261803898434 292098224414 414603068 1522470 401303 181053 0 928224 4031683 +linux_tuned 0 20 0x1a00 135 60.0 63.4 93.1 221.9 281.8 445.9 199996.0 200000 20 2316.38 4238832 520291234 12151659 365007762 311945262417 468966383419 523079953446 814480470 2374288 352930 107533 0 365150 7182449 +linux_tuned 0 20 0x1a00 75 60.2 63.6 95.1 231.3 296.8 468.0 199916.4 200000 20 2318.37 4241280 520307276 12153925 365096664 312553042132 469830667359 524057088639 824996489 2295675 335029 106623 0 382933 7163797 +linux_tuned 0 20 0x1a00 55 64.3 69.3 114.4 317.4 434.8 2127.2 199861.4 200000 20 2130.43 4284537 523288273 12274576 369121374 310508351827 465568093560 568950463384 829682892 1897307 308832 96270 0 327952 7247223 +linux_tuned 0 30 0x1a00 135 56.8 59.1 75.2 133.1 166.1 202.2 50083.5 50000 20 1803.38 1606725 166229416 3009713 90305658 88135446574 143812409064 160994512245 217669001 938106 356938 180178 0 848559 2385804 +linux_tuned 0 30 0x1a00 75 56.4 58.5 74.6 128.3 165.5 201.6 49962.0 50000 20 1801.39 1604662 165960321 3002353 90084720 87990053489 143730647226 160935669331 213880813 929862 367752 185419 0 853727 2388270 +linux_tuned 0 30 0x1a00 55 56.8 59.1 75.4 132.8 167.6 203.5 49904.6 50000 20 1800.6 1607230 166085828 2999114 89987976 87953472443 143588914431 160774673221 214379119 932981 370186 184334 0 846561 2386157 +linux_tuned 0 30 0x1a00 135 57.4 60.3 79.3 155.6 183.7 235.7 99961.5 100000 20 1989.86 2291457 271454623 6013725 180460392 165065309028 261004269555 291169681476 412794393 1549519 411941 169569 0 881461 3788542 +linux_tuned 0 30 0x1a00 75 57.5 60.4 79.3 155.0 183.1 237.1 100055.2 100000 20 1988.65 2288433 271331166 6019458 180632862 165316762490 261821723838 292082403174 412898593 1544626 413550 170881 0 882257 3787687 +linux_tuned 0 30 0x1a00 55 57.7 60.6 80.0 156.8 184.8 242.5 100078.6 100000 20 1989.51 2294002 271700042 6020458 180661428 165478194408 262450478774 292812845372 417173460 1555320 421208 174060 0 876878 3801597 +linux_tuned 0 30 0x1a00 135 60.0 64.0 98.2 227.7 288.3 452.2 199922.6 200000 20 2315.52 4244417 520615852 12155275 365145096 311391048277 467034358855 520924879781 810523160 2328868 344729 92456 0 332058 6285262 +linux_tuned 0 30 0x1a00 75 59.9 63.9 97.3 224.8 282.7 444.3 200003.0 200000 20 2317.17 4241553 520477930 12150031 364956852 311824113999 469190305222 523329627293 817683463 2389226 351142 91769 0 314465 6324226 +linux_tuned 0 30 0x1a00 55 62.7 67.8 110.3 327.6 559.2 6078.7 200066.7 200000 20 2131.13 4301265 524701817 12321293 370667214 310705287964 463336860353 565126380965 825126331 1936734 314995 85300 0 281782 6382431 +linux_tuned 0 40 0x1a00 135 57.9 60.8 77.7 133.7 170.8 207.6 50033.2 50000 20 1797.43 1598850 165679732 3007239 90233070 87943658101 144165475882 161902790998 215181635 804749 503526 177442 0 827137 2277571 +linux_tuned 0 40 0x1a00 75 57.4 60.2 77.2 132.3 168.1 205.1 49967.2 50000 20 1798.47 1596760 165432370 3003222 90112422 87863197742 144351652318 162068903723 214894068 803131 490503 174223 0 815341 2269379 +linux_tuned 0 40 0x1a00 55 56.3 58.3 74.8 128.7 159.4 196.5 50033.3 50000 20 1785.15 1607978 166273209 3007094 90228402 86479628776 138830838468 155971112757 194240018 816379 499469 170728 0 829308 2274792 +linux_tuned 0 40 0x1a00 135 57.8 60.8 82.5 157.9 187.1 242.6 99914.7 100000 20 1987.92 2284489 270912394 6013072 180447480 165143100766 262427069898 292767602124 413911160 1506156 527062 160886 0 830083 3521467 +linux_tuned 0 40 0x1a00 75 57.9 60.9 82.6 159.1 187.5 244.9 99998.8 100000 20 1989.77 2287508 271227810 6018343 180606390 165546220302 262938897216 293353750153 417738269 1501629 523131 158973 0 828986 3516000 +linux_tuned 0 40 0x1a00 55 57.0 59.7 80.2 150.0 178.5 227.4 99887.2 100000 20 1975.06 2287498 271054183 6010461 180366444 162981086721 253515613354 282855358595 382293855 1539805 530584 159318 0 847426 3507795 +linux_tuned 0 40 0x1a00 135 61.0 65.8 103.8 226.2 278.2 416.6 199730.4 200000 20 2314.35 4243288 520253683 12143747 364808538 311568004233 470792905116 525123770003 814287469 2216088 536454 74318 0 265719 5473362 +linux_tuned 0 40 0x1a00 55 62.0 67.0 106.1 240.3 303.8 521.7 200045.0 200000 20 2131.06 4256153 521520352 12198424 366583332 307432637890 449482895746 541337453837 766178113 2103936 478835 76257 0 258503 5484476 +linux_tuned 0 10 0x1b00 135 55.6 57.2 70.6 123.1 156.7 187.5 49958.5 50000 20 1877.73 1621615 167074422 3001921 90070938 87919612995 144308752171 155543376184 205430623 978030 345614 192682 0 887000 2549050 +linux_tuned 0 10 0x1b00 75 55.9 57.6 71.2 122.7 156.6 188.5 50062.4 50000 20 1880.58 1620039 167070676 3008173 90258540 87922624646 144489459413 155762326445 208972046 978866 346895 189842 0 880113 2548619 +linux_tuned 0 10 0x1b00 55 56.5 58.5 72.4 123.9 159.2 188.4 50084.2 50000 20 1881.27 1618499 167019331 3009459 90297138 88083786225 145144516891 156417715280 210111034 976018 338446 190239 0 874765 2548848 +linux_tuned 0 10 0x1b00 135 56.4 58.4 75.0 146.1 172.4 221.7 100015.8 100000 20 2070.96 2307141 272518079 6015645 180513882 164802020867 261849840595 281316718616 397642629 1540555 406542 183115 0 979588 4128169 +linux_tuned 0 10 0x1b00 75 56.2 58.2 74.0 142.9 169.1 213.7 99927.9 100000 20 2071.16 2301310 272033574 6009887 180339714 164669260316 261501418909 280953175950 402433089 1526270 407426 186225 0 991787 4125557 +linux_tuned 0 10 0x1b00 55 56.7 59.1 75.3 148.0 173.3 222.8 99832.5 100000 20 2060.46 2301580 271955059 6004315 180172944 164885816831 263390235752 284467303440 406129966 1535691 413748 187878 0 986535 4126610 +linux_tuned 0 10 0x1b00 135 58.0 61.3 87.0 201.1 249.6 377.6 199903.6 200000 20 2415.9 4229259 519538394 12133324 364418808 311695880715 472152743053 507126542176 795760521 2405099 366259 122623 0 470311 7632759 +linux_tuned 0 10 0x1b00 75 59.0 62.1 86.9 203.3 256.9 421.7 199810.4 200000 20 2414.02 4232023 519586405 12140648 364677216 311767745047 470833618292 505709675343 800060399 2335914 363481 124513 0 483652 7639409 +linux_tuned 0 10 0x1b00 55 62.8 67.9 128.5 3767.0 6515.1 11425.7 199826.5 200000 20 2054.98 4429385 534759186 12860725 388859994 309103108913 462697598125 590310462945 824883724 1607149 295863 93219 0 298186 8014727 +linux_tuned 0 20 0x1b00 135 56.5 58.5 72.9 125.0 160.5 193.2 50024.0 50000 20 1879.04 1610704 166422545 3005930 90191466 88031004272 145295764411 156673721683 211837576 969574 371001 195885 0 885118 2495786 +linux_tuned 0 20 0x1b00 75 56.4 58.5 73.0 125.9 160.2 189.0 50104.6 50000 20 1880.75 1617529 166991471 3010607 90331284 88400729443 145819582242 157227791391 214142500 973454 346359 188644 0 869403 2501186 +linux_tuned 0 20 0x1b00 55 56.1 58.0 72.6 126.4 161.1 190.1 50127.1 50000 20 1879.66 1614376 166776123 3012061 90375162 88423502865 146379923203 157753076656 215120584 966293 345163 189340 0 872615 2499375 +linux_tuned 0 20 0x1b00 135 56.7 59.0 75.9 146.5 172.5 221.3 100129.5 100000 20 2072.76 2311511 272962715 6021563 180688752 165236192718 263781890686 283392971035 408829488 1577417 411489 183112 0 941728 4044963 +linux_tuned 0 20 0x1b00 75 56.9 59.4 76.4 151.0 176.2 223.5 100289.6 100000 20 2075.42 2297047 272216152 6032233 181012020 165995866694 265222812427 284960803893 414856215 1545646 403156 178270 0 937996 4031262 +linux_tuned 0 20 0x1b00 55 56.9 59.4 76.5 150.0 177.3 228.2 99895.7 100000 20 2147.4 2294119 271492910 6008200 180289944 165483672828 264962748548 285842851392 424731998 1561815 415944 179566 0 943447 4022909 +linux_tuned 0 20 0x1b00 135 58.5 62.0 88.9 211.6 271.5 427.8 200058.5 200000 20 2418.28 4240929 520514693 12150312 364952460 312629885885 474358414782 509510364999 819469060 2439493 354701 109002 0 398255 7162782 +linux_tuned 0 20 0x1b00 55 63.8 69.1 125.3 3150.2 6233.1 11277.7 200147.8 200000 20 2070.56 4424699 534587453 12817857 387334488 309713157764 464985969241 588341014046 835003172 1650018 286114 85742 0 267551 7426707 +linux_tuned 0 30 0x1b00 135 56.4 58.5 74.2 125.7 162.2 194.5 50022.3 50000 20 1877.42 1613765 166620860 3006043 90195516 88055638961 145437436810 156824209643 213859184 948251 367577 181635 0 847490 2388507 +linux_tuned 0 30 0x1b00 75 56.2 58.0 73.9 126.8 162.0 197.8 50020.4 50000 20 1879.56 1611011 166447237 3005800 90187902 88313578781 145793534186 157246470639 215187946 948049 368156 182504 0 852650 2390726 +linux_tuned 0 30 0x1b00 55 56.5 58.6 74.4 125.9 161.6 195.7 49989.3 50000 20 1879.9 1610117 166378767 3003928 90131688 88264932237 145927808543 157412073979 215966054 946922 380066 183900 0 852077 2389997 +linux_tuned 0 30 0x1b00 135 56.6 59.0 77.9 149.8 177.9 227.9 99990.2 100000 20 2072.37 2295308 271727109 6014133 180468714 165429693769 264758523760 284430469525 414083079 1578593 414469 171093 0 888155 3784944 +linux_tuned 0 30 0x1b00 75 57.2 60.0 78.6 150.7 178.8 226.9 99994.0 100000 20 2073.59 2297285 271850034 6014824 180490788 165524396632 264856506632 284556685268 414796742 1593984 427185 174776 0 891349 3790783 +linux_tuned 0 30 0x1b00 55 57.1 59.9 78.4 152.7 180.3 236.2 100014.1 100000 20 2066.2 2296232 271801207 6016017 180526806 165642094699 265117434836 285751304134 416628700 1570022 411576 172164 0 891056 3783943 +linux_tuned 0 30 0x1b00 135 60.0 63.5 94.4 210.8 260.8 391.3 200002.4 200000 20 2418.11 4236805 520154793 12139426 364606668 312151665413 474900939830 510078467952 815300548 2456081 355678 94469 0 331236 6285158 +linux_tuned 0 30 0x1b00 75 58.8 62.7 93.7 209.2 259.2 393.9 199818.9 200000 20 2418.03 4230095 519462527 12123942 364129824 311951234696 474974162607 510157003809 814436344 2505192 363329 94240 0 320551 6295360 +linux_tuned 0 30 0x1b00 55 65.2 72.5 131.6 3736.5 6523.2 11328.4 200137.4 200000 20 2149.91 4451338 536699614 12894763 390060750 308994072098 463126145940 593043191351 835386598 1587721 274191 70870 0 212051 6516002 +linux_tuned 0 40 0x1b00 135 56.3 58.3 75.4 130.1 165.2 201.0 50029.9 50000 20 1877.35 1605871 166108491 3006946 90223944 88075651342 146447520200 158387078919 214763089 811062 491813 171483 0 815632 2274252 +linux_tuned 0 40 0x1b00 75 57.0 59.5 76.5 132.1 166.2 203.4 50010.4 50000 20 1879.84 1600906 165766561 3005718 90187002 88084704789 146809866924 158920460433 216663082 804785 509866 174897 0 829276 2277154 +linux_tuned 0 40 0x1b00 55 56.3 58.3 74.7 126.4 156.0 188.9 49978.1 50000 20 1861.09 1610329 166362232 3003778 90128808 86363202360 139708172463 151317630831 193942548 811518 511944 166177 0 824683 2272173 +linux_tuned 0 40 0x1b00 135 57.6 60.6 81.8 153.4 181.0 229.1 99882.7 100000 20 2075.04 2291251 271287575 6010227 180359880 165076280320 264360090382 283999273658 413874393 1524027 538056 158898 0 839341 3508633 +linux_tuned 0 40 0x1b00 75 57.4 60.3 81.5 154.9 182.7 236.7 99956.2 100000 20 2076.9 2287850 271191125 6015471 180519414 165498987485 266064643770 285860936517 415747294 1509665 531184 155909 0 835288 3501479 +linux_tuned 0 40 0x1b00 55 56.3 58.6 79.0 146.7 172.7 218.7 99978.0 100000 20 2056.45 2294470 271670711 6015061 180502068 163341494022 255827865790 275074435614 382342496 1551005 575783 157335 0 855721 3510367 +linux_tuned 0 40 0x1b00 135 60.5 65.0 102.1 221.7 273.7 414.8 200111.7 200000 20 2419.87 4245900 520867118 12157187 365182830 312739481927 476946742004 512275683083 816157940 2305024 567391 76036 0 270818 5465822 +linux_tuned 0 40 0x1b00 55 64.5 71.6 124.7 1859.8 5499.6 10863.8 200012.3 200000 20 2079.52 4373673 530682005 12597726 380344692 305774593705 451951003220 564326727599 768758920 1774062 437022 65666 0 218060 5537621 +linux_tuned 0 10 0x1c00 135 55.1 56.4 68.8 119.3 151.2 181.8 50000.2 50000 20 1963.84 1624282 167257274 3004284 90141474 87805046491 142185692353 147820705532 208446125 1005900 332272 185975 0 887587 2552660 +linux_tuned 0 10 0x1c00 75 55.0 56.3 68.7 119.2 151.8 183.3 50073.9 50000 20 1965.11 1628086 167658851 3008700 90273906 88035367186 142998211973 148690878836 207812631 1004469 345167 188359 0 889501 2556717 +linux_tuned 0 10 0x1c00 55 54.9 56.2 68.7 118.8 150.9 182.9 49936.7 50000 20 1965.82 1626651 167396484 3000580 90030612 88419263158 143621828728 149295957456 211253105 1005544 337322 187834 0 886436 2550812 +linux_tuned 0 10 0x1c00 135 55.2 56.8 71.8 137.6 162.9 208.3 99904.0 100000 20 2156.54 2314980 272910591 6007272 180257802 164806836404 258205573882 267535725650 397603319 1595745 408285 185319 0 1002363 4133586 +linux_tuned 0 10 0x1c00 75 55.7 57.4 72.6 140.3 165.0 207.3 100077.4 100000 20 2157.92 2314557 273082642 6017867 180576108 165295314141 259705447404 269071534388 403783084 1595898 409996 185429 0 1003193 4138964 +linux_tuned 0 10 0x1c00 55 56.3 58.3 74.0 143.2 169.4 216.1 100069.8 100000 20 2102.18 2315517 273127772 6018293 180591510 165407209750 259656398313 275600961412 408090395 1568723 408414 184225 0 987107 4139264 +linux_tuned 0 10 0x1c00 135 56.4 59.0 82.6 189.0 232.9 349.9 200157.1 200000 20 2510.05 4223251 519489198 12125835 364126164 313318085750 469340344195 486105381631 795481814 2628568 384012 125350 0 485132 7626415 +linux_tuned 0 10 0x1c00 75 56.5 59.0 82.1 185.7 226.1 338.2 199955.5 200000 20 2510.75 4219043 518923544 12111429 363686724 313407554026 469451377946 486220082518 803228524 2618127 383597 126220 0 484398 7618490 +linux_tuned 0 10 0x1c00 55 62.2 66.7 122.0 3689.6 6604.8 11073.6 199741.6 200000 20 2051.17 4445123 535864636 12915190 390700878 308977072994 455252625006 580798169146 822512281 1569549 292979 94689 0 317092 8024254 +linux_tuned 0 20 0x1c00 135 55.2 56.7 69.9 120.7 153.3 184.2 49978.5 50000 20 1963.13 1624651 167279610 3003025 90103842 88057930163 142965632567 148643120911 210415309 994359 336441 186336 0 875071 2501980 +linux_tuned 0 20 0x1c00 75 55.9 57.5 71.2 122.0 155.6 185.8 50099.6 50000 20 1966.66 1623589 167374103 3010324 90322872 88546100185 143961109365 149716450264 214009066 997273 344822 187830 0 883089 2506271 +linux_tuned 0 20 0x1c00 55 55.5 57.0 70.4 121.1 155.0 187.0 50126.0 50000 20 1965.76 1619175 167119614 3011876 90369390 88662278441 144264350178 150025710614 214459553 994658 340093 184213 0 870808 2500350 +linux_tuned 0 20 0x1c00 135 55.6 57.5 73.7 142.2 168.3 218.5 100027.6 100000 20 2157.71 2314822 273056459 6014914 180487668 165358322164 259940592221 269313389052 406614331 1616802 407737 179836 0 952738 4033675 +linux_tuned 0 20 0x1c00 75 55.7 57.5 73.7 141.1 166.7 213.2 100224.5 100000 20 2162.53 2312287 273091127 6026365 180830214 166179276388 261810146342 271266046922 415548825 1609652 407110 180208 0 952557 4042264 +linux_tuned 0 20 0x1c00 55 56.1 58.1 74.7 145.4 170.7 220.9 100016.7 100000 20 2108.76 2304809 272387927 6014502 180476022 165888550516 260835603765 276154690428 413259052 1598170 417294 182217 0 948006 4035259 +linux_tuned 0 20 0x1c00 135 57.0 60.1 83.5 184.2 222.7 335.3 199947.2 200000 20 2512.53 4219969 518975992 12108874 363604794 313609793512 472036817350 488905404276 814834824 2762488 373526 106940 0 373157 7161707 +linux_tuned 0 20 0x1c00 75 57.5 60.8 85.9 196.9 243.4 367.7 200217.6 200000 20 2514.02 4230642 520012507 12135293 364428018 314418984858 473418188203 490328141492 818715309 2650762 370515 109910 0 397595 7159349 +linux_tuned 0 20 0x1c00 55 63.4 68.7 127.4 4072.7 6836.3 11287.5 200021.4 200000 20 2043.07 4458270 537149536 12941653 391550418 310023094485 458813094285 590377784415 839265348 1623885 286234 82020 0 235918 7469563 +linux_tuned 0 30 0x1c00 135 55.5 57.0 71.8 123.0 156.7 187.6 50010.5 50000 20 1968.2 1620658 167093673 3005038 90164526 88497419814 144183334858 150003061701 214238998 970949 377253 183984 0 865710 2393480 +linux_tuned 0 30 0x1c00 75 55.6 57.1 71.9 122.0 155.6 187.1 50070.1 50000 20 1968.57 1618996 167019490 3008497 90267846 88413285892 144213782614 149997160114 215131102 971105 369654 180787 0 856538 2391553 +linux_tuned 0 30 0x1c00 55 55.5 57.0 72.0 121.8 156.3 187.3 49927.3 50000 20 1969.15 1613556 166524114 3000010 90013500 88326280654 144164811412 150004269145 215456208 962493 375046 185378 0 865205 2387001 +linux_tuned 0 30 0x1c00 135 55.7 57.5 75.4 143.0 169.6 217.1 99945.8 100000 20 2162.72 2299070 271911419 6011114 180377118 165684587000 261185395642 270577597188 411629406 1636686 423003 171484 0 912529 3765146 +linux_tuned 0 30 0x1c00 75 56.9 59.4 77.4 145.5 171.9 219.0 100226.4 100000 20 2164.84 2306897 272741216 6027853 180878970 166274929886 262447615102 271911380210 415660795 1651743 423181 168602 0 908518 3776869 +linux_tuned 0 30 0x1c00 55 56.5 58.8 77.4 147.5 174.7 225.4 99979.4 100000 20 2106.74 2300087 272014975 6013584 180452658 165720885881 261551339293 277314068465 416218609 1640481 445963 171760 0 896399 3780780 +linux_tuned 0 30 0x1c00 135 57.3 60.7 88.9 193.2 234.7 348.6 200267.0 200000 20 2515.47 4234037 520287185 12135497 364430538 313787532635 471193284038 488023289555 809071310 2702745 368432 93438 0 352559 6260520 +linux_tuned 0 30 0x1c00 75 57.2 60.5 87.4 187.3 224.6 327.5 200009.2 200000 20 2519.05 4224867 519342207 12115192 363804996 313762372180 472823975144 489713126805 817156495 2752918 383173 95397 0 328971 6280418 +linux_tuned 0 30 0x1c00 55 64.3 71.7 138.6 4684.1 7302.6 11559.1 200028.0 200000 20 2040.48 4468662 537990825 12963959 392458566 308727158864 454962021025 584520710501 827794656 1616620 279000 70384 0 216537 6497389 +linux_tuned 0 40 0x1c00 135 55.9 57.7 74.5 126.6 159.4 195.6 50036.6 50000 20 1962.73 1611386 166483899 3007158 90229782 88128550889 144264518896 150796516375 215210789 808003 526479 168483 0 826621 2275763 +linux_tuned 0 40 0x1c00 75 55.9 57.6 74.0 126.5 159.2 194.5 50005.4 50000 20 1967.74 1607225 166185441 3005357 90175890 88145814409 144617594254 151114547949 215645755 804092 514254 169445 0 824604 2270927 +linux_tuned 0 40 0x1c00 55 55.4 57.0 72.3 121.3 150.0 184.3 49905.2 50000 20 1951.82 1611038 166322870 2999306 89994324 86402641697 141290604644 147819439450 194228701 803355 540385 173986 0 837613 2275446 +linux_tuned 0 40 0x1c00 135 56.2 58.4 78.4 144.9 172.1 220.3 99939.5 100000 20 2163.7 2293011 271499819 6013017 180441258 165432522731 261738379894 271177373854 414566475 1546931 575561 154222 0 847231 3503226 +linux_tuned 0 40 0x1c00 75 56.4 58.7 79.1 151.1 177.7 226.2 100124.3 100000 20 2165.89 2290240 271520560 6024761 180796132 165633646275 262404729454 271854287069 414048138 1525410 557445 151083 0 845753 3480507 +linux_tuned 0 40 0x1c00 55 56.2 58.5 78.6 144.9 171.7 220.6 99999.6 100000 20 2102.03 2299663 272017711 6016677 180551688 163210109126 257337841785 272730782518 381510830 1546693 570159 159334 0 845986 3499596 +linux_tuned 0 40 0x1c00 135 59.2 63.6 97.1 200.6 241.3 349.1 199966.4 200000 20 2514.08 4231977 519777462 12126660 364197864 313710612105 471587987766 488432977978 813420654 2449752 644115 76400 0 280437 5441728 +linux_tuned 0 40 0x1c00 55 64.5 72.1 136.0 4580.9 7325.7 11735.2 200067.9 200000 20 2030.73 4472911 538491970 12934833 391985820 304553107606 447715704454 576895152584 769239697 1526601 382747 60821 0 203824 5549074 +linux_tuned 0 10 0x1d00 135 54.5 55.9 67.4 116.4 148.4 178.3 50023.9 50000 20 2038.36 1631596 167813951 3005723 90184608 88038125906 144102431292 144710695391 205854445 1016165 348002 186641 0 891519 2556414 +linux_tuned 0 10 0x1d00 75 55.1 56.5 68.6 119.1 150.5 179.3 50059.4 50000 20 2053.28 1636189 168166352 3007809 90247020 88083827890 145018646727 145617816145 207770803 1014571 347448 187934 0 897475 2561777 +linux_tuned 0 10 0x1d00 55 54.8 56.1 67.9 118.4 148.7 177.7 49997.4 50000 20 2044.88 1627509 167509434 3004093 90135630 88052043723 144607103616 145505252790 210258768 1010743 334344 186150 0 886852 2553499 +linux_tuned 0 10 0x1d00 135 55.9 57.6 72.3 135.6 160.3 201.2 100012.8 100000 20 2252.29 2314787 273022384 6013161 180432522 165432632884 262560781677 262675438141 402304475 1643943 421293 186894 0 1017280 4136012 +linux_tuned 0 10 0x1d00 75 55.1 56.6 70.9 134.4 158.8 204.1 99968.5 100000 20 2254.95 2319279 273264603 6010581 180355302 165160135383 262852273456 262944682058 402858602 1613793 409896 185444 0 1007761 4138357 +linux_tuned 0 10 0x1d00 55 55.8 57.7 74.0 147.3 172.5 231.1 100060.8 100000 20 2126.05 2306124 272541435 6019435 180631698 165155824323 262509149532 276981169055 406676524 1556258 408909 183099 0 989898 4130285 +linux_tuned 0 10 0x1d00 135 55.9 58.3 79.3 174.1 209.5 306.9 200116.2 200000 20 2621.58 4219826 519185148 12116493 363825252 313541597630 474015302043 474015367471 790436445 2683978 385684 126881 0 510475 7609736 +linux_tuned 0 10 0x1d00 75 55.9 58.1 79.3 175.9 213.0 316.5 200034.9 200000 20 2623.76 4220054 519079451 12117404 363870060 313616849286 475850661035 475850772077 799361150 2623498 382408 127053 0 507848 7604458 +linux_tuned 0 10 0x1d00 55 62.9 68.0 128.0 3977.7 6601.1 11292.9 199953.4 200000 20 2049.3 4443398 536027227 12903684 390285372 309231377533 459463381455 588726336147 826811056 1589831 296206 94071 0 291373 8038672 +linux_tuned 0 20 0x1d00 135 55.0 56.3 68.8 115.6 148.6 177.4 50076.8 50000 20 2048.33 1633343 167981064 3008843 90278112 88288364205 144840077462 145535308100 211040347 1012496 346852 187761 0 874800 2508408 +linux_tuned 0 20 0x1d00 75 55.2 56.7 69.6 118.4 151.9 182.0 49971.7 50000 20 2051.13 1627119 167435122 3002499 90087594 88466450754 145887155158 146531774780 213808914 1000969 337230 188471 0 883717 2503742 +linux_tuned 0 20 0x1d00 55 55.2 56.7 70.0 117.8 151.7 183.9 50000.0 50000 20 2025.64 1628581 167595062 3004376 90144534 88861891600 147423697993 148549672065 215716696 1057159 474972 199421 0 875007 2508904 +linux_tuned 0 20 0x1d00 135 55.3 57.0 72.1 136.7 161.8 202.1 99938.5 100000 20 2253.06 2313589 272869725 6008705 180298884 165431456458 262846329967 262942024092 407801670 1637079 410117 181225 0 962771 4028455 +linux_tuned 0 20 0x1d00 75 55.6 57.3 72.6 136.8 163.1 205.9 100017.5 100000 20 2259.96 2309392 272682266 6013396 180439452 166009078153 264718657064 264805936539 413966183 1623070 408917 182873 0 963064 4028334 +linux_tuned 0 20 0x1d00 55 56.5 58.8 75.0 144.0 170.7 219.2 99982.4 100000 20 2127.03 2296216 271759767 6013003 180432870 165775384509 263821498358 278645809742 416343329 1589617 418196 183310 0 942086 4028999 +linux_tuned 0 20 0x1d00 75 56.8 59.9 83.0 183.9 223.6 328.3 200106.4 200000 20 2629.76 4219434 519098941 12113729 363736308 314615489018 477903050210 477915094360 818404227 2743491 375287 112172 0 411179 7125107 +linux_tuned 0 20 0x1d00 55 64.1 69.5 130.9 4269.1 6855.3 11879.3 199903.2 200000 20 2054.9 4450822 536347788 12923162 390931758 309769087970 459895140201 587257360867 836370024 1572796 279187 84887 0 267291 7426034 +linux_tuned 0 30 0x1d00 135 55.1 56.6 70.7 119.7 152.2 183.9 49913.1 50000 20 2053.02 1623220 167122978 2999258 89991282 88198550575 145668547229 146395499314 214189859 976806 373757 181767 0 862712 2391516 +linux_tuned 0 30 0x1d00 75 55.3 56.8 71.0 120.0 153.8 186.0 50089.2 50000 20 2056.34 1624468 167411869 3009759 90306246 88550602851 146141838918 146829583639 214629664 977475 368916 180734 0 855155 2388463 +linux_tuned 0 30 0x1d00 55 55.1 56.5 70.8 121.2 152.7 184.3 50030.5 50000 20 2047.53 1630038 167733202 3006398 90205716 88374918665 145721306454 146869817282 215279011 984376 368812 178447 0 854735 2392991 +linux_tuned 0 30 0x1d00 135 55.9 57.7 75.6 140.7 167.0 213.7 100053.5 100000 20 2261.51 2309968 272776362 6016798 180545448 165778390415 265765592744 265848082540 412470230 1668488 422857 171776 0 911113 3769970 +linux_tuned 0 30 0x1d00 75 55.6 57.4 75.1 141.2 167.8 214.7 99988.9 100000 20 2262.4 2309215 272632181 6012906 180428656 166091007530 266012203227 266132391058 423956345 1675947 431584 172242 0 915286 3769632 +linux_tuned 0 30 0x1d00 55 56.2 58.5 77.6 151.6 179.1 238.9 100070.8 100000 20 2126.04 2298591 272013562 6020768 180674220 165722598631 263652741481 278649742838 415446930 1599469 419748 172213 0 897913 3779200 +linux_tuned 0 30 0x1d00 135 57.0 60.1 86.1 182.7 219.0 318.9 199997.5 200000 20 2628.86 4222499 519240804 12109603 363624006 313969302942 477921062089 477921238503 811195668 2792900 383910 96464 0 353753 6237099 +linux_tuned 0 30 0x1d00 75 57.1 60.3 86.9 187.8 226.0 332.7 199800.6 200000 20 2628.82 4220367 518827393 12101187 363382212 313605943241 476593193281 476593119964 814390688 2741305 375769 97254 0 369796 6205609 +linux_tuned 0 30 0x1d00 55 65.0 72.4 139.4 4592.6 7158.3 11475.8 199839.7 200000 20 2037.08 4465019 537564281 12946381 391894326 308841720243 459111296673 592019305577 835172669 1596302 279195 68363 0 199036 6525085 +linux_tuned 0 40 0x1d00 135 55.5 57.1 73.1 124.1 156.2 190.6 50015.2 50000 20 2044.42 1616030 166806616 3005891 90191874 88257514509 146810379437 148373463715 216093546 809885 558801 170154 0 826060 2279471 +linux_tuned 0 40 0x1d00 75 55.5 57.2 73.3 124.4 156.0 191.8 50035.7 50000 20 2048.89 1618726 166994119 3007178 90230556 88240677554 146612372876 148182542998 216177692 802023 542515 171329 0 827576 2278042 +linux_tuned 0 40 0x1d00 55 53.7 55.8 70.8 120.1 147.7 181.7 49851.9 50000 20 2024.54 1622645 166987354 2996044 89896230 86315292343 139140218026 141128625425 193013206 795017 561830 166803 0 835579 2269314 +linux_tuned 0 40 0x1d00 135 55.9 58.1 78.0 144.0 170.8 217.5 99927.4 100000 20 2260.58 2298529 271836396 6011852 180405528 165438654285 265399344661 265498879214 414228302 1545253 577101 153937 0 851909 3486203 +linux_tuned 0 40 0x1d00 75 56.1 58.3 78.2 144.1 170.6 214.4 99895.4 100000 20 2261.02 2298060 271764710 6009756 180341910 165621774822 266392690048 266494625215 417219202 1556914 586756 156551 0 853785 3500569 +linux_tuned 0 40 0x1d00 55 55.4 57.3 77.0 141.8 167.7 214.1 99977.0 100000 20 2127.54 2295392 271711999 6014716 180490674 163259010871 253541028474 265525524617 380133739 1549877 582494 155887 0 853800 3493781 +linux_tuned 0 40 0x1d00 135 57.5 61.2 92.2 187.4 222.7 323.1 200178.5 200000 20 2625.54 4231307 519995567 12133580 364391496 314481803515 478754252258 478754059343 816390404 2490092 671501 77596 0 290115 5426780 +linux_tuned 0 40 0x1d00 55 62.8 69.4 125.0 3898.7 6788.6 11112.0 200070.1 200000 20 2039.72 4437642 535871356 12818086 387975510 305268490882 443627857817 567398224116 769504846 1696343 425862 61005 0 188806 5570854 +linux_tuned 0 10 0x1300 135 64.2 67.4 86.0 172.0 211.5 261.2 50025.3 50000 20 1333.06 1561131 163159517 3007496 90242886 86792296381 136799013261 209118116544 204760981 825103 363278 191665 0 823870 2498323 +linux_tuned 0 10 0x1300 75 64.3 67.5 86.2 170.2 210.7 260.7 49907.8 50000 20 1331.24 1560262 162957681 3000191 90023028 86709030483 136627680855 208828988110 207395546 827229 371347 196650 0 831577 2496523 +linux_tuned 0 10 0x1300 55 64.5 67.6 86.4 171.7 213.2 264.2 49969.4 50000 20 1332.05 1557384 162839470 3004104 90141036 87235746816 137815944305 210628699922 212459640 821196 350685 191990 0 823315 2494895 +linux_tuned 0 10 0x1300 135 66.5 69.5 95.4 213.6 251.5 360.4 99961.7 100000 20 1472.63 2249042 268624765 6026739 180889776 161965214434 245051089592 374052018456 397368130 1213751 369157 168132 0 835790 4086827 +linux_tuned 0 10 0x1300 75 66.4 69.3 93.9 207.0 242.8 332.9 99892.4 100000 20 1471.12 2254477 268904980 6023102 180782436 161979276467 245030874962 374014378486 400178283 1215631 368633 168263 0 828526 4089688 +linux_tuned 0 10 0x1300 55 66.3 69.1 93.9 207.7 244.8 343.8 100201.2 100000 20 1474.11 2256056 269338596 6040678 181306674 162994639246 246784313491 376689652179 408597462 1206979 365649 170307 0 834316 4100038 +linux_tuned 0 10 0x1300 135 73.0 80.0 193.3 717.1 1095.4 2444.6 199825.6 200000 20 1722.68 4390798 530096843 12486765 376097130 305310383622 438315463885 669008057411 810646479 1165440 238752 80344 0 241110 7917612 +linux_tuned 0 10 0x1300 75 70.8 77.0 162.5 606.0 953.2 2131.3 199937.4 200000 20 1721.61 4410670 531562434 12522066 377242410 305744640058 436315127118 665954773242 815852668 1167316 244447 81516 0 244529 7946653 +linux_tuned 0 10 0x1300 55 73.7 80.3 184.8 665.7 1008.3 2078.6 199905.2 200000 20 1724.18 4400958 530839233 12503602 376639410 306221708196 439172099369 670320124392 826418789 1147277 240557 79931 0 238240 7936388 +linux_tuned 0 20 0x1300 135 65.7 68.4 87.9 176.9 216.5 271.9 50014.7 50000 20 1331.76 1558375 162966838 3006523 90212718 86962164900 137205749043 209671080105 210141701 839432 347790 187106 0 797467 2467726 +linux_tuned 0 10 0x1500 135 62.6 65.6 82.0 155.5 194.0 235.6 49967.6 50000 20 1457.43 1570019 163669733 3003539 90122868 87027715908 141498211910 195777842494 205121219 871398 373493 193673 0 830744 2503100 +linux_tuned 0 10 0x1500 75 62.8 65.9 82.4 155.5 195.0 240.7 50112.4 50000 20 1456.85 1574132 164138009 3012159 90381120 87137671700 141898396643 196302356098 207513639 858349 345810 188929 0 829455 2512233 +linux_tuned 0 10 0x1500 55 62.7 65.8 82.4 157.8 196.1 241.7 49883.6 50000 20 1454.58 1569531 163553737 2998504 89971656 86894112067 141727103699 196056709887 208392954 853289 341149 186053 0 818827 2504344 +linux_tuned 0 10 0x1500 135 63.8 67.2 88.8 188.0 219.8 300.5 99964.9 100000 20 1613.93 2258739 269266171 6021461 180714954 162714306295 255016623837 352205374071 399716256 1292589 384190 174192 0 880330 4093977 +linux_tuned 0 10 0x1500 75 64.8 68.0 89.8 191.0 223.0 304.1 99890.8 100000 20 1612.22 2267344 269735370 6017369 180593346 162884614386 256174330749 353795795928 408921159 1281548 379471 175175 0 876322 4102337 +linux_tuned 0 10 0x1500 55 63.9 67.3 88.5 189.5 221.9 302.6 100039.7 100000 20 1613.47 2263263 269657328 6026390 180864024 163284039668 256967607911 354887401023 407349534 1289493 384446 175056 0 874738 4102269 +linux_tuned 0 10 0x1500 135 67.9 73.1 134.9 1183.1 4795.1 7926.9 200085.4 200000 20 2000.16 4388211 530303911 12477551 375755364 307563462590 455455743761 628976527040 810467289 1434232 276071 94379 0 307786 7898398 +linux_tuned 0 10 0x1500 75 68.6 74.1 132.3 379.1 504.3 857.0 199886.4 200000 20 1888.05 4306295 524603769 12303633 370042494 307016524779 457753605269 632134397302 817211482 1528393 282074 94812 0 283914 7793381 +linux_tuned 0 10 0x1500 55 67.9 73.3 128.1 366.5 489.4 864.3 200161.1 200000 20 1888.54 4324788 526147527 12343987 371325186 307995060973 458717639421 633465555331 824418141 1474919 277290 93464 0 285847 7818465 +linux_tuned 0 20 0x1500 75 62.9 66.0 83.5 161.2 197.4 243.6 50019.6 50000 20 1455.47 1569736 163730160 3006405 90207876 87542715208 143002000003 197805766152 213339939 870016 356191 191688 0 813145 2475990 +linux_tuned 0 20 0x1500 55 63.0 66.2 83.0 159.3 196.8 243.7 49992.1 50000 20 1565.25 1570878 163778689 3004766 90158820 87618854096 142716013657 197421005781 216708270 869278 347008 186089 0 809163 2472800 +linux_tuned 0 20 0x1500 75 65.1 68.4 91.1 193.3 225.7 309.5 100072.7 100000 20 1613.96 2262431 269645584 6027911 180908358 163633815641 258159631318 356525227353 414835399 1311857 387877 173209 0 827328 4034996 +linux_tuned 0 20 0x1500 55 65.0 68.3 90.6 193.1 224.5 307.0 99973.6 100000 20 1615.38 2259303 269330028 6021877 180727206 163807371681 258710521028 357287978248 416479204 1319562 395560 173878 0 824999 4031517 +linux_tuned 0 20 0x1500 135 70.7 76.3 141.9 434.3 607.4 1205.4 199877.8 200000 20 1889.14 4335826 526519551 12358051 371840994 307414513525 458642965382 633362529148 826187273 1490587 279413 84149 0 236445 7382916 +linux_tuned 0 20 0x1500 75 69.7 75.5 142.1 416.6 564.3 1033.2 200221.4 200000 20 1892.09 4330201 526583219 12356039 371712006 308518514042 459988105427 635220158687 834385703 1510511 275244 82354 0 232065 7392076 +linux_tuned 0 20 0x1500 55 70.0 75.6 139.9 426.2 590.6 1154.7 199725.9 200000 20 1980.55 4324505 525575544 12334419 371087040 307567052987 457795499915 632211001034 834760048 1497798 275511 84689 0 240474 7378630 +linux_tuned 0 30 0x1500 135 63.4 66.7 85.0 158.8 198.8 246.8 49955.8 50000 20 1456.56 1569427 163672032 3002801 90100608 87399043500 142745543176 197497945449 213832433 848210 367268 187475 0 806234 2388175 +linux_tuned 0 30 0x1500 75 63.5 66.8 84.8 158.9 198.4 243.8 49901.9 50000 20 1456.48 1566003 163339052 2999369 89996922 87217599224 142625022714 197335239789 213655229 846527 357358 185804 0 798787 2383721 +linux_tuned 0 30 0x1500 55 63.6 66.9 85.1 160.6 199.4 246.6 50030.2 50000 20 1455.76 1567111 163545024 3007227 90233184 87329691421 142966705122 197807287057 215526091 848484 365078 186462 0 800427 2389083 +linux_tuned 0 30 0x1500 135 65.8 69.2 93.3 196.5 229.5 318.9 99990.9 100000 20 1617.51 2263417 269602874 6022209 180734922 163392532889 258029633676 356348224287 415133654 1330758 390587 166904 0 781861 3851877 +linux_tuned 0 30 0x1500 75 65.2 68.7 92.6 195.9 229.3 315.0 100021.9 100000 20 1615.48 2260540 269464613 6024905 180818706 163525177526 258176285603 356544345545 416009796 1315674 384070 165183 0 784531 3847054 +linux_tuned 0 30 0x1500 55 65.2 68.7 92.2 193.8 226.1 310.1 99858.7 100000 20 1616.58 2259547 269226288 6015712 180544464 163371937586 257927621972 356209584190 415516886 1309539 380048 160002 0 781780 3835330 +linux_tuned 0 30 0x1500 135 70.6 76.8 133.1 361.9 475.6 808.1 199715.0 200000 20 1888.22 4329978 525939605 12327286 370860216 306925722111 457303458671 631513329706 825530445 1549741 266003 73208 0 196167 6554463 +linux_tuned 0 30 0x1500 75 71.4 77.8 143.2 413.4 550.9 947.7 199878.2 200000 20 1889.8 4324741 525822380 12325020 370754712 307237290812 458782765173 633555611670 828392635 1522806 259603 72167 0 200603 6545388 +linux_tuned 0 30 0x1500 55 71.3 77.9 141.9 405.6 564.5 1335.9 200086.2 200000 20 1889.87 4352963 527947340 12380162 372541542 307452083358 457736578628 632110796406 830697240 1542716 265616 72640 0 196357 6557484 +linux_tuned 0 40 0x1500 135 63.8 67.1 87.0 161.2 200.8 247.3 50042.8 50000 20 1455.57 1559685 163086521 3008301 90266328 87362971428 143040793295 198094988575 215209303 766020 434130 179924 0 787873 2270922 +linux_tuned 0 40 0x1500 75 65.0 68.0 88.1 158.7 200.5 248.4 49996.5 50000 20 1455.49 1557292 162895251 3005473 90181476 87349166968 143674679044 198974495674 216105791 762659 433307 182008 0 791269 2272103 +linux_tuned 0 40 0x1500 55 61.5 63.9 83.6 151.1 187.9 227.9 50027.1 50000 20 1443.21 1577592 164278101 3007077 90228762 85958201254 134520898668 186373858620 194513486 793422 447824 179901 0 807236 2277065 +linux_tuned 0 40 0x1500 135 65.3 68.7 95.9 198.5 233.7 321.5 100134.1 100000 20 1616.78 2255884 269297826 6033199 181072194 163560606177 258702136618 357278252228 416862741 1289048 437829 155003 0 755575 3577447 +linux_tuned 0 40 0x1500 75 65.2 68.6 95.5 197.4 231.3 311.9 100022.5 100000 20 1614.91 2254416 269050216 6025997 180854562 163469690276 259195113362 357953999585 416760895 1300767 442136 155755 0 758251 3579569 +linux_tuned 0 40 0x1500 55 62.5 65.8 92.2 188.3 219.3 295.2 99926.5 100000 20 1600.05 2263826 269538017 6017824 180602850 161543046104 244381740358 337505493688 382354268 1366952 448959 158403 0 785292 3546365 +linux_tuned 0 40 0x1500 135 70.4 78.4 141.1 388.5 510.2 861.8 199976.7 200000 20 1888.32 4329269 526208042 12323161 370692552 307144854693 458832774831 633625157759 826471792 1436826 337090 62069 0 178424 5652423 +linux_tuned 0 40 0x1500 55 66.8 73.4 124.9 313.1 402.5 641.2 199957.2 200000 20 1864.25 4292045 523787138 12245898 368141814 304826115517 435360130898 601209771586 770894520 1751197 392314 67033 0 197827 5606309 +linux_tuned 0 10 0x1700 135 60.0 61.9 77.5 142.6 179.1 217.5 50049.4 50000 20 1574.18 1589559 165050140 3007899 90251868 87259294452 139183105740 175971350719 204698010 909862 347349 188943 0 850192 2520877 +linux_tuned 0 10 0x1700 75 58.5 61.2 76.8 142.9 178.7 218.4 50058.9 50000 20 1577.93 1593903 165350282 3008427 90267654 87541817106 139578979233 176444982837 207431125 921369 353049 193353 0 859790 2526410 +linux_tuned 0 10 0x1700 55 59.5 61.6 77.3 144.0 180.1 218.4 49960.0 50000 20 1576.18 1594828 165319700 3002409 90086766 87600411408 140087153983 177102806309 209227002 916793 359747 194655 0 860636 2525161 +linux_tuned 0 10 0x1700 135 60.6 62.8 81.9 170.8 199.4 267.6 99953.9 100000 20 1737.09 2281215 270745886 6016617 180557040 163547627274 251389776435 317075847680 398968308 1387155 383901 176487 0 926065 4109312 +linux_tuned 0 10 0x1700 75 60.0 62.2 81.2 168.1 197.5 260.6 99977.5 100000 20 1740.57 2278906 270609619 6016970 180564400 164005027992 252259436919 318132699668 408669088 1408610 396848 181062 0 937220 4110822 +linux_tuned 0 10 0x1700 55 60.2 62.3 81.3 168.2 199.0 266.5 99941.5 100000 20 1741.15 2278813 270573803 6015755 180531006 163897636980 253503066337 319684549553 405475059 1390290 394362 180440 0 933970 4107486 +linux_tuned 0 10 0x1700 135 62.0 65.9 104.5 296.7 411.5 835.5 200154.3 200000 20 2028.98 4282743 523355333 12253797 368349258 309326452140 450374881091 567867471869 798521165 1892977 322778 109433 0 391237 7732485 +linux_tuned 0 10 0x1700 75 62.4 66.5 106.6 299.8 403.4 748.0 200155.3 200000 20 2032.15 4287546 523698972 12267050 368787474 309839004336 452634602587 570725840252 808610906 1860186 321755 109671 0 392712 7731792 +linux_tuned 0 10 0x1700 55 62.2 66.3 105.2 278.9 364.5 603.7 199997.9 200000 20 2035.02 4266580 522089782 12219447 367244598 310319092907 454127787545 572649154269 815067956 1929590 328417 109952 0 373207 7713636 +linux_tuned 0 20 0x1700 135 60.7 62.5 78.2 142.1 179.3 218.0 49989.2 50000 20 1577.52 1597248 165518986 3004121 90138018 87367520922 139412128672 176171441091 209728663 922133 339818 188425 0 839259 2489394 +linux_tuned 0 20 0x1700 75 60.0 61.9 77.9 145.8 180.4 221.1 49995.9 50000 20 1580.62 1590494 165062540 3004603 90152670 87926750312 140363480219 177426095989 213201517 918230 342729 189817 0 839186 2483014 +linux_tuned 0 20 0x1700 55 59.9 61.9 78.2 144.8 183.1 222.2 50005.8 50000 20 1581.44 1587507 164869116 3005072 90166470 88018342505 141292844824 178683819000 214148951 912299 353349 190852 0 841502 2480644 +linux_tuned 0 20 0x1700 135 60.7 63.0 82.5 170.6 201.6 267.0 100085.9 100000 20 1740.85 2284292 271074395 6023631 180764784 164349247425 253627410944 319836205053 409156725 1433139 394825 178746 0 884612 4034263 +linux_tuned 0 20 0x1700 75 60.7 63.0 82.8 172.3 202.0 268.6 100019.0 100000 20 1744.97 2280360 270757614 6019617 180644370 164634483703 254474681630 320903678552 413251469 1425847 398127 179868 0 883525 4030781 +linux_tuned 0 20 0x1700 55 60.9 63.3 83.2 173.7 203.1 270.4 100000.7 100000 20 1744.45 2277956 270586419 6018707 180618618 164708002428 254934837100 321487643548 415073316 1449825 407502 180915 0 883499 4029894 +linux_tuned 0 20 0x1700 135 63.3 68.2 113.6 317.0 424.9 749.6 199990.8 200000 20 2035.6 4273209 522556565 12229145 367565718 309658747388 454741319905 573373170995 815100164 1977015 321661 98036 0 309807 7250852 +linux_tuned 0 20 0x1700 75 64.3 69.0 108.7 280.1 361.7 582.8 199997.2 200000 20 2038.04 4268528 522180686 12218226 367204236 310500109759 455381069035 574179438536 824471836 1987727 319717 98227 0 304230 7257734 +linux_tuned 0 20 0x1700 55 63.3 67.7 104.7 266.6 340.8 539.2 200206.7 200000 20 2039.43 4272818 522765115 12226999 367456146 310456070913 455507855869 574398306350 826678214 2006280 323208 97766 0 299616 7264201 +linux_tuned 0 30 0x1700 135 59.5 61.9 79.4 146.1 182.4 223.2 49964.3 50000 20 1578.78 1589359 164987243 3002791 90098610 87622599158 140464375542 177613019955 213617340 890591 371968 186672 0 824057 2385707 +linux_tuned 0 30 0x1700 75 59.8 62.0 79.2 145.4 183.8 222.4 49994.1 50000 20 1577.96 1590969 165083574 3004505 90149790 87849009233 140564259498 177726618668 214134418 892485 372802 189277 0 831524 2390447 +linux_tuned 0 30 0x1700 55 59.8 62.0 79.3 143.6 182.7 223.4 49860.0 50000 20 1579.26 1588993 164784643 2996638 89914362 87651551312 140739234204 177998816438 215397761 888889 390493 189306 0 833061 2388708 +linux_tuned 0 30 0x1700 135 60.5 62.8 84.0 170.9 201.1 260.9 99743.6 100000 20 1741.84 2269317 269694991 6003779 180171450 163831903549 253782755991 320028755686 409810450 1424602 401653 168631 0 836284 3798443 +linux_tuned 0 30 0x1700 75 60.8 63.4 85.6 177.1 206.7 276.0 100026.6 100000 20 1743.49 2278109 270636124 6020593 180675348 164480476806 254192356383 320543114563 413907135 1439072 407006 169603 0 841115 3819688 +linux_tuned 0 30 0x1700 55 61.5 64.0 85.9 176.4 205.6 273.5 100030.6 100000 20 1744.58 2273227 270311841 6021012 180688470 164407563265 254546545967 320994383070 415169051 1427361 401313 169578 0 839476 3813724 +linux_tuned 0 30 0x1700 135 63.7 69.9 116.6 303.5 393.6 638.0 200114.3 200000 20 2035.11 4271180 522580614 12214929 367078536 310273734603 452444797978 570485945292 817232454 2010730 317037 84903 0 263955 6414697 +linux_tuned 0 30 0x1700 75 63.7 69.2 110.9 278.3 357.7 571.4 200031.5 200000 20 2035.42 4268676 522284693 12206608 366819054 309756235391 453698184951 572057574598 821356744 2028301 321528 83585 0 249537 6437511 +linux_tuned 0 30 0x1700 55 63.2 68.7 109.5 275.3 353.9 567.2 199787.6 200000 20 2033.76 4268171 521976807 12199984 366644682 309491847533 453504792006 571885668050 819158722 1991132 316865 84524 0 261427 6411432 +linux_tuned 0 40 0x1700 135 60.4 62.4 81.2 147.7 186.7 229.8 50042.9 50000 20 1578.88 1579083 164377382 3007889 90252804 87916503891 141630741200 179393363189 216415497 792449 495295 184371 0 824002 2281260 +linux_tuned 0 40 0x1700 75 60.7 62.7 81.3 145.5 185.0 226.0 50064.8 50000 20 1578.9 1580304 164463311 3009359 90297324 87794391434 141508408591 179150066210 215594476 797483 444004 174627 0 802106 2268928 +linux_tuned 0 40 0x1700 55 60.4 62.4 80.4 144.6 177.9 218.6 50077.1 50000 20 1576.29 1584024 164740828 3009839 90310950 86120247925 139797966698 176991050981 194489859 809604 450517 179129 0 810776 2275922 +linux_tuned 0 40 0x1700 135 61.1 63.7 88.2 178.4 207.6 276.4 99915.9 100000 20 1743.42 2269071 269900862 6016519 180561150 164125847900 254383037034 320783902897 412205824 1402415 472251 155031 0 799521 3521530 +linux_tuned 0 40 0x1700 75 61.1 63.6 87.7 176.3 207.3 269.7 99980.5 100000 20 1745.44 2265770 269766610 6019812 180658746 164600159677 256444792166 323389602023 416846483 1419135 469182 158705 0 796552 3536878 +linux_tuned 0 40 0x1700 55 61.7 64.4 88.3 173.0 201.6 265.9 100028.1 100000 20 1832.17 2271237 270175909 6022572 180740640 162307766121 255277679948 321933816523 389566199 1426915 469670 157187 0 803719 3535600 +linux_tuned 0 40 0x1700 135 64.8 71.3 119.5 299.9 391.8 649.0 200122.6 200000 20 2033.32 4282426 523319581 12231793 367648662 309720687786 453253303666 571496818766 820570969 1885014 428778 72921 0 233186 5530915 +linux_tuned 0 40 0x1700 55 65.6 71.8 120.6 317.4 435.4 845.5 199945.2 200000 20 2034.69 4290866 523626276 12246975 368188788 305318929706 454263033439 572803589651 766222365 1825954 408352 74719 0 251827 5513515 +linux_tuned 0 50 0xd00 135 81.0 85.1 120.1 258.2 308.0 400.6 49916.3 50000 20 1042.87 1468116 156893383 3003656 90136026 83678485650 128166505292 286217834239 197638078 217867 455336 154234 0 675527 2165493 +linux_tuned 1 50 0xd00 135 81.5 85.6 121.4 259.4 311.5 412.3 50104.6 50000 20 1044.23 1473471 157476847 3015096 90479460 83855167770 128610762909 287201986359 199665537 219983 451589 152003 0 676986 2173039 +linux_tuned 2 50 0xd00 135 81.2 85.4 121.6 262.2 313.9 410.9 50037.6 50000 20 1044.27 1474098 157443251 3011161 90361638 84093595015 128802981027 287637748102 200348084 220229 464633 155253 0 679863 2175663 +linux_tuned 0 50 0xd00 95 81.3 85.5 121.8 264.1 315.5 416.7 50063.0 50000 20 1043.89 1462800 156695680 3012751 90409608 84134438545 128747930437 287501158131 200799750 221401 451579 151395 0 674773 2164309 +linux_tuned 1 50 0xd00 95 81.5 86.0 122.3 262.5 316.0 423.1 50074.6 50000 20 1145.6 1471724 157343574 3013617 90436110 84190973648 129009537556 288097987005 201859534 231552 470769 153022 0 673396 2172794 +linux_tuned 2 50 0xd00 95 81.1 85.4 121.2 262.7 316.2 423.5 50005.6 50000 20 1044.49 1466636 156915216 3009297 90305958 84170689417 129214265752 288556980300 201986760 218527 469197 155492 0 682946 2171378 +linux_tuned 0 50 0xd00 75 81.5 85.7 121.8 259.7 315.2 417.4 50014.6 50000 20 1043.38 1467759 156980900 3009769 90320032 84018833628 128994756117 288067155307 202048232 219487 450541 152045 0 673760 2166136 +linux_tuned 1 50 0xd00 75 81.3 85.5 120.8 259.4 313.9 411.3 50016.6 50000 20 1043.17 1470109 157149095 3009883 90323196 84046040308 129098297361 288284587215 202785773 218279 454250 152730 0 673815 2170489 +linux_tuned 2 50 0xd00 75 81.5 85.9 121.6 264.0 318.5 420.3 50039.9 50000 20 1044.08 1468490 157072698 3011221 90363174 84181217639 129407330542 288974260372 203411640 218163 454562 155726 0 679259 2174161 +linux_tuned 0 50 0xd00 55 81.4 85.6 122.8 265.1 319.9 425.9 50045.8 50000 20 1043.1 1464123 156767971 3011973 90387054 84282135306 129423334810 289011795478 203973840 218675 455839 154159 0 677797 2168724 +linux_tuned 1 50 0xd00 55 82.4 86.9 122.0 258.5 313.7 404.2 50076.1 50000 20 1043.08 1469272 157163291 3013586 90434838 84397113023 129981805648 290259730235 216921857 223326 458948 156767 0 684824 2176785 +linux_tuned 2 50 0xd00 55 81.3 85.4 121.1 258.8 312.8 407.8 49937.6 50000 20 1042.64 1473162 157261701 3004984 90175848 83991653738 129209657538 288537289909 203520311 218881 454645 155377 0 677409 2174631 +linux_tuned 0 100 0xd00 135 84.4 90.4 140.1 277.8 335.5 437.7 49951.1 50000 20 1041.84 1434571 154716790 3009210 90313314 83710586549 129775666858 290117391594 201943756 103804 557328 175729 0 718940 1681350 +linux_tuned 1 100 0xd00 135 84.1 90.3 141.7 281.1 337.8 438.9 49924.0 50000 20 1042.39 1434409 154676141 3007510 90262176 84054332044 129925549601 290437660779 202833340 104826 560265 173158 0 719187 1679599 +linux_tuned 2 100 0xd00 135 83.6 89.4 139.2 275.9 334.4 437.2 49991.0 50000 20 1041.91 1435973 154866365 3011427 90379188 83820013930 129866625735 290324772914 202003039 104818 558104 173634 0 722772 1683462 +linux_tuned 0 100 0xd00 95 84.2 90.4 140.7 275.4 332.1 432.8 50036.3 50000 20 1042.83 1436042 154909920 3014082 90458652 83911474911 130091941759 290809780601 203405756 106794 563228 176416 0 723414 1688726 +linux_tuned 1 100 0xd00 95 84.0 90.0 142.0 283.5 340.3 442.6 50024.7 50000 20 1042.74 1437402 155010456 3013472 90440688 83886776195 129941244461 290467274767 203272433 105587 561388 173301 0 717188 1682277 +linux_tuned 2 100 0xd00 95 83.8 89.6 137.9 274.5 333.5 433.0 50017.0 50000 20 1042.85 1438930 155067676 3013028 90427332 83858204126 130010883124 290621348925 203813728 103930 554684 172906 0 716729 1678246 +linux_tuned 0 100 0xd00 75 83.9 89.9 141.4 278.9 337.9 443.8 50040.4 50000 20 1042.51 1433588 154763818 3014665 90477144 83856244709 129922632348 290414930724 204238151 110317 570098 173298 0 722402 1689274 +linux_tuned 1 100 0xd00 75 83.7 89.4 138.6 275.4 332.9 435.3 49963.0 50000 20 1042.21 1438206 154994957 3009744 90328728 83928447306 130183458294 291022948639 203730633 99700 543822 174808 0 720101 1677131 +linux_tuned 2 100 0xd00 75 83.7 89.6 139.5 279.8 335.9 441.8 50138.8 50000 20 1042.88 1439452 155269759 3020341 90646530 84081442358 130373137998 291436736937 204730359 102884 552290 174981 0 723007 1685460 +linux_tuned 0 100 0xd00 55 84.0 90.1 141.2 275.4 333.2 435.1 49980.5 50000 20 1042.11 1437907 154964263 3010814 90361056 83957733032 130331069570 291353566764 204584673 103734 563414 178802 0 726302 1691516 +linux_tuned 1 100 0xd00 55 83.9 89.8 138.7 274.7 332.6 435.7 50005.4 50000 20 1042.91 1433675 154713574 3012419 90409440 83881739066 130190630510 291048326887 205309117 104918 555376 172705 0 715323 1678072 +linux_tuned 2 100 0xd00 55 86.2 92.4 143.4 282.7 340.5 443.5 50044.2 50000 20 1043.07 1437663 155040403 3014408 90468198 84146928375 130353781048 291392209525 205023441 102949 554497 176109 0 721369 1684891 +linux_tuned 0 200 0xd00 135 93.3 105.6 218.0 345.3 417.7 530.3 49984.0 50000 20 1037.56 1365852 150228764 3020968 90697644 82225344399 126730731979 283650895614 203676911 19578 364355 165118 0 726854 1185797 +linux_tuned 1 200 0xd00 135 93.9 106.7 219.2 349.4 418.8 530.3 50055.3 50000 20 1037.98 1369727 150577234 3025432 90831630 82298431099 126703560850 283580765813 203230545 19004 360491 163879 0 727501 1185326 +linux_tuned 2 200 0xd00 135 93.6 107.0 218.0 342.1 413.3 525.0 49930.1 50000 20 1038.35 1365484 150122365 3017642 90597420 82252168070 126861823805 283942383691 204070896 20250 368844 169573 0 737553 1195907 +linux_tuned 0 200 0xd00 95 93.5 106.2 219.4 346.7 418.3 527.4 49921.6 50000 20 1038.1 1366808 150229936 3017140 90582726 82300930167 126700948950 283573644167 203189391 18336 356310 161667 0 724265 1184008 +linux_tuned 1 200 0xd00 95 93.0 105.0 215.4 340.2 412.6 522.0 49887.5 50000 20 1037.39 1363836 149976512 3014880 90514320 82146182092 126515075100 283156110026 203942502 19038 359960 163490 0 726831 1184536 +linux_tuned 2 200 0xd00 95 93.5 106.4 219.6 346.1 421.5 532.3 50044.4 50000 20 1037.67 1367423 150395222 3024681 90809076 82654823692 127136566775 284530289903 204909219 18184 351954 160603 0 723183 1182836 +linux_tuned 0 200 0xd00 75 93.3 105.6 218.7 347.8 418.7 528.9 50097.5 50000 20 1037.85 1366199 150400554 3027727 90899886 82684188730 127301691611 284917189952 205578507 19144 356970 162605 0 724943 1186433 +linux_tuned 1 200 0xd00 75 93.7 106.0 216.9 342.2 414.1 524.0 50014.4 50000 20 1038.97 1365961 150282705 3022786 90752112 82600158625 127348170452 285012163937 205896237 19338 365541 167809 0 730319 1193363 +linux_tuned 2 200 0xd00 75 93.7 106.6 218.5 346.6 417.3 529.7 50016.6 50000 20 1038.54 1365634 150246597 3022950 90757776 82497477358 127035054200 284312701047 205722126 18865 357433 162305 0 723539 1183839 +linux_tuned 0 200 0xd00 55 92.4 104.2 215.3 340.0 410.5 523.0 49934.1 50000 20 1037.21 1364505 150071533 3017913 90606132 82521806861 127205444093 284694685596 218259070 18329 351963 159172 0 720004 1178802 +linux_tuned 1 200 0xd00 55 93.7 106.7 220.0 348.0 418.8 531.6 50053.9 50000 20 1037.8 1367814 150421328 3025330 90829164 82537662005 127131935176 284534961078 206387368 18292 352537 160995 0 722689 1182911 +linux_tuned 2 200 0xd00 55 93.5 105.9 218.9 344.6 415.8 523.9 49998.9 50000 20 1038.86 1367510 150353072 3021860 90724692 82657248225 127260309339 284811865113 207388644 18758 361465 165522 0 726948 1188069 +linux_tuned 0 300 0xd00 135 106.9 131.2 290.8 430.4 492.2 608.1 50015.8 50000 20 1033.26 1316133 146995993 3033812 91118772 81976112406 123821860072 277075870208 207646226 13552 206026 132061 0 702968 896544 +linux_tuned 1 300 0xd00 135 108.5 133.2 292.9 432.1 493.4 615.7 50062.9 50000 20 1034.4 1316307 147072913 3036557 91202280 82207708479 123927338688 277300169722 207823884 13385 203023 130294 0 699295 894288 +linux_tuned 2 300 0xd00 135 109.2 133.7 294.8 433.6 496.0 622.0 49979.6 50000 20 1034.07 1316783 146971474 3031105 91035480 81963146105 123796788661 277018402039 207925243 13690 205645 133105 0 704106 897149 +linux_tuned 0 300 0xd00 95 109.8 136.6 294.7 433.0 493.3 612.8 49933.2 50000 20 1033.53 1315033 146814559 3028792 90968382 81918725493 123710133461 276820324064 207427237 13434 201573 130257 0 700743 895970 +linux_tuned 1 300 0xd00 95 107.2 131.3 291.8 431.7 492.2 615.2 50020.5 50000 20 1034.04 1316565 147024590 3034275 91133556 82070938767 123782307371 276984046435 207656986 13538 205151 132595 0 701584 893817 +linux_tuned 2 300 0xd00 95 108.7 133.4 290.4 430.2 491.6 611.8 49954.5 50000 20 1034.31 1315869 146906536 3029765 90997422 81915350749 124259645308 278066930099 211771141 14425 215426 138189 0 710267 897673 +linux_tuned 0 300 0xd00 75 107.1 131.4 291.8 433.3 496.3 619.3 49939.3 50000 20 1032.76 1314844 146800416 3029017 90974838 81895264745 123702518846 276805676744 208111249 13845 207762 132113 0 699184 891138 +linux_tuned 1 300 0xd00 75 107.6 132.2 292.0 433.2 495.9 610.7 49970.9 50000 20 1033.59 1316253 146936213 3031282 91044768 81869943202 123732627303 276870257163 207883998 13183 200014 128879 0 696056 894373 +linux_tuned 2 300 0xd00 75 108.7 134.5 292.0 431.4 490.2 613.2 50124.0 50000 20 1033.63 1321128 147442624 3040080 91306998 82038112606 124126902368 277757244609 209142215 13555 207700 132510 0 701854 895831 +linux_tuned 0 300 0xd00 55 109.7 136.0 293.7 432.2 491.3 616.3 49978.6 50000 20 1034.55 1313120 146740146 3031235 91040472 81911624733 123981637035 277434319604 208599582 13506 204289 131062 0 704722 897896 +linux_tuned 1 300 0xd00 55 108.0 132.0 290.8 433.1 493.9 617.4 49936.2 50000 20 1034.69 1312386 146635740 3028784 90968100 81885288951 124053351793 277595700051 208731591 13822 205912 131881 0 704004 897180 +linux_tuned 2 300 0xd00 55 107.5 131.7 292.0 431.9 492.3 615.4 50003.5 50000 20 1035.6 1316469 146984198 3033416 91107936 81992232755 123946565858 277344342493 208453066 13430 203090 131149 0 701128 896510 +linux_tuned 0 50 0xf00 135 75.2 78.7 109.8 225.3 275.4 356.9 50100.8 50000 20 1131.59 1494747 158878836 3013860 90439392 85618069013 133228464498 257987770754 209367121 443466 468413 165753 0 712541 2158094 +linux_tuned 1 50 0xf00 135 75.9 79.8 110.5 228.4 276.0 360.3 50010.0 50000 20 1131.22 1489114 158337225 3008541 90280242 85573176296 133119811019 257787094234 209943687 449490 485292 167584 0 710434 2155616 +linux_tuned 2 50 0xf00 135 75.3 78.8 110.0 224.3 273.6 351.7 49894.4 50000 20 1131.27 1495507 158667505 3001430 90066324 85501020469 133231988796 258008437256 209727736 450638 488940 169810 0 717344 2163006 +linux_tuned 0 50 0xf00 95 75.3 78.9 110.5 226.7 276.0 353.5 49989.0 50000 20 1132.65 1499230 159018928 3006835 90227748 85775918712 133798486506 259109178685 211149599 451166 502514 175300 0 730432 2174429 +linux_tuned 1 50 0xf00 95 75.5 79.1 110.0 224.0 274.7 353.0 50040.9 50000 20 1132.43 1495874 158885188 3010129 90327102 85810889162 133660841974 258833686950 209914785 447834 488089 169971 0 722817 2167390 +linux_tuned 2 50 0xf00 95 75.3 78.9 110.5 229.4 275.5 355.6 49992.6 50000 20 1132.02 1501953 159210557 3007201 90239088 85880964052 133651419423 258790235496 209993866 453089 478796 170794 0 718576 2168641 +linux_tuned 0 50 0xf00 75 75.4 79.0 110.5 223.7 271.6 351.3 49985.1 50000 20 1131.92 1496729 158851542 3006849 90228948 85751636742 133961890409 259429796885 222747997 450010 491129 174888 0 725022 2169234 +linux_tuned 1 50 0xf00 75 75.8 79.6 111.3 227.3 275.5 354.5 49991.7 50000 20 1131.17 1492182 158569293 3007204 90239490 85827219278 133767299224 259048373457 210782256 446590 494105 172752 0 727527 2165575 +linux_tuned 2 50 0xf00 75 75.5 79.1 110.5 226.7 275.7 356.9 50064.2 50000 20 1132.13 1494879 158832264 3011778 90377226 85850855848 133868029427 259254165403 211091304 447636 488050 169101 0 721874 2166819 +linux_tuned 0 50 0xf00 55 75.3 78.9 110.7 228.9 275.8 355.0 50025.2 50000 20 1131.71 1497657 158984020 3009118 90296526 85808397228 133867971681 259230346746 210763447 449409 490152 172511 0 721810 2170424 +linux_tuned 1 50 0xf00 55 75.4 78.9 110.4 226.5 275.6 353.0 50051.2 50000 20 1226.78 1497137 158976154 3010821 90348096 85952782968 134141951311 259760880315 211556175 447900 484065 173613 0 724079 2170833 +linux_tuned 2 50 0xf00 55 75.5 79.1 111.1 230.0 277.3 360.0 50047.3 50000 20 1132.02 1500523 159192062 3010524 90339000 85854394369 133799579851 259104316458 210704962 445044 485551 169102 0 717970 2167039 +linux_tuned 0 100 0xf00 135 78.4 83.9 132.0 252.2 302.8 392.9 50018.2 50000 20 1127.72 1458072 156322904 3012252 90401466 84541888038 134788530499 262940623363 208630523 104468 629071 183039 0 751372 1672917 +linux_tuned 1 100 0xf00 135 78.6 83.8 129.0 246.0 298.7 385.5 50167.3 50000 20 1128.78 1457092 156486323 3021001 90663396 85202996169 135757080943 264769808687 218750398 106673 634092 184154 0 759447 1681477 +linux_tuned 2 100 0xf00 135 78.2 83.4 128.8 247.3 299.8 389.1 49965.0 50000 20 1127.12 1459891 156412412 3009267 90312708 84549926920 134836804764 263085829379 208813578 100030 617792 179576 0 741601 1661095 +linux_tuned 0 100 0xf00 95 78.3 83.5 128.5 246.6 299.7 389.6 49998.6 50000 20 1127.47 1458758 156376024 3011209 90370500 84510089525 134648705866 262699606074 207584949 98877 612680 178151 0 745937 1660014 +linux_tuned 1 100 0xf00 95 78.8 84.2 131.4 248.8 301.1 390.8 50120.5 50000 20 1128.12 1461315 156673638 3018335 90583806 85067746919 135490135468 264283733742 209696639 102671 627650 183435 0 751952 1676100 +linux_tuned 2 100 0xf00 95 78.3 83.7 131.3 250.3 302.7 393.2 50115.8 50000 20 1127.85 1462119 156748234 3018091 90576546 84905500179 135253344288 263908227967 208565034 94427 605165 175200 0 743495 1656917 +linux_tuned 0 100 0xf00 75 78.6 83.9 129.8 248.8 303.1 393.8 49971.9 50000 20 1127.04 1461508 156514450 3009425 90316932 84579555131 134942033133 263239102557 208423935 101069 618511 180909 0 747903 1664855 +linux_tuned 1 100 0xf00 75 79.0 84.3 130.0 249.2 302.6 390.5 50039.1 50000 20 1127.63 1460803 156555011 3013498 90439062 84966522285 135397669007 264122042647 209712778 104111 624857 181957 0 752372 1671826 +linux_tuned 2 100 0xf00 75 78.4 83.8 130.4 249.0 301.4 392.6 50071.9 50000 20 1127.77 1455544 156250918 3015612 90502764 84802490325 134864185446 263036278558 209254273 102409 614954 176603 0 738459 1655473 +linux_tuned 0 100 0xf00 55 78.7 84.2 132.1 250.3 302.3 392.2 49946.8 50000 20 1127.8 1457887 156243344 3007687 90263712 84740431609 134989385199 263315662383 208461401 104284 625910 183354 0 757690 1675080 +linux_tuned 1 100 0xf00 55 78.3 83.6 128.9 246.6 300.0 392.0 49962.7 50000 20 1128.22 1458229 156307219 3009071 90306732 84703986390 134900975168 263163268775 208138853 97068 606885 177324 0 744722 1656123 +linux_tuned 2 100 0xf00 55 78.3 83.7 129.3 248.6 300.4 388.4 49902.0 50000 20 1128.84 1455373 156032362 3004985 90182730 84666418725 134969559048 263269127417 209030518 104070 620792 181371 0 754859 1669775 +linux_tuned 0 200 0xf00 135 87.6 100.2 209.2 322.5 379.3 479.1 49976.3 50000 20 1120.72 1380123 151161981 3020099 90670434 82868934703 131124593982 256772251965 207785102 19073 363369 164787 0 747631 1178683 +linux_tuned 2 200 0xf00 135 85.8 97.0 200.6 316.0 369.0 475.0 49992.1 50000 20 1120.52 1378246 151024715 3021458 90712596 82840216470 131125191172 256766859589 208260966 19226 365067 163454 0 750510 1180962 +linux_tuned 0 200 0xf00 95 87.3 99.8 209.1 323.6 380.1 477.1 50008.5 50000 20 1121.48 1380157 151191143 3022082 90730212 82841130359 131242751990 256980926047 208551727 19068 364677 165869 0 752000 1183508 +linux_tuned 1 200 0xf00 95 88.0 100.8 208.9 321.7 379.0 479.6 49956.4 50000 20 1119.44 1375292 150834678 3018733 90629304 82748712319 131014851234 256538795150 207386979 19367 366230 166811 0 751125 1182477 +linux_tuned 2 200 0xf00 95 87.5 100.5 209.2 324.2 382.4 480.7 50015.4 50000 20 1120.28 1378925 151145671 3022706 90749448 82940329017 131377390918 257230145464 208620865 18902 362441 164108 0 751373 1181691 +linux_tuned 0 200 0xf00 75 86.6 98.8 206.0 321.6 378.5 477.8 50001.8 50000 20 1119.78 1377924 151060481 3021494 90712332 82869913034 131432819791 257301435004 208924314 22664 384855 171281 0 748558 1178849 +linux_tuned 1 200 0xf00 75 87.9 100.9 208.5 323.3 380.0 477.0 49999.1 50000 20 1121.51 1378719 151100628 3021487 90711402 82929152655 131526342122 257553137314 208606861 19682 368367 168020 0 755981 1186884 +linux_tuned 2 200 0xf00 75 87.0 99.2 207.5 320.7 375.8 477.8 50052.4 50000 20 1121.62 1376517 151027496 3024760 90810318 82954812956 131307209486 257090527458 208216981 18620 359975 161637 0 747704 1179077 +linux_tuned 0 200 0xf00 55 86.7 99.6 209.2 324.0 382.5 481.6 49980.6 50000 20 1121.12 1378326 151042887 3020407 90679296 82841980328 131402472145 257277184885 211947305 18981 363750 165693 0 753257 1184066 +linux_tuned 1 200 0xf00 55 86.7 98.7 205.2 320.6 379.9 482.1 50034.1 50000 20 1120.75 1379873 151238458 3023477 90771684 82829082327 131099367915 256649404456 208296561 18810 359875 162098 0 745404 1173752 +linux_tuned 2 200 0xf00 55 87.7 100.7 209.5 325.0 384.8 484.3 49991.2 50000 20 1120.68 1378625 151060167 3020910 90694710 82933373256 131476367808 257424783807 208369260 19141 362449 163955 0 751923 1181267 +linux_tuned 0 300 0xf00 135 101.8 124.4 280.0 406.0 457.1 567.1 49915.6 50000 20 1115.6 1320781 147163211 3027158 90917892 82059059744 127755262204 249940210289 215016897 13265 198641 128531 0 725192 894890 +linux_tuned 1 300 0xf00 135 104.5 128.9 283.9 413.7 464.8 570.5 49973.5 50000 20 1115.47 1323983 147453191 3030609 91020552 82261899005 127956667109 250327000080 210104395 13407 198484 128397 0 721545 893867 +linux_tuned 2 300 0xf00 135 104.5 128.5 283.2 411.2 464.5 574.6 50018.3 50000 20 1116.55 1325539 147604242 3033453 91106670 82510396114 128477317801 251352029962 211306484 13916 204925 132190 0 722812 891875 +linux_tuned 0 300 0xf00 95 102.6 125.7 280.9 406.8 457.4 566.7 50067.6 50000 20 1115.68 1326253 147695243 3036675 91204704 82443486084 128432493936 251271870424 212831133 13865 204669 132858 0 732929 900021 +linux_tuned 1 300 0xf00 95 100.6 121.9 278.4 405.1 454.0 566.5 49984.5 50000 20 1116.8 1323850 147456260 3031653 91052904 82315416579 128293589324 251000455248 210810135 14046 209960 136176 0 732597 895913 +linux_tuned 2 300 0xf00 95 103.6 127.8 284.4 413.8 464.6 571.2 49966.9 50000 20 1116.47 1321556 147278567 3030264 91010520 82370401263 128035585015 250457390373 210040456 13528 200175 129064 0 722315 891903 +linux_tuned 0 300 0xf00 75 102.1 124.4 280.3 408.5 459.0 572.0 49958.2 50000 20 1116.31 1322445 147323658 3029735 90994836 82240452547 128204678824 250804051356 210709863 13571 206146 132610 0 724840 894207 +linux_tuned 1 300 0xf00 75 102.7 125.7 280.7 409.0 460.8 572.8 49998.5 50000 20 1116.42 1321683 147325615 3032539 91080150 82240797812 128297842127 251000478564 210614146 13967 209181 134633 0 729812 896257 +linux_tuned 0 300 0xf00 55 102.3 126.5 279.8 403.5 448.1 546.0 50107.8 50000 20 1109.58 1327626 147850556 3038958 91272522 80801872972 123224740765 241499254413 190244269 13654 203489 131097 0 736896 895585 +linux_tuned 1 300 0xf00 55 100.5 124.3 278.3 402.8 445.4 548.1 50043.2 50000 20 1110.1 1328221 147828422 3034924 91150512 81095135014 123746802937 242488964151 194234174 13843 207085 132551 0 732479 892081 +linux_tuned 2 300 0xf00 55 99.9 121.1 277.2 402.7 448.8 549.6 50007.5 50000 20 1110.71 1326208 147631056 3032749 91085436 81317510460 124444521444 243808993778 195816078 14146 208735 134097 0 738066 895711 +linux_tuned 0 50 0x1100 135 71.0 74.5 102.4 202.8 247.9 317.2 50060.2 50000 20 1228.53 1523603 160735471 3010494 90335616 86658720107 137011864310 234286513120 214303385 567380 521262 177914 0 755860 2171907 +linux_tuned 1 50 0x1100 135 70.6 74.2 101.4 199.3 243.6 304.2 49955.2 50000 20 1228.05 1518066 160209730 3004279 90149430 86480850964 136563298438 233520513732 213322981 564752 508589 174325 0 752228 2163116 +linux_tuned 2 50 0x1100 135 71.0 74.5 102.1 201.4 247.3 314.4 50061.0 50000 20 1228.59 1513702 160069716 3010529 90336558 86646221879 136851359429 234009897780 214375859 564616 518135 175975 0 750213 2161154 +linux_tuned 0 50 0x1100 95 70.8 74.4 102.2 198.4 244.5 312.1 50046.2 50000 20 1229.11 1523008 160675255 3009810 90315480 86648064961 137028432841 234334344473 213982224 566097 544340 181996 0 757841 2175040 +linux_tuned 1 50 0x1100 95 70.9 74.4 101.9 198.5 244.3 305.7 50021.2 50000 20 1228.43 1520852 160488566 3008311 90270576 86283614549 136168180101 232852853025 212200100 562988 513920 172343 0 742969 2157022 +linux_tuned 2 50 0x1100 95 71.4 74.7 102.3 203.9 248.7 312.1 50067.3 50000 20 1228.79 1518798 160406517 3010970 90349962 86619855016 136787249758 233923324631 214244959 564470 518361 174024 0 751251 2162972 +linux_tuned 0 50 0x1100 75 70.6 74.3 102.1 200.7 246.1 316.9 49991.3 50000 20 1229.16 1520690 160473608 3006522 90216876 86553658487 136792172303 233921932196 213918034 567476 520989 177165 0 750442 2167148 +linux_tuned 1 50 0x1100 75 71.2 74.6 102.3 202.5 247.2 306.8 49933.2 50000 20 1229.78 1520259 160340031 3002855 90106296 86578405493 137016947200 234314659882 213646077 565712 504738 177715 0 746180 2162297 +linux_tuned 2 50 0x1100 75 71.9 74.9 102.4 199.5 245.7 311.2 50058.1 50000 20 1230.85 1520462 160508824 3010452 90334632 86734550436 137233002473 234663528287 214633841 565443 506673 179839 0 753488 2169142 +linux_tuned 0 50 0x1100 55 70.7 74.3 101.9 199.7 244.6 308.6 50012.3 50000 20 1230.46 1517987 160282537 3007773 90254550 86466780197 136802766528 233953161020 214030685 559071 504646 174245 0 747288 2157823 +linux_tuned 1 50 0x1100 55 70.8 74.4 102.2 202.2 247.5 313.9 50073.2 50000 20 1230.83 1521363 160587724 3011333 90360858 86610537077 137048576790 234330335368 214323079 571163 513331 174767 0 747634 2166691 +linux_tuned 2 50 0x1100 55 70.4 74.1 101.6 198.3 244.0 307.9 49971.3 50000 20 1230.5 1517905 160237294 3005308 90180510 86462011867 136878229424 234050947860 213630130 565834 509842 178669 0 749767 2163784 +linux_tuned 0 50 0x1100 135 73.4 77.0 114.7 251.8 299.4 428.5 99968.0 100000 20 1360.03 2225847 267106167 6037185 181234194 161438673381 244783123191 417582211922 408932812 980876 472134 133004 0 650220 3328335 +linux_tuned 1 50 0x1100 135 73.6 77.2 116.0 257.3 304.4 434.0 100157.0 100000 20 1361.45 2230121 267619688 6047197 181530252 161958497952 246774922994 420980870692 412686139 1016557 501938 140452 0 646600 3365829 +linux_tuned 2 50 0x1100 135 73.8 77.8 117.7 262.6 312.0 441.6 99965.9 100000 20 1360.4 2230425 267421968 6035806 181188774 161526426077 245354474642 418560356692 410583848 1001938 493461 136757 0 640974 3345455 +linux_tuned 0 50 0x1100 95 73.6 77.2 115.5 254.8 303.0 429.6 99910.7 100000 20 1360.51 2227481 267117449 6033285 181115490 161521987003 245615344692 418998491360 411391366 990540 474550 137722 0 649271 3341335 +linux_tuned 1 50 0x1100 95 74.7 78.7 117.7 257.7 303.9 433.0 100000.1 100000 20 1361.46 2228708 267341296 6036626 181209912 161948511210 246860434585 421129951601 417732332 1016400 501085 143450 0 645993 3364794 +linux_tuned 2 50 0x1100 95 73.7 77.2 115.1 252.0 300.1 420.8 99979.5 100000 20 1361.7 2231012 267449203 6036046 181194438 161696894113 246304055900 420175902032 414369922 1017846 494962 141325 0 645915 3364538 +linux_tuned 0 50 0x1100 75 74.1 77.9 116.6 255.7 302.6 430.7 99973.7 100000 20 1361.94 2226896 267148056 6035519 181178064 161590542231 246247662397 420079169458 412831478 1013893 500421 140392 0 646630 3360851 +linux_tuned 1 50 0x1100 75 73.7 77.5 116.9 263.9 319.5 483.9 100024.8 100000 20 1361.79 2230168 267444082 6041191 181355976 161717046602 245683135149 419116973238 412514260 988405 483883 136832 0 652892 3342691 +linux_tuned 2 50 0x1100 75 73.9 77.7 117.5 262.7 314.3 458.4 100158.9 100000 20 1362.89 2229638 267602702 6047542 181541448 161829313264 246407592780 420353241788 413971678 1000478 480339 139449 0 649621 3355832 +linux_tuned 0 50 0x1100 55 74.0 77.8 117.3 258.2 307.1 446.9 100081.8 100000 20 1361.41 2233885 267738162 6042517 181389672 161945338435 246363926168 420285938327 415734509 1002146 489477 139189 0 646820 3354432 +linux_tuned 1 50 0x1100 55 74.2 78.0 116.3 253.5 300.5 430.4 99975.7 100000 20 1363.99 2229506 267370093 6036304 181203342 161706362757 247103166107 421537530506 414619522 1016278 511772 140141 0 641698 3366451 +linux_tuned 2 50 0x1100 55 73.9 77.7 117.3 261.5 309.6 447.6 99982.4 100000 20 1362.85 2225514 267138008 6037030 181226628 161664408344 246858129510 421122682258 414401986 1000979 492222 141445 0 644166 3355811 +linux_tuned 0 100 0x1100 135 74.6 79.2 124.9 230.2 276.4 357.9 48920.2 50000 20 1195.15 1238683 132203697 2527031 75859065 71690462047 117542808503 204803867773 200373371 69200 549474 149842 0 645055 1381767 +linux_tuned 1 100 0x1100 135 73.5 77.7 120.3 219.2 262.7 333.8 49997.2 50000 20 1211.29 1482957 157937830 3010307 90341286 83382918520 134211117692 234639753652 190160051 79228 671946 178321 0 767948 1641706 +linux_tuned 2 100 0x1100 135 74.1 78.5 121.1 218.5 262.4 334.5 50006.7 50000 20 1213.35 1480213 157797768 3010778 90355290 83827464109 135279969914 236290885124 194372439 84768 676438 185840 0 782482 1656819 +linux_tuned 0 100 0x1100 95 73.8 78.4 123.9 223.4 269.0 348.7 50101.6 50000 20 1214.54 1478390 157804818 3016712 90533790 84323669217 135794840856 236895127297 197119691 96582 690415 186631 0 784972 1664328 +linux_tuned 1 100 0x1100 95 74.1 78.6 123.7 225.2 269.0 348.9 49942.3 50000 20 1213.89 1477521 157513150 3007174 90247578 83870636443 135628476727 236773943344 196259646 82236 667361 181303 0 771742 1645470 +linux_tuned 2 100 0x1100 95 73.7 78.1 122.8 222.3 270.0 349.3 49932.6 50000 20 1213.81 1472167 157189717 3006319 90221232 83732945334 135569752484 236582620049 196861541 88162 671657 179591 0 769419 1646415 +linux_tuned 0 100 0x1100 75 74.0 78.4 122.1 222.6 267.7 349.2 49923.7 50000 20 1307.77 1476070 157429751 3005926 90210144 84078847804 136429564918 238103531052 210217465 83042 669070 182715 0 772739 1646929 +linux_tuned 1 100 0x1100 75 74.1 78.5 122.2 222.9 269.7 350.1 50006.7 50000 20 1214.63 1476433 157564676 3010810 90355992 84347813973 136687644157 238485064651 199251915 85323 665771 178240 0 769745 1646129 +linux_tuned 2 100 0x1100 75 73.9 78.3 123.5 224.4 270.6 351.2 50070.9 50000 20 1214.36 1477513 157676170 3015085 90485790 84206151146 136289715046 237816049615 199671941 84107 669525 180105 0 760061 1642514 +linux_tuned 0 100 0x1100 55 74.0 78.3 122.5 223.3 269.8 349.8 50027.2 50000 20 1215.13 1476510 157598699 3012395 90404808 84241801987 136449624537 238109800910 199769979 82544 668218 181246 0 769122 1646294 +linux_tuned 1 100 0x1100 55 74.0 78.3 122.4 222.8 268.2 350.6 50068.4 50000 20 1214.41 1474867 157517694 3014597 90469992 84195572319 136635299627 238436366314 206054129 77486 656860 176203 0 759402 1635986 +linux_tuned 2 100 0x1100 55 74.1 78.5 122.3 223.5 270.0 351.5 50025.8 50000 20 1214.54 1479934 157808886 3012042 90393432 84442062828 136925499308 238942565722 200632450 79654 662916 182657 0 766682 1646583 +linux_tuned 0 100 0x1100 135 78.5 87.2 154.6 285.0 335.4 461.0 100092.9 100000 20 1358.18 2215821 266596453 6060390 181986684 159842865080 242387137286 414104388227 408601217 192856 1063835 136069 0 612143 2381154 +linux_tuned 2 100 0x1100 135 76.5 84.2 149.0 269.6 318.1 431.2 100069.4 100000 20 1345.06 2219994 266833117 6056020 181843524 156915191619 232243956628 397225776936 366191673 164475 1119606 135740 0 625495 2371984 +linux_tuned 0 100 0x1100 95 77.3 85.4 151.2 275.2 326.0 443.5 100014.6 100000 20 1347.16 2218045 266643093 6054019 181786776 157491130087 233765300378 399669914220 375425292 165188 1089434 131861 0 624848 2358518 +linux_tuned 1 100 0x1100 95 77.0 85.2 150.7 274.0 325.1 435.1 100007.0 100000 20 1348.62 2217919 266618395 6053024 181755414 157694752311 234585812279 401022735916 379808405 171038 1106429 135060 0 621955 2373111 +linux_tuned 2 100 0x1100 95 77.6 86.1 152.1 275.3 327.1 438.3 100106.5 100000 20 1349.32 2218340 266778407 6058845 181930062 158174596721 234852448722 401490305990 381602612 173609 1097278 134943 0 622343 2369481 +linux_tuned 0 100 0x1100 75 77.0 84.9 150.5 273.2 323.3 437.2 99957.4 100000 20 1350.23 2214728 266373227 6050517 181681524 157909924934 235776029247 403000263548 384313354 165909 1087103 132440 0 621340 2366763 +linux_tuned 1 100 0x1100 75 76.6 84.3 150.4 275.9 326.9 442.4 100056.7 100000 20 1349.63 2217927 266694811 6057183 181884132 158355478885 235204365687 401995902343 384853309 178871 1079076 132454 0 628686 2360220 +linux_tuned 2 100 0x1100 75 77.7 86.4 153.2 279.7 329.7 446.6 100183.3 100000 20 1350.82 2219703 266969764 6064244 182094246 158314695761 236271417422 403845457615 387348164 171784 1098877 134980 0 618650 2374473 +linux_tuned 0 100 0x1100 55 77.0 84.7 150.4 274.2 325.6 439.7 100070.4 100000 20 1349.89 2216347 266614372 6058591 181928364 158296794205 235224737748 402049373065 386856717 168589 1081376 130821 0 624539 2360556 +linux_tuned 1 100 0x1100 55 76.6 84.4 150.0 271.9 321.2 429.1 100002.5 100000 20 1349.44 2214079 266370590 6053357 181767654 158351480253 235865421505 403135908061 388653731 175921 1098900 134659 0 623240 2375297 +linux_tuned 2 100 0x1100 55 77.6 85.9 151.8 277.9 329.9 450.0 100004.4 100000 20 1348.94 2220023 266782181 6053261 181764696 158404981156 236043296435 403429693257 390915568 178012 1098171 135109 0 623589 2375893 +linux_tuned 0 200 0x1100 135 81.1 93.6 197.9 301.7 347.5 436.8 50060.9 50000 20 1205.08 1388573 151847055 3024983 90816648 82323746558 132761016715 232753886523 200591947 19534 371324 168978 0 781082 1184912 +linux_tuned 1 200 0x1100 135 81.6 93.2 198.6 301.8 345.0 436.5 50026.2 50000 20 1205.22 1392705 152032093 3022487 90739968 82251149550 132499706825 232306256240 199446798 18286 359567 162639 0 772445 1181133 +linux_tuned 2 200 0x1100 135 81.7 93.6 198.7 302.3 347.2 439.4 49905.2 50000 20 1204.59 1392360 151879450 3015559 90532716 82307267900 132626097192 232566297065 203516760 19287 368290 167728 0 780003 1181821 +linux_tuned 0 200 0x1100 95 80.9 92.1 196.6 300.9 344.7 435.8 50001.2 50000 20 1205.92 1388808 151764033 3021014 90696324 82224604962 132458135821 232207182090 200303642 18961 363318 166121 0 775038 1180002 +linux_tuned 1 200 0x1100 95 81.7 93.2 198.0 300.5 343.6 435.4 50024.9 50000 20 1205.33 1393903 152133771 3022859 90752256 82253624041 132498485296 232316115544 200011134 19213 366471 167065 0 779425 1182548 +linux_tuned 2 200 0x1100 95 81.2 92.9 198.7 302.6 346.5 436.6 50184.7 50000 20 1206.17 1395681 152426645 3032087 91028598 82668857947 132799884564 232764609244 201709354 18932 367077 165531 0 776292 1182500 +linux_tuned 0 200 0x1100 75 81.0 92.0 196.8 300.3 344.6 435.8 49986.5 50000 20 1306.56 1392529 151989194 3020444 90679944 82324399344 132421319039 232130909074 203923492 18151 359028 163214 0 763395 1168661 +linux_tuned 1 200 0x1100 75 81.2 92.5 196.8 301.5 345.5 438.1 49954.3 50000 20 1206.48 1390779 151855888 3018459 90620322 82094057275 132201825946 231762661535 200654923 18538 361036 164410 0 770070 1174179 +linux_tuned 2 200 0x1100 75 82.0 94.5 199.4 302.2 348.7 439.4 50036.9 50000 20 1206.54 1392797 152089775 3023727 90779226 82229698876 132640198210 232536864733 201545051 19334 369824 168638 0 777373 1183216 +linux_tuned 0 200 0x1100 55 81.1 92.0 196.7 300.6 344.7 438.6 50055.0 50000 20 1206.78 1394616 152206485 3024292 90794238 82488496795 132875857264 232905212090 204978970 18235 358339 160818 0 768333 1176006 +linux_tuned 1 200 0x1100 55 82.5 94.9 200.3 304.0 352.3 443.6 49994.9 50000 20 1205.81 1391761 151968237 3020746 90687978 82357666014 132703316606 232584224894 202386489 19170 363967 166170 0 773855 1177256 +linux_tuned 2 200 0x1100 55 81.1 93.1 197.2 301.7 346.5 437.0 49944.2 50000 20 1206.58 1391305 151860432 3017752 90598518 82247624870 132591621651 232388705676 202815910 20092 373425 168913 0 774555 1174079 +linux_tuned 0 200 0x1100 135 103.5 126.7 231.7 349.2 403.6 516.6 100079.0 100000 20 1336.17 2204590 265819312 6098124 183252540 155559457145 225259783997 385623955852 387295087 23603 482711 161151 0 735782 1411319 +linux_tuned 1 200 0x1100 135 104.8 128.1 232.7 353.2 405.9 517.1 99964.3 100000 20 1335.06 2201333 265478259 6091884 183068580 155578022423 224369767017 384172474880 386263231 23984 480635 161130 0 733128 1406619 +linux_tuned 2 200 0x1100 135 103.8 127.7 234.0 359.1 416.6 535.8 100066.6 100000 20 1334.53 2204353 265799268 6098622 183273826 155624459324 224180600224 383743839017 385091054 23389 474403 156713 0 731552 1400963 +linux_tuned 0 200 0x1100 95 104.8 128.1 233.5 356.7 412.5 527.1 100197.5 100000 20 1335.91 2204454 265969379 6106408 183504840 155909882988 225951839355 386787986388 389251970 24377 480910 159773 0 732316 1407598 +linux_tuned 1 200 0x1100 95 103.3 126.5 232.7 352.4 404.9 519.0 100072.1 100000 20 1335.26 2203873 265782604 6097661 183239892 155903965998 225156350992 385496876025 389105631 24606 480225 160952 0 734690 1409240 +linux_tuned 2 200 0x1100 95 105.4 129.3 234.4 360.6 416.1 528.1 99977.6 100000 20 1336.5 2203110 265593979 6093153 183107904 155649162127 225828432656 386580493215 389823923 22584 471810 158987 0 728951 1407784 +linux_tuned 0 200 0x1100 75 101.9 124.9 229.9 349.5 405.0 515.3 100121.2 100000 20 1337.3 2204496 265847762 6101135 183345204 156263470619 226613729274 387912731378 392227446 23340 476905 158746 0 729063 1409333 +linux_tuned 1 200 0x1100 75 103.5 128.8 234.7 359.1 416.9 532.1 99960.9 100000 20 1335.88 2201954 265506743 6091652 183060342 156184248265 225794056782 386569723456 392990852 22763 471626 156340 0 725523 1403264 +linux_tuned 2 200 0x1100 75 101.5 123.7 231.1 354.4 408.3 517.1 100039.3 100000 20 1337.27 2201048 265558301 6095970 183190734 156375893625 226666094625 388073851173 395731367 25670 482307 160629 0 732942 1405431 +linux_tuned 0 200 0x1100 55 103.6 127.1 235.0 365.1 422.6 546.7 100182.7 100000 20 1337.82 2204767 265958635 6105578 183480804 156704491481 226855724713 388263794213 395040517 23842 476718 157729 0 726443 1403883 +linux_tuned 1 200 0x1100 55 104.5 128.4 233.6 358.2 413.0 526.9 100011.9 100000 20 1337.25 2201012 265527835 6094125 183131670 156357984155 226928863360 388407666722 396121425 24136 480262 161000 0 728871 1409408 +linux_tuned 2 200 0x1100 55 101.2 123.0 229.3 350.2 406.6 519.8 100092.4 100000 20 1338.08 2203678 265791496 6099065 183283602 156572221967 226703097852 388035178745 395020710 23809 478723 161113 0 726674 1407504 +linux_tuned 0 300 0x1100 135 97.8 119.4 270.1 396.1 434.4 524.8 50070.6 50000 20 1200.82 1331795 148068760 3036159 91187022 82030745425 129717916573 227005123723 204943314 14440 214480 137288 0 756357 897047 +linux_tuned 1 300 0x1100 135 97.2 119.3 271.7 397.2 436.7 529.0 50071.8 50000 20 1199.57 1334796 148268142 3036341 91192074 82110176659 129285551049 226150876407 204786458 13411 199888 127221 0 740361 890355 +linux_tuned 2 300 0x1100 135 96.3 118.0 269.4 395.0 433.7 528.0 50006.0 50000 20 1200.13 1330996 147927803 3032681 91083510 81794098121 129111452452 225931431841 203791711 13662 203161 130099 0 746305 895475 +linux_tuned 0 300 0x1100 95 97.4 119.4 270.8 397.3 436.7 531.8 50020.8 50000 20 1200.01 1331280 148005616 3033449 91106844 82090924534 129573903931 226703428168 205523740 14004 209172 132932 0 747691 891462 +linux_tuned 1 300 0x1100 95 95.6 116.8 269.0 395.0 433.4 526.4 50082.8 50000 20 1200.07 1332891 148173703 3036826 91206948 81983948948 129441827994 226449696090 205260172 13414 200857 128545 0 743184 892567 +linux_tuned 2 300 0x1100 95 96.3 118.0 270.5 396.8 436.1 530.3 49931.3 50000 20 1199.74 1329943 147780935 3027563 90927612 81879923630 129264934380 226175560627 204913882 13774 204711 131282 0 747401 893568 +linux_tuned 0 300 0x1100 75 95.4 117.7 268.8 394.9 431.8 524.2 50033.6 50000 20 1199.15 1332883 148140046 3034214 91128318 81985881150 129727028174 226963881547 205663303 13690 205972 131444 0 746234 893743 +linux_tuned 1 300 0x1100 75 97.9 120.4 272.6 397.6 436.5 530.1 50106.4 50000 20 1200.26 1333028 148220545 3038686 91263222 82164386499 129716127831 226892455591 206015692 13982 206778 132107 0 749268 893640 +linux_tuned 2 300 0x1100 75 96.4 117.8 270.2 396.1 434.8 529.4 50101.3 50000 20 1199.96 1333801 148259901 3038026 91242396 82127329856 129687018138 226788079168 205946368 13427 199785 128529 0 740325 891502 +linux_tuned 0 300 0x1100 55 97.1 118.8 271.3 396.7 436.9 531.9 50014.3 50000 20 1200.45 1330689 147937428 3033001 91092330 82300020204 130012132317 227409162279 206753092 13734 202981 129126 0 746979 896793 +linux_tuned 1 300 0x1100 55 96.4 118.2 269.5 396.0 435.3 527.7 49917.7 50000 20 1200.26 1329421 147740573 3027206 90918606 81959082854 129243630117 226044082544 205403874 13242 197893 126244 0 739611 892437 +linux_tuned 2 300 0x1100 55 97.1 119.4 271.1 396.4 436.4 529.7 49936.2 50000 20 1200.25 1331773 147925909 3028469 90957468 82142533653 129695195968 226871803154 206411045 14082 208304 132425 0 748154 893551 +linux_tuned 0 300 0x1100 135 135.6 167.1 304.9 434.0 488.7 602.0 99820.0 100000 20 1326.5 2202628 265397060 6119358 184029744 155770190387 219440643895 375638209619 396014479 14111 220388 132420 0 695238 967977 +linux_tuned 1 300 0x1100 135 137.2 168.9 305.9 434.1 488.5 592.7 99973.7 100000 20 1327.59 2203392 265631019 6127726 184282998 156076554255 219788231847 376180194447 396185557 14040 219174 131081 0 691048 965535 +linux_tuned 2 300 0x1100 135 142.2 172.9 308.7 436.5 492.9 605.6 100014.7 100000 20 1328.58 2203902 265671391 6131014 184378098 156029813102 220428793668 377318730416 398106815 14028 220610 133257 0 693352 968898 +linux_tuned 0 300 0x1100 95 141.9 173.5 310.2 439.4 496.8 616.4 99959.6 100000 20 1328.85 2204336 265701281 6127305 184269150 156137577226 220587312235 377536179463 397614597 14568 223461 133416 0 695411 968598 +linux_tuned 1 300 0x1100 95 135.2 167.5 306.1 434.6 490.0 610.1 100003.9 100000 20 1328.66 2205185 265782347 6130441 184365108 156277708031 220749540963 377845902566 396620131 14085 220154 132541 0 692085 967634 +linux_tuned 2 300 0x1100 95 136.3 169.1 307.5 438.0 496.0 612.3 99967.5 100000 20 1327.79 2204203 265707425 6129280 184331580 156143430843 220615794154 377550345180 396975069 14181 219217 129726 0 690652 966432 +linux_tuned 0 300 0x1100 75 138.5 170.0 308.1 435.8 490.8 596.3 100109.4 100000 20 1329.26 2205615 265915033 6137186 184567770 156297376918 220943202008 378134658582 399217154 13911 219699 133061 0 693678 968940 +linux_tuned 1 300 0x1100 75 134.7 165.9 303.4 433.0 489.3 599.3 100083.2 100000 20 1330.51 2204352 265834477 6134297 184475700 156492456724 221478719929 379104889339 399965734 15255 229201 135399 0 696439 968059 +linux_tuned 2 300 0x1100 75 142.7 173.7 309.8 438.6 495.8 607.2 99893.4 100000 20 1330.22 2202879 265475837 6122816 184133214 156130497867 220324711055 377052113116 398143031 14020 220074 131906 0 693138 967962 +linux_tuned 0 300 0x1100 55 140.8 173.3 310.7 441.7 503.1 623.1 100010.6 100000 20 1329.27 2206394 265855184 6133114 184452642 156366165035 220538519779 377448680518 398894257 13851 218327 130351 0 690958 967511 +linux_tuned 1 300 0x1100 55 133.3 165.6 305.0 432.3 486.7 601.2 99999.1 100000 20 1328.16 2206056 265832764 6131296 184395876 156335319194 219827114890 376208084066 397182438 13773 216636 128545 0 687463 964808 +linux_tuned 2 300 0x1100 55 141.0 172.4 308.2 436.0 491.6 605.3 100104.6 100000 20 1330.17 2206465 266009742 6136365 184541760 156549831296 221630416624 379307791116 401231406 14310 223691 134410 0 693115 968706 +linux_tuned 0 50 0x1300 135 66.7 69.6 94.2 176.8 217.8 273.6 49906.3 50000 20 1327.86 1544353 161902510 3000860 90045390 86109988988 136747871256 209675347716 205380739 586187 556255 175645 0 767180 2158175 +linux_tuned 1 50 0x1300 135 67.0 69.9 94.0 175.3 217.5 272.8 49980.9 50000 20 1328.28 1538975 161636024 3005305 90178734 86247982554 137022189897 210063748399 206062628 586908 566876 176798 0 770909 2159790 +linux_tuned 2 50 0x1300 135 67.3 70.2 95.0 176.5 220.1 275.3 50080.7 50000 20 1328.28 1546783 162292759 3011332 90359598 86238044657 136969764004 209997173824 206063530 586524 549838 172762 0 762026 2157002 +linux_tuned 0 50 0x1300 95 66.8 69.6 94.2 175.6 217.5 270.8 50006.1 50000 20 1328.67 1544831 162066069 3006728 90221052 86505162042 137594362697 210926735010 207722820 592147 572534 180442 0 775188 2170259 +linux_tuned 1 50 0x1300 95 67.0 69.9 94.3 175.0 219.0 272.5 49979.0 50000 20 1327.74 1540787 161753570 3005463 90184278 86361184590 137088735631 210152429790 206959241 590038 558001 175563 0 768875 2159262 +linux_tuned 2 50 0x1300 95 67.0 70.0 95.2 179.3 220.1 274.6 49978.7 50000 20 1328.45 1538945 161634831 3005037 90170382 86227854083 136870902334 209848903175 206747901 585407 563747 173990 0 763509 2152217 +linux_tuned 0 50 0x1300 75 66.8 69.7 94.3 174.6 216.8 270.0 49970.0 50000 20 1328.33 1542851 161877372 3004535 90155316 86365258554 137234456874 210421918641 207638369 585156 572356 179135 0 782295 2167254 +linux_tuned 1 50 0x1300 75 66.7 69.5 93.6 175.2 217.9 272.3 49941.3 50000 20 1328.1 1545202 162015186 3002847 90104532 86397734048 137246830742 210406856853 207709479 588405 560553 174751 0 770138 2160743 +linux_tuned 2 50 0x1300 75 66.5 69.4 94.0 176.8 219.0 275.2 50060.8 50000 20 1328.78 1542475 161969080 3010135 90323682 86600448652 137728775010 211171424279 208780346 588427 582544 183175 0 780180 2171820 +linux_tuned 0 50 0x1300 55 67.2 70.1 95.0 179.7 220.5 273.9 50054.2 50000 20 1328.96 1542986 161999319 3009763 90312534 86389320836 137294414663 210440127654 208019465 587892 557820 175740 0 767403 2160329 +linux_tuned 1 50 0x1300 55 67.3 70.2 95.0 176.6 218.9 273.6 50045.3 50000 20 1328.39 1546751 162255822 3009196 90295554 86551041723 137547399547 210829272535 208216735 592047 557926 176592 0 771787 2167424 +linux_tuned 2 50 0x1300 55 67.3 70.3 95.3 178.7 220.7 276.5 50002.7 50000 20 1328.47 1542596 161900943 3006518 90214806 86539394553 137658012175 211009943376 208415950 588746 556607 180618 0 771740 2164263 +linux_tuned 0 50 0x1300 135 69.1 73.3 106.9 224.0 264.8 366.1 99892.8 100000 20 1473.96 2238807 267861385 6025055 180847188 162213583760 249830924851 381343762100 407202141 1103997 541648 144905 0 699883 3312787 +linux_tuned 1 50 0x1300 135 69.0 73.3 106.7 221.2 260.1 357.2 100068.3 100000 20 1474.61 2240952 268245090 6034807 181137390 162696958715 251184422826 383413022561 410738159 1120221 554869 148175 0 694217 3333280 +linux_tuned 2 50 0x1300 135 68.8 73.0 106.4 225.7 266.7 371.3 99915.7 100000 20 1472.77 2238803 267893049 6027464 180922620 162214227746 249867604986 381398030815 410062785 1089031 541430 143643 0 696442 3304256 +linux_tuned 0 50 0x1300 95 70.1 74.3 108.4 228.5 269.6 373.9 100073.3 100000 20 1474.4 2240906 268229069 6036140 181180842 162379227562 250508201315 382388351061 408572418 1107964 548649 142716 0 692674 3316617 +linux_tuned 1 50 0x1300 95 69.4 73.7 106.9 222.7 263.9 362.8 99989.4 100000 20 1473.93 2237852 267935153 6030292 181003038 162363717806 250978466784 383097010518 410075531 1118656 557192 146503 0 691767 3328048 +linux_tuned 2 50 0x1300 95 69.0 73.2 107.0 223.7 264.7 365.7 100068.5 100000 20 1475.18 2244477 268479130 6035206 181150752 162640541156 251111964199 383301646350 410658471 1112543 549345 144834 0 695312 3326636 +linux_tuned 0 50 0x1300 75 69.6 73.9 108.1 225.4 268.3 367.6 100035.6 100000 20 1475.86 2238077 267976859 6033356 181095834 162794424440 251285951530 383571322396 411727819 1115736 556549 147270 0 699188 3330298 +linux_tuned 1 50 0x1300 75 69.5 73.7 107.0 222.6 261.7 362.1 99982.5 100000 20 1475.99 2242954 268258563 6030345 181006332 162532344422 251042972286 383189431865 410630629 1107830 546945 144992 0 692028 3322341 +linux_tuned 2 50 0x1300 75 69.1 73.3 107.2 223.7 264.8 363.0 99939.3 100000 20 1476.77 2241802 268113204 6027243 180911004 162557998152 251254580515 383516337526 410756868 1111954 558529 146359 0 694772 3329364 +linux_tuned 0 50 0x1300 55 69.4 73.7 108.2 228.8 271.3 391.0 99923.3 100000 20 1477.12 2245365 268340491 6027516 180923022 162471559050 250828278540 382864438087 411626236 1095645 546062 144024 0 701038 3316118 +linux_tuned 1 50 0x1300 55 69.3 73.5 107.3 222.2 263.6 364.1 100066.1 100000 20 1478.46 2244453 268437997 6034544 181129206 162873389300 251885012351 384480571663 414511134 1117528 549286 148480 0 693552 3335401 +linux_tuned 2 50 0x1300 55 69.4 73.8 108.5 230.4 272.6 385.2 99996.7 100000 20 1477.27 2242332 268233692 6031937 181056030 162699567784 251211761153 383458623959 413298230 1102319 551589 143443 0 699543 3319934 +linux_tuned 0 100 0x1300 135 70.4 75.5 118.8 212.2 252.7 327.9 49976.6 50000 20 1309.1 1486393 158186728 3008846 90296838 84724750657 140323899487 222495373076 207083825 81969 703852 186183 0 790425 1648484 +linux_tuned 1 100 0x1300 135 71.0 76.0 120.1 211.3 251.1 326.7 49981.4 50000 20 1308.93 1486793 158207916 3009208 90307896 84756909816 140325434601 222593950050 206956106 75267 697437 182420 0 776156 1636932 +linux_tuned 2 100 0x1300 135 70.4 75.7 121.3 213.5 256.1 330.7 50019.5 50000 20 1309.67 1487902 158322361 3011374 90372504 84821104032 140208220873 222180445401 207926411 84410 708323 186479 0 790533 1649397 +linux_tuned 0 100 0x1300 95 70.2 75.2 117.9 209.4 250.5 324.4 50070.6 50000 20 1310.31 1487464 158366332 3014394 90462714 85095407523 140834747961 223298794876 208446326 77494 700442 184590 0 790541 1648977 +linux_tuned 1 100 0x1300 95 70.0 75.1 118.8 210.6 252.4 324.1 50143.5 50000 20 1309.65 1486351 158354984 3018816 90595800 85083323498 141013456792 223691877174 214341261 72609 694931 181595 0 782098 1641508 +linux_tuned 2 100 0x1300 95 70.1 75.2 117.7 210.2 252.5 327.1 49912.8 50000 20 1309.48 1486649 158088383 3005024 90182148 84733977228 140363740543 222792732319 207534856 70700 688199 181383 0 777216 1633158 +linux_tuned 0 100 0x1300 75 70.5 75.5 119.0 210.1 251.5 326.1 50162.6 50000 20 1311.33 1487830 158482737 3020190 90637536 85075645538 140967204410 223579049687 209712001 71605 688770 180283 0 781429 1637952 +linux_tuned 1 100 0x1300 75 70.4 75.4 118.1 212.4 252.5 325.3 49950.2 50000 20 1310.99 1485091 158064236 3007316 90250752 84766857049 140443837315 222796132105 208960514 73532 688988 179285 0 778971 1632974 +linux_tuned 2 100 0x1300 75 69.9 75.0 117.8 210.1 249.6 325.3 50036.4 50000 20 1311.17 1491132 158546941 3012503 90406824 85116538284 141062430147 223723769547 209307392 72293 694980 186221 0 788088 1646259 +linux_tuned 0 100 0x1300 55 71.3 76.2 118.6 212.6 254.2 327.9 49982.3 50000 20 1310.12 1484004 158025102 3009177 90306876 85025325963 140620860190 222993665663 208897122 72142 691057 180034 0 775009 1632491 +linux_tuned 1 100 0x1300 55 71.4 76.1 117.9 211.5 251.5 327.7 49988.2 50000 20 1308.65 1487484 158263405 3009294 90309300 84898579201 140906104153 223511934465 220991661 70497 686493 177995 0 770773 1627935 +linux_tuned 2 100 0x1300 55 70.2 75.2 119.6 213.3 253.7 330.1 49962.6 50000 20 1309.12 1487314 158208308 3007946 90269538 84935788691 140670096642 223145431624 208588494 70923 688556 179764 0 766406 1626208 +linux_tuned 0 100 0x1300 135 74.7 82.2 146.6 254.8 300.5 399.3 100094.8 100000 20 1468.42 2221231 266961782 6055209 181811274 159719759404 245094416549 375813655153 405444887 142453 1177155 136506 0 643158 2358643 +linux_tuned 1 100 0x1300 135 74.8 82.5 146.7 257.4 303.0 398.2 100046.3 100000 20 1469.27 2223029 266992488 6051776 181706382 160144117168 246186898142 377568019781 407924890 146711 1188784 139245 0 645761 2373403 +linux_tuned 2 100 0x1300 135 75.9 83.5 148.1 258.4 303.5 399.2 99929.4 100000 20 1469.53 2215285 266345172 6044888 181501320 159827363559 245530842167 376468731397 405670578 145120 1180971 138773 0 647292 2365962 +linux_tuned 0 100 0x1300 95 74.6 81.7 144.6 251.9 298.7 393.0 99930.4 100000 20 1470.12 2218911 266616193 6044788 181496154 159754701368 245732386361 376857238365 406537136 140217 1183326 138651 0 646858 2369308 +linux_tuned 1 100 0x1300 95 75.2 82.4 146.8 259.6 305.3 404.7 99904.5 100000 20 1467.91 2221226 266725838 6044182 181481562 159764489708 244674156749 375094908249 404640090 142504 1145754 133068 0 648325 2337871 +linux_tuned 2 100 0x1300 95 74.1 81.0 145.5 254.8 303.5 401.0 100105.8 100000 20 1469.28 2224567 267213439 6056743 181860456 159973210347 245734440284 376749339892 405319435 141574 1151154 135765 0 638248 2342231 +linux_tuned 0 100 0x1300 75 74.9 82.2 146.0 253.6 299.5 395.0 100061.8 100000 20 1470.62 2219921 266872375 6052749 181736784 160230008009 246679026847 378135904596 408259121 150902 1171201 139298 0 647539 2363725 +linux_tuned 1 100 0x1300 75 74.3 80.3 141.3 246.0 294.0 386.2 99954.9 100000 20 1469.18 2221757 266849462 6046811 181559748 160035469355 246114979496 377315138476 408442017 148897 1166680 137073 0 644736 2355842 +linux_tuned 0 100 0x1300 55 73.2 80.1 142.7 244.1 288.8 382.7 99945.7 100000 20 1452.15 2224414 266980866 6044900 181498380 156980404179 236624141664 363741288740 367799614 135885 1188118 137585 0 667368 2345569 +linux_tuned 1 100 0x1300 55 74.0 81.1 144.1 248.4 294.3 391.5 100085.6 100000 20 1457.77 2223943 267110949 6053681 181763880 157898073439 239008973194 366740093074 376123981 143964 1182948 134886 0 665128 2346409 +linux_tuned 2 100 0x1300 55 72.9 79.5 140.9 241.5 285.9 370.8 100057.8 100000 20 1459.49 2223653 267080038 6051789 181705644 158172737949 240496613188 369076143356 382106705 136474 1199975 138115 0 652810 2360758 +linux_tuned 0 200 0x1300 135 77.4 88.0 191.0 293.5 328.3 406.4 50000.4 50000 20 1297.6 1397029 152288135 3021031 90696486 82988158881 136289375240 217214784449 207177371 19112 366639 167072 0 791716 1177943 +linux_tuned 1 200 0x1300 135 77.9 89.6 192.1 294.7 330.9 412.0 50006.3 50000 20 1297.08 1395181 152174323 3021385 90706860 82913528949 136399408214 217391984773 207602237 18924 367025 167181 0 794034 1182373 +linux_tuned 2 200 0x1300 135 79.0 91.1 193.6 296.9 332.3 419.6 50011.2 50000 20 1297.32 1397144 152356524 3021583 90713208 82904501106 136456550963 217504788161 207640250 19420 369780 169470 0 796362 1181427 +linux_tuned 0 200 0x1300 95 79.3 91.6 193.0 295.2 332.7 418.9 49921.0 50000 20 1288.49 1395097 152055112 3016264 90553662 82975872852 137205400091 218566944510 214957615 28140 425585 190140 0 805924 1184764 +linux_tuned 1 200 0x1300 95 77.5 87.8 190.0 293.8 327.9 409.8 49940.3 50000 20 1297.02 1395873 152160203 3017593 90594288 82861399397 136393095870 217359579506 207758590 18295 364440 166611 0 792587 1179417 +linux_tuned 2 200 0x1300 95 77.8 88.3 188.7 293.1 328.2 411.6 49988.0 50000 20 1297.16 1396736 152260659 3020164 90669552 82752315112 136396040486 217389646648 207570758 19250 369750 168468 0 795795 1180346 +linux_tuned 0 200 0x1300 75 78.3 90.4 194.9 296.4 333.4 421.4 50000.7 50000 20 1297.32 1399141 152461016 3021284 90704610 82773535711 136286955361 217230746832 208149570 19571 369969 167181 0 792484 1178972 +linux_tuned 1 200 0x1300 75 78.9 91.5 194.9 295.7 332.6 416.4 49979.2 50000 20 1298.35 1395513 152170831 3019720 90656826 82856323069 136651497853 217796672958 208238712 19162 367827 168874 0 796758 1183183 +linux_tuned 2 200 0x1300 75 78.5 91.0 195.1 296.3 333.2 416.8 49955.4 50000 20 1297.19 1399976 152435170 3018371 90616824 82909390494 136425005207 217349403899 208187363 19264 364950 165954 0 787170 1174839 +linux_tuned 0 200 0x1300 55 78.7 90.5 195.0 296.3 332.8 418.7 50061.6 50000 20 1298.8 1398574 152467123 3024566 90802494 82920837838 136630859381 217726816569 208175432 18820 363297 165244 0 792871 1181175 +linux_tuned 1 200 0x1300 55 79.7 91.9 195.7 295.6 330.1 415.5 49927.6 50000 20 1297.67 1396026 152124583 3016194 90549642 82943950409 136595886456 217681642515 208811315 20566 375115 172596 0 800420 1183913 +linux_tuned 2 200 0x1300 55 78.1 89.7 192.3 296.5 333.7 420.8 50009.9 50000 20 1297.6 1398466 152401632 3021473 90708750 83068054011 136793623075 217959342107 208519627 19236 366992 168258 0 795067 1181376 +linux_tuned 0 200 0x1300 135 94.8 116.5 220.0 321.4 368.6 459.1 99960.3 100000 20 1441.35 2200332 265397885 6087946 182934294 155306955560 228404995475 351486819118 377070661 23361 480891 164305 0 780709 1408485 +linux_tuned 1 200 0x1300 135 98.2 119.6 221.3 324.0 371.8 464.1 100141.8 100000 20 1442.44 2205076 265950664 6098565 183254652 155377218887 228784358136 352034337639 380035524 23133 477672 164996 0 777385 1407154 +linux_tuned 2 200 0x1300 135 96.8 118.1 220.0 323.1 370.0 461.4 99846.6 100000 20 1442.68 2199644 265243922 6080645 182716122 155285822003 229170730907 352551892867 382609994 24320 484312 165817 0 781140 1409230 +linux_tuned 0 200 0x1300 95 97.7 120.4 222.5 326.5 374.4 470.2 99851.1 100000 20 1441.77 2198821 265185787 6081381 182739858 155138199073 228565224551 351660112765 381690406 22363 475716 161131 0 773736 1402892 +linux_tuned 1 200 0x1300 95 98.6 120.1 222.5 325.5 372.8 467.4 100035.4 100000 20 1442.89 2203622 265751268 6092232 183066390 155331868825 228603300859 351643439766 382646950 23572 482860 164836 0 780111 1406465 +linux_tuned 2 200 0x1300 95 98.3 120.1 223.3 328.6 378.2 471.9 100081.5 100000 20 1443.76 2203149 265721956 6095859 183176532 155821054296 229439902332 352896584779 384714783 23370 480960 163733 0 775789 1405302 +linux_tuned 0 200 0x1300 75 100.8 123.6 225.1 329.9 379.4 474.0 99974.9 100000 20 1443.46 2201876 265528003 6088336 182947050 155416468799 229172757155 352586083287 384739573 23042 480544 163111 0 775289 1405891 +linux_tuned 1 200 0x1300 75 96.3 117.5 219.9 324.5 372.4 464.4 100087.2 100000 20 1445.65 2205363 265893400 6096563 183199962 155853523939 230352806620 354326153844 386945389 25482 492190 167983 0 779161 1408628 +linux_tuned 2 200 0x1300 75 96.4 118.4 221.0 324.4 374.0 466.5 100037.2 100000 20 1446.57 2204800 265774724 6092901 183086190 155962163494 230505835122 354525732192 392039935 24372 490270 166607 0 779146 1410294 +linux_tuned 0 200 0x1300 55 93.5 115.8 218.2 317.7 366.0 458.5 99966.1 100000 20 1445.48 2202271 265571180 6088466 182952324 155800795719 229896633113 353585930149 389202543 23671 480540 163982 0 773167 1405241 +linux_tuned 1 200 0x1300 55 100.0 122.8 224.9 328.8 378.2 471.1 100017.5 100000 20 1447.29 2201589 265591769 6091462 183042552 155952633808 230479835114 354431976294 389652635 23319 481302 164749 0 774926 1406987 +linux_tuned 2 200 0x1300 55 95.7 116.7 220.7 324.9 372.3 468.4 100220.0 100000 20 1447.42 2208187 266266188 6104257 183430116 156254069302 230613524734 354574106367 391030668 23926 483607 163384 0 772550 1403167 +linux_tuned 0 300 0x1300 135 93.0 114.4 264.8 389.8 422.6 513.3 49987.1 50000 20 1288.18 1334862 148167007 3031089 91034706 82368594762 132489858741 210477342977 209146956 13160 198006 125884 0 749580 889626 +linux_tuned 1 300 0x1300 135 94.4 116.9 265.2 391.0 426.2 514.4 50000.5 50000 20 1290.22 1339250 148479871 3032171 91068240 82747522107 133562811955 212264982727 211273256 14508 216005 136369 0 771957 896490 +linux_tuned 2 300 0x1300 135 89.3 111.3 262.5 390.0 423.2 510.7 49955.7 50000 20 1288.19 1334972 148143602 3029247 90978828 82326868723 132312400385 210141890987 209219426 13004 195847 122223 0 743649 886999 +linux_tuned 0 300 0x1300 95 90.8 113.3 262.5 389.0 422.2 508.6 50061.6 50000 20 1289.19 1336056 148340139 3035662 91172220 82559285387 132996601505 211254030779 209833056 13594 203068 128585 0 758688 895028 +linux_tuned 1 300 0x1300 95 91.3 112.8 262.8 388.5 419.6 506.3 49988.2 50000 20 1288.85 1336877 148314755 3031636 91051698 82391526554 132690050087 210741792991 209213879 13281 196531 124667 0 752013 893978 +linux_tuned 2 300 0x1300 95 92.0 113.4 262.2 389.3 423.7 515.0 50011.4 50000 20 1288.84 1337748 148398066 3032833 91087350 82559127580 133203803546 211639308887 210563219 14389 211308 133504 0 763602 893387 +linux_tuned 0 300 0x1300 75 92.5 114.3 262.4 388.8 422.0 510.9 49910.1 50000 20 1287.97 1334738 148070593 3026680 90903726 82432466874 133050310771 211391672261 210540788 13889 205503 131392 0 759993 894932 +linux_tuned 1 300 0x1300 75 91.0 112.3 261.8 387.5 419.4 510.8 49967.3 50000 20 1288.32 1338363 148403918 3029940 90999912 82440599016 133113676439 211522480096 210678398 13985 204977 130496 0 762245 896195 +linux_tuned 2 300 0x1300 75 89.9 110.4 261.7 388.0 419.0 510.7 50143.3 50000 20 1289.58 1337791 148577501 3040479 91316532 82596041964 133024607218 211333855541 210321005 13309 200163 127150 0 753303 889719 +linux_tuned 0 300 0x1300 55 92.1 113.4 262.7 388.7 419.6 502.0 49951.2 50000 20 1289.14 1337244 148315820 3029051 90974052 82373149087 132805559705 210978622651 210085536 13075 198193 125760 0 751553 893235 +linux_tuned 1 300 0x1300 55 91.5 112.7 263.2 389.4 422.8 510.0 50005.2 50000 20 1289.21 1336533 148299706 3032229 91068804 82517316976 133085033797 211453568788 211299418 13745 206988 130917 0 756692 891856 +linux_tuned 2 300 0x1300 55 92.6 114.2 262.7 388.6 420.9 509.6 49924.7 50000 20 1289.31 1335726 148182981 3027055 90912762 82433184794 133264588983 211758660649 211663560 14205 208865 132420 0 760540 893842 +linux_tuned 0 300 0x1300 135 129.7 160.0 294.5 406.0 456.4 548.8 99816.6 100000 20 1435.23 2200963 265264595 6117238 183960558 155548751737 223237884269 343131845980 392529085 13988 217452 131170 0 727731 968187 +linux_tuned 1 300 0x1300 135 135.5 165.6 300.5 417.6 468.2 566.9 99988.4 100000 20 1435.27 2204210 265705723 6128230 184294800 156192107125 223876318597 344124338036 394314565 14147 219582 131932 0 724353 966768 +linux_tuned 2 300 0x1300 135 131.0 160.4 293.6 404.5 454.2 543.3 99969.7 100000 20 1435.99 2203215 265590917 6125196 184194684 156115193430 224305219847 344748095440 396185808 14624 222142 133664 0 729074 968682 +linux_tuned 0 300 0x1300 95 131.0 161.1 295.1 411.1 461.7 554.6 99999.4 100000 20 1436.56 2204577 265741828 6128418 184293792 156239167430 224399822277 344880838445 397865800 14298 219382 131775 0 726458 967698 +linux_tuned 1 300 0x1300 95 129.1 160.3 295.7 410.1 461.1 558.6 100061.4 100000 20 1436.83 2206606 265965152 6132246 184412946 156548544105 224462159177 344933017739 397433960 14102 220876 130806 0 723193 965287 +linux_tuned 2 300 0x1300 95 134.0 163.3 296.6 414.4 464.3 567.1 99847.3 100000 20 1435.8 2199715 265270993 6118528 183995844 156209916145 224238801442 344585514364 397308867 14217 223544 133608 0 727758 967707 +linux_tuned 0 300 0x1300 75 133.1 164.7 299.1 415.7 466.6 565.2 100103.8 100000 20 1436.08 2204276 265837927 6135156 184501224 156617667364 225105580810 345988075120 398260025 14706 222567 133162 0 726381 967901 +linux_tuned 1 300 0x1300 75 131.8 162.0 298.2 417.3 470.3 578.7 99925.7 100000 20 1434.43 2202429 265509279 6123952 184162938 156291453158 224291270324 344757259411 397775721 14540 220856 130862 0 725293 966287 +linux_tuned 2 300 0x1300 75 126.1 156.5 292.2 407.3 460.6 559.3 100013.6 100000 20 1435.26 2202052 265568023 6128265 184287408 156499244685 225453936546 346573999414 399009465 15151 228949 135548 0 734182 968568 +linux_tuned 0 300 0x1300 55 132.1 162.1 296.2 411.9 464.4 564.4 100104.9 100000 20 1434.04 2204268 265822406 6134649 184483134 156339574604 224962459330 345701108404 397364775 14364 220374 132182 0 722559 967638 +linux_tuned 1 300 0x1300 55 130.4 161.3 296.5 413.0 463.8 563.3 99900.9 100000 20 1433.83 2200362 265341265 6121675 184091976 156228579908 224470018467 344979274363 402173774 13825 217580 129935 0 723066 966601 +linux_tuned 2 300 0x1300 55 130.0 160.5 296.1 409.8 462.3 558.4 99990.1 100000 20 1433.75 2202950 265629926 6127627 184274856 156606971622 225057091389 345920959840 398806497 14344 220919 132374 0 723443 966904 +linux_tuned 0 50 0x1500 135 62.8 65.9 88.6 163.6 202.6 252.6 50042.1 50000 20 1450.26 1555537 162804196 3008793 90282684 87048500990 139553014714 194213793788 212725708 574408 606256 175790 0 784463 2154243 +linux_tuned 1 50 0x1500 135 63.1 66.4 89.0 162.7 202.9 254.2 49812.9 50000 20 1447.98 1551824 162289008 2994805 89862426 86690361696 138722652791 193033720684 210982098 573389 607088 175870 0 786622 2150689 +linux_tuned 2 50 0x1500 135 62.8 66.0 88.0 163.8 202.9 250.6 50013.1 50000 20 1447.46 1558600 162988382 3007090 90231786 87045022114 139256077103 193827996319 211912181 573784 612697 174638 0 780643 2152787 +linux_tuned 0 50 0x1500 95 63.6 67.0 89.7 165.8 204.8 253.4 50001.0 50000 20 1446.98 1556772 162853539 3006261 90206586 87026543781 139127495137 193608092683 212958788 575862 611937 175675 0 784103 2154082 +linux_tuned 1 50 0x1500 95 63.1 66.5 89.1 165.0 204.9 254.3 49981.5 50000 20 1446.55 1558111 162926190 3005089 90171462 87044038788 139452210861 194114000844 212652860 577678 620320 177734 0 789792 2159292 +linux_tuned 2 50 0x1500 95 63.0 66.3 89.0 164.2 204.7 255.9 50011.8 50000 20 1446.89 1553587 162670349 3006805 90222594 87039277035 139400328516 193945282875 212237211 576069 611504 176497 0 791104 2157246 +linux_tuned 0 50 0x1500 75 63.0 66.3 89.5 163.3 204.9 255.2 50026.6 50000 20 1447.1 1556776 162871265 3007884 90255582 87280349669 139964528634 194748647516 213711765 576651 624366 177288 0 788505 2159965 +linux_tuned 1 50 0x1500 75 62.7 65.9 88.1 160.8 202.2 251.0 49946.3 50000 20 1445.76 1559924 162994247 3003064 90111066 86938613315 139344802141 193865893838 212218591 575312 605915 177047 0 783536 2156478 +linux_tuned 2 50 0x1500 75 62.8 66.0 88.9 163.6 203.1 251.2 49980.1 50000 20 1447.43 1556671 162797196 3004883 90165102 87269459756 139903935988 194700726524 213578771 580026 638004 183506 0 798257 2168580 +linux_tuned 0 50 0x1500 55 64.4 67.8 90.3 164.7 204.4 255.4 49965.9 50000 20 1445.64 1552534 162524182 3004188 90144552 86952797209 139175098605 193708955350 212698830 575295 613055 174707 0 781759 2148492 +linux_tuned 1 50 0x1500 55 63.1 66.5 89.3 165.5 204.0 254.9 50114.8 50000 20 1447.31 1563803 163449719 3013151 90413454 87576745838 140311797522 195213624062 214877933 581014 629596 181085 0 788176 2170178 +linux_tuned 2 50 0x1500 55 63.0 66.4 89.2 164.0 204.2 258.2 49985.9 50000 20 1446.73 1562017 163189069 3005288 90177174 87138876611 139745231406 194462168928 212770146 575040 612589 177985 0 788979 2161931 +linux_tuned 0 50 0x1500 135 64.7 68.5 98.9 196.9 230.2 303.4 100030.0 100000 20 1604.41 2253186 269007200 6027860 180915258 162776885100 249158760125 344123290701 402188586 1188092 654322 149592 0 732283 3304738 +linux_tuned 1 50 0x1500 135 65.1 68.8 99.3 196.7 231.0 307.6 100065.1 100000 20 1603.57 2257457 269297724 6030148 180984660 162757951756 248799224730 343622655494 403667181 1181274 655332 148168 0 734476 3300227 +linux_tuned 2 50 0x1500 135 64.3 68.2 99.4 198.1 231.6 315.9 100064.2 100000 20 1603.09 2256597 269277429 6030269 180988674 162947512176 249034433956 343945117746 404040950 1184868 646417 146743 0 730954 3300124 +linux_tuned 0 50 0x1500 95 64.1 67.9 97.7 194.9 229.2 306.6 100068.8 100000 20 1605.44 2257685 269328325 6030573 180997638 163111980222 248937807002 343813596946 404820524 1178571 651343 146580 0 735401 3295772 +linux_tuned 1 50 0x1500 95 64.5 68.3 98.0 193.5 227.6 299.3 99944.3 100000 20 1604.65 2254780 268982301 6022111 180741096 162986333881 248981413136 343899352005 412984661 1191453 654342 148177 0 734377 3304796 +linux_tuned 2 50 0x1500 95 64.5 68.4 98.5 196.1 229.8 308.6 100107.6 100000 20 1603.91 2256800 269315571 6033203 181077582 163489146017 249315827062 344346411191 408541227 1172211 648765 143005 0 730344 3289744 +linux_tuned 0 50 0x1500 75 64.3 68.1 98.1 196.2 229.6 311.0 99987.1 100000 20 1604.31 2257609 269285223 6025663 180850698 163169837529 249437005942 344502431510 408638017 1176709 656186 147126 0 725127 3294688 +linux_tuned 1 50 0x1500 75 64.3 68.2 99.3 200.3 235.4 316.0 99892.6 100000 20 1604.77 2254204 268850782 6019777 180673518 163137695379 249690354829 344863587824 407677520 1170974 637072 144050 0 733881 3291221 +linux_tuned 2 50 0x1500 75 65.4 69.0 99.5 197.8 232.6 310.2 100117.6 100000 20 1605.42 2256908 269344335 6033410 181083108 163623774877 250252137672 345629376614 410670023 1171334 647690 145163 0 727983 3294634 +linux_tuned 0 50 0x1500 55 64.7 68.6 99.1 197.0 232.5 313.8 99917.8 100000 20 1604.73 2253131 268842107 6021646 180730842 162991650371 249909485643 345159375267 407753420 1169949 653691 147726 0 731991 3296110 +linux_tuned 1 50 0x1500 55 64.2 68.0 98.6 197.3 232.8 313.3 100032.2 100000 20 1606.0 2254498 269061503 6028465 180935478 163165747884 249953485138 345221218144 408430890 1170405 642176 148394 0 730933 3295493 +linux_tuned 2 50 0x1500 55 64.8 68.7 100.0 200.8 237.1 320.7 100026.5 100000 20 1604.17 2252939 268966698 6028292 180930306 163092753947 249682317784 344843347970 408379920 1168122 646169 143589 0 729713 3286738 +linux_tuned 0 100 0x1500 135 68.0 73.3 116.6 203.3 240.4 310.9 50014.3 50000 20 1416.63 1498573 159016768 3010713 90351378 85295270778 143924961852 210629734091 217214529 65340 712840 183183 0 793475 1637637 +linux_tuned 1 100 0x1500 135 67.3 72.3 114.3 200.3 234.7 302.7 50025.9 50000 20 1414.39 1498535 159041811 3011644 90380160 85195784367 143397680174 210211122890 210561873 57654 700251 178286 0 784838 1625560 +linux_tuned 2 100 0x1500 135 67.0 72.1 114.3 202.0 237.8 303.9 49902.5 50000 20 1416.24 1494448 158620853 3004256 90158676 85085085079 143867899193 210586620400 215955052 68155 713253 184210 0 800515 1641241 +linux_tuned 0 100 0x1500 95 67.9 73.3 116.5 202.8 238.5 304.3 49972.7 50000 20 1416.52 1495811 158823502 3008627 90290166 85121313610 143297862444 209504212655 212082992 75392 724724 184787 0 798525 1643851 +linux_tuned 1 100 0x1500 95 67.5 72.8 115.6 202.7 239.6 305.6 49960.0 50000 20 1416.14 1493157 158606589 3007566 90257562 85237518151 143704103746 210347542196 212418344 64745 708024 183068 0 796527 1636613 +linux_tuned 2 100 0x1500 95 67.3 72.6 116.0 204.5 241.6 311.3 50002.5 50000 20 1417.65 1494207 158720960 3009915 90327510 85101597027 143566744795 210211406481 210880647 61956 702353 179454 0 785102 1625818 +linux_tuned 0 100 0x1500 75 67.2 72.3 114.3 200.6 234.2 303.7 50007.9 50000 20 1417.18 1495028 158778113 3010425 90343170 85213598784 143975368728 210751603430 212530115 61737 706185 180714 0 787451 1630304 +linux_tuned 1 100 0x1500 75 68.0 73.3 116.3 202.3 238.4 305.2 50021.7 50000 20 1419.02 1496581 158907708 3011335 90370710 85124807516 143758154370 210422046154 212361034 65709 709915 181951 0 792027 1635448 +linux_tuned 2 100 0x1500 75 67.1 72.1 112.4 199.8 234.8 303.0 50079.6 50000 20 1417.39 1497918 159073995 3014869 90477054 85439524893 143898297390 210815815462 212492596 61432 705351 180477 0 791607 1632932 +linux_tuned 0 100 0x1500 55 67.4 72.5 115.0 202.5 238.8 307.7 50047.7 50000 20 1419.86 1496065 158904020 3012897 90417552 85299745683 144056393571 210980091965 211650168 58623 700324 177880 0 781516 1624064 +linux_tuned 1 100 0x1500 55 67.5 72.7 116.9 203.4 240.4 313.9 50011.3 50000 20 1421.18 1498552 159046251 3010817 90355662 85229493632 143804822531 210369107843 213051271 66084 706568 179638 0 786237 1627406 +linux_tuned 2 100 0x1500 55 67.9 73.2 115.4 202.9 239.5 307.3 49876.2 50000 20 1419.29 1496590 158745675 3002682 90111468 85034661891 143599630797 210068821630 212027856 67829 711247 184883 0 798230 1638073 +linux_tuned 0 100 0x1500 135 71.5 78.4 139.7 233.2 276.4 361.0 99931.4 100000 20 1591.53 2223564 266910542 6041995 181404936 159978710987 244786711418 341974218934 402684572 115203 1265720 139136 0 679544 2351366 +linux_tuned 1 100 0x1500 135 69.6 76.9 137.3 231.1 273.9 356.0 100082.0 100000 20 1591.03 2225097 267207971 6050574 181660104 160254753503 244082057214 340821747508 403463933 122813 1272501 140752 0 683005 2352770 +linux_tuned 2 100 0x1500 135 69.4 76.5 136.4 227.4 271.9 352.8 99960.5 100000 20 1591.15 2224205 266971802 6042975 181431540 160207270634 245002528609 342104844011 404050418 124761 1283416 143287 0 680341 2362946 +linux_tuned 0 100 0x1500 95 69.8 76.6 136.8 229.8 273.9 352.9 100095.8 100000 20 1591.73 2226605 267286286 6051970 181704864 160294627614 244332276840 341182768305 404750653 126342 1257812 138426 0 684255 2343752 +linux_tuned 1 100 0x1500 95 69.6 76.3 135.6 226.9 270.9 351.3 99965.0 100000 20 1590.28 2228408 267265805 6043164 181437534 160115742198 243831359603 340500138262 404467478 120749 1284615 142493 0 682803 2360236 +linux_tuned 2 100 0x1500 95 70.3 77.7 138.4 231.9 274.8 355.1 100101.7 100000 20 1591.77 2226657 267322881 6051688 181694298 160462149402 245226457054 342421724633 405730141 111838 1275083 139428 0 674651 2355882 +linux_tuned 0 100 0x1500 75 70.5 78.2 139.1 234.1 277.2 356.5 100159.2 100000 20 1591.82 2229591 267613803 6055415 181806816 160361940105 245510112173 342832116480 405278582 114673 1275945 140331 0 676942 2359124 +linux_tuned 1 100 0x1500 75 70.0 77.2 138.9 233.9 278.2 359.6 100120.5 100000 20 1590.07 2226056 267295522 6053651 181755834 160259049916 245020155011 342329841613 405524758 110806 1250439 137180 0 673581 2339037 +linux_tuned 2 100 0x1500 75 69.7 76.6 137.5 231.8 275.5 357.7 100085.9 100000 20 1589.48 2229255 267487705 6052244 181716246 160046266385 243921026215 340580527190 404161676 111474 1244087 134841 0 669968 2328286 +linux_tuned 0 100 0x1500 55 69.9 77.1 137.5 230.5 275.0 353.2 100029.3 100000 20 1592.27 2225764 267166794 6047182 181558530 160375119503 244769453836 341681336711 407128710 125946 1270036 140320 0 684572 2354828 +linux_tuned 1 100 0x1500 55 72.2 79.9 141.2 236.6 278.8 360.0 100091.2 100000 20 1591.65 2225017 267176269 6051155 181678578 160217635021 245172317187 342415841258 406025143 112696 1258612 137879 0 675187 2345900 +linux_tuned 2 100 0x1500 55 69.8 76.7 136.6 229.3 274.4 357.3 99924.2 100000 20 1592.32 2224642 266987842 6041303 181383444 160358357446 245207147203 342335919636 406950340 114255 1260406 139357 0 672934 2344531 +linux_tuned 0 200 0x1500 135 74.8 85.5 186.2 287.4 317.5 399.8 50047.7 50000 20 1404.22 1402346 152738204 3023830 90780702 83258799798 139787902678 205154223240 211639814 19463 370628 169075 0 806346 1179747 +linux_tuned 1 200 0x1500 135 74.9 86.2 188.2 289.0 321.7 400.0 49974.3 50000 20 1404.1 1399817 152425314 3019393 90646782 83008992344 139418512883 204745368944 210710417 19069 364986 166290 0 805580 1180746 +linux_tuned 2 200 0x1500 135 76.0 87.5 188.8 289.8 321.8 399.3 49858.3 50000 20 1402.87 1402666 152505444 3012489 90439968 82847682661 139064279349 204223452087 220284696 19395 365804 166715 0 795564 1168974 +linux_tuned 0 200 0x1500 95 75.6 87.1 188.2 288.6 320.3 401.4 49969.0 50000 20 1403.9 1405061 152802783 3019330 90645852 83157135022 139720494871 205134106201 211511078 19324 367637 168103 0 805883 1182404 +linux_tuned 1 200 0x1500 95 75.5 86.8 188.8 289.6 321.7 399.0 49872.8 50000 20 1401.25 1399451 152315844 3012840 90448980 83057701229 139557916137 204934308439 211329115 20340 373130 169312 0 807167 1181887 +linux_tuned 0 200 0x1500 75 74.8 85.5 187.3 285.9 313.0 388.0 50100.3 50000 20 1391.54 1408814 153203615 3027266 90883992 81725997806 134550119903 198841821457 190212501 18695 363338 163074 0 803247 1172460 +linux_tuned 1 200 0x1500 75 74.6 85.0 185.3 283.0 308.6 386.2 50061.7 50000 20 1395.33 1405815 152927595 3024122 90787302 81872644706 135561005873 200144967255 193643421 18920 368255 166953 0 817097 1183060 +linux_tuned 2 200 0x1500 75 74.5 85.2 185.7 286.5 317.2 391.3 50008.8 50000 20 1396.77 1403597 152717240 3020903 90690666 82080191035 136207701413 200991527860 196389913 19193 367038 167051 0 813293 1178937 +linux_tuned 0 200 0x1500 55 74.1 84.4 184.6 285.8 315.7 393.5 50073.1 50000 20 1395.82 1408410 153127098 3025111 90817968 82062659721 136341564854 201011766380 196494488 19603 370837 166338 0 809463 1180979 +linux_tuned 1 200 0x1500 55 75.0 86.1 186.7 287.4 316.6 392.9 49948.5 50000 20 1394.55 1405565 152777289 3017711 90596346 81829926329 136302526933 200972285954 196818000 23220 392531 177623 0 811352 1174734 +linux_tuned 2 200 0x1500 55 74.0 83.9 184.0 284.4 314.4 391.5 50070.5 50000 20 1396.5 1409663 153213677 3024864 90810432 82376085912 136634672784 201357409492 198190280 18922 367092 165483 0 805845 1177271 +linux_tuned 0 200 0x1500 135 91.9 113.2 213.9 307.0 354.5 435.0 100141.5 100000 20 1573.24 2205636 265986343 6098021 183235512 157179022899 234200309803 327815148125 403283043 23999 483645 167764 0 805393 1408385 +linux_tuned 1 200 0x1500 135 89.5 109.6 211.5 304.2 351.8 433.1 100006.7 100000 20 1572.28 2203581 265715720 6088987 182962842 157098857749 233916389667 327662320880 404901481 22781 477724 164437 0 802623 1400868 +linux_tuned 2 200 0x1500 135 93.1 114.7 216.0 313.3 360.7 442.6 99748.4 100000 20 1572.15 2199905 265136085 6073595 182500854 156524290541 233408889723 326693741064 401075175 23123 478258 165140 0 801612 1402080 +linux_tuned 1 200 0x1500 95 90.8 112.0 211.4 301.4 341.8 412.4 99920.7 100000 20 1562.92 2205389 265710675 6084007 182813592 154035278305 227726665853 319450336273 365053874 23927 491799 170171 0 819896 1409136 +linux_tuned 2 200 0x1500 95 97.5 119.6 218.6 310.4 355.5 439.2 100016.1 100000 20 1566.67 2203658 265718812 6089579 182979744 154765407625 229183645250 321186125618 371129436 23067 480695 164094 0 810182 1400187 +linux_tuned 0 200 0x1500 75 91.5 112.8 213.0 303.4 346.7 426.0 99942.1 100000 20 1569.03 2201489 265467525 6084958 182840292 154887249617 230666885415 323323801980 375902733 23243 486887 167155 0 812475 1409496 +linux_tuned 1 200 0x1500 75 96.4 119.2 217.7 313.3 358.5 440.3 99915.5 100000 20 1569.51 2201155 265387658 6082404 182761692 155316696146 231225576365 323923130190 378388208 23065 481717 163670 0 810803 1403553 +linux_tuned 2 200 0x1500 75 93.9 115.5 214.3 306.8 353.0 435.7 100034.5 100000 20 1570.0 2203853 265748181 6090517 183007008 155485603277 232116628952 325149340090 381287840 23868 488359 166008 0 811746 1407321 +linux_tuned 0 200 0x1500 55 91.6 112.8 214.1 304.5 350.9 432.8 100045.2 100000 20 1572.0 2203375 265719976 6091812 183047946 155478346383 231816906544 324680077286 381603583 22714 481810 162857 0 809568 1402288 +linux_tuned 1 200 0x1500 55 94.5 116.3 215.1 306.2 351.9 430.9 100023.6 100000 20 1570.44 2203750 265685492 6089821 182986548 155585335055 232549188492 325783244739 385112535 22997 483766 165392 0 808902 1406057 +linux_tuned 2 200 0x1500 55 97.0 119.2 217.8 311.6 357.0 438.6 100143.5 100000 20 1571.69 2207092 266076316 6097046 183203694 155544917491 232303881705 325366430320 385109626 22510 482735 165149 0 810713 1408224 +linux_tuned 0 300 0x1500 135 89.4 112.3 258.1 382.0 406.8 484.2 50085.2 50000 20 1389.15 1342285 148791083 3036982 91211574 82018241282 132903524485 194886266204 200375292 13356 199021 124264 0 763992 893302 +linux_tuned 1 300 0x1500 135 87.8 108.6 254.1 379.5 404.5 484.0 50054.2 50000 20 1389.51 1344059 148847499 3035220 91158240 81825815594 133378323961 195743158233 201570414 13930 208566 129369 0 774719 896368 +linux_tuned 2 300 0x1500 135 87.5 109.6 255.0 379.3 404.3 483.6 49915.5 50000 20 1388.83 1341263 148531371 3027028 90912750 81706827444 133061404444 195167651928 201586125 13557 200598 124856 0 764838 895421 +linux_tuned 0 300 0x1500 95 85.4 105.5 253.0 378.9 403.5 481.4 49808.7 50000 20 1386.38 1337720 148145315 3020037 90702078 81395721284 132422549791 194268912435 201152176 13519 203107 125332 0 761243 889656 +linux_tuned 1 300 0x1500 95 85.8 105.8 254.5 379.1 403.2 482.6 49981.5 50000 20 1388.42 1341216 148596401 3030754 91024128 81851433583 133138830479 195271309064 202294131 13580 207021 128700 0 766368 890213 +linux_tuned 2 300 0x1500 95 88.3 110.3 256.5 381.5 404.9 482.2 50040.6 50000 20 1389.52 1339836 148572251 3034364 91132704 81970736632 133233107040 195394269702 202893807 13843 204771 126503 0 766007 892814 +linux_tuned 0 300 0x1500 75 87.9 109.4 255.9 380.3 405.0 485.5 50168.7 50000 20 1480.75 1345522 149109105 3042309 91372350 82121511097 133917262945 196315858295 221923580 13488 201553 124614 0 764837 894768 +linux_tuned 1 300 0x1500 75 89.9 111.9 259.8 382.1 406.4 485.3 49993.2 50000 20 1389.29 1338977 148469561 3031653 91051866 81807931227 132932282275 194908021250 202742423 13234 200654 124082 0 763481 891618 +linux_tuned 2 300 0x1500 75 89.4 111.0 258.8 382.7 406.9 485.0 49954.2 50000 20 1389.54 1343094 148672969 3028966 90969588 81747546655 133000883362 195037723953 203268309 13928 203989 126306 0 764141 891152 +linux_tuned 0 300 0x1500 55 88.0 109.7 255.6 380.3 404.6 485.3 50003.5 50000 20 1389.38 1343907 148794711 3032324 91072482 81981039366 133269999242 195434011215 204375274 13572 203153 126083 0 765365 893276 +linux_tuned 1 300 0x1500 55 87.0 108.2 254.9 379.9 404.6 481.6 49914.9 50000 20 1390.15 1338773 148353486 3026846 90907782 81741745478 133185541349 195334505632 203629168 13507 204072 126609 0 768673 894477 +linux_tuned 2 300 0x1500 55 88.6 110.5 257.7 380.3 404.5 483.7 50048.7 50000 20 1390.12 1343333 148817808 3034489 91135632 82074433898 133659420748 195982535195 204691245 14059 207921 128354 0 768142 894183 +linux_tuned 0 300 0x1500 135 123.4 152.6 285.6 395.9 433.9 521.0 99993.3 100000 20 1557.07 2203832 265663710 6126417 184229172 155816150166 225647832784 315904115991 387571342 14143 220393 130143 0 753975 967659 +linux_tuned 1 300 0x1500 135 126.9 156.0 286.6 397.3 436.9 523.1 100129.5 100000 20 1557.2 2205722 265995624 6135247 184498206 155708032326 225462429349 315386633060 388234873 13959 215803 128837 0 751439 968760 +linux_tuned 2 300 0x1500 135 122.0 151.1 284.6 396.2 434.9 520.7 99916.4 100000 20 1556.22 2203189 265552631 6122666 184119516 155530303766 225268635824 315107532594 388845357 14005 222046 130929 0 750525 966555 +linux_tuned 0 300 0x1500 95 122.0 151.4 284.3 394.7 430.9 515.7 100088.0 100000 20 1557.64 2205325 265897889 6132982 184429968 155828203162 225480077746 315379991161 390422413 14130 218346 130300 0 750780 967733 +linux_tuned 1 300 0x1500 95 128.8 156.2 286.9 396.8 435.8 519.5 99911.4 100000 20 1556.38 2201578 265431906 6122251 184105234 155707655807 226196329089 316427442045 391874139 14239 218150 129738 0 752025 968333 +linux_tuned 2 300 0x1500 95 122.7 152.9 285.1 396.1 435.6 524.2 100037.7 100000 20 1559.43 2203246 265701782 6128984 184305444 155994550427 226528943477 316846986872 392082999 14411 221401 131736 0 752927 967956 +linux_tuned 0 300 0x1500 75 123.2 152.7 285.2 396.6 435.5 523.7 100016.0 100000 20 1557.96 2202620 265641126 6127473 184260354 156023920063 226775718826 317207522673 392804800 15006 224844 131758 0 756335 968344 +linux_tuned 1 300 0x1500 75 127.3 157.1 288.6 398.6 439.5 529.0 99940.7 100000 20 1558.29 2201252 265464801 6123620 184147698 156067771643 226638417860 316934837703 393524310 14200 222437 132471 0 752512 967058 +linux_tuned 2 300 0x1500 75 126.8 155.7 287.7 398.5 439.4 525.8 99951.1 100000 20 1557.62 2202747 265580294 6123397 184141242 156293236245 227076918775 317562010611 395169191 14499 223746 131761 0 752176 967953 +linux_tuned 0 300 0x1500 55 126.4 155.9 289.3 400.0 442.3 530.2 99966.5 100000 20 1558.16 2203287 265594851 6125252 184197138 156341282688 227583911226 318248086347 398081185 14443 221531 131718 0 751013 968790 +linux_tuned 1 300 0x1500 55 120.9 150.1 283.3 396.5 436.4 519.9 99997.5 100000 20 1559.18 2202243 265542603 6126452 184232520 156652183711 227907847850 318728026922 399754710 14736 223547 132791 0 756348 968510 +linux_tuned 2 300 0x1500 55 125.7 154.1 287.1 397.7 438.1 524.8 100006.0 100000 20 1560.19 2202401 265597771 6128128 184280898 156682038254 228154554264 319040546466 400924351 14618 221753 131862 0 753408 968899 +linux_tuned 0 50 0x1700 135 59.7 62.1 82.5 147.2 183.2 226.5 50008.4 50000 20 1567.76 1578323 164252033 3006472 90212310 86043433551 137309387854 175428712463 196749632 559162 645311 169443 0 793830 2132090 +linux_tuned 1 50 0x1700 135 60.9 63.1 83.4 149.0 184.8 227.5 50050.4 50000 20 1570.29 1577438 164282254 3008957 90286680 86369769081 138377391275 176782873988 199729494 561366 660818 173637 0 806217 2145510 +linux_tuned 2 50 0x1700 135 60.6 62.9 84.0 149.4 185.2 228.9 50094.9 50000 20 1571.02 1579915 164510266 3011670 90368238 86395217426 139033035480 177658138387 200828921 558407 658955 174544 0 804375 2146136 +linux_tuned 0 50 0x1700 95 61.0 63.1 83.1 149.1 185.6 230.1 49988.4 50000 20 1569.66 1570734 163763314 3005283 90176616 86206477282 138575403366 176980954950 200875739 553199 635158 169444 0 789433 2126590 +linux_tuned 1 50 0x1700 95 61.0 63.3 83.8 150.4 187.8 231.1 49959.3 50000 20 1570.49 1573484 163873180 3003370 90118590 86408844783 139049604323 177616597366 202196795 562322 648636 174034 0 807228 2144950 +linux_tuned 2 50 0x1700 95 60.8 63.2 84.2 151.8 187.4 233.4 50074.7 50000 20 1571.24 1577126 164318073 3010440 90331308 86685623461 139582908740 178306149158 203639163 561151 666903 175068 0 802725 2147433 +linux_tuned 0 50 0x1700 75 60.6 62.8 83.8 151.4 186.6 233.0 49974.4 50000 20 1570.08 1572851 163874442 3004326 90147570 86540430324 139368371185 178048779436 203268361 554590 657591 173584 0 802995 2140479 +linux_tuned 1 50 0x1700 75 61.0 63.3 84.3 153.0 189.0 232.6 50026.1 50000 20 1570.98 1569571 163724902 3007534 90244260 86644478155 139922010944 178734287526 208012429 559663 649174 173154 0 800229 2141941 +linux_tuned 2 50 0x1700 75 61.1 63.3 84.1 151.9 188.5 233.7 50081.5 50000 20 1570.85 1577227 164286983 3010812 90342336 86785189467 139779168727 178520622486 204305767 563293 665127 173457 0 800518 2146255 +linux_tuned 0 50 0x1700 55 60.6 62.8 83.9 150.6 186.7 233.6 50030.6 50000 20 1571.05 1578698 164357791 3007787 90251748 86760367488 139792046693 178606528838 205626393 561673 679099 176442 0 810352 2154503 +linux_tuned 1 50 0x1700 55 60.6 62.8 83.8 151.8 188.5 236.0 49814.3 50000 20 1570.98 1569471 163482217 2994814 89862588 86347168443 139406113995 178005163233 203839408 555407 647209 173543 0 797435 2138062 +linux_tuned 2 50 0x1700 55 61.0 63.2 84.4 151.0 188.6 236.2 50024.2 50000 20 1571.78 1572231 163898981 3007421 90240768 86557001921 139528697758 178197113334 205260884 555368 658579 174339 0 800843 2144775 +linux_tuned 0 50 0x1700 135 63.0 66.6 93.9 179.2 210.3 276.2 99987.9 100000 20 1740.04 2263983 269640698 6022897 180759366 162475075099 254499670477 320985821081 394229964 1198488 710680 147452 0 754822 3282608 +linux_tuned 1 50 0x1700 135 62.8 66.2 93.5 177.5 207.8 273.4 100060.7 100000 20 1740.59 2261863 269582336 6027699 180904968 162655901580 254496117140 320974571169 393951899 1193096 718146 143981 0 755879 3275529 +linux_tuned 2 50 0x1700 135 62.6 66.0 94.4 179.4 211.3 275.8 99813.7 100000 20 1739.61 2265094 269511926 6012286 180440676 162309773547 254213218728 320622446644 395560084 1203648 730065 147293 0 752514 3285277 +linux_tuned 0 50 0x1700 95 62.6 66.0 93.3 178.6 210.8 274.8 99964.7 100000 20 1739.5 2266231 269785549 6021361 180712662 162630183420 254105558449 320502258087 396900087 1204186 721621 145320 0 752708 3283361 +linux_tuned 1 50 0x1700 95 62.2 65.4 93.5 177.7 209.0 276.7 99980.6 100000 20 1740.43 2264211 269678848 6022728 180754926 162749772574 254942412599 321549816996 398709958 1200581 709426 147208 0 756034 3284748 +linux_tuned 2 50 0x1700 95 62.2 65.4 93.5 178.5 210.4 275.4 100039.0 100000 20 1741.92 2268597 270008908 6026201 180859380 162856183097 254716007003 321252887475 398981187 1203431 714554 145949 0 749843 3284039 +linux_tuned 0 50 0x1700 75 62.7 66.1 93.8 181.0 213.3 278.8 99921.5 100000 20 1740.87 2263065 269489141 6019228 180650370 162689181869 254521628461 321008144150 399685044 1197103 717810 145241 0 752749 3275855 +linux_tuned 1 50 0x1700 75 63.9 67.6 95.4 181.5 213.6 285.8 99988.5 100000 20 1740.38 2262639 269555763 6023471 180778206 162827695336 254518127194 321004531272 400668460 1189204 716379 142033 0 751766 3266101 +linux_tuned 2 50 0x1700 75 62.8 66.2 95.3 183.4 215.3 288.9 99975.3 100000 20 1741.42 2269118 269952844 6022724 180755700 163155080606 254966223300 321578598510 400239278 1198492 714024 144268 0 752332 3279463 +linux_tuned 0 50 0x1700 55 62.7 66.0 94.1 179.3 211.2 275.8 99858.2 100000 20 1742.22 2267138 269678711 6015280 180531360 162971641293 255447029322 322195300098 402038833 1207873 717208 148108 0 751626 3293844 +linux_tuned 1 50 0x1700 55 63.1 66.8 95.3 184.3 216.6 289.2 100046.2 100000 20 1743.61 2268363 269993503 6026867 180880416 163531795622 256688637003 323726394770 404527742 1196421 712967 147946 0 745443 3286939 +linux_tuned 2 50 0x1700 55 62.4 65.6 94.1 183.6 216.9 283.7 99940.2 100000 20 1743.93 2264519 269632509 6020858 180700614 163196375438 256784333538 323891900815 404510957 1195719 722350 145344 0 741646 3284332 +linux_tuned 0 100 0x1700 135 64.2 69.4 112.9 193.8 223.8 289.8 50009.0 50000 20 1528.2 1506817 159531565 3010365 90341088 84616782576 144273972346 197822790863 203341857 61068 723038 181613 0 802379 1628578 +linux_tuned 1 100 0x1700 135 64.0 69.2 112.1 193.1 222.6 290.3 49916.0 50000 20 1527.1 1502866 159184402 3004889 90176958 84335973622 143891108593 197670494637 202625754 53523 712918 180634 0 798381 1622912 +linux_tuned 2 100 0x1700 135 65.1 70.2 113.2 193.4 223.4 290.6 49949.9 50000 20 1530.63 1503042 159217327 3006920 90237990 84465693514 144432682229 197822036051 203532126 62370 722676 183403 0 810760 1633375 +linux_tuned 0 100 0x1700 95 64.3 69.6 112.8 193.4 224.7 288.5 49970.1 50000 20 1528.59 1507520 159553237 3008186 90276186 84589553749 144681742822 198296467235 204261277 59481 723053 182713 0 803755 1631347 +linux_tuned 1 100 0x1700 95 64.5 69.8 113.1 192.5 223.1 290.0 50037.8 50000 20 1527.63 1507756 159638536 3011929 90387762 84817831610 144879503477 198503416948 205184874 61787 727849 186101 0 817528 1644325 +linux_tuned 2 100 0x1700 95 64.1 69.2 113.6 195.0 225.8 290.2 50051.7 50000 20 1526.19 1509222 159763351 3013121 90424110 84419431668 144314331065 198160579615 203301041 50458 705359 176726 0 788714 1614483 +linux_tuned 0 100 0x1700 75 64.5 69.7 112.6 193.8 224.1 289.6 50010.6 50000 20 1527.48 1507292 159573415 3010482 90345126 84622449943 144591058230 198034449103 205369902 61123 725273 182466 0 802433 1630774 +linux_tuned 1 100 0x1700 75 64.0 69.1 111.4 192.9 223.7 288.4 49938.8 50000 20 1527.99 1505546 159393838 3006462 90224838 84683409457 144561564865 197887770844 205470216 64194 726636 184961 0 810597 1634131 +linux_tuned 2 100 0x1700 75 65.8 71.0 113.1 196.0 226.4 292.7 49913.9 50000 20 1527.89 1505491 159338581 3004732 90172278 84548351415 144737076360 198412338707 205462836 58891 722576 183444 0 802318 1632043 +linux_tuned 0 100 0x1700 55 64.6 69.7 112.2 193.6 224.1 290.6 49999.2 50000 20 1527.7 1503463 159307319 3009991 90330372 84662243659 144405339854 197709013017 205670233 62377 724999 182578 0 799996 1627902 +linux_tuned 1 100 0x1700 55 65.0 70.4 114.0 195.3 226.0 292.4 50030.2 50000 20 1529.59 1504023 159406529 3011589 90377694 84749842210 144787084253 198252182147 206852042 66321 736030 187603 0 817412 1646550 +linux_tuned 2 100 0x1700 55 64.3 69.5 112.4 193.5 225.0 290.1 49957.8 50000 20 1529.96 1502902 159255673 3007485 90255186 84792347361 144983745493 198567090682 205716355 59948 720967 182781 0 807421 1632163 +linux_tuned 0 100 0x1700 135 67.7 75.2 133.3 215.0 255.1 323.1 100088.6 100000 20 1723.12 2231272 267608441 6049425 181621398 160177758819 251326317031 322872977956 400285651 104805 1317980 143070 0 698914 2354185 +linux_tuned 1 100 0x1700 135 68.4 75.8 135.0 218.0 258.2 328.8 100060.7 100000 20 1723.96 2231216 267565801 6047361 181558248 160242642487 251155802171 322554028416 401010931 106774 1325046 144528 0 700137 2359376 +linux_tuned 2 100 0x1700 135 68.5 75.8 135.0 218.7 258.3 329.0 99842.4 100000 20 1723.43 2228760 267161006 6034286 181166304 160028743439 250857259321 322390245696 399842443 100026 1313127 142196 0 698489 2349876 +linux_tuned 0 100 0x1700 95 68.7 75.9 135.2 218.1 258.0 330.5 99871.2 100000 20 1723.53 2228136 267127090 6035961 181216476 160130222131 250499100748 321610502251 400984576 109122 1306743 142522 0 703737 2343942 +linux_tuned 1 100 0x1700 95 68.0 75.2 134.4 218.3 259.7 329.6 100052.7 100000 20 1723.84 2230924 267559875 6047216 181555680 160135503454 250686839711 321973167522 401378032 100521 1296129 140394 0 696419 2335684 +linux_tuned 2 100 0x1700 95 66.7 73.1 130.2 212.2 254.1 320.9 100061.5 100000 20 1723.95 2231271 267571009 6048492 181594050 160187579045 250338634081 321325022945 400430795 101955 1284594 137661 0 693007 2322448 +linux_tuned 0 100 0x1700 75 67.3 74.6 133.6 214.6 254.4 322.1 99952.7 100000 20 1722.92 2229838 267332241 6040453 181349256 159879045731 250782970357 322123279468 401426316 104878 1309784 141975 0 699902 2348480 +linux_tuned 1 100 0x1700 75 67.6 75.0 133.6 215.8 256.7 327.7 100123.4 100000 20 1725.62 2230975 267602056 6051320 181677054 160409612258 252748324718 324505686714 404110711 111379 1310962 142733 0 700956 2353917 +linux_tuned 2 100 0x1700 75 68.0 75.3 134.1 217.2 259.9 331.6 99913.7 100000 20 1725.17 2231441 267420400 6038300 181286058 160085775862 252091077843 323691255557 403034305 102908 1318809 144996 0 696272 2357321 +linux_tuned 0 100 0x1700 55 67.5 74.6 133.2 217.5 258.0 329.6 99940.3 100000 20 1723.04 2229858 267339324 6040586 181356288 160352590065 251732523813 323262368960 403945041 102608 1305705 143253 0 698336 2348483 +linux_tuned 1 100 0x1700 55 68.6 75.4 133.5 217.0 258.5 329.2 100022.0 100000 20 1723.68 2229052 267381198 6045173 181493454 160307994093 251616762386 322965790455 402260399 104986 1296074 140782 0 700208 2340743 +linux_tuned 2 100 0x1700 55 68.0 75.3 135.1 218.3 258.4 330.3 100022.7 100000 20 1724.39 2228761 267347519 6045790 181513428 160172103881 251279394750 322611885881 402440206 97947 1302586 141024 0 693794 2341424 +linux_tuned 0 200 0x1700 135 73.4 84.4 184.9 282.0 309.5 383.2 50130.6 50000 20 1509.18 1412770 153512542 3028630 90923580 82745087110 140302310259 192644176299 205100699 19448 375065 169584 0 821864 1183713 +linux_tuned 1 200 0x1700 135 72.3 82.5 181.8 279.5 306.4 375.9 50079.7 50000 20 1509.4 1414256 153549287 3025660 90835134 82741494213 140175558659 192546606524 205084133 19842 376507 171886 0 824672 1186035 +linux_tuned 2 200 0x1700 135 73.4 84.5 185.3 283.0 309.9 382.8 50037.0 50000 20 1508.52 1410694 153262470 3022650 90743382 82784294732 140293907201 192544518466 206203497 19487 372391 167446 0 815634 1180455 +linux_tuned 0 200 0x1700 95 72.2 82.4 182.0 279.6 306.6 383.2 49970.1 50000 20 1508.81 1408227 153014443 3018666 90623616 82583878397 140322885322 192554086291 204764629 19520 369938 166934 0 815115 1178483 +linux_tuned 1 200 0x1700 95 73.5 85.1 187.6 284.8 312.6 383.4 49998.5 50000 20 1509.57 1409187 153100389 3020346 90674772 82462004119 139617584508 191596799481 204325699 18685 362879 163844 0 799042 1163996 +linux_tuned 2 200 0x1700 95 71.5 81.9 179.5 279.5 305.8 379.6 49938.2 50000 20 1509.83 1409080 153025332 3017387 90587454 82499798494 139889619650 192138265760 205398919 19949 374634 169500 0 823183 1182728 +linux_tuned 0 200 0x1700 75 73.1 83.9 184.9 282.7 310.0 384.2 50118.2 50000 20 1511.36 1410503 153332123 3027800 90898758 82948367308 140716891561 193017984820 206381392 19235 369775 166153 0 816978 1181231 +linux_tuned 1 200 0x1700 75 72.6 82.7 183.1 280.8 307.7 379.5 50024.5 50000 20 1508.8 1410926 153270293 3022084 90726780 82887431426 140209559601 192497719513 206471344 18959 366566 164332 0 810637 1178615 +linux_tuned 2 200 0x1700 75 71.8 81.5 179.8 278.7 306.4 378.6 50102.3 50000 20 1509.28 1411061 153344158 3026927 90872616 83398257350 141039295203 193472097879 208005305 19581 371533 168051 0 819874 1181119 +linux_tuned 0 200 0x1700 55 72.8 83.5 183.9 282.4 308.8 382.8 49985.5 50000 20 1507.7 1408880 153095995 3019594 90651312 82715091895 139818301440 191809828145 206053664 18755 366817 163672 0 807256 1168502 +linux_tuned 1 200 0x1700 55 72.7 83.0 183.2 282.8 309.5 382.5 50141.7 50000 20 1510.35 1415616 153710815 3029393 90946662 83136583175 140978688965 193348658857 207936018 19865 372575 167640 0 818740 1183098 +linux_tuned 2 200 0x1700 55 72.4 83.2 184.1 280.8 307.8 383.5 50056.9 50000 20 1509.48 1409727 153186366 3024466 90799764 82937601198 140173125553 192377890179 207749568 19207 369902 165508 0 813802 1177604 +linux_tuned 0 200 0x1700 135 90.3 111.5 211.9 302.6 345.1 423.8 99987.4 100000 20 1702.07 2202694 265583202 6087336 182910174 156796001765 238223439389 306672941348 397954993 22669 481457 162487 0 825936 1401171 +linux_tuned 1 200 0x1700 135 91.9 113.8 211.3 299.7 339.0 412.2 100040.6 100000 20 1700.92 2204769 265808140 6090749 183013536 156815561428 238792123569 307514933815 399546161 22728 484447 167548 0 826775 1409053 +linux_tuned 2 200 0x1700 135 90.7 112.1 210.4 299.1 336.5 409.6 99941.3 100000 20 1700.51 2203814 265653375 6083768 182799300 156744045269 239284481692 308171706298 399076964 23283 487330 167319 0 827925 1407256 +linux_tuned 0 200 0x1700 95 89.6 110.7 209.4 299.9 339.6 412.1 99928.8 100000 20 1701.58 2202168 265478315 6083083 182779110 156923335871 239564715734 308457472994 400468823 22735 484832 167881 0 828111 1406188 +linux_tuned 1 200 0x1700 95 87.4 108.2 207.9 298.5 335.5 413.5 99930.7 100000 20 1701.21 2205270 265706806 6084441 182823186 156828147319 239786852568 308720268732 399840756 22636 483650 166089 0 822541 1403881 +linux_tuned 2 200 0x1700 95 88.7 110.4 209.9 300.0 338.3 410.5 100085.6 100000 20 1701.22 2208467 266119879 6092948 183076338 156892104074 239708160069 308533408682 400969998 22773 483381 165515 0 825846 1404697 +linux_tuned 0 200 0x1700 75 88.7 110.4 209.5 300.3 339.7 415.5 99998.2 100000 20 1702.07 2205224 265767427 6087629 182921436 157155746825 240251932911 309189841169 401226875 23500 488194 167727 0 826766 1405895 +linux_tuned 1 200 0x1700 75 87.8 108.7 209.0 298.6 336.6 410.1 100112.0 100000 20 1701.48 2209032 266179215 6094740 183132330 157185465351 240552927736 309503361271 403296575 23396 487974 166405 0 825621 1403417 +linux_tuned 2 200 0x1700 75 89.8 111.4 210.9 300.5 340.2 414.8 100091.0 100000 20 1701.04 2208041 266081094 6092966 183078318 157328561026 240126505611 309067369993 402865076 23139 486789 167084 0 828148 1407478 +linux_tuned 0 200 0x1700 55 92.0 113.9 212.7 301.6 341.7 413.5 100015.5 100000 20 1702.84 2204608 265745991 6088483 182943132 156948008627 241119234603 310276893438 403243143 22748 484977 167302 0 824344 1407844 +linux_tuned 2 200 0x1700 55 87.3 108.0 205.1 292.2 325.2 394.0 99870.6 100000 20 1682.62 2201381 265383032 6079659 182677596 153847248594 227319199332 294125849662 362843484 22582 482934 164362 0 839656 1404712 +linux_tuned 0 300 0x1700 135 85.1 105.4 250.3 374.1 400.7 475.6 50052.8 50000 20 1501.42 1345804 148983599 3035067 91153584 82353073184 136298042223 185821027212 209473340 13122 198763 120991 0 762375 892724 +linux_tuned 1 300 0x1700 135 86.3 107.9 252.8 376.3 402.1 476.8 50041.1 50000 20 1502.34 1344489 148896498 3034517 91137480 82710682204 137228525362 187125623300 211601147 14069 208680 127535 0 775355 896660 +linux_tuned 2 300 0x1700 135 85.5 106.3 250.5 375.8 402.3 476.8 49972.3 50000 20 1500.9 1345398 148867558 3029842 90996264 82407440000 137507975550 187531753133 210517922 14276 214727 132329 0 779458 895277 +linux_tuned 0 300 0x1700 95 85.8 108.0 253.1 375.9 402.1 474.9 50067.9 50000 20 1502.85 1344375 148902508 3035809 91175088 82837986761 137714236701 187741505377 211270482 14294 213131 130739 0 780268 897990 +linux_tuned 1 300 0x1700 95 86.2 108.4 251.5 376.2 401.5 472.6 50001.1 50000 20 1502.3 1343962 148792876 3031859 91056924 82470061973 136889492840 186533640384 210232736 13356 200827 122917 0 767082 893242 +linux_tuned 2 300 0x1700 95 84.5 106.1 252.6 376.1 402.3 475.5 49980.6 50000 20 1502.2 1344669 148831245 3030894 91029000 82582993978 136695193979 186171161641 209606019 13136 197578 120046 0 755531 885994 +linux_tuned 0 300 0x1700 75 84.3 105.4 252.9 377.1 402.6 477.2 50090.1 50000 20 1503.68 1346644 149085448 3037191 91216866 82579014056 137020993993 186595347918 210988705 13292 200605 122058 0 759493 887203 +linux_tuned 1 300 0x1700 75 83.7 104.5 249.9 373.1 400.3 475.4 49944.8 50000 20 1504.03 1343724 148704943 3028899 90968958 82450391243 136650579748 186311736995 210186225 13452 201480 122968 0 765416 889441 +linux_tuned 2 300 0x1700 75 87.2 109.7 255.3 377.6 402.5 478.4 50040.9 50000 20 1502.15 1345058 148898616 3034357 91133100 82806370398 137281429184 187050922200 211122317 13058 200085 122882 0 766724 893439 +linux_tuned 0 300 0x1700 55 84.5 106.4 253.3 377.0 402.7 478.0 50052.6 50000 20 1503.1 1344596 148915688 3034789 91144602 82402828363 137155840271 186826235420 209990814 13286 198927 122040 0 762114 889467 +linux_tuned 1 300 0x1700 55 87.1 110.0 252.3 376.1 402.3 477.1 50045.8 50000 20 1503.72 1346084 148985723 3034503 91135920 82514066831 137679150625 187688494218 210959189 14075 212491 130573 0 780597 897383 +linux_tuned 2 300 0x1700 55 87.3 108.8 252.2 376.5 402.8 477.8 50083.2 50000 20 1502.83 1347151 149115915 3036876 91208106 82602173490 137586257638 187419500977 211907294 13867 208904 127304 0 771751 894833 +linux_tuned 0 300 0x1700 135 115.1 144.1 274.6 384.7 413.8 485.8 100070.7 100000 20 1670.59 2203291 265754960 6130260 184343940 154983907570 223126098121 287695722466 373843859 14860 223261 129241 0 775360 967184 +linux_tuned 1 300 0x1700 135 122.3 150.6 279.4 388.5 420.3 489.7 99920.2 100000 20 1671.73 2200724 265392730 6121830 184090494 155043425211 224306844846 289138607287 378486998 14024 214771 126439 0 771472 967832 +linux_tuned 2 300 0x1700 135 118.1 145.8 277.6 387.9 419.1 491.7 99916.5 100000 20 1676.55 2200458 265366271 6120741 184055034 155379622096 225429466037 290444556428 381695040 14597 223027 129778 0 775039 967585 +linux_tuned 0 300 0x1700 95 120.2 148.5 277.1 387.0 419.3 497.9 100049.5 100000 20 1675.67 2202667 265672090 6128941 184306956 155471490376 225497340796 290447969090 383238450 14045 214415 125521 0 771767 968653 +linux_tuned 1 300 0x1700 95 119.1 147.4 276.6 388.1 420.7 490.0 99957.1 100000 20 1677.75 2201494 265504528 6123902 184152936 155500991485 225540871889 290366367527 382793016 14426 218625 127585 0 771699 966285 +linux_tuned 2 300 0x1700 95 121.9 149.6 279.2 388.2 419.6 489.7 99884.1 100000 20 1677.46 2197519 265149041 6119751 184029612 155479968621 225565581368 290536427123 385313459 13926 213292 125852 0 771732 967452 +linux_tuned 0 300 0x1700 75 118.8 147.3 277.9 386.9 416.7 486.5 99963.2 100000 20 1677.2 2202130 265510736 6123325 184134450 155447859938 225643701975 290712015946 387033982 14266 218908 128727 0 774481 968520 +linux_tuned 1 300 0x1700 75 117.6 146.2 278.3 387.2 418.5 490.8 100008.8 100000 20 1675.94 2201891 265595008 6127462 184262286 155750049975 225390139988 290333172416 387201719 14079 217512 126842 0 771434 967517 +linux_tuned 2 300 0x1700 75 121.4 151.2 281.4 390.6 424.1 500.5 99962.3 100000 20 1679.19 2199161 265353962 6123356 184133946 155675369606 226279377530 291308460599 388266424 14615 218170 127168 0 770024 966715 +linux_tuned 0 300 0x1700 55 122.0 150.4 280.9 391.3 425.2 500.2 100063.0 100000 20 1679.43 2203306 265726891 6130338 184344888 155845042668 226558202674 291711966918 390805544 14372 222112 130398 0 775780 968199 +linux_tuned 1 300 0x1700 55 121.7 150.9 281.6 389.6 422.1 498.1 100032.4 100000 20 1679.77 2202393 265616319 6128632 184297848 155947717575 226490711433 291584744875 389784861 14095 216047 126076 0 767719 967034 +linux_tuned 2 300 0x1700 55 121.2 149.9 279.5 390.4 423.2 494.8 99889.0 100000 20 1677.24 2199458 265272144 6120507 184053054 155755740096 226132291832 291244964780 390270995 14195 217594 127522 0 771841 966987 +linux_tuned 0 50 0x1900 135 58.8 61.6 81.2 141.8 177.0 220.5 49863.1 50000 20 1709.51 1583385 164455914 2997550 89944104 86872505582 142042897614 167872104598 210498915 540080 687039 174974 0 812606 2132678 +linux_tuned 1 50 0x1900 135 58.8 61.6 81.3 143.2 177.9 221.2 50084.9 50000 20 1709.15 1589673 165106337 3010736 90339114 87333907058 142873506444 168844569544 215355590 540967 686199 174731 0 812658 2140259 +linux_tuned 2 50 0x1900 135 58.4 61.2 80.7 143.5 178.0 220.9 50072.0 50000 20 1709.3 1585425 164836644 3010130 90321600 87312476586 142961136624 168897653406 212477620 542050 688820 175755 0 814478 2140495 +linux_tuned 0 50 0x1900 95 59.5 62.0 81.4 142.4 177.9 220.3 49897.6 50000 20 1707.76 1580362 164274788 2999712 90009174 86992803537 142248110449 168099749030 211110889 537075 672596 169104 0 802338 2127624 +linux_tuned 1 50 0x1900 95 58.8 61.6 81.3 145.2 178.4 221.9 49904.3 50000 20 1710.35 1583634 164498694 3000022 90018186 87048714460 142840895029 168723286469 212051920 539514 686912 174278 0 810718 2136811 +linux_tuned 2 50 0x1900 95 58.9 61.6 81.3 143.1 177.7 221.0 49871.1 50000 20 1710.45 1582087 164366951 2998033 89958582 86957956071 142451672128 168326160005 212454257 539073 689026 175778 0 814218 2135300 +linux_tuned 0 50 0x1900 75 58.5 61.4 81.2 143.5 179.1 221.5 50010.3 50000 20 1710.59 1583999 164659095 3006326 90207120 87284314552 142990062549 168893094171 212355402 541043 682457 172901 0 807427 2133808 +linux_tuned 1 50 0x1900 75 58.4 61.2 80.0 139.8 176.5 218.4 50013.4 50000 20 1713.14 1583299 164635474 3006695 90218676 87284468842 142754710489 168639351943 212546508 537130 667237 172019 0 799858 2127039 +linux_tuned 2 50 0x1900 75 58.8 61.5 81.3 146.2 179.9 221.7 50050.8 50000 20 1713.73 1587886 164960109 3008852 90283050 87319222348 143449166365 169447971732 212033935 535344 667430 171618 0 806061 2132214 +linux_tuned 0 50 0x1900 55 58.8 61.6 81.1 142.8 178.0 221.8 50032.5 50000 20 1712.93 1580544 164437241 3007890 90254868 87280477310 142892265540 168810146921 212829549 541941 682533 171416 0 802443 2126360 +linux_tuned 1 50 0x1900 55 58.4 61.3 81.0 144.7 178.7 221.7 49908.3 50000 20 1711.8 1582932 164471049 3000292 90026292 87104567065 143102867497 169109727042 224595832 535314 663803 170696 0 798636 2124704 +linux_tuned 2 50 0x1900 55 58.5 61.3 81.0 141.3 175.3 217.4 49922.7 50000 20 1711.0 1578558 164198236 3001199 90053652 87098179561 142359491586 168225554336 212607973 540601 677377 170541 0 811835 2126030 +linux_tuned 1 50 0x1900 135 58.8 61.9 86.5 160.0 189.2 242.3 99922.4 100000 20 1880.98 2274212 270246208 6016017 180544278 161443028361 247459669819 287274760322 375857174 1179994 851686 144782 0 773948 3260976 +linux_tuned 2 50 0x1900 135 58.4 61.6 86.7 162.3 191.4 245.7 100109.1 100000 20 1890.87 2275470 270538016 6027453 180888030 162267802921 251219623552 291602986832 384325361 1183498 843277 143981 0 773521 3268858 +linux_tuned 0 50 0x1900 95 58.7 61.9 87.4 164.6 194.1 251.4 99973.9 100000 20 1892.25 2279271 270640474 6019898 180662694 162178367437 251036080695 291456233086 398320637 1170705 829749 142847 0 771637 3257062 +linux_tuned 1 50 0x1900 95 58.7 61.9 87.0 161.9 192.4 246.7 99980.9 100000 20 1894.04 2278185 270589024 6020062 180668796 162916284145 252339603653 292913202105 389766000 1183993 850070 144739 0 771737 3266896 +linux_tuned 2 50 0x1900 95 59.7 62.6 88.7 167.2 197.5 255.7 99972.5 100000 20 1894.37 2272636 270187264 6019878 180662616 162536961435 251998390429 292511431813 389890739 1173414 834138 142779 0 773446 3255862 +linux_tuned 0 50 0x1900 75 59.1 62.3 88.1 166.2 196.5 255.9 99985.2 100000 20 1895.72 2281111 270761552 6020506 180681876 162837588827 252934037630 293594186282 392861131 1175605 843162 143073 0 768704 3264529 +linux_tuned 1 50 0x1900 75 59.8 62.7 88.1 164.7 195.9 253.9 100064.7 100000 20 1896.23 2276150 270548843 6025276 180823842 162894167016 252841807201 293476567734 395752782 1192496 843711 143830 0 768071 3269677 +linux_tuned 2 50 0x1900 75 59.4 62.4 88.1 164.8 195.8 253.8 99980.4 100000 20 1893.79 2274796 270350556 6020296 180675240 162866707971 251991754895 292484485651 393874247 1175004 830562 143699 0 770364 3256786 +linux_tuned 0 50 0x1900 55 59.6 62.6 88.8 167.3 196.9 251.3 100048.1 100000 20 1894.22 2274220 270389455 6024213 180792726 163083417200 253858354629 294650455428 395224080 1183307 831213 144314 0 770236 3268362 +linux_tuned 1 50 0x1900 55 58.9 62.1 87.7 165.4 195.6 250.9 100052.2 100000 20 1896.01 2273663 270381258 6025006 180817596 162960666252 253289522872 293999283657 396945554 1167753 813992 142326 0 771884 3254962 +linux_tuned 2 50 0x1900 55 59.9 62.6 87.9 165.5 196.0 251.4 100025.7 100000 20 1894.9 2279007 270646928 6023398 180769116 163097159079 253745991957 294538734958 397244752 1167280 822730 140633 0 760074 3248625 +linux_tuned 0 100 0x1900 135 62.2 66.9 109.9 187.9 216.3 274.9 50060.4 50000 20 1656.08 1511359 159918783 3013297 90428688 85373249116 148617031603 191516733120 210783733 57363 731507 185831 0 818960 1638378 +linux_tuned 1 100 0x1900 135 62.7 67.7 111.5 189.1 217.7 278.4 49910.3 50000 20 1654.93 1509567 159620400 3004469 90164226 85232323929 148666399522 191699948189 210646544 52706 717433 183872 0 812612 1629037 +linux_tuned 2 100 0x1900 135 62.8 67.8 110.2 188.8 217.1 275.1 50152.1 50000 20 1656.9 1514075 160195299 3019164 90605436 85316319485 149015154156 191720192567 216894146 58368 728383 181931 0 809872 1630644 +linux_tuned 0 100 0x1900 95 63.7 68.9 113.1 190.7 218.8 281.1 49974.4 50000 20 1654.59 1508368 159613671 3008237 90277680 84908362518 148334823893 191356463837 210246828 51799 719085 179512 0 799216 1618758 +linux_tuned 1 100 0x1900 95 62.8 67.8 110.2 188.1 215.8 277.1 50056.7 50000 20 1651.64 1514934 160137476 3013306 90429468 84987476613 148682237631 191693425712 210630603 51102 719222 179092 0 796230 1618135 +linux_tuned 2 100 0x1900 95 62.6 67.6 111.8 188.6 217.3 279.5 49952.3 50000 20 1654.62 1505868 159450118 3006962 90238908 85009653610 148391082925 191028959906 217246443 59397 730403 185235 0 811926 1630585 +linux_tuned 0 100 0x1900 75 63.1 68.1 112.1 190.7 219.1 279.2 49905.3 50000 20 1654.64 1506237 159401040 3004030 90150648 85025603074 148625396932 191426100667 210743858 55015 723887 182806 0 809458 1627125 +linux_tuned 1 100 0x1900 75 63.1 68.1 110.4 188.1 215.9 274.9 49996.7 50000 20 1653.3 1512184 159899489 3009690 90321144 85193234297 148856318499 191976596299 210643147 49259 715822 179204 0 800202 1621168 +linux_tuned 2 100 0x1900 75 62.8 68.0 112.8 188.3 215.9 277.3 49981.8 50000 20 1745.36 1506896 159528908 3008910 90298104 85323644110 148124151091 189532112030 220460914 92563 786276 194125 0 814469 1655395 +linux_tuned 0 100 0x1900 55 62.5 67.3 110.6 188.9 217.9 279.5 49947.3 50000 20 1653.0 1508837 159616923 3006736 90232596 85164173949 148752030802 191960048638 210841114 49423 713635 181444 0 806455 1623741 +linux_tuned 1 100 0x1900 55 62.9 68.0 112.1 189.0 218.4 279.5 50026.0 50000 20 1652.5 1509639 159756422 3011458 90374292 85206297751 148601243495 191479226869 211345329 53560 722686 180955 0 807201 1625503 +linux_tuned 2 100 0x1900 55 62.9 68.0 112.1 189.7 218.9 281.9 50042.7 50000 20 1654.62 1514201 160084923 3012683 90411402 85302744251 148782495852 191640011801 211560412 55303 726298 181843 0 810103 1628857 +linux_tuned 0 100 0x1900 135 63.8 71.3 128.4 202.3 239.0 301.3 99986.9 100000 20 1866.19 2236516 267809610 6042175 181400406 159602707902 249601831112 298686386443 394043751 95160 1351794 148169 0 732915 2350734 +linux_tuned 1 100 0x1900 135 64.0 70.8 126.5 199.5 235.3 297.6 100102.3 100000 20 1864.96 2236106 267949499 6049429 181619748 159595975473 249552928992 298770374010 395438958 94494 1328624 143773 0 730378 2333380 +linux_tuned 2 100 0x1900 135 63.9 71.0 126.9 200.3 235.9 297.8 100109.5 100000 20 1865.21 2242162 268364422 6049103 181606602 160002000022 250069703186 299362654553 395776928 85062 1344827 146971 0 724365 2345983 +linux_tuned 0 100 0x1900 95 64.0 71.8 128.8 203.8 240.7 301.4 100022.8 100000 20 1864.09 2237954 267952949 6044138 181460118 159571244178 249602049198 298709625129 395563695 90080 1329393 143998 0 729400 2333192 +linux_tuned 1 100 0x1900 95 64.4 71.8 128.5 204.7 242.3 300.2 99965.9 100000 20 1866.98 2232841 267563143 6040620 181352616 159940500255 250435118968 299601431911 397949225 92472 1339624 146318 0 730453 2343883 +linux_tuned 2 100 0x1900 95 63.6 70.6 127.2 201.4 237.6 300.1 99974.6 100000 20 1865.51 2233447 267627384 6040927 181361748 159975245574 249930096897 299136551645 397993076 86535 1341108 145782 0 726072 2343399 +linux_tuned 0 100 0x1900 75 63.9 71.1 128.0 201.7 238.8 300.3 99993.1 100000 20 1959.21 2234030 267700943 6042377 181405626 160437491047 251360883095 300785190277 400446254 86927 1346157 145852 0 727085 2349923 +linux_tuned 1 100 0x1900 75 64.3 72.1 130.0 206.8 245.4 308.5 100018.3 100000 20 1866.24 2233182 267640860 6043579 181442244 160081264566 250447823611 299475234960 400252556 85801 1318017 142137 0 725608 2324305 +linux_tuned 2 100 0x1900 75 64.6 72.0 129.3 206.1 242.9 302.5 100181.5 100000 20 1868.43 2234314 267894480 6054077 181757784 160709962625 250993176059 300105833957 401897663 91311 1332430 145441 0 728979 2338832 +linux_tuned 0 100 0x1900 55 63.7 70.5 127.0 201.0 238.2 299.3 100005.0 100000 20 1868.55 2233214 267625242 6043260 181433988 160417136802 251234094342 300420275389 402234286 85609 1323857 143735 0 727312 2335045 +linux_tuned 1 100 0x1900 55 64.6 72.0 128.8 205.1 241.7 300.9 99882.8 100000 20 1869.48 2233190 267444105 6035475 181197810 160303103252 251445002911 300462977889 401547362 97636 1334837 146565 0 730191 2340434 +linux_tuned 2 100 0x1900 55 64.7 72.1 128.9 203.4 240.1 302.7 100086.9 100000 20 1869.73 2236271 267933109 6048095 181577946 160752076039 251891990914 301179976644 402861025 90379 1348394 146912 0 727241 2353048 +linux_tuned 0 200 0x1900 135 70.4 80.8 177.6 275.9 301.8 365.3 49976.5 50000 20 1627.99 1412286 153270741 3018807 90627144 83074676554 143751514650 185193942870 209762680 18173 363006 161006 0 814390 1176721 +linux_tuned 1 200 0x1900 135 70.2 81.1 180.0 276.5 302.7 368.3 50044.4 50000 20 1630.19 1414986 153562146 3023263 90761976 83229298420 143694819022 185351128699 209819326 19949 373336 168435 0 829676 1182874 +linux_tuned 2 200 0x1900 135 69.7 80.0 178.2 275.9 302.0 367.6 50015.0 50000 20 1627.45 1417279 153655397 3021365 90704838 82976564468 143190970767 184609569061 209572898 19005 372045 167265 0 819486 1178184 +linux_tuned 0 200 0x1900 95 70.2 80.5 179.3 276.3 302.2 367.2 49962.5 50000 20 1627.78 1413275 153342759 3018346 90614586 83024506582 143483410742 184924399093 210291933 19459 371828 166820 0 826200 1181060 +linux_tuned 1 200 0x1900 95 70.4 80.9 179.7 276.5 302.3 368.7 49976.0 50000 20 1628.33 1412788 153331807 3019310 90643878 83076486282 143768825872 185304246458 210148365 19554 371385 168240 0 827562 1181310 +linux_tuned 2 200 0x1900 95 70.4 81.0 178.5 276.1 302.3 370.2 49949.0 50000 20 1628.77 1411748 153201019 3017439 90587526 82912449826 143352126145 184829948471 209557742 18692 365429 163488 0 815935 1176876 +linux_tuned 0 200 0x1900 75 71.3 81.6 179.2 277.6 304.0 371.4 50004.0 50000 20 1629.08 1411959 153310937 3020929 90692394 83065006342 143403593038 185088888891 209758758 19932 378588 171458 0 832593 1183636 +linux_tuned 1 200 0x1900 75 70.6 80.9 179.9 277.6 303.6 373.0 49994.0 50000 20 1630.13 1412450 153314646 3020250 90671950 82893369710 142907226627 184176826160 210128312 20048 376817 171668 0 819077 1166592 +linux_tuned 2 200 0x1900 75 71.7 82.8 181.0 278.4 304.2 369.6 50082.0 50000 20 1631.12 1411578 153338688 3025452 90827730 83261939548 143978097544 185578034414 211581680 20004 376508 170331 0 833165 1185842 +linux_tuned 0 200 0x1900 55 70.2 80.8 179.2 276.3 302.5 369.3 49978.5 50000 20 1631.94 1413150 153333485 3019253 90642216 82941266006 143745318781 185152951863 209711068 18685 368352 165278 0 816184 1175678 +linux_tuned 1 200 0x1900 55 70.4 81.0 180.1 276.5 302.3 368.1 50046.6 50000 20 1633.46 1413046 153396605 3023002 90753258 83216762799 144012595504 185443487203 210810133 17959 358056 158157 0 810586 1174754 +linux_tuned 2 200 0x1900 55 70.5 81.3 179.6 276.2 303.4 370.7 50056.3 50000 20 1631.47 1416398 153662192 3023596 90771132 83069540100 143901321675 185166390579 210816259 18344 365390 163353 0 810476 1169429 +linux_tuned 0 200 0x1900 135 84.8 106.1 203.9 290.8 323.1 392.3 100104.9 100000 20 1841.02 2209172 266175592 6093680 183096450 156677383526 237538525043 284693981679 395272575 22719 483713 164627 0 849037 1401000 +linux_tuned 1 200 0x1900 135 85.8 107.9 206.0 293.6 328.0 395.3 100021.7 100000 20 1843.82 2205996 265848233 6088228 182933202 156493544049 238009400984 285003541077 395632221 22168 477928 162917 0 844467 1395380 +linux_tuned 2 200 0x1900 135 84.6 107.0 204.8 292.1 325.2 394.9 99937.2 100000 20 1841.81 2205943 265774432 6083017 182774856 156503348323 238244126821 285425978277 396058465 22622 481710 163971 0 845783 1398412 +linux_tuned 0 200 0x1900 95 84.9 106.8 204.2 291.4 325.5 394.3 100030.9 100000 20 1843.7 2205660 265836487 6089588 182976018 156937739557 238362249871 285600982953 397237244 23129 485878 166040 0 850660 1401703 +linux_tuned 1 200 0x1900 95 87.4 109.3 206.0 293.1 326.1 392.9 99862.4 100000 20 1843.77 2201572 265410429 6078064 182625366 156575576692 238140222331 285339358322 397396693 23374 485135 165677 0 850372 1404227 +linux_tuned 2 200 0x1900 95 86.1 106.9 204.6 292.0 325.7 395.4 99972.2 100000 20 1846.34 2205836 265792161 6085008 182833422 156848624720 239193671927 286474830180 399006763 23388 490440 168399 0 850809 1404859 +linux_tuned 0 200 0x1900 75 86.8 107.7 203.9 291.5 325.2 393.6 100135.7 100000 20 1844.28 2207029 266077920 6094738 183128862 156865176724 239329627364 286841585089 400079190 22959 486800 167093 0 847226 1406715 +linux_tuned 1 200 0x1900 75 85.6 106.6 206.1 294.3 327.9 397.7 100034.7 100000 20 1844.18 2203816 265739398 6088854 182952630 156770200987 238990335221 286151161354 399257293 23827 486332 165185 0 844724 1394453 +linux_tuned 2 200 0x1900 75 87.2 109.0 204.9 292.4 326.3 394.4 99833.0 100000 20 1845.32 2201423 265352844 6076353 182574942 156372985630 238455211689 285657725658 398890041 23458 489099 167287 0 850168 1403655 +linux_tuned 0 200 0x1900 55 85.0 106.4 204.2 291.3 325.0 393.6 100097.2 100000 20 1846.07 2206707 265993628 6092812 183071454 156863640481 239865535475 287233534009 399670125 22355 481688 164601 0 846583 1401014 +linux_tuned 1 200 0x1900 55 86.9 109.0 205.9 293.7 327.6 400.0 100111.4 100000 20 1846.17 2205511 265922761 6093858 183103134 157106645759 239362530557 286425149990 400516855 22549 481477 164224 0 847353 1403360 +linux_tuned 2 200 0x1900 55 86.8 107.6 204.4 292.6 326.5 394.1 100044.1 100000 20 1846.68 2207958 265990166 6090269 182996376 156930813446 239696781056 287045328881 402190528 24525 493572 169753 0 850256 1405949 +linux_tuned 0 300 0x1900 135 82.8 104.8 248.7 370.3 398.5 465.6 49970.0 50000 20 1620.14 1348590 149084784 3030053 91003740 82601475886 140028685705 178978726931 213524808 13880 206684 125208 0 772761 892067 +linux_tuned 1 300 0x1900 135 83.6 105.3 248.5 371.3 399.2 467.4 49997.1 50000 20 1619.34 1347045 149007522 3031502 91046418 82675533651 139728709975 178876026795 213546228 14063 211684 127277 0 780571 893576 +linux_tuned 2 300 0x1900 135 82.2 104.0 247.5 370.0 398.3 465.0 50061.9 50000 20 1620.9 1348447 149165387 3035706 91172748 82942047629 140421192728 179227536636 214219788 13509 203580 122440 0 768708 891325 +linux_tuned 0 300 0x1900 95 84.0 106.0 250.9 371.3 398.4 465.3 49988.1 50000 20 1620.05 1345719 148907652 3031039 91033050 82643649067 139554393655 178461052600 215991705 13089 201208 120348 0 767269 891336 +linux_tuned 1 300 0x1900 95 84.3 106.8 250.3 372.4 398.9 466.4 49963.5 50000 20 1620.09 1346747 148934006 3029496 90985758 82762154592 140035135095 179014842197 213318341 13630 206747 124991 0 775667 895073 +linux_tuned 2 300 0x1900 95 83.1 105.3 249.5 370.3 398.2 465.5 49861.0 50000 20 1621.14 1345720 148767505 3023075 90792870 82521361037 139676672179 178531450796 213095462 13255 200586 120412 0 769034 894095 +linux_tuned 0 300 0x1900 75 87.6 111.2 254.7 373.7 399.9 467.3 50010.9 50000 20 1622.12 1351219 149288288 3032780 91085178 82784175778 139958537115 178823658450 213796007 13825 207121 125314 0 771320 892858 +linux_tuned 1 300 0x1900 75 83.9 105.7 249.0 370.9 398.6 465.4 50016.4 50000 20 1620.71 1348157 149110502 3032613 91078944 82787269092 139884981825 178949553279 213340339 13978 206917 125414 0 779100 896131 +linux_tuned 2 300 0x1900 75 83.9 106.0 249.4 370.9 398.6 464.2 49923.8 50000 20 1621.27 1346367 148866362 3027569 90928998 82624972522 139994937416 179114479150 214160806 13839 211053 127491 0 778154 892458 +linux_tuned 0 300 0x1900 55 82.7 103.5 249.2 371.6 398.7 464.6 50077.6 50000 20 1619.17 1345497 149009458 3036703 91204266 82890638314 140644014825 179462191267 215522636 13706 203400 122544 0 768170 890175 +linux_tuned 2 300 0x1900 55 80.3 101.3 245.1 367.4 395.6 453.5 49920.5 50000 20 1603.18 1347616 148959699 3027162 90916518 80884936134 134453660574 173439473384 191221826 13309 203460 121224 0 770961 891563 +linux_tuned 0 300 0x1900 135 116.2 143.4 273.2 382.1 407.9 481.6 100070.0 100000 20 1829.64 2202600 265684691 6130315 184342836 157224616898 233569221365 278720950337 411042324 15065 224771 128990 0 785733 967193 +linux_tuned 1 300 0x1900 135 119.5 147.3 276.2 384.5 414.0 486.6 100157.2 100000 20 1828.84 2205221 265940740 6136648 184537662 157761299613 234233949840 279214621281 406551556 13929 214689 125788 0 775744 967274 +linux_tuned 2 300 0x1900 135 117.7 145.6 275.4 383.1 410.8 484.7 99988.4 100000 20 1828.73 2201898 265536936 6126109 184219560 157043328488 233595220001 278686726219 406643973 14178 217271 126003 0 782869 968787 +linux_tuned 0 300 0x1900 95 114.8 143.8 275.5 384.4 412.0 484.9 100040.7 100000 20 1826.1 2202594 265619542 6129214 184315518 157187756374 232688645434 277636955103 404752113 13492 213545 123356 0 769532 961779 +linux_tuned 1 300 0x1900 95 114.5 142.6 273.3 382.9 411.2 484.9 99914.5 100000 20 1828.49 2201162 265408900 6120639 184055808 157026824107 233579327041 278384138102 404842894 14038 215834 126402 0 780109 967147 +linux_tuned 0 300 0x1900 75 118.7 146.2 274.5 382.3 408.0 479.7 100023.6 100000 20 1809.7 2204796 265793789 6128806 184303128 154234551565 227198613430 271826240067 365044454 14552 224152 126305 0 784973 964781 +linux_tuned 1 300 0x1900 75 117.1 145.3 274.4 383.8 411.6 483.5 100039.3 100000 20 1816.56 2202557 265684827 6128133 184278738 155113203185 229757468484 274684990670 374579562 13898 218950 125656 0 783482 967184 +linux_tuned 2 300 0x1900 75 116.1 144.0 273.6 380.5 404.7 478.3 99903.2 100000 20 1818.81 2199579 265296828 6119922 184031070 154910549364 229455955991 274204732777 376206899 14011 217564 125979 0 783854 967616 +linux_tuned 0 300 0x1900 55 117.2 145.0 273.2 382.9 409.8 482.7 100107.7 100000 20 1822.32 2205202 265890943 6132933 184423552 155863623649 232276903011 277361523485 381775834 14367 218550 126388 0 782547 968192 +linux_tuned 1 300 0x1900 55 117.6 145.2 274.8 383.3 410.8 483.4 99936.2 100000 20 1820.92 2200372 265384656 6122407 184105056 155387845741 231495634742 276534850326 381495002 14089 219100 125059 0 784502 967402 +linux_tuned 2 300 0x1900 55 114.8 141.7 272.7 381.7 407.3 482.7 100013.6 100000 20 1821.12 2200571 265492290 6127041 184247838 155671967759 232112944269 277381801793 385286300 14375 222717 128599 0 789669 968465 +linux_tuned 0 50 0x1b00 135 57.3 60.1 79.1 138.1 169.5 209.6 49983.4 50000 20 1859.38 1592323 165165085 3004700 90158526 87541632743 144491948170 159314479257 214865905 516710 707168 171619 0 818421 2113832 +linux_tuned 1 50 0x1b00 135 57.1 59.7 78.9 137.8 168.4 210.6 50003.2 50000 20 1856.63 1591992 165175365 3005803 90191076 87323290988 144221889757 159042768020 217427638 512277 687462 166762 0 808744 2103912 +linux_tuned 2 50 0x1b00 135 57.3 60.1 78.9 139.0 169.2 210.0 49984.2 50000 20 1861.72 1592328 165175093 3004771 90160470 87435160042 144578306309 159370801578 214568145 512444 694610 170184 0 821051 2113782 +linux_tuned 0 50 0x1b00 95 57.0 59.6 78.9 137.4 168.2 210.2 49939.6 50000 20 1862.58 1595752 165358353 3002019 90077886 87342649539 144246175302 159044956157 214795057 511044 691515 166163 0 813355 2110048 +linux_tuned 1 50 0x1b00 95 56.9 59.3 78.0 137.6 167.9 207.6 50055.6 50000 20 1863.67 1594230 165392504 3009105 90290748 87428127072 144496695607 159257428572 215310208 514243 695208 169538 0 817282 2112497 +linux_tuned 2 50 0x1b00 95 56.9 59.4 78.9 139.7 169.4 213.2 50165.2 50000 20 1865.19 1595830 165629675 3015479 90481320 87731010538 145192772850 160019673040 216135089 518592 707235 170126 0 822688 2121044 +linux_tuned 0 50 0x1b00 75 57.1 59.6 78.2 136.5 167.5 207.8 49947.1 50000 20 1864.08 1590329 164992588 3002428 90089754 87398114587 144500438974 159339841613 214490444 508296 685859 167634 0 815854 2105606 +linux_tuned 1 50 0x1b00 75 57.2 60.0 79.1 137.8 168.7 210.0 50033.6 50000 20 1860.27 1592220 165214544 3007741 90249786 87431476839 144735579849 159500880411 215410742 528410 726044 171319 0 813359 2120195 +linux_tuned 2 50 0x1b00 75 57.1 59.8 78.9 139.5 169.9 211.2 49987.9 50000 20 1865.74 1598323 165600164 3005113 90171024 87623468639 145074200425 159877505760 215412219 517632 704103 172732 0 826478 2121615 +linux_tuned 0 50 0x1b00 55 57.1 59.8 78.5 137.2 168.5 209.9 50006.9 50000 20 1867.68 1589093 164992991 3005964 90195960 87676290816 145254307491 160053465203 216322040 513453 696917 170076 0 817525 2114365 +linux_tuned 1 50 0x1b00 55 56.8 59.2 78.2 136.6 168.7 209.3 50013.7 50000 20 1866.2 1585534 164774933 3006355 90207492 87478001537 145189034672 159981108318 215710099 513334 691733 172495 0 825773 2114240 +linux_tuned 2 50 0x1b00 55 56.9 59.4 78.7 140.4 170.1 208.9 49992.0 50000 20 1866.63 1593840 165280309 3005284 90176088 87636868897 145050100534 159772619285 216300975 519163 706248 171414 0 824887 2118063 +linux_tuned 0 50 0x1b00 135 57.8 61.0 85.6 158.7 188.1 244.1 100165.0 100000 20 2067.82 2282984 271107928 6030123 180966204 164664187534 259996124075 279556314651 412511733 1167405 889927 140463 0 781592 3252338 +linux_tuned 1 50 0x1b00 135 57.8 61.0 85.6 157.8 187.5 241.6 100146.4 100000 20 2067.96 2282063 271023972 6029026 180933042 164570136135 260489418335 280087108437 412462548 1179524 899514 142174 0 777326 3258335 +linux_tuned 2 50 0x1b00 135 58.4 61.6 85.8 157.6 186.8 237.3 100012.4 100000 20 2067.58 2281803 270874810 6021005 180692730 164389806764 260520859382 280107637653 412688820 1176799 900178 144132 0 772141 3260415 +linux_tuned 0 50 0x1b00 95 58.0 61.1 85.0 158.9 187.6 242.4 100006.1 100000 20 2067.52 2280433 270738407 6021170 180699186 164502165582 260941174180 280541270071 412570986 1172252 891980 141014 0 779757 3252599 +linux_tuned 1 50 0x1b00 95 57.8 61.0 85.7 158.5 187.2 238.6 99872.8 100000 20 2067.91 2280212 270570388 6012820 180447126 164525264305 261267113131 280920571366 413651880 1174582 886118 143673 0 776512 3256663 +linux_tuned 2 50 0x1b00 95 58.2 61.4 85.7 157.7 187.2 240.5 100030.0 100000 20 2070.12 2278306 270643134 6022166 180727728 164527254678 261777000854 281428120550 415063739 1178279 895845 143291 0 776387 3260539 +linux_tuned 0 50 0x1b00 75 58.0 61.3 86.4 158.9 188.1 239.5 100018.1 100000 20 2070.25 2278924 270657030 6021767 180716076 164694407428 261640358384 281320623408 415559548 1172220 885235 143682 0 777446 3254096 +linux_tuned 2 50 0x1b00 75 56.7 59.3 82.7 149.2 177.4 227.1 100033.0 100000 20 2048.5 2289109 271381142 6021540 180706734 161765662348 250815681252 269811138295 385823893 1158137 935371 139493 0 789548 3244761 +linux_tuned 0 50 0x1b00 55 57.5 60.6 84.3 152.6 181.0 232.0 100092.1 100000 20 2051.58 2286442 271273832 6025594 180829584 162535397738 252963068806 272838585813 382897097 1164205 930026 141171 0 779663 3246197 +linux_tuned 1 50 0x1b00 55 57.3 60.3 83.2 150.6 179.5 226.2 99856.5 100000 20 2056.76 2287014 271016197 6010587 180376446 162364292199 255084606382 274794318669 386302668 1171622 933144 144875 0 775774 3263945 +linux_tuned 2 50 0x1b00 55 57.4 60.4 83.8 152.8 181.3 228.2 100012.4 100000 20 2054.59 2286709 271183558 6020718 180683166 163133901860 256047022140 276152866937 390393014 1168102 924940 141463 0 779118 3255870 +linux_tuned 0 100 0x1b00 135 61.1 65.7 109.9 185.3 211.0 269.5 49986.8 50000 20 1794.84 1515025 160079170 3009011 90300240 85477347235 151393527693 184782735804 214955984 55084 729950 183172 0 818923 1626379 +linux_tuned 1 100 0x1b00 135 61.7 66.6 110.3 186.2 210.7 270.5 50062.7 50000 20 1791.97 1512797 159999075 3013580 90437544 85322901298 151419108600 185214431254 213869205 51294 720978 181457 0 821428 1628446 +linux_tuned 2 100 0x1b00 135 61.1 65.8 109.7 185.2 209.8 269.4 50067.9 50000 20 1790.69 1516240 160275879 3013802 90443820 85357330504 151284047458 184804908276 214448023 55724 731271 183286 0 819595 1630495 +linux_tuned 1 100 0x1b00 95 60.2 64.8 107.8 181.9 204.5 257.6 49944.6 50000 20 1772.05 1520478 160397576 3006547 90226650 83799717109 146212504752 181072375319 191924854 50263 727154 181782 0 826244 1626504 +linux_tuned 2 100 0x1b00 95 60.3 65.2 109.6 183.7 206.8 265.0 50022.4 50000 20 1778.04 1517399 160254494 3011183 90365730 84286257973 147696132693 181823521371 196890689 55004 733487 182043 0 824320 1626100 +linux_tuned 0 100 0x1b00 75 60.7 65.4 110.1 184.6 207.1 264.8 49953.7 50000 20 1776.77 1516282 160135879 3006932 90237540 84096813195 147709955049 182029035990 196387743 47652 717703 176777 0 808763 1611546 +linux_tuned 1 100 0x1b00 75 61.1 65.8 109.0 183.4 207.0 266.0 50027.1 50000 20 1775.81 1516907 160239220 3011166 90364224 84153054155 147662375943 181988538505 198160735 50378 723624 178419 0 815596 1619681 +linux_tuned 2 100 0x1b00 75 60.3 64.9 107.8 183.2 206.8 263.9 49978.4 50000 20 1780.6 1517225 160239169 3008651 90290094 84235307539 148187698094 182365597926 199162318 53582 730167 182541 0 822227 1625960 +linux_tuned 0 100 0x1b00 55 60.8 65.4 108.8 183.6 207.5 264.4 50092.9 50000 20 1781.83 1519977 160523248 3015513 90495876 84791711963 148716609686 181854309076 201640360 71172 763923 189893 0 837095 1650585 +linux_tuned 1 100 0x1b00 55 60.7 65.6 109.7 184.9 209.4 268.3 49964.8 50000 20 1783.71 1518133 160254792 3007495 90254856 84564238208 149142869183 183582037581 200520680 48749 722068 181312 0 818221 1625633 +linux_tuned 2 100 0x1b00 55 60.7 65.4 109.2 182.7 206.2 263.8 50055.4 50000 20 1778.96 1516522 160256661 3013138 90423936 84646538027 148994855510 182477687392 202376281 61186 743602 185502 0 818112 1629999 +linux_tuned 0 100 0x1b00 135 62.8 69.7 125.6 195.3 227.6 284.3 99982.2 100000 20 2024.1 2238527 267950728 6041257 181371276 159022852572 252618429900 283612041356 385083879 86215 1362945 148404 0 748557 2342031 +linux_tuned 1 100 0x1b00 135 62.4 68.9 124.8 194.1 225.6 284.8 99972.1 100000 20 2018.41 2236541 267825123 6040229 181339470 159086686300 251687067643 282954244042 387257555 79813 1345879 143207 0 742560 2325504 +linux_tuned 2 100 0x1b00 135 63.2 70.0 125.6 194.2 227.1 287.6 100030.9 100000 20 2025.46 2240126 268149701 6044039 181453368 159312669233 252642800672 283317180376 388805171 95648 1348774 146152 0 756479 2330138 +linux_tuned 0 100 0x1b00 95 62.7 69.7 125.6 193.6 225.9 281.7 99980.2 100000 20 2025.14 2239449 268009015 6040786 181355544 159417228130 252572176960 283529202247 391288152 93479 1367867 148941 0 753010 2344750 +linux_tuned 1 100 0x1b00 95 63.9 71.0 126.6 196.4 228.6 290.1 100015.7 100000 20 2021.48 2238394 267987832 6042967 181420662 159267145572 252875310478 283927641715 390216366 80662 1342090 142586 0 744036 2325221 +linux_tuned 2 100 0x1b00 95 62.3 68.7 124.0 192.8 225.3 282.0 99919.1 100000 20 2024.19 2236806 267768131 6036954 181240764 159395434034 253485757939 284575717533 392934451 89203 1367513 149946 0 748282 2347043 +linux_tuned 0 100 0x1b00 75 62.4 69.3 125.2 195.3 226.7 286.7 99933.1 100000 20 2025.53 2236401 267754997 6037265 181247856 159276359712 253356195786 284394931649 393024326 82202 1356232 146469 0 750623 2341945 +linux_tuned 1 100 0x1b00 75 62.6 69.5 125.5 195.0 226.7 286.6 100100.4 100000 20 2026.98 2241404 268270512 6048028 181573320 159924836789 254497687719 285565089392 395258610 83481 1359771 145525 0 744441 2340081 +linux_tuned 2 100 0x1b00 75 63.0 70.1 126.9 197.4 228.9 290.3 100120.2 100000 20 2024.46 2240404 268256593 6048550 181587642 159823507960 253851686076 284962056432 395102971 81890 1365362 147917 0 743579 2346589 +linux_tuned 0 100 0x1b00 55 63.4 70.8 127.4 200.3 234.0 299.8 100021.9 100000 20 2014.04 2238359 267996443 6042604 181407948 159535672401 253869259045 286996050283 395605334 77230 1350617 143787 0 737114 2338815 +linux_tuned 1 100 0x1b00 55 63.4 70.7 127.2 197.5 229.9 288.1 99952.3 100000 20 2017.39 2238497 267946508 6038628 181289790 159762304157 253718682981 285756738850 396574702 82597 1361258 146026 0 741752 2343218 +linux_tuned 2 100 0x1b00 55 62.3 68.6 124.3 195.8 228.7 289.6 100121.0 100000 20 2019.66 2239151 268169079 6048935 181599816 160180190784 254284310504 286076373950 399322997 83223 1351314 145343 0 745066 2337135 +linux_tuned 0 200 0x1b00 135 67.5 78.3 174.9 272.3 297.4 355.8 50056.7 50000 20 1755.82 1418928 153786372 3024400 90797376 82264749736 143052731850 175759909080 199594323 19398 375116 166573 0 839081 1181844 +linux_tuned 1 200 0x1b00 135 69.7 80.3 177.1 274.3 299.1 357.5 50073.1 50000 20 1848.64 1417204 153716169 3024958 90813054 82432895830 143759129666 176246824590 201481172 18632 366298 159547 0 822287 1174175 +linux_tuned 2 200 0x1b00 135 67.8 78.8 176.0 272.5 297.5 356.0 49944.2 50000 20 1754.43 1418423 153641973 3016936 90571512 82442533293 143904120324 176403198680 201549590 18433 366395 161261 0 822484 1175112 +linux_tuned 0 200 0x1b00 95 67.2 77.8 174.1 272.8 297.4 357.3 49961.1 50000 20 1728.59 1417099 153558088 3017969 90602934 82276409729 143881203715 176307066667 201804108 19694 373449 163023 0 824281 1174911 +linux_tuned 1 200 0x1b00 95 67.7 78.4 175.2 272.9 297.5 357.7 49895.7 50000 20 1755.76 1417048 153495678 3013839 90477816 82225809088 143265318548 175807526471 201796184 19286 371448 163544 0 827548 1176647 +linux_tuned 2 200 0x1b00 95 68.4 79.4 175.2 272.7 297.5 354.5 49938.6 50000 20 1757.21 1415443 153454718 3016618 90561954 82244163686 143573664848 176178123358 201982870 19450 375334 165648 0 831029 1179244 +linux_tuned 0 200 0x1b00 75 68.8 79.4 176.7 274.0 298.8 357.3 49988.3 50000 20 1760.07 1418058 153669559 3019503 90648252 82500147904 143980173748 176698893816 202575804 19707 376564 168892 0 838030 1183209 +linux_tuned 1 200 0x1b00 75 69.1 79.2 176.2 274.3 299.5 359.0 50022.5 50000 20 1756.52 1419110 153779369 3022074 90726792 82275581634 143741289395 176145847141 202684260 19068 373324 162956 0 821022 1170374 +linux_tuned 2 200 0x1b00 75 68.5 79.5 177.0 273.4 297.7 357.6 49908.2 50000 20 1755.64 1417465 153517823 3015085 90516930 82339170940 143588428805 175950136726 202712088 19237 369569 162023 0 820705 1168001 +linux_tuned 0 200 0x1b00 55 68.6 79.1 176.1 273.5 298.2 355.7 49954.0 50000 20 1755.57 1413540 153304192 3017907 90601590 82469021184 143858503412 176270623092 202840328 18663 367754 161987 0 820678 1169906 +linux_tuned 1 200 0x1b00 55 68.7 79.6 175.7 273.2 297.7 356.1 49918.9 50000 20 1755.26 1415035 153399338 3015423 90525978 82279260464 143980634203 176561196103 203254549 20960 384603 169932 0 834782 1179867 +linux_tuned 2 200 0x1b00 55 67.6 78.2 176.3 273.0 297.9 357.6 49972.4 50000 20 1757.68 1416515 153526619 3018840 90628974 82426427324 143790223607 176344370208 202954231 18888 369586 161995 0 827172 1176925 +linux_tuned 0 200 0x1b00 135 84.2 105.0 200.3 283.7 313.3 376.4 99976.5 100000 20 1996.23 2204453 265727893 6084811 182829978 156351220748 241995281156 271608980564 394812515 23573 492787 168248 0 863596 1406544 +linux_tuned 1 200 0x1b00 135 84.0 105.5 199.9 284.8 314.7 376.3 100061.7 100000 20 1995.53 2207776 266060639 6089506 182967336 156420896318 241644987337 271060588969 395193803 23568 492674 166550 0 866658 1403825 +linux_tuned 2 200 0x1b00 135 84.0 105.7 199.9 284.8 314.7 376.0 99923.8 100000 20 2086.03 2206489 265825856 6080829 182707392 156402635677 241206647337 270824298999 407427419 23141 489651 165696 0 863173 1398115 +linux_tuned 0 200 0x1b00 95 81.7 103.0 199.8 284.3 314.7 378.0 100060.2 100000 20 1995.12 2206676 265926209 6089315 182961570 156369251427 242009049325 271495592681 395854280 22529 485756 163556 0 858455 1402243 +linux_tuned 1 200 0x1b00 95 85.8 107.5 203.3 287.8 317.9 376.6 100025.5 100000 20 1998.12 2202822 265644575 6087711 182915466 156619671126 241561414865 270984307804 396508207 22940 487937 165407 0 865043 1402428 +linux_tuned 2 200 0x1b00 95 82.7 103.3 198.9 281.9 310.3 372.2 100004.6 100000 20 1995.83 2203478 265664783 6085997 182863038 156514913910 241997356762 271451435425 396890857 22479 486148 164570 0 863179 1406399 +linux_tuned 0 200 0x1b00 75 85.8 107.7 202.7 286.9 317.8 384.0 100044.7 100000 20 1996.59 2205273 265832022 6089129 182958468 156587043754 242029511760 271386885427 397539046 22055 481137 162437 0 857508 1399581 +linux_tuned 1 200 0x1b00 75 86.4 108.4 204.0 287.9 318.3 377.9 99851.4 100000 20 1997.08 2203297 265485968 6077575 182611194 156024621557 240555368211 269963393457 394697677 22582 485964 164328 0 857980 1400788 +linux_tuned 2 200 0x1b00 75 86.0 107.9 202.8 286.4 316.0 377.1 99949.4 100000 20 1997.6 2206876 265838513 6083342 182784204 156315060700 241837602288 271297977488 397904400 22544 487242 164817 0 859552 1405292 +linux_tuned 0 200 0x1b00 55 84.5 106.0 201.4 287.2 317.8 380.8 99905.1 100000 20 1992.81 2206388 265781768 6080453 182696598 156573515941 242389153893 272922022380 396465589 23788 487813 164827 0 863665 1402079 +linux_tuned 1 200 0x1b00 55 83.8 104.5 199.7 283.8 313.7 378.2 100075.6 100000 20 1996.0 2205589 265881568 6091307 183025776 156783023910 243139429968 273154561461 398761415 23222 489911 167470 0 862872 1406911 +linux_tuned 2 200 0x1b00 55 83.0 104.0 200.4 286.3 317.3 383.8 99946.2 100000 20 1992.78 2208725 265962938 6082787 182767434 156634990295 242468053822 273224362286 397946537 24381 496796 167859 0 862416 1399696 +linux_tuned 0 300 0x1b00 135 79.3 99.9 242.8 365.3 393.4 450.2 49958.6 50000 20 1834.62 1350348 149167724 3029631 90991476 82041427793 139471853686 169156645272 205874488 12873 199812 117441 0 766893 889475 +linux_tuned 1 300 0x1b00 135 79.9 102.1 243.7 366.3 394.4 450.3 50094.0 50000 20 1744.76 1350058 149315970 3037598 91229178 82440676476 140313058308 170225757793 207303392 13713 207728 123365 0 781139 895502 +linux_tuned 2 300 0x1b00 135 81.9 103.8 245.2 366.8 395.0 452.2 50098.7 50000 20 1746.65 1350962 149383103 3037858 91237326 82131362486 140618518151 170564828867 206619171 13572 205714 122829 0 782386 898075 +linux_tuned 0 300 0x1b00 95 83.2 106.6 248.2 368.0 396.0 454.0 49954.3 50000 20 1744.72 1348568 149053791 3029246 90979014 82165335470 140411986187 170364436460 207043738 14065 209531 124860 0 782825 894174 +linux_tuned 1 300 0x1b00 95 82.6 105.0 247.9 368.0 396.2 455.9 50011.6 50000 20 1741.43 1350632 149262978 3032041 91060830 82242291140 140311915286 170034035835 207524205 13401 206482 122575 0 773340 889374 +linux_tuned 2 300 0x1b00 95 81.0 102.2 243.7 365.5 393.3 452.6 49995.1 50000 20 1744.04 1350111 149196711 3031902 91060056 82165630453 140163372222 170242146389 207101121 13723 210959 125904 0 785353 895509 +linux_tuned 0 300 0x1b00 75 79.8 101.6 245.3 366.6 394.5 449.6 49953.7 50000 20 1744.67 1347946 149030243 3029034 90973446 82093021287 140458690787 170323373504 207097880 13676 204964 122159 0 779944 894600 +linux_tuned 1 300 0x1b00 75 82.7 104.5 245.3 366.4 395.0 453.7 50038.2 50000 20 1742.29 1350622 149298347 3034581 91140444 82346650124 140521661199 170400868232 208555779 13716 207420 123874 0 779303 893583 +linux_tuned 2 300 0x1b00 75 79.2 100.3 243.8 366.3 394.9 455.2 49964.2 50000 20 1742.97 1349215 149124296 3029441 90985158 82132315596 140290204874 169978784513 207339081 13081 201323 119217 0 769297 890299 +linux_tuned 0 300 0x1b00 55 82.1 104.9 244.9 367.0 395.0 452.9 50085.6 50000 20 1742.13 1349915 149297533 3036884 91207416 82388276504 140544719788 170116828544 208635254 13241 204429 120172 0 769844 887440 +linux_tuned 1 300 0x1b00 55 84.4 107.9 248.2 368.8 396.5 457.0 49956.0 50000 20 1746.22 1350449 149177025 3029258 90979950 82280290488 140458968282 170350017051 207558456 13448 204474 121970 0 777665 893956 +linux_tuned 2 300 0x1b00 55 78.9 99.3 241.4 366.0 394.1 450.0 50021.8 50000 20 1743.37 1348673 149149227 3033217 91097604 82489050662 140583655632 170374954644 208630196 13402 205944 122878 0 776426 890396 +linux_tuned 0 300 0x1b00 135 112.0 139.7 269.5 376.6 402.6 471.9 100068.9 100000 20 1977.95 2204382 265804734 6130847 184361730 157391904948 237073330709 264527554983 404845077 14488 221470 126992 0 794957 967910 +linux_tuned 1 300 0x1b00 135 112.9 141.6 271.4 378.8 404.9 476.3 100052.8 100000 20 1979.11 2202094 265665695 6129516 184323192 157075241513 236218070105 263194645441 401098394 13965 216848 122753 0 782359 962627 +linux_tuned 2 300 0x1b00 135 114.7 141.6 269.7 377.4 403.3 476.0 100080.7 100000 20 1979.86 2204722 265824286 6130737 184357266 157443712898 236737102041 264190004756 404994270 14801 224004 127338 0 794324 968134 +linux_tuned 0 300 0x1b00 95 115.7 143.5 271.4 378.1 404.2 475.4 100160.4 100000 20 1980.74 2203891 265904124 6135688 184506228 157431713591 236609483534 263807207308 404549718 14128 218875 125169 0 790953 967698 +linux_tuned 1 300 0x1b00 95 116.3 143.3 271.4 378.8 404.1 477.4 100023.2 100000 20 1980.36 2201668 265589902 6127422 184255908 157278144175 236914564323 264260492330 404127415 13833 213726 123593 0 786871 967102 +linux_tuned 2 300 0x1b00 95 118.9 146.0 273.9 379.0 403.8 473.9 100088.0 100000 20 1980.8 2202462 265728958 6131131 184369344 157254046328 236542913373 263653212494 405327552 13603 212213 121690 0 784955 967678 +linux_tuned 0 300 0x1b00 75 116.7 144.7 273.7 380.7 405.1 476.8 99974.6 100000 20 1978.69 2200892 265483855 6125220 184192128 156989556200 235673405538 262729051050 404606728 13953 216109 122845 0 786152 966481 +linux_tuned 1 300 0x1b00 75 116.1 142.8 271.5 377.8 403.8 473.9 99989.3 100000 20 1976.59 2201862 265555421 6126222 184225464 157030445342 236366038277 263673585661 405837818 14122 217090 123152 0 783639 965028 +linux_tuned 2 300 0x1b00 75 115.1 143.3 271.1 377.8 403.6 475.0 100230.3 100000 20 1981.72 2205581 266060141 6140661 184660068 157779148784 237917119021 265265779563 408531477 13851 213470 122651 0 786951 967821 +linux_tuned 0 300 0x1b00 55 117.5 144.4 272.4 379.2 404.9 477.6 99924.3 100000 20 1981.63 2198130 265218783 6121147 184064916 157515213200 237754564671 265684607220 407480729 14680 220431 127352 0 796266 968464 +linux_tuned 1 300 0x1b00 55 111.4 138.0 267.1 374.0 402.5 471.1 100027.7 100000 20 1980.69 2199284 265440583 6127279 184251846 157271919595 237344369576 264792267882 406792264 14054 215617 124396 0 790527 967314 +linux_tuned 2 300 0x1b00 55 112.4 139.5 269.5 375.9 402.4 473.4 99926.4 100000 20 1978.37 2199445 265338284 6121804 184090770 157146710860 237336329064 264897829100 407247711 14046 214333 124120 0 786958 967951 +linux_tuned 0 50 0x1d00 135 55.8 57.7 76.4 132.2 158.0 200.1 49945.4 50000 20 2015.46 1596049 165370941 3002285 90085530 86851264569 144372638969 149860806261 207424199 489943 733535 171603 0 843157 2098868 +linux_tuned 1 50 0x1d00 135 55.8 57.7 76.1 130.5 158.3 200.1 50049.3 50000 20 2021.88 1603037 165964147 3008557 90273702 86900710368 144416997526 149959668102 207740600 491840 744451 169314 0 825366 2098478 +linux_tuned 2 50 0x1d00 135 56.3 58.4 76.9 132.8 159.8 201.7 49945.1 50000 20 2025.48 1606243 166046973 3002425 90090168 86785960771 144132709190 149694075923 207051506 484480 731186 170066 0 834516 2094648 +linux_tuned 0 50 0x1d00 95 56.4 58.5 76.8 133.5 160.7 200.2 50194.0 50000 20 2026.59 1603893 166191832 3017008 90526686 87118931225 144798058428 150275206807 208764713 483293 721275 168709 0 842050 2100355 +linux_tuned 1 50 0x1d00 95 56.4 58.5 77.2 133.2 160.5 200.1 50048.6 50000 20 2027.99 1595314 165445586 3008442 90270162 86893898983 144211754912 149700643756 207947900 479265 708479 164882 0 823907 2082019 +linux_tuned 2 50 0x1d00 95 55.7 57.6 76.3 131.5 158.4 201.6 49995.0 50000 20 2028.31 1604697 166003489 3005332 90177090 86900145441 144492794505 150061243792 209103872 481307 716908 167502 0 829381 2089214 +linux_tuned 0 50 0x1d00 75 55.6 57.4 76.0 130.7 159.0 200.1 49985.7 50000 20 2028.18 1603639 165930227 3004785 90160644 86904341767 144559136167 150029440362 208714284 482021 715068 167698 0 829760 2089028 +linux_tuned 1 50 0x1d00 75 55.6 57.5 76.2 130.9 158.6 199.0 49951.1 50000 20 2025.29 1597344 165433539 3002419 90088974 86775808687 144239147056 149732441792 208510787 486842 721167 167109 0 834114 2089487 +linux_tuned 2 50 0x1d00 75 55.6 57.4 76.0 130.8 159.1 199.8 50148.3 50000 20 2028.64 1605126 166223807 3014382 90448020 87238466531 145129007059 150563248466 209882931 487871 734524 171012 0 832813 2103326 +linux_tuned 0 50 0x1d00 55 55.7 57.5 76.2 131.4 158.9 199.2 50034.1 50000 20 2013.3 1602754 165903420 3007731 90249228 86954188689 144459261277 150953998497 209282646 484619 718261 164909 0 823302 2088952 +linux_tuned 1 50 0x1d00 55 55.8 57.7 76.5 133.2 160.6 201.9 49975.6 50000 20 2017.68 1603715 165919892 3003999 90136506 86900197700 144588704170 150748616234 210235804 481241 712356 168019 0 822334 2088719 +linux_tuned 2 50 0x1d00 55 56.8 59.1 77.8 132.3 160.1 201.6 50119.1 50000 20 2016.19 1606716 166283573 3012800 90401166 87097294559 144595846037 150997726553 210000204 486286 722300 165978 0 823101 2095341 +linux_tuned 0 50 0x1d00 135 56.5 58.9 81.2 146.2 173.2 223.0 100044.8 100000 20 2240.78 2291353 271553602 6021926 180716982 164372411360 260069201097 260637912148 409695497 1121741 995806 136357 0 783360 3232398 +linux_tuned 1 50 0x1d00 135 56.5 58.9 81.5 145.8 173.4 219.8 100023.2 100000 20 2251.71 2287216 271214161 6020244 180665412 164692349825 260976154051 261463010665 411856525 1128904 999897 139920 0 783619 3245993 +linux_tuned 2 50 0x1d00 135 56.3 58.7 81.5 147.6 175.2 223.2 100096.4 100000 20 2251.66 2292162 271640508 6024989 180808320 164714534964 260816459989 261380544409 411271687 1118080 992524 136835 0 780799 3236801 +linux_tuned 0 50 0x1d00 95 56.7 59.2 82.3 147.3 175.1 222.2 99980.2 100000 20 2253.42 2298655 271934955 6017922 180596508 164591237339 260665898858 261271013526 411502250 1121912 1002185 138046 0 782755 3243060 +linux_tuned 1 50 0x1d00 95 56.7 59.2 81.5 144.9 171.9 218.2 100107.4 100000 20 2253.57 2296548 271943481 6025295 180817020 164807430829 261211867579 261701627102 413132951 1132268 1014935 139697 0 785659 3252031 +linux_tuned 2 50 0x1d00 95 56.8 59.4 82.8 148.6 176.3 223.7 100030.9 100000 20 2254.22 2292567 271553781 6020537 180673662 164759209304 261153743836 261657507024 412739267 1127241 1005308 140032 0 783882 3248158 +linux_tuned 0 50 0x1d00 75 56.2 58.5 80.7 146.0 172.8 220.0 100074.8 100000 20 2255.31 2290082 271472897 6023419 180760836 164802893471 261534369729 262053677793 419226146 1126547 1001010 140317 0 784600 3247805 +linux_tuned 1 50 0x1d00 75 56.3 58.6 80.9 146.3 173.5 221.2 99984.0 100000 20 2255.98 2289780 271368819 6018252 180606636 164616732625 260758670156 261245485609 412502144 1115162 981921 137687 0 784522 3232300 +linux_tuned 2 50 0x1d00 75 56.5 58.9 81.2 146.0 174.2 220.0 100197.9 100000 20 2258.27 2292136 271752739 6030666 180977880 164803553555 261554020537 262045799914 414514849 1124506 990514 140313 0 784701 3246136 +linux_tuned 0 50 0x1d00 55 57.4 60.5 85.4 156.2 186.7 242.4 99974.9 100000 20 2123.73 2279061 270654379 6019423 180647574 164183091931 259248553577 273971913324 412095888 1150462 930977 141215 0 776103 3249835 +linux_tuned 1 50 0x1d00 55 57.3 60.4 84.8 157.4 185.9 244.9 99933.5 100000 20 2123.93 2283012 270836115 6017956 180607314 164388182775 259589181531 274134112702 413087814 1151420 931007 140655 0 777077 3249512 +linux_tuned 2 50 0x1d00 55 57.4 60.3 83.5 154.6 183.6 234.9 100102.1 100000 20 2125.79 2287804 271354225 6027638 180896184 164631903214 260318723358 274550925294 412652117 1140852 913309 136623 0 771755 3236141 +linux_tuned 0 100 0x1d00 135 59.1 63.9 108.6 182.2 204.3 259.3 50057.1 50000 20 1936.74 1525207 160843968 3013003 90419844 84976994579 151693304945 177899297370 208367241 51674 733003 183793 0 831100 1630034 +linux_tuned 1 100 0x1d00 135 59.5 64.4 108.9 182.1 205.1 262.9 50023.0 50000 20 1935.81 1518339 160332262 3011109 90363366 85018462330 152021022726 178133685694 209589908 53981 735407 183453 0 839108 1635994 +linux_tuned 2 100 0x1d00 135 59.8 64.6 108.4 181.7 205.2 262.0 49940.9 50000 20 1932.54 1520123 160352231 3005987 90208782 84877861487 151464502936 177651505043 208079981 50874 728524 181946 0 827360 1624760 +linux_tuned 0 100 0x1d00 95 59.3 64.1 109.6 183.0 205.3 258.9 49954.1 50000 20 1935.91 1519601 160329660 3006900 90236574 84988916485 151868580040 177985426268 209098707 52480 730113 184317 0 833151 1628551 +linux_tuned 1 100 0x1d00 95 59.0 63.7 107.9 181.1 204.2 259.0 49967.4 50000 20 1935.13 1523366 160573864 3007909 90267564 85185221817 151811024511 177450388796 210263906 60487 751033 190098 0 846536 1645953 +linux_tuned 2 100 0x1d00 95 59.3 64.1 108.7 182.3 204.8 259.8 49974.0 50000 20 1932.62 1522025 160527989 3008030 90270558 84944374691 151813956368 177995608719 209740870 51958 727851 182759 0 829829 1627579 +linux_tuned 0 100 0x1d00 75 59.1 63.9 108.5 181.1 204.1 259.7 49966.1 50000 20 1934.03 1520187 160389896 3007612 90257826 85147600255 151923437025 178026047583 210163159 52894 729586 185500 0 832909 1630281 +linux_tuned 1 100 0x1d00 75 60.8 65.6 110.1 184.4 207.4 266.5 49992.4 50000 20 1937.66 1519697 160391173 3009323 90309606 85066739793 152072163581 177887650477 210455266 56442 736058 184262 0 836907 1632861 +linux_tuned 2 100 0x1d00 75 59.3 64.0 108.7 182.0 204.9 257.6 49892.7 50000 20 1930.47 1519708 160262444 3003409 90132672 84933426628 152027754491 178418262259 210278475 45525 716418 178917 0 819353 1614877 +linux_tuned 0 100 0x1d00 55 59.7 64.4 109.1 182.4 204.9 262.1 49967.4 50000 20 1922.31 1518246 160230794 3007588 90256842 85092276131 152140120716 179612569578 210784587 45154 717591 179780 0 815358 1619231 +linux_tuned 1 100 0x1d00 55 59.8 64.9 110.5 185.1 207.8 267.2 50085.2 50000 20 2020.02 1522738 160696178 3014810 90473982 85312411302 151718054208 178945866190 212318847 64334 748378 187419 0 835968 1640448 +linux_tuned 2 100 0x1d00 55 59.5 64.5 109.9 183.0 205.2 262.7 50146.4 50000 20 1926.65 1523936 160848413 3018521 90585900 85481687856 152526346086 179675582344 211473462 46161 722309 179198 0 820580 1623953 +linux_tuned 0 100 0x1d00 135 61.2 67.7 123.4 189.8 221.5 274.8 99935.6 100000 20 2204.2 2238258 267917201 6037010 181239912 160788624220 260154882026 275265039796 411175926 77109 1377914 150238 0 757627 2351241 +linux_tuned 1 100 0x1d00 135 61.4 68.2 123.8 191.8 222.6 275.7 99990.7 100000 20 2205.77 2242015 268195989 6041786 181387152 160829571204 259557887209 274363004134 409277352 81994 1358573 146975 0 755649 2330712 +linux_tuned 0 100 0x1d00 95 61.8 68.3 123.8 188.0 215.3 269.6 100046.7 100000 20 2177.09 2242899 268350096 6043962 181448520 158091937170 250988840945 267646290354 369435108 78931 1374070 149245 0 773516 2341837 +linux_tuned 1 100 0x1d00 95 61.2 68.1 124.4 189.8 219.1 272.3 100006.4 100000 20 2187.75 2243166 268280591 6041489 181374708 158587684860 252816433927 268988433177 378449067 76700 1356927 145541 0 766578 2325903 +linux_tuned 2 100 0x1d00 95 62.2 69.3 125.4 191.3 220.4 274.4 99971.5 100000 20 2194.93 2243220 268282474 6039318 181308822 158902260609 254627976101 270530520647 381538875 80950 1375593 147676 0 764055 2340841 +linux_tuned 0 100 0x1d00 75 61.9 69.5 124.0 188.8 218.4 272.2 99974.3 100000 20 2159.15 2238539 267937937 6039347 181309320 159776120185 257377078757 270360866065 390974062 260270 1423116 155954 0 757016 2315102 +linux_tuned 1 100 0x1d00 75 60.5 66.7 122.4 187.9 218.1 272.2 99919.5 100000 20 2285.08 2240480 268007421 6036297 181218210 159010777833 254905243469 270800523581 384745128 69857 1350466 145346 0 758128 2322917 +linux_tuned 2 100 0x1d00 75 60.5 66.9 121.8 187.3 217.4 270.2 99943.8 100000 20 2196.4 2235971 267750295 6037882 181265982 159389831929 256437368060 272423511771 387823441 71890 1375802 148213 0 760127 2346110 +linux_tuned 0 100 0x1d00 55 62.9 70.2 127.2 202.1 240.0 343.5 100025.6 100000 20 2059.44 2233937 267726683 6046484 181539342 159144034906 252833153932 287658284365 389196574 93855 1336943 147051 0 749528 2345210 +linux_tuned 1 100 0x1d00 55 61.4 68.2 124.9 197.7 232.5 327.4 99874.5 100000 20 2052.02 2235000 267593350 6036173 181224378 158967306767 252218009039 288823886440 387936297 84683 1324849 144727 0 741829 2337522 +linux_tuned 2 100 0x1d00 55 62.0 69.0 125.7 199.4 235.9 332.1 100015.0 100000 20 2054.85 2239127 268061679 6044983 181490064 159411566729 253208962738 289385145915 391692634 91204 1345662 147875 0 742297 2351325 +linux_tuned 0 200 0x1d00 135 67.1 78.1 176.1 272.5 296.6 352.6 50078.2 50000 20 1900.81 1421391 154011305 3025479 90828798 83390278076 147019945865 171606977797 209437042 18539 370574 162439 0 830626 1176741 +linux_tuned 1 200 0x1d00 135 67.6 79.0 176.6 272.4 296.9 351.5 50012.1 50000 20 1904.48 1420958 153889343 3021176 90699090 83073868511 147030500907 171768537116 209771596 19378 373580 165223 0 839039 1182111 +linux_tuned 2 200 0x1d00 135 66.8 77.5 174.0 271.5 296.2 354.0 49943.9 50000 20 1899.53 1422356 153904705 3017023 90573966 82965414148 146668692552 171312349397 209301910 18610 370577 161790 0 828710 1179695 +linux_tuned 0 200 0x1d00 95 66.3 76.9 172.7 271.3 295.7 351.1 49989.9 50000 20 1902.73 1420230 153818042 3019936 90662304 83024776371 146806818730 171303989770 209223362 18434 365327 158491 0 820545 1172060 +linux_tuned 1 200 0x1d00 95 65.4 76.0 172.8 270.8 294.8 350.7 50001.5 50000 20 1904.15 1419942 153798064 3020589 90681420 83169578292 146931598216 171609070437 209713112 18958 369975 163551 0 833487 1178510 +linux_tuned 2 200 0x1d00 95 66.9 78.4 176.0 272.2 296.7 351.7 50012.5 50000 20 1901.77 1419858 153831616 3021336 90704190 82932916412 146747909069 171275487758 209664410 19675 377004 168231 0 832978 1173925 +linux_tuned 0 200 0x1d00 75 68.1 78.9 176.5 273.3 297.7 355.7 49853.7 50000 20 1902.08 1416390 153403920 3011606 90411120 82740340676 146474155883 171058301335 209226507 18382 366928 158970 0 823756 1169795 +linux_tuned 1 200 0x1d00 75 67.1 78.6 175.1 272.5 296.9 352.9 50001.7 50000 20 1889.42 1419265 153767537 3020385 90675174 83087996127 147027641481 171426688915 210966535 23555 402339 175239 0 835997 1172929 +linux_tuned 2 200 0x1d00 75 67.3 78.4 175.6 272.7 297.4 353.4 50099.5 50000 20 1902.05 1424189 154225514 3026566 90861378 83049217800 147023889044 171695384007 210100668 19379 374518 163576 0 835150 1178821 +linux_tuned 0 200 0x1d00 55 67.0 78.1 174.9 273.4 297.9 356.4 49980.7 50000 20 1895.69 1419899 153802801 3019149 90638526 82750852295 146573194782 171870164832 209938416 18431 369169 161915 0 830214 1175312 +linux_tuned 1 200 0x1d00 55 67.2 78.2 175.0 272.6 297.8 358.3 49948.5 50000 20 1899.37 1419831 153751944 3017240 90580974 82960643611 147062391331 172145979714 209593245 19208 373672 165240 0 833361 1179477 +linux_tuned 2 200 0x1d00 55 66.6 77.8 174.0 272.5 297.3 356.3 49969.3 50000 20 1898.35 1414940 153415052 3018811 90628746 82915947768 146694444349 171421288332 209728502 18969 370508 162410 0 827669 1173384 +linux_tuned 0 200 0x1d00 135 82.8 104.1 197.8 278.3 305.1 364.4 100013.6 100000 20 2162.5 2208410 266042025 6086707 182882280 155373116039 243085360496 257582304789 386108104 23789 497254 165964 0 878952 1403685 +linux_tuned 1 200 0x1d00 135 79.7 100.5 195.8 276.5 303.7 364.2 100175.6 100000 20 2162.99 2209454 266268391 6095484 183143670 155839801720 243353805496 257889287363 388491933 23530 494414 163941 0 876347 1401352 +linux_tuned 2 200 0x1d00 135 81.0 102.7 197.6 280.7 308.5 366.1 100018.0 100000 20 2162.92 2203152 265657598 6087476 182907420 155717067434 242562753121 257028334232 387592813 22551 492082 161652 0 876181 1399563 +linux_tuned 0 200 0x1d00 95 83.9 105.4 199.6 281.4 308.6 365.9 100020.0 100000 20 2161.88 2205674 265804680 6086650 182882112 155741242299 243711843122 258216243948 389671459 23324 493637 165414 0 877713 1407101 +linux_tuned 1 200 0x1d00 95 81.3 102.6 197.0 278.3 305.4 363.8 100057.9 100000 20 2165.83 2209218 266104999 6090089 182987352 155778733247 242915909932 257181837931 390256472 22144 487079 162692 0 867623 1399062 +linux_tuned 2 200 0x1d00 95 81.3 102.4 197.7 279.0 307.8 366.6 100048.3 100000 20 2160.9 2204623 265818334 6088890 182950242 155711506727 243083909311 257268798781 389299706 22937 486314 160552 0 862208 1387527 +linux_tuned 0 200 0x1d00 75 83.0 104.5 198.1 279.8 308.1 367.4 100068.9 100000 20 2167.74 2206174 265934449 6089819 182978370 156004820901 243676676709 257976225071 390956148 22227 484988 160716 0 871267 1398782 +linux_tuned 1 200 0x1d00 75 83.5 103.9 197.7 277.9 304.9 365.3 99897.9 100000 20 2166.68 2203404 265570317 6079510 182666232 156286006048 244108232700 258385317196 393516932 23548 496093 165908 0 878163 1404123 +linux_tuned 2 200 0x1d00 75 82.3 103.1 197.9 278.3 305.6 366.1 100124.1 100000 20 2165.39 2207331 266093917 6093873 183100440 156284681758 245112231253 259576001690 394724459 23964 498202 165614 0 876768 1406645 +linux_tuned 0 200 0x1d00 55 81.0 102.3 198.6 287.3 320.6 408.7 99939.2 100000 20 2039.05 2203070 265584172 6084717 182833524 156421685921 242457760878 276545574526 396425828 24171 491938 166723 0 854540 1404994 +linux_tuned 1 200 0x1d00 55 83.4 104.6 199.8 288.3 323.6 408.5 99844.8 100000 20 2042.42 2199041 265165994 6078244 182635932 156083225491 241903881407 275314619385 394879658 24049 490285 165757 0 858949 1404961 +linux_tuned 2 200 0x1d00 55 83.6 104.8 200.6 288.9 324.5 429.6 100047.8 100000 20 2030.35 2204055 265776564 6090780 183013242 156723074787 243022809265 279656222667 397562290 24646 495821 166279 0 855634 1407444 +linux_tuned 0 300 0x1d00 135 83.0 106.0 246.7 366.4 394.0 446.3 50092.6 50000 20 1885.71 1351715 149431438 3037477 91226898 82856110353 143072255431 165096813936 212168829 13248 206069 121248 0 781499 896896 +linux_tuned 1 300 0x1d00 135 81.3 105.2 245.8 365.6 393.5 444.8 49918.5 50000 20 1884.44 1351140 149171338 3026786 90904968 82613202301 143066281642 165287256327 220039709 13777 209611 124563 0 786673 895916 +linux_tuned 2 300 0x1d00 135 79.0 101.1 241.5 364.2 391.7 444.8 50101.7 50000 20 1884.99 1354490 149638285 3037452 91226496 82912329213 143286573169 165244318695 213686806 13819 209408 122706 0 779784 894074 +linux_tuned 0 300 0x1d00 95 79.8 100.6 239.9 364.8 392.9 444.8 49910.3 50000 20 1779.5 1353517 149341489 3026077 90881892 82648684354 143183493029 165473990256 212527591 16600 244209 139054 0 783900 875771 +linux_tuned 1 300 0x1d00 95 80.9 104.8 245.6 366.2 393.9 444.8 50053.6 50000 20 1886.3 1352701 149422068 3035039 91152882 82876052509 143089456683 165113038827 212808317 13395 207620 122712 0 783214 893473 +linux_tuned 2 300 0x1d00 95 81.8 105.7 242.7 365.2 393.2 444.9 50091.6 50000 20 1885.96 1352851 149473819 3037422 91224276 82957102026 143312430848 165308766816 213733541 13669 210042 123506 0 784022 893856 +linux_tuned 0 300 0x1d00 75 79.2 101.4 242.9 363.9 391.1 444.0 49904.4 50000 20 1885.38 1348623 149007732 3025611 90868122 82363675859 142550207743 164468276085 212263135 13204 204397 119057 0 775432 893427 +linux_tuned 1 300 0x1d00 75 82.3 106.1 244.9 366.1 394.3 448.4 49984.3 50000 20 1884.46 1351336 149276479 3031061 91032870 82663546128 142818302139 164723559460 212969696 13414 203443 119504 0 776944 893023 +linux_tuned 2 300 0x1d00 75 79.6 102.3 242.3 364.2 391.8 444.1 50075.3 50000 20 1884.06 1354135 149554523 3036490 91196868 82840119102 143383171908 165364752099 213716028 14042 212699 125660 0 783974 894353 +linux_tuned 0 300 0x1d00 55 82.5 106.4 245.8 366.2 394.0 445.2 49921.7 50000 20 1882.02 1350408 149149179 3027114 90914790 82675982610 142975478824 165118630230 212654963 13493 204185 121338 0 777800 894179 +linux_tuned 1 300 0x1d00 55 79.3 102.5 242.2 364.8 392.9 446.2 50123.9 50000 20 1884.34 1352017 149490128 3039539 91288140 82902732751 143662697077 165734456163 214557436 14083 210107 125202 0 785093 895664 +linux_tuned 2 300 0x1d00 55 77.4 98.4 238.9 364.6 392.3 447.0 50007.9 50000 20 1878.56 1352968 149407129 3032661 91082256 82519762009 142297901989 163816303972 213510372 13287 203696 118894 0 762172 880329 +linux_tuned 0 300 0x1d00 135 112.6 139.8 267.5 372.4 400.2 466.4 100069.4 100000 20 2144.96 2202192 265648182 6129974 184330806 157184422060 239056340989 251096020369 400263396 14092 219330 124197 0 801016 967741 +linux_tuned 1 300 0x1d00 135 112.9 139.7 266.1 372.1 400.1 466.5 99923.5 100000 20 2143.6 2198895 265257550 6120847 184056912 156928937488 238720857402 250900963150 400938226 14866 223060 124473 0 801637 967172 +linux_tuned 2 300 0x1d00 135 110.3 137.7 265.7 371.9 400.1 465.6 99879.8 100000 20 2145.92 2197914 265156690 6117877 183968688 156759740950 238808657448 250708091429 404307497 14566 221997 124004 0 802079 966591 +linux_tuned 0 300 0x1d00 95 111.2 137.2 265.1 369.1 397.9 460.6 100048.2 100000 20 2149.59 2202749 265678952 6127757 184264404 157108652957 239103906050 251098081776 401274456 14591 224533 126265 0 802264 967677 +linux_tuned 1 300 0x1d00 95 110.9 137.0 265.3 369.2 398.8 464.5 99977.4 100000 20 2148.98 2201067 265510457 6124165 184159788 156922364668 239244362466 251251847461 401597900 14732 222842 124458 0 800140 966372 +linux_tuned 2 300 0x1d00 95 112.1 138.6 266.8 372.1 399.8 464.3 99956.7 100000 20 2146.26 2201734 265494892 6122806 184114896 157090760621 239091848242 251025150749 401523599 14174 218141 123464 0 798661 966317 +linux_tuned 0 300 0x1d00 75 111.6 137.6 264.3 370.4 398.4 460.1 99802.5 100000 20 2147.23 2197843 265039051 6113951 183851928 157090949538 238751323841 250361859356 401513025 14041 215563 122128 0 796227 967408 +linux_tuned 1 300 0x1d00 75 109.9 136.6 265.9 372.6 399.8 468.0 100004.9 100000 20 2147.68 2201239 265531311 6125410 184193044 157011023023 239012662595 250963749571 400819242 14264 219983 122958 0 796164 966836 +linux_tuned 2 300 0x1d00 75 112.7 138.2 264.3 370.5 398.9 462.5 99867.0 100000 20 2146.81 2198406 265180518 6117574 183961452 156923104788 238648753650 250486294528 403806837 14058 216285 121678 0 798621 967954 +linux_tuned 0 300 0x1d00 55 112.9 140.4 270.7 383.6 418.8 527.7 100010.0 100000 20 2029.89 2202917 265636862 6127800 184272876 156981342694 236184004552 266872966523 402342452 14039 217935 123459 0 777993 965059 +linux_tuned 1 300 0x1d00 55 111.6 138.6 269.8 380.5 410.3 508.7 99980.2 100000 20 2029.2 2199433 265354296 6126566 184237884 156960266013 236279196449 267735689495 403630640 13588 211320 120128 0 776038 966020 +linux_tuned 2 300 0x1d00 55 112.8 140.6 270.2 382.6 415.3 514.4 99986.6 100000 20 2030.16 2201512 265546359 6126250 184226400 157067852824 237515233509 268736886539 405031606 14766 227670 128536 0 788421 967950 linux_tuned 0 50 0x1d00 135 59.6 64.8 101.8 192.3 225.0 316.4 200142.7 200000 20 2607.76 4235590 520224046 12136378 364497174 309419334508 462089718209 462092800297 759220603 1736751 1399139 61961 0 237491 4760085 linux_tuned 1 50 0x1d00 135 60.4 65.5 102.5 194.2 228.1 325.0 199964.8 200000 20 2608.89 4231339 519730418 12125551 364171188 310631756406 463214569688 463232344741 783849682 1741647 1406484 61040 0 234580 4764936 linux_tuned 2 50 0x1d00 135 59.1 64.1 102.0 196.4 233.4 332.3 200254.7 200000 20 2612.5 4240641 520682505 12147414 364843902 311154050894 464478696728 464478967277 789428316 1719420 1369246 62283 0 245083 4744091 @@ -880,29 +2391,6 @@ linux_tuned 2 50 0x1d00 75 60.3 65.6 103.8 200.5 238.8 340.9 200145.2 200000 20 linux_tuned 0 50 0x1d00 55 68.0 75.9 132.5 3117.0 6121.5 10804.4 199819.1 200000 20 2045.8 4433347 535178661 12756519 386338668 305581278228 449411715275 576097993479 791914252 1258770 748753 50123 0 152604 4862919 linux_tuned 1 50 0x1d00 55 69.6 78.6 149.5 4424.2 7066.8 11994.4 200007.0 200000 20 2046.69 4436875 535692364 12777613 387134442 304744947213 446715233369 569360465529 766696453 1226706 708166 52348 0 186714 4804553 linux_tuned 2 50 0x1d00 55 68.0 76.8 143.2 4594.2 7440.8 11192.7 199895.4 200000 20 2014.61 4478099 538886744 12902207 391442226 303862569317 447994381689 583210977498 778079503 1190819 711712 46778 0 131823 4890475 -linux_tuned 0 100 0x1d00 135 58.1 61.7 100.6 171.2 193.8 245.8 49881.7 50000 20 2013.83 1534978 161271773 3002235 90095712 85026567710 145378036900 159522467170 208107890 40308 734903 169288 0 833142 1604580 -linux_tuned 1 100 0x1d00 135 58.1 61.9 100.8 170.3 192.0 244.8 50017.2 50000 20 2008.81 1538969 161700819 3010409 90340956 85202929202 146996948067 161031376117 206724912 47731 751876 175077 0 849653 1624762 -linux_tuned 2 100 0x1d00 135 58.1 61.9 100.3 171.1 194.6 245.2 50008.9 50000 20 2006.62 1540973 161807831 3009942 90327102 85071606118 146608535834 160789026597 206720254 40309 736385 170293 0 834023 1608497 -linux_tuned 0 100 0x1d00 95 57.9 61.6 98.2 170.0 191.8 246.0 49941.4 50000 20 2021.65 1535373 161356693 3006060 90211578 85440453951 145258534801 159259433516 211369318 42261 739840 170659 0 840241 1610345 -linux_tuned 1 100 0x1d00 95 59.3 63.2 104.0 172.5 196.0 249.3 49904.8 50000 20 2017.89 1533345 161173218 3003555 90135294 85490089126 147692629972 161577508235 211080939 42412 737410 173119 0 835320 1608533 -linux_tuned 2 100 0x1d00 95 59.1 62.8 100.3 170.7 193.0 247.3 49970.8 50000 20 2013.89 1538893 161627627 3007844 90264924 85471403514 147780948439 161773057497 211415069 42822 741428 172375 0 833526 1609498 -linux_tuned 0 100 0x1d00 75 58.2 62.2 103.2 173.0 197.1 248.9 49986.2 50000 20 2013.69 1532992 161260825 3008691 90289938 85610192966 146960720751 160805067077 212313351 64122 780060 181298 0 840117 1629330 -linux_tuned 1 100 0x1d00 75 58.5 62.4 103.9 174.7 197.8 248.0 49982.7 50000 20 2020.27 1533899 161295189 3008334 90278796 85562806436 148226187170 162104604144 212584664 41287 736121 172554 0 836107 1609979 -linux_tuned 2 100 0x1d00 75 58.9 62.7 100.4 171.4 194.5 248.4 49973.7 50000 20 2014.32 1532832 161246677 3007784 90262290 85599044451 148573596215 162378701298 212863734 45606 745930 172203 0 845247 1617111 -linux_tuned 0 100 0x1d00 55 58.6 62.5 101.8 171.5 194.8 249.6 49997.7 50000 20 2002.42 1530552 161114670 3009400 90311370 85667619170 147001025391 161607522974 213218851 49817 753229 177068 0 848512 1624379 -linux_tuned 2 100 0x1d00 55 57.3 60.8 99.3 168.3 189.0 239.8 49939.8 50000 20 1990.45 1540072 161670899 3006006 90209754 84154632311 142136240288 156917447195 204918044 44569 749011 172362 0 847109 1614480 -linux_tuned 0 100 0x1d00 135 59.8 65.2 118.6 181.6 210.7 265.2 100033.7 100000 20 2241.81 2243379 268315292 6042823 181413096 160696849587 252720686113 259544373913 400034462 68410 1410915 139505 0 755897 2329445 -linux_tuned 1 100 0x1d00 135 60.8 66.2 121.5 185.2 215.6 269.7 100008.4 100000 20 2230.03 2243255 268299234 6041443 181372686 159904865921 256486800833 264330519558 394626856 70303 1416003 143042 0 749660 2337561 -linux_tuned 2 100 0x1d00 135 61.2 67.1 121.5 184.5 215.6 270.5 99999.0 100000 20 2234.32 2242999 268271652 6040945 181357566 160124216918 256682239002 264193449028 397115949 77124 1415537 142800 0 755495 2339976 -linux_tuned 0 100 0x1d00 95 60.3 65.8 120.1 183.0 213.2 267.6 99976.7 100000 20 2238.76 2245472 268434344 6039149 181302444 160831923795 255563138026 263626245481 410077573 71451 1417832 144070 0 756628 2344747 -linux_tuned 1 100 0x1d00 95 60.9 66.4 120.2 184.4 213.5 268.1 99938.3 100000 20 2236.61 2240643 268020117 6037238 181246314 160974367008 257933680705 265373644657 405456868 69667 1412649 143512 0 749074 2339304 -linux_tuned 2 100 0x1d00 95 60.1 65.3 118.9 182.3 211.4 265.9 100103.8 100000 20 2237.98 2245818 268583089 6047582 181558380 160690445213 257331442350 264558348372 402652556 68706 1389203 138323 0 749664 2319840 -linux_tuned 0 100 0x1d00 75 61.4 66.9 121.2 185.3 216.3 270.3 99926.8 100000 20 2238.47 2236447 267774051 6036652 181229526 160495389891 254884697001 262870753895 403484965 62687 1394428 137784 0 743637 2320550 -linux_tuned 1 100 0x1d00 75 61.1 66.3 120.6 184.6 216.4 269.8 99918.9 100000 20 2240.21 2245718 268354567 6035860 181204806 161066737379 257273671826 263021818781 421695374 71670 1398541 139508 0 752241 2318594 -linux_tuned 2 100 0x1d00 75 62.1 67.9 122.3 186.2 219.4 273.7 99969.4 100000 20 2238.04 2243948 268302794 6039370 181311084 161076964584 259897299841 267742161483 409253408 69123 1416596 143695 0 746022 2346013 -linux_tuned 0 100 0x1d00 55 61.7 67.8 123.2 191.9 226.4 293.4 100120.6 100000 20 2103.32 2240603 268293212 6049621 181623768 160837776353 253998889787 277554761273 406763886 77850 1392821 144709 0 741835 2348000 -linux_tuned 1 100 0x1d00 55 61.6 67.4 121.9 187.9 222.0 283.3 100039.2 100000 20 2104.41 2239909 268122473 6044875 181482192 158851223575 248082431161 269556651202 380201017 70671 1389355 140327 0 745548 2330515 -linux_tuned 2 100 0x1d00 55 62.3 69.1 124.9 194.1 228.3 307.8 99899.0 100000 20 2093.03 2241735 268091858 6039718 181343124 158917172719 249598926251 274114258635 382191176 82811 1386886 144546 0 749963 2342770 linux_tuned 0 100 0x1d00 135 74.5 86.5 143.5 234.9 271.7 371.4 200117.3 200000 20 2587.49 4278774 523079689 12211910 367048248 304708532376 443429507201 444019917125 776703002 151074 1780324 111081 0 349566 2809968 linux_tuned 1 100 0x1d00 135 74.4 86.9 143.9 231.1 267.1 358.4 200041.7 200000 20 2584.68 4277688 522905048 12204373 366812322 304052965143 447035786187 447586766528 769359531 144176 1801354 111906 0 336213 2818355 linux_tuned 2 100 0x1d00 135 74.2 86.4 143.9 235.4 271.3 368.4 199914.8 200000 20 2588.49 4274315 522521479 12198210 366630474 304379728164 448170753504 448894570277 773192567 143628 1795429 111566 0 336745 2820659 @@ -915,29 +2403,6 @@ linux_tuned 2 100 0x1d00 75 73.9 86.3 144.2 238.5 274.8 378.0 199956.6 200000 20 linux_tuned 0 100 0x1d00 55 82.7 98.8 172.2 3488.9 6413.9 11173.0 200278.4 200000 20 2037.61 4449604 536956823 12674682 385539990 303174909489 431778336959 550032972670 787488049 155482 1203048 77191 0 223971 2812106 linux_tuned 1 100 0x1d00 55 85.0 101.6 174.4 3541.3 6524.0 10817.5 199748.0 200000 20 2031.52 4429045 534779887 12605230 382861092 299832086104 423017780008 539232651904 753046240 157974 1283595 80285 0 219532 2825981 linux_tuned 2 100 0x1d00 55 81.3 97.8 167.5 3207.4 6243.8 10752.9 200056.1 200000 20 2035.8 4432850 535354305 12627438 383687142 300108049744 423056800459 537776063278 759260168 149716 1287918 80742 0 223473 2821492 -linux_tuned 0 200 0x1d00 135 62.8 71.0 165.9 263.9 285.5 338.7 49950.5 50000 20 1992.63 1427451 154232609 3017514 90589326 83342302080 140336340217 153283432661 211042590 17706 356252 149488 0 843105 1172109 -linux_tuned 1 200 0x1d00 135 63.4 72.0 166.5 264.0 284.7 338.5 49962.3 50000 20 1985.09 1429228 154356545 3017959 90601938 82886078739 140198996990 153670147371 205968771 18280 360274 153245 0 852145 1173259 -linux_tuned 2 200 0x1d00 135 62.8 70.4 163.2 263.4 284.4 334.8 50072.0 50000 20 1988.38 1432249 154679795 3024800 90807708 82941278333 139209510435 152319364346 206183148 17947 355833 149151 0 843579 1168153 -linux_tuned 0 200 0x1d00 95 63.4 71.8 168.0 264.5 286.2 340.0 50029.6 50000 20 1996.57 1431339 154614478 3022368 90735972 83412517185 140051090428 152762742533 213010430 17719 354264 148681 0 839581 1166557 -linux_tuned 1 200 0x1d00 95 64.7 74.0 169.5 266.3 288.4 344.2 50005.2 50000 20 1989.28 1428154 154373756 3020536 90679320 83535220675 141814946453 155299369778 210646788 18229 362737 155462 0 855212 1178187 -linux_tuned 2 200 0x1d00 95 64.1 74.0 169.0 266.0 288.3 343.1 50025.8 50000 20 1987.37 1429292 154475237 3021719 90714834 83605096967 141319962444 154634306862 210393610 18864 363826 157326 0 861264 1180486 -linux_tuned 0 200 0x1d00 75 63.5 72.7 168.3 265.0 286.4 339.7 50063.8 50000 20 1994.69 1429393 154503584 3024282 90791808 83711654125 141966073230 155053089511 213060691 18465 359312 152092 0 851976 1176720 -linux_tuned 1 200 0x1d00 75 64.7 73.9 172.1 266.0 287.4 341.3 49984.9 50000 20 1991.7 1427658 154284517 3019233 90639708 83490046579 141342940157 154514982447 212081405 17772 357367 151279 0 841710 1170224 -linux_tuned 2 200 0x1d00 75 64.9 74.9 169.6 266.2 288.5 338.8 49949.5 50000 20 1973.15 1423222 153964927 3017467 90587778 83662557092 142391010698 155931755059 212491965 23880 398809 173915 0 871447 1176388 -linux_tuned 0 200 0x1d00 55 64.7 74.2 173.1 267.2 289.5 344.9 49912.3 50000 20 1978.75 1426421 154133803 3014936 90511260 83445047722 142091880471 156100983950 213643198 19311 363382 157423 0 845818 1165325 -linux_tuned 1 200 0x1d00 55 62.4 70.6 165.3 261.9 280.9 333.1 50114.8 50000 20 1968.18 1432048 154748939 3027441 90887214 82051015107 135936592862 150062875390 191766044 18416 361857 151081 0 855675 1176402 -linux_tuned 2 200 0x1d00 55 62.4 70.9 164.2 261.6 281.3 333.8 49978.9 50000 20 1970.94 1430269 154459143 3019055 90634848 82341333292 136498109554 150636703291 195587860 18475 361580 152423 0 859275 1175975 -linux_tuned 0 200 0x1d00 135 80.7 101.9 195.3 275.3 302.5 362.3 100215.3 100000 20 2203.8 2213189 266516033 6099336 183264018 157750517388 242723997614 250342969945 402743257 22278 487886 161810 0 885418 1403548 -linux_tuned 1 200 0x1d00 135 80.4 101.1 194.6 273.2 298.9 356.3 100078.7 100000 20 2202.76 2208559 266104369 6090879 183012414 156383677605 239245082668 246791318688 403018653 22348 490237 162610 0 895279 1408580 -linux_tuned 2 200 0x1d00 135 79.5 100.4 194.3 273.8 300.6 357.8 100046.1 100000 20 2201.85 2207215 265986408 6088752 182945934 156508424861 239869260977 247970688542 392902672 22497 488021 161928 0 889978 1402985 -linux_tuned 0 200 0x1d00 95 78.8 99.1 193.0 272.7 299.1 359.3 100114.7 100000 20 2208.87 2210212 266241778 6092862 183070872 157423783363 241711272278 248260372181 405085547 22779 490513 163483 0 890686 1404433 -linux_tuned 1 200 0x1d00 95 79.3 99.6 194.2 273.9 300.6 359.6 100047.3 100000 20 2200.98 2209040 266103000 6088670 182941392 157375858838 241881363903 249652664530 400513164 23026 495260 165845 0 892805 1405647 -linux_tuned 2 200 0x1d00 95 80.4 101.5 195.8 275.4 303.0 363.0 100021.6 100000 20 2200.25 2207170 265927742 6087198 182898942 157167723626 241298826995 249102458521 398671866 22750 489500 163109 0 890100 1402263 -linux_tuned 0 200 0x1d00 75 81.2 102.2 196.0 274.4 301.5 360.5 99931.1 100000 20 2210.48 2204237 265592723 6081398 182722014 157471133672 243311536421 250502891373 405861343 22617 488127 164226 0 889660 1407562 -linux_tuned 1 200 0x1d00 75 79.4 99.9 194.3 274.0 300.6 362.1 99970.6 100000 20 2202.68 2205733 265767424 6084193 182809248 157217808853 242832300014 250985370289 402935606 23139 491572 164198 0 891202 1403176 -linux_tuned 2 200 0x1d00 75 81.5 103.0 196.8 275.3 303.0 360.6 99902.1 100000 20 2208.84 2206288 265753458 6079921 182679168 157220064331 241541912951 248662088475 403350998 22510 486671 161286 0 887778 1405379 -linux_tuned 1 200 0x1d00 55 79.1 99.7 195.7 276.3 304.2 367.4 100104.0 100000 20 2094.72 2211778 266323801 6091899 183040278 155168364574 233713378807 251941154319 376037298 23513 495564 164496 0 888891 1399025 -linux_tuned 2 200 0x1d00 55 80.8 101.6 195.8 276.1 304.3 367.0 100122.7 100000 20 2096.2 2208709 266156018 6094148 183110502 155814739541 234935131749 253590435678 380369274 24178 496265 165709 0 890897 1404925 linux_tuned 0 200 0x1d00 135 109.7 130.3 219.9 308.6 347.8 433.7 199977.3 200000 20 2542.35 4361689 528322428 12338386 371404164 303363876558 423914470592 424711683282 796469983 29175 642622 212642 0 588636 1461358 linux_tuned 1 200 0x1d00 135 107.9 129.1 219.8 311.4 351.6 442.8 200043.6 200000 20 2690.66 4362233 528438760 12342564 371536782 302105239959 418204094265 419350888891 765204935 30420 646397 213910 0 596744 1461932 linux_tuned 2 200 0x1d00 135 110.8 132.0 222.2 312.9 352.7 440.6 200020.6 200000 20 2536.78 4360546 528363007 12340369 371463018 302517365679 419069491157 420380366938 773734681 31429 650901 213982 0 593805 1462093 @@ -950,30 +2415,6 @@ linux_tuned 2 200 0x1d00 75 109.5 130.7 221.2 314.1 354.4 439.4 199925.8 200000 linux_tuned 0 200 0x1d00 55 121.7 147.0 251.2 3414.2 6417.8 10484.6 199888.9 200000 20 2017.11 4493560 539269940 12629096 384842478 295231539487 396690184225 503109557238 710386653 26433 529429 162562 0 408979 1461824 linux_tuned 1 200 0x1d00 55 123.8 148.6 253.7 3614.2 6505.7 10893.1 200040.7 200000 20 2027.4 4489652 539096293 12625323 384515784 297420448651 398029628741 501974896336 740445866 25671 533306 164526 0 414259 1461602 linux_tuned 2 200 0x1d00 55 121.0 146.6 254.9 3947.1 6684.3 10914.9 199843.1 200000 20 2014.37 4496823 539478001 12640312 385477494 297049805240 397196313693 504460016739 740327293 26055 519121 157625 0 404947 1460468 -linux_tuned 0 300 0x1d00 135 74.0 94.0 235.3 358.0 382.1 434.8 49987.9 50000 20 1965.67 1357156 149665347 3030842 91027068 81625726675 135012177989 147283025522 199435399 13202 200609 113172 0 785065 891584 -linux_tuned 1 300 0x1d00 135 77.2 100.4 238.8 358.3 382.9 435.4 50041.1 50000 20 1971.53 1359007 149842743 3034185 91126614 82254918566 134163063879 146148494540 206860858 12683 199533 110276 0 777165 889465 -linux_tuned 2 300 0x1d00 135 74.0 93.8 236.5 357.9 382.0 435.8 50030.6 50000 20 1975.65 1356943 149698197 3033882 91119168 82287540719 134645660657 146389022425 207552222 13304 203549 113333 0 786217 891277 -linux_tuned 0 300 0x1d00 95 72.2 90.9 233.1 356.4 379.4 432.1 49869.7 50000 20 1975.91 1356204 149431749 3023958 90819954 81853634842 135320831277 147383508434 203180853 12944 201762 111546 0 783105 892338 -linux_tuned 1 300 0x1d00 95 75.8 97.8 238.6 358.6 383.2 437.2 49923.4 50000 20 1976.68 1356549 149535751 3027312 90920892 82518349777 135441736359 147478193550 210585423 12724 197873 108926 0 778799 892611 -linux_tuned 2 300 0x1d00 95 75.3 96.3 237.6 359.0 384.0 435.6 49986.7 50000 20 1977.78 1356723 149638653 3030982 91030284 82661638235 136305451196 148295124072 211437751 13407 205389 114657 0 791344 896401 -linux_tuned 0 300 0x1d00 75 75.2 96.2 235.1 358.1 382.9 437.3 50060.9 50000 20 1972.99 1357788 149789708 3035283 91159836 82203643886 136067479467 148115411362 205290196 13216 203678 115558 0 789592 888642 -linux_tuned 1 300 0x1d00 75 73.8 94.2 237.6 357.4 380.5 434.9 50169.6 50000 20 1983.59 1360159 150071981 3041924 91359432 83008618715 136295186379 147718753422 213524288 12900 200588 111898 0 783276 889843 -linux_tuned 2 300 0x1d00 75 74.1 94.3 235.6 357.5 381.4 436.5 49993.5 50000 20 1973.87 1357235 149667795 3031679 91052010 82735350116 135784827834 147607758909 212544501 12944 201064 111031 0 780707 892092 -linux_tuned 0 300 0x1d00 55 75.6 96.7 238.4 359.0 384.0 437.5 50055.7 50000 20 1976.52 1357184 149724511 3035232 91158174 82446459556 135874117496 147843882448 207299735 13254 205717 114988 0 789389 892873 -linux_tuned 1 300 0x1d00 55 75.4 98.2 237.9 357.2 380.7 431.7 50071.6 50000 20 1958.77 1360309 149948410 3035922 91178772 81209614553 131654874357 143914492039 192863320 12803 201698 109864 0 782390 889524 -linux_tuned 2 300 0x1d00 55 73.9 93.8 234.9 357.5 381.9 433.6 49957.0 50000 20 1957.58 1356104 149557403 3028926 90968142 81475125925 133136788857 145663503644 196821446 12948 202781 112320 0 786993 890771 -linux_tuned 0 300 0x1d00 135 108.5 133.4 260.8 364.6 392.2 444.5 100009.6 100000 20 2270.59 2202238 265620072 6126581 184231974 155839490833 230608638326 236881885317 406190256 13973 213787 118313 0 808579 968078 -linux_tuned 1 300 0x1d00 135 110.5 136.7 263.0 365.3 393.8 450.3 100035.0 100000 20 2180.83 2203638 265744588 6127889 184271058 156368835580 229353741628 235401242670 394211647 13791 214871 119753 0 808428 967404 -linux_tuned 2 300 0x1d00 135 106.6 131.6 260.5 365.0 393.1 448.7 99985.2 100000 20 2181.41 2199565 265417089 6125371 184197858 156188063329 228188118381 234268157570 393429921 13693 211063 116187 0 803780 967052 -linux_tuned 0 300 0x1d00 95 107.7 134.1 261.2 364.5 392.2 446.8 100077.6 100000 20 2184.02 2201361 265619143 6130142 184336026 155877130632 230823373116 236620278898 387097103 13968 211158 116644 0 802576 967155 -linux_tuned 1 300 0x1d00 95 109.0 136.1 262.4 365.7 394.1 452.0 99998.4 100000 20 2181.08 2200994 265513123 6126485 184231614 157031928864 231703177155 237888842573 400953709 13854 213007 117672 0 804787 967192 -linux_tuned 2 300 0x1d00 95 110.5 137.0 264.3 366.6 396.1 455.2 99871.8 100000 20 2185.29 2198593 265163061 6116982 183937266 156904028657 231564620857 237158943400 402620065 14036 215282 119730 0 809332 967986 -linux_tuned 0 300 0x1d00 75 109.9 135.1 261.9 364.7 392.3 449.3 99878.1 100000 20 2183.35 2199587 265280630 6118970 184000992 155809046773 232185469006 238367859730 390991957 13858 213343 120217 0 806779 968504 -linux_tuned 1 300 0x1d00 75 110.0 135.6 263.2 365.5 394.1 450.5 99864.2 100000 20 2182.73 2199168 265222436 6117461 183956376 157106672777 231990266387 237733152390 404599343 13926 212364 118067 0 806459 968033 -linux_tuned 2 300 0x1d00 75 109.5 136.0 263.8 366.7 395.5 454.8 99900.1 100000 20 2185.38 2198551 265257307 6119626 184021590 157211533751 231835079922 237312848488 405611702 13966 213232 117999 0 802298 966600 -linux_tuned 0 300 0x1d00 55 110.8 136.9 264.2 367.9 397.8 462.6 99986.3 100000 20 2094.0 2201491 265492018 6124919 184179234 156310057262 232275086905 247650175351 392554846 14296 216072 121359 0 799739 966751 -linux_tuned 1 300 0x1d00 55 112.4 139.6 265.4 367.7 397.1 462.3 100123.5 100000 20 2094.24 2201761 265688475 6133902 184453746 155243034117 227629422626 241669423281 379986301 13700 210307 116327 0 802717 967596 -linux_tuned 2 300 0x1d00 55 110.1 135.5 263.8 368.2 397.2 459.2 100166.9 100000 20 2092.86 2205141 265979902 6135684 184504200 156134012182 230539207032 245700722967 383910905 18336 237916 126332 0 804384 961378 linux_tuned 0 300 0x1d00 135 142.7 172.3 293.2 395.6 431.7 511.4 199762.6 200000 20 2506.89 4437065 533089967 12447914 375316932 301628145669 405896229922 406643205529 743800853 17447 303680 189885 0 591966 976441 linux_tuned 1 300 0x1d00 135 136.9 167.2 292.4 399.5 439.3 530.7 199965.2 200000 20 2509.6 4440416 533586534 12458730 375642562 303585823192 406972550876 407710661425 769149880 18626 312552 191136 0 592145 976385 linux_tuned 2 300 0x1d00 135 136.2 166.5 290.3 393.3 429.9 513.4 199950.4 200000 20 2508.16 4441949 533615219 12460206 375699156 304099525671 403838028854 404652320236 769761172 16968 301252 184092 0 593080 976238 @@ -986,30 +2427,6 @@ linux_tuned 2 300 0x1d00 75 144.6 174.4 298.5 401.6 443.0 534.7 199947.7 200000 linux_tuned 0 300 0x1d00 55 170.1 204.0 347.8 4359.0 7352.4 10758.6 199941.8 200000 20 1997.37 4575134 545012925 12708396 388618776 300708153584 399483530875 512868230339 761733324 13720 259997 151892 0 386242 976361 linux_tuned 1 300 0x1d00 55 157.8 191.8 335.1 3569.1 6515.3 10321.3 199928.8 200000 20 2004.69 4568264 544448194 12695817 387971460 299663905134 395550381087 504297511295 745565760 14246 262512 153021 0 402778 976352 linux_tuned 2 300 0x1d00 55 160.5 196.1 342.0 4236.3 7178.6 10607.9 199697.1 200000 20 1999.87 4565841 544007166 12681210 387739812 299522010527 395285551016 505859917441 749543888 14053 259632 152392 0 397575 976399 -linux_tuned 0 400 0x1d00 135 96.8 128.6 301.6 454.4 480.4 532.9 50052.9 50000 20 1965.5 1312710 146791206 3045904 91515984 81362231032 131598785365 141882910698 208811391 9057 120751 110962 0 656122 705857 -linux_tuned 1 400 0x1d00 135 93.3 124.7 302.2 455.8 481.1 533.5 49840.3 50000 20 1970.13 1311235 146445402 3032176 91102014 80875619045 130291064229 140395969976 207155527 9853 129812 114481 0 672807 703442 -linux_tuned 2 400 0x1d00 135 94.4 126.4 299.5 455.1 480.3 532.8 49994.6 50000 20 1968.89 1310296 146567987 3041873 91392888 81187962293 130986947311 141172396024 210383698 9777 128665 113826 0 673271 703708 -linux_tuned 0 400 0x1d00 95 90.4 121.9 300.0 454.3 479.3 533.3 50003.7 50000 20 1973.61 1311925 146697605 3042125 91400760 81652464137 132125655667 142322487246 210790647 9965 132227 114768 0 675952 704380 -linux_tuned 1 400 0x1d00 95 96.0 128.4 301.6 453.2 478.7 531.5 49998.0 50000 20 1972.99 1311520 146660276 3042559 91415940 81598350253 132015439604 142351318495 211941945 9287 123350 111781 0 662514 704536 -linux_tuned 2 400 0x1d00 95 88.3 121.9 299.3 453.7 479.4 532.3 50016.2 50000 20 1965.85 1310594 146626316 3043807 91454760 81334039474 131516368654 141786651897 211034965 9209 122885 111476 0 661360 702986 -linux_tuned 0 400 0x1d00 75 94.4 126.4 301.7 455.2 480.7 533.0 49959.5 50000 20 1971.22 1309931 146483837 3039952 91336242 81435762296 132684882149 143206870724 210749184 9336 124779 111533 0 660795 704155 -linux_tuned 1 400 0x1d00 75 93.5 127.0 303.4 455.1 480.2 535.8 50145.7 50000 20 1972.89 1313824 146978074 3051460 91684770 81829444402 132246301903 142302197686 214397997 9060 123264 111713 0 660029 704937 -linux_tuned 2 400 0x1d00 75 94.8 126.6 302.2 456.0 480.7 534.6 49945.7 50000 20 1963.51 1310406 146501325 3039228 91315824 81409758058 132402701015 142724739547 213245306 9191 121060 110521 0 655978 704850 -linux_tuned 0 400 0x1d00 55 96.4 128.5 303.6 456.4 480.9 535.0 49902.1 50000 20 2058.56 1310241 146480620 3036447 91232130 81511261914 132487765216 143061115187 211357729 9369 124064 110985 0 664776 705344 -linux_tuned 1 400 0x1d00 55 93.7 125.5 297.6 451.1 477.7 528.7 50079.6 50000 20 1949.62 1315920 147028950 3047750 91572486 80170271609 126944694205 137448812989 193435790 9403 125084 114526 0 668659 705023 -linux_tuned 2 400 0x1d00 55 95.1 126.2 300.5 452.7 478.6 532.0 49963.0 50000 20 1949.4 1311758 146611155 3040058 91339296 80355169429 128385218946 139222058038 195535793 9594 126940 114260 0 669302 703223 -linux_tuned 0 400 0x1d00 135 135.3 168.6 329.6 466.1 486.6 551.4 99998.9 100000 20 2173.22 2205724 265814828 6161487 185427876 155518638594 225694983898 230568316139 394895940 9614 127690 92734 0 674637 730445 -linux_tuned 1 400 0x1d00 135 135.8 168.0 328.1 463.4 485.1 545.0 100090.8 100000 20 2175.1 2206013 265923449 6168315 185636118 154856059636 222364227131 226653598077 394538241 9139 123226 89720 0 668725 730631 -linux_tuned 2 400 0x1d00 135 134.6 167.3 329.5 464.8 485.6 549.4 99908.9 100000 20 2171.32 2204390 265615886 6156463 185276100 154902880406 224675747938 229981292223 407970661 9556 129891 92388 0 676420 730398 -linux_tuned 0 400 0x1d00 95 132.4 164.3 327.0 465.0 486.5 544.2 99843.3 100000 20 2178.19 2201506 265381043 6151739 185132058 155438202316 225486288247 230165210542 398498836 9320 125601 89459 0 670423 730548 -linux_tuned 1 400 0x1d00 95 132.5 165.1 327.5 464.9 486.3 548.0 100035.8 100000 20 2169.61 2205668 265869692 6164178 185511498 155495771751 226585793614 232047168848 400542851 9697 127823 90549 0 677152 730839 -linux_tuned 2 400 0x1d00 95 134.3 169.0 330.5 465.6 486.6 549.4 100101.8 100000 20 2174.41 2208249 266073297 6168242 185634408 155922388585 225912524942 230828231568 401077207 9572 128246 93071 0 676111 730558 -linux_tuned 0 400 0x1d00 75 136.3 168.9 330.1 465.5 486.2 554.1 99955.7 100000 20 2175.01 2206030 265793205 6160207 185395860 155452848060 226293071462 231314972208 398440907 9322 125988 90050 0 673572 730510 -linux_tuned 1 400 0x1d00 75 133.2 166.7 329.0 464.5 486.2 550.0 100012.2 100000 20 2175.01 2205473 265808602 6164041 185509296 155779482470 226140123435 230485904606 405794335 9555 127789 92973 0 675530 730107 -linux_tuned 2 400 0x1d00 75 133.6 164.9 327.7 465.4 486.6 553.1 99978.5 100000 20 2170.34 2205246 265759590 6161183 185424384 156052051269 227333423801 232525795375 405854266 9873 131272 91538 0 681218 730668 -linux_tuned 0 400 0x1d00 55 140.7 173.3 335.0 468.8 489.2 567.9 100030.0 100000 20 2091.98 2207105 265934824 6164734 185530488 155848184976 225821417955 240118633839 400676024 9572 126576 89844 0 668259 729962 -linux_tuned 1 400 0x1d00 55 134.4 166.5 328.5 465.9 487.5 559.1 100046.5 100000 20 2088.51 2205036 265828385 6163599 185493126 153770080284 219312560050 232330317859 376225816 9826 129477 92449 0 678503 730716 -linux_tuned 2 400 0x1d00 55 133.9 167.4 329.9 467.8 488.8 561.4 99944.9 100000 20 2086.39 2204498 265672030 6158177 185330304 154302962763 221791905644 234968216033 378475771 9816 131392 91705 0 680028 730804 linux_tuned 0 400 0x1d00 135 175.7 214.3 372.6 492.2 534.9 631.8 200248.9 200000 20 2503.59 4526522 539580753 12583272 380082390 303415844690 404217597738 406452355862 773103347 12265 184867 136068 0 533257 732399 linux_tuned 1 400 0x1d00 135 186.2 223.3 376.0 490.4 532.9 622.0 199836.3 200000 20 2501.87 4513740 538227250 12558303 379313766 302223117928 400065370178 400507893377 767144838 12290 178590 135273 0 541252 732412 linux_tuned 2 400 0x1d00 135 173.3 210.3 368.8 488.7 530.0 620.1 199873.3 200000 20 2506.45 4515294 538376800 12560843 379396494 302808269586 401397084674 402098942910 772345525 12152 182561 136711 0 540086 732409 @@ -1022,30 +2439,6 @@ linux_tuned 2 400 0x1d00 75 171.7 212.0 371.9 490.6 534.3 628.7 200030.7 200000 linux_tuned 0 400 0x1d00 55 198.4 243.9 427.8 3726.8 6564.7 10593.2 199997.1 200000 20 1996.9 4628590 548215440 12732081 389574984 301804907134 396084658820 509055422018 774268297 10094 162156 117032 0 368022 732403 linux_tuned 1 400 0x1d00 55 207.0 250.3 422.7 3647.1 6631.2 10365.5 200134.4 200000 20 2001.14 4625415 548252327 12732704 389517366 299120037831 388265072507 494219795390 743519876 10132 161922 119965 0 381784 732403 linux_tuned 2 400 0x1d00 55 201.4 243.4 420.2 3314.8 6308.5 10563.1 200017.2 200000 20 2017.67 4613020 546974889 12711032 388182624 299474647376 388830085163 490187574200 747257860 10037 165687 122255 0 393666 732387 -linux_tuned 0 50 0x1800 135 61.1 63.2 82.9 146.3 182.5 225.2 49932.8 50000 20 1654.46 1575888 164052140 3001797 90071820 87167619249 143160485920 173973841565 211596965 560433 672449 173010 0 799385 2150969 -linux_tuned 1 50 0x1800 135 60.7 62.7 82.0 145.4 181.2 224.2 49959.1 50000 20 1648.65 1580880 164394448 3003335 90117678 86761210366 140185494742 170673759305 207824013 551066 669740 172308 0 812063 2139774 -linux_tuned 2 50 0x1800 135 60.3 62.3 81.9 143.5 179.8 224.2 50090.0 50000 20 1646.71 1581646 164602329 3011183 90352950 87003636786 140229567193 170413732516 208360112 553199 668841 171162 0 806928 2137031 -linux_tuned 0 50 0x1800 95 60.9 63.0 82.8 147.5 182.9 224.8 50046.0 50000 20 1651.85 1581327 164515661 3008663 90277788 87218433637 143586071620 174657303821 212785032 565304 645375 169476 0 798330 2142208 -linux_tuned 1 50 0x1800 95 60.5 62.7 82.8 147.1 182.3 225.3 49996.3 50000 20 1648.98 1584522 164688024 3005680 90188328 87444221523 141137258555 171705151294 212288781 549733 657390 169276 0 803435 2136187 -linux_tuned 2 50 0x1800 95 60.4 62.4 81.8 144.9 180.3 224.0 50093.2 50000 20 1646.54 1580242 164497771 3011435 90360804 87435208806 141368735608 171993120598 212527225 551609 660351 173581 0 808300 2139482 -linux_tuned 0 50 0x1800 75 60.8 63.0 83.4 148.1 183.8 225.8 50139.4 50000 20 1652.57 1578595 164450280 3014301 90447024 87555618713 144596975870 175797846387 214281903 564101 664746 173824 0 808736 2153834 -linux_tuned 1 50 0x1800 75 60.4 62.5 82.8 146.1 183.1 227.0 49916.4 50000 20 1639.71 1580236 164289097 3000781 90041016 87300404841 141717267426 172406695398 214045774 576122 741283 179704 0 805419 2160373 -linux_tuned 2 50 0x1800 75 60.3 62.3 82.0 143.0 181.1 225.8 50061.7 50000 20 1648.24 1576960 164244014 3009412 90299784 87492568749 141745024217 172463594732 214489497 555221 672524 172733 0 802968 2139643 -linux_tuned 0 50 0x1800 55 61.3 63.5 83.2 147.6 183.6 226.6 50028.2 50000 20 1654.1 1581502 164537690 3007500 90242718 87749717769 144675361296 175725841852 214589130 564966 647519 171968 0 802274 2142988 -linux_tuned 1 50 0x1800 55 59.2 61.7 80.4 141.9 175.3 215.2 49993.9 50000 20 1641.45 1581972 164483556 3005320 90177066 85789292624 137942769080 167803948392 192574345 552727 661936 168039 0 806329 2127569 -linux_tuned 2 50 0x1800 55 60.5 62.5 81.7 145.6 178.4 221.3 50000.5 50000 20 1642.66 1582814 164559064 3005650 90186708 86256781752 139245378804 169627169170 197824950 551461 670819 173303 0 811768 2141044 -linux_tuned 0 50 0x1800 135 62.2 65.3 91.4 173.5 204.8 267.5 99845.7 100000 20 1826.68 2269480 269828508 6014178 180496860 163615047992 259132779080 313146794077 407149907 1193850 745354 144485 0 757630 3272344 -linux_tuned 1 50 0x1800 135 61.2 64.0 90.7 172.8 202.6 261.8 100010.6 100000 20 1817.42 2274245 270375404 6022659 180747408 163253373866 253582509566 306457285313 401379935 1208248 801282 147051 0 763446 3287364 -linux_tuned 2 50 0x1800 135 61.1 63.7 90.1 171.3 202.1 261.9 100120.5 100000 20 1818.58 2269189 270118846 6029646 180958074 163669490571 254089448155 307074927603 403375998 1205566 784494 147739 0 762611 3281601 -linux_tuned 0 50 0x1800 95 61.8 64.8 91.7 175.4 205.2 267.6 99944.7 100000 20 1826.82 2267642 269837259 6019621 180659184 163893677427 259619496783 313739641831 409364993 1197425 743142 145531 0 763670 3276852 -linux_tuned 1 50 0x1800 95 61.2 64.0 91.4 174.9 206.6 271.3 100014.4 100000 20 1820.13 2271963 270220478 6023958 180789546 163961770947 255215458546 308445559388 408716477 1186067 769365 144455 0 761594 3266294 -linux_tuned 2 50 0x1800 95 61.7 64.6 91.5 173.0 204.6 265.8 100034.6 100000 20 1820.55 2267572 269948637 6024910 180817086 163901439152 255221065397 308422898440 408871735 1185042 759513 143131 0 761418 3265618 -linux_tuned 0 50 0x1800 75 61.8 64.9 93.0 178.6 209.2 277.6 100118.5 100000 20 1825.02 2270771 270247178 6030762 180995790 163804597282 259743640388 313902713065 407430519 1183024 729135 142796 0 755956 3261627 -linux_tuned 1 50 0x1800 75 61.4 64.2 90.5 172.9 203.0 262.7 100016.5 100000 20 1820.31 2274517 270393417 6023881 180786906 164057799408 256092519732 309488680166 412389308 1189072 772656 145496 0 754914 3274120 -linux_tuned 2 50 0x1800 75 61.0 63.8 90.5 173.1 204.3 266.1 100077.2 100000 20 1819.52 2266698 269912079 6027136 180883770 164291999976 255998045786 309364492741 411489041 1187384 759440 144184 0 759201 3272118 -linux_tuned 0 50 0x1800 55 61.6 64.4 91.5 176.4 206.5 269.2 100060.4 100000 20 1827.53 2267677 270006511 6026917 180879060 164016717145 261085304149 315512288001 411008020 1197082 739241 146538 0 757613 3281719 -linux_tuned 1 50 0x1800 55 60.9 63.6 89.1 167.6 198.2 254.3 99995.2 100000 20 1814.94 2273546 270280264 6021721 180719706 161952025786 251741543986 304225785570 386080229 1206969 790836 145656 0 761379 3274820 -linux_tuned 2 50 0x1800 55 61.5 64.4 90.5 168.9 199.1 255.0 99957.9 100000 20 1818.24 2269733 269966169 6019545 180653784 162540700141 253404844276 306239700082 389300446 1208404 775149 147256 0 762762 3282334 linux_tuned 0 50 0x1800 135 66.7 74.0 120.9 268.7 335.2 518.4 200136.0 200000 20 2127.9 4275498 522834314 12214003 367074642 310074833318 461264056525 557363494476 805190668 1571998 859578 60928 0 203618 4829826 linux_tuned 1 50 0x1800 135 64.9 71.8 115.3 248.7 303.6 461.5 199851.6 200000 20 2111.28 4265164 521834072 12182643 366085398 309474423213 451942355153 546100535376 794833253 1610009 938729 61537 0 198783 4824316 linux_tuned 2 50 0x1800 135 64.4 70.7 113.6 247.9 311.2 490.7 199964.5 200000 20 2111.59 4270903 522413948 12200454 366656196 309303680600 451530217634 545601809529 797945660 1555820 905489 61591 0 216592 4793155 @@ -1058,30 +2451,6 @@ linux_tuned 2 50 0x1800 75 65.8 73.1 117.2 254.7 316.5 469.8 200120.5 200000 20 linux_tuned 0 50 0x1800 55 67.2 74.6 123.8 289.4 375.0 644.7 200182.0 200000 20 2110.41 4283178 523475427 12230590 367615500 310163786948 463948306382 566105259174 810078957 1543072 838389 59347 0 190698 4847912 linux_tuned 1 50 0x1800 55 65.9 73.2 118.2 261.9 326.5 500.0 199900.3 200000 20 2099.64 4262322 521697348 12180145 365991798 308280226111 451802635831 548626735990 775477320 1635072 935493 60829 0 192565 4840968 linux_tuned 2 50 0x1800 55 65.2 72.3 116.4 252.4 314.2 473.3 200035.0 200000 20 2102.46 4266008 522117447 12189487 366276138 308200387582 450979424215 547666143818 782234391 1626161 923176 60319 0 200755 4831110 -linux_tuned 0 100 0x1800 135 63.1 67.2 104.8 184.0 210.6 274.6 49951.9 50000 20 1632.52 1518670 160279783 3007021 90241242 85444035043 144779451681 183882097014 213139468 46062 728945 175413 0 814140 1618081 -linux_tuned 1 100 0x1800 135 62.8 66.9 107.7 184.8 211.1 272.7 50018.6 50000 20 1626.36 1522472 160601369 3010767 90352602 85055906500 142231950613 180941112242 206802834 57075 750040 179131 0 823553 1628162 -linux_tuned 2 100 0x1800 135 64.1 68.5 106.5 184.0 210.2 274.9 49982.7 50000 20 1624.87 1521666 160488187 3008467 90283020 84990315978 142565761042 181721393956 206378316 47130 731442 173630 0 814494 1613815 -linux_tuned 0 100 0x1800 95 63.6 67.9 105.8 184.6 213.8 274.9 50042.3 50000 20 1632.85 1523226 160695255 3012353 90400590 85678695897 145665175060 185208117693 213581549 46342 729706 174484 0 813473 1619470 -linux_tuned 1 100 0x1800 95 62.6 66.5 104.5 183.1 208.6 273.0 50108.0 50000 20 1627.24 1520061 160552474 3015904 90506064 85453091801 143402833184 182527148893 211248281 45357 731253 173327 0 806872 1610916 -linux_tuned 2 100 0x1800 95 63.1 67.3 106.9 185.1 212.3 273.1 50030.4 50000 20 1626.2 1517379 160252242 3011261 90366636 85489806112 143227994412 182273042570 211261698 46285 728384 173416 0 813025 1613332 -linux_tuned 0 100 0x1800 75 63.1 67.3 106.2 184.8 211.8 274.4 49960.7 50000 20 1760.24 1516827 160164996 3007190 90244992 85493057795 145476454713 185016910456 226219521 43466 724985 169855 0 807152 1608982 -linux_tuned 1 100 0x1800 75 62.8 66.7 103.5 182.8 209.4 273.5 49921.1 50000 20 1628.21 1516594 160086041 3004779 90172362 85377582701 143602322679 182533378499 212449960 46348 728342 173151 0 811449 1611177 -linux_tuned 2 100 0x1800 75 63.0 67.2 107.2 185.0 213.5 274.5 50082.8 50000 20 1625.6 1518321 160416298 3014628 90468714 85552241383 144323604531 183726875587 213612953 50222 741298 175158 0 812016 1620323 -linux_tuned 0 100 0x1800 55 61.9 65.7 104.9 179.6 203.5 264.7 50002.7 50000 20 1616.27 1524705 160723364 3009645 90318360 83888115084 137497635273 176039981220 191485315 42293 727376 169322 0 806927 1598198 -linux_tuned 1 100 0x1800 55 61.6 65.3 103.4 178.8 203.1 263.1 50012.1 50000 20 1613.31 1527802 160974570 3010386 90340986 83868930984 137391493597 176036224241 192756667 43423 735922 172013 0 818063 1612891 -linux_tuned 2 100 0x1800 55 61.7 65.3 103.9 180.2 204.5 264.2 50022.8 50000 20 1615.99 1526183 160861060 3010940 90357414 84458209979 138628348153 177448944306 196193210 44410 736486 174847 0 822340 1617454 -linux_tuned 0 100 0x1800 135 65.9 72.6 129.4 205.8 245.4 308.3 99864.3 100000 20 1814.19 2234502 267596879 6034717 181176078 160483507134 253293173548 309187896806 407643083 86717 1360297 143174 0 700691 2351972 -linux_tuned 1 100 0x1800 135 65.9 72.5 129.1 204.4 243.1 305.3 100014.3 100000 20 1804.73 2232183 267562385 6043336 181435860 159800099111 247567407226 302346257899 397974302 94653 1370420 142885 0 717532 2347149 -linux_tuned 2 100 0x1800 135 65.0 71.5 127.9 202.3 242.0 301.7 100050.2 100000 20 1805.63 2235083 267802683 6045827 181509522 159942942473 247516755280 302525433616 396614705 84345 1367116 141933 0 710496 2346579 -linux_tuned 0 100 0x1800 95 66.4 73.1 130.6 207.6 247.2 306.4 99947.5 100000 20 1813.12 2231788 267467708 6039964 181335252 160738356965 253294811781 309647172213 407510831 91161 1344769 140700 0 704656 2342733 -linux_tuned 1 100 0x1800 95 66.2 73.3 130.8 206.1 246.1 314.1 100073.3 100000 20 1806.08 2234222 267808462 6047330 181555170 160597433285 249166351397 304283331227 404524301 93835 1356843 142733 0 712732 2343320 -linux_tuned 2 100 0x1800 95 65.3 72.1 129.0 205.0 246.5 308.9 100003.8 100000 20 1806.76 2233439 267649566 6043307 181434864 160379158316 249120233745 303843213104 403116717 88936 1338149 138049 0 714544 2329664 -linux_tuned 0 100 0x1800 75 66.4 73.3 130.4 207.0 246.3 308.1 100091.4 100000 20 1816.69 2231868 267659730 6049026 181607436 160895884413 253618308261 309220630672 408636130 87110 1346344 139745 0 701294 2341515 -linux_tuned 1 100 0x1800 75 66.5 73.2 131.1 206.9 246.5 310.5 100245.8 100000 20 1808.13 2242512 268559416 6058267 181884252 161095356673 250026599717 304990467401 407276011 87166 1349258 139583 0 710609 2338375 -linux_tuned 2 100 0x1800 75 66.1 73.2 130.2 205.7 244.9 307.4 99966.5 100000 20 1805.31 2234884 267671397 6040355 181344000 160700004816 250208787125 305857960409 408165221 89377 1355892 141452 0 710271 2344311 -linux_tuned 0 100 0x1800 55 65.1 72.1 128.1 199.1 235.6 295.2 100014.3 100000 20 1799.55 2237843 267922388 6042638 181410846 158571432182 241670750319 296102213611 374988525 89871 1389295 144119 0 722092 2354809 -linux_tuned 1 100 0x1800 55 65.5 72.7 129.4 203.1 241.4 302.2 100014.9 100000 20 1793.13 2234079 267715065 6042904 181419768 158690844025 241630276014 296015656586 379901016 89072 1371076 142601 0 725165 2343355 -linux_tuned 2 100 0x1800 55 64.3 71.1 127.4 201.7 239.4 300.3 100148.9 100000 20 1795.43 2239844 268283639 6051064 181665798 159350129414 242552922371 297191032705 382163722 87885 1358735 142036 0 721368 2335630 linux_tuned 0 100 0x1800 135 81.7 97.4 160.0 291.6 350.2 504.8 199910.2 200000 20 2094.82 4309651 524839440 12247401 368280984 304814706898 441163257757 533110066199 790375963 198362 1468604 91902 0 261525 2820028 linux_tuned 1 100 0x1800 135 79.3 93.5 156.1 283.0 338.3 490.3 199990.5 200000 20 2083.78 4309059 524911884 12248084 368289924 303507691570 431447128792 521365097693 773278567 194381 1499714 94059 0 276162 2811733 linux_tuned 2 100 0x1800 135 81.3 96.1 160.3 304.6 383.9 628.1 199920.1 200000 20 2083.65 4316476 525311184 12262330 368800722 303987948071 431711059555 521800182054 777806884 188860 1454088 93961 0 290084 2799642 @@ -1093,30 +2462,6 @@ linux_tuned 2 100 0x1800 75 82.2 97.8 159.7 288.0 343.0 486.0 199849.4 200000 20 linux_tuned 0 100 0x1800 55 79.9 93.5 153.8 268.4 315.9 437.0 199985.5 200000 20 2075.25 4301203 524387778 12235128 367854012 301366772328 421112013616 509429315016 737517183 195438 1568490 97297 0 282857 2818367 linux_tuned 1 100 0x1800 55 80.1 95.1 155.3 276.0 327.9 465.0 199894.6 200000 20 2073.36 4297785 524059981 12228856 367660104 302314221467 423535782020 512646678575 752607274 184113 1577552 97384 0 275147 2826373 linux_tuned 2 100 0x1800 55 80.3 94.7 156.1 279.2 333.4 481.0 199878.7 200000 20 2075.03 4298486 524058355 12231076 367743288 301917013263 423112303531 512034483152 756057801 187723 1566973 96954 0 278363 2823266 -linux_tuned 0 200 0x1800 135 68.2 77.7 176.7 271.8 297.0 359.8 50065.9 50000 20 1613.41 1420166 153934103 3024151 90787140 82803557389 135603381950 173299471534 200616456 20185 375284 165993 0 847381 1182000 -linux_tuned 1 200 0x1800 135 68.7 77.9 176.6 271.4 297.2 364.1 49921.4 50000 20 1607.3 1417480 153584994 3015767 90537144 82711851432 135905862457 173674463777 204687925 19114 361944 160916 0 836819 1175140 -linux_tuned 2 200 0x1800 135 68.5 77.1 174.5 271.7 297.1 364.9 50049.3 50000 20 1608.03 1419587 153849892 3023645 90773436 82954191227 136016537381 173796396713 205382264 20079 370982 167077 0 849575 1177873 -linux_tuned 0 200 0x1800 95 69.0 78.3 177.0 272.0 297.7 362.7 50031.5 50000 20 1608.08 1417434 153685981 3022965 90755730 82748471519 135853094848 173483398234 203330772 22451 386487 171759 0 852290 1180443 -linux_tuned 1 200 0x1800 95 68.7 78.1 176.8 272.0 297.8 364.3 49942.6 50000 20 1609.09 1419768 153738922 3016891 90570600 83219764383 136662376020 174589468355 209179410 19456 367567 165206 0 843502 1177360 -linux_tuned 2 200 0x1800 95 68.3 77.0 174.9 272.0 297.9 362.0 49981.9 50000 20 1611.63 1419428 153793567 3019516 90649524 83278508621 135644934155 172851045491 209502735 18619 362005 159061 0 837295 1176551 -linux_tuned 0 200 0x1800 75 69.8 78.8 177.4 273.5 299.1 364.0 50031.2 50000 20 1616.36 1418212 153750331 3022112 90726870 82861785997 135642621069 173155799328 204794388 18931 360453 158927 0 841150 1176508 -linux_tuned 1 200 0x1800 75 68.8 77.7 176.2 272.7 298.9 365.1 49920.2 50000 20 1607.9 1417167 153541599 3015449 90526332 83226521540 137255361541 175306600220 210884311 18585 361007 160514 0 835911 1175707 -linux_tuned 2 200 0x1800 75 68.0 76.6 174.5 271.9 297.1 361.9 50018.4 50000 20 1611.67 1415762 153541600 3021399 90705252 83279373119 136211607534 173636160606 210766898 18412 356898 157600 0 830074 1170012 -linux_tuned 0 200 0x1800 55 68.4 77.8 175.6 271.6 296.7 361.5 50046.9 50000 20 1612.25 1419197 153825756 3023218 90760128 83104760199 136346047428 174033794338 206281671 18786 360679 160121 0 836174 1174647 -linux_tuned 1 200 0x1800 55 67.4 75.9 173.8 270.6 295.0 355.2 50057.0 50000 20 1600.32 1425801 154277048 3024043 90785442 81954730551 131709949615 168638938635 192039307 19219 365190 159221 0 849502 1179769 -linux_tuned 2 200 0x1800 55 67.8 76.8 175.2 270.3 295.6 359.9 50049.2 50000 20 1604.37 1425655 154261316 3023746 90776922 82348754483 132612303216 169632753584 196224909 19447 367946 162803 0 849408 1178312 -linux_tuned 0 200 0x1800 135 83.8 104.6 203.3 289.6 323.8 391.9 100039.9 100000 20 1778.32 2207220 265976978 6089154 182959314 155832301587 231528441442 284735627941 380313004 23323 487825 166743 0 852856 1397346 -linux_tuned 1 200 0x1800 135 82.7 103.0 201.4 286.2 320.8 390.5 99946.9 100000 20 1777.4 2203403 265608545 6084107 182810478 156198253550 230036574480 281067470924 391662003 22680 481935 163220 0 858469 1401269 -linux_tuned 2 200 0x1800 135 86.9 108.2 204.5 289.3 322.8 392.6 100250.2 100000 20 1778.24 2209740 266379392 6102165 183350190 156680271309 232015965054 284128294745 392664461 23322 486897 166777 0 860713 1405964 -linux_tuned 0 200 0x1800 95 84.5 106.0 203.0 285.8 317.9 387.4 100086.1 100000 20 1781.45 2206467 265949798 6091485 183027870 156276991289 230701784068 282370801133 385195909 22090 482595 164661 0 859805 1406999 -linux_tuned 1 200 0x1800 95 85.8 107.4 203.1 288.6 322.9 391.8 100096.1 100000 20 1779.22 2207789 266076019 6093083 183080172 157410713089 233801184416 286440272272 400039878 23224 489008 168688 0 860504 1409674 -linux_tuned 2 200 0x1800 95 84.1 104.9 203.6 290.8 325.7 395.3 100014.9 100000 20 1777.59 2207267 265951578 6088405 182942190 157040340908 234142369885 287603235956 397623401 24364 488980 167764 0 851213 1399782 -linux_tuned 0 200 0x1800 75 84.6 106.2 202.8 287.4 319.9 387.8 99992.4 100000 20 1783.03 2202974 265609297 6085983 182864742 156210650875 232526474315 285304994917 389530962 23474 489302 167813 0 856905 1405894 -linux_tuned 1 200 0x1800 75 87.4 108.9 205.8 292.8 327.6 396.5 100072.5 100000 20 1781.75 2207464 266013208 6092647 183071148 157329758998 234242463576 286727384307 402925827 22694 484497 166353 0 854546 1404657 -linux_tuned 2 200 0x1800 75 86.8 108.0 204.7 290.9 325.5 392.2 100034.4 100000 20 1781.12 2204490 265761907 6089016 182956242 157342163337 234419950562 287054911177 402115548 22406 478656 163965 0 851536 1403025 -linux_tuned 0 200 0x1800 55 85.3 106.4 202.7 288.0 321.2 390.8 99835.2 100000 20 1779.26 2203771 265509974 6076815 182589696 156441806521 232512994284 285111260765 391232171 23508 488905 167633 0 857874 1406454 -linux_tuned 1 200 0x1800 55 85.8 107.7 203.9 288.6 322.6 392.1 100145.2 100000 20 1771.66 2208181 266147835 6095672 183157248 155441466883 228432935625 280297400255 375825391 22549 484486 165664 0 860073 1404574 -linux_tuned 2 200 0x1800 55 84.6 105.7 201.9 286.6 319.5 386.9 100136.9 100000 20 1773.89 2206020 265996122 6094999 183135336 156045152035 229591523270 281588492029 380062851 23335 486894 167963 0 866667 1409058 linux_tuned 0 200 0x1800 135 120.9 143.7 236.7 353.1 401.6 526.9 199900.2 200000 20 2048.49 4383355 529720853 12352430 371915154 300363595227 403553284167 487764488393 747720940 30185 625475 194651 0 500803 1462401 linux_tuned 1 200 0x1800 135 119.5 142.9 238.9 365.6 425.9 590.8 199883.1 200000 20 2045.67 4391606 530225066 12363130 372288636 301933124239 405878897257 490610148715 764941125 31858 608882 186068 0 500337 1460594 linux_tuned 2 200 0x1800 135 119.8 143.7 239.2 363.8 419.5 562.1 199938.0 200000 20 2050.37 4389863 530198653 12363002 372265332 301996437745 407210449132 492317990702 773101170 31236 614743 187467 0 495576 1461276 @@ -1129,30 +2474,6 @@ linux_tuned 2 200 0x1800 75 122.3 145.6 238.8 360.2 411.4 544.2 200256.6 200000 linux_tuned 0 200 0x1800 55 117.4 140.7 233.9 350.8 399.7 525.3 199976.7 200000 20 2048.37 4390906 530323229 12365521 372340884 301957538041 407241975172 492674700417 765741923 31335 614531 188301 0 494686 1461439 linux_tuned 1 200 0x1800 55 117.5 141.9 235.8 353.9 401.5 536.5 199800.6 200000 20 2037.46 4381925 529485206 12350516 371870934 299922533652 401904510444 486482968214 744045445 32669 622708 190716 0 505155 1461720 linux_tuned 2 200 0x1800 55 119.3 143.1 236.9 356.7 406.6 536.3 200142.1 200000 20 2040.82 4388277 530351020 12369024 372413070 300478584458 402262515525 486591019691 749053331 31920 629331 193573 0 503472 1462249 -linux_tuned 0 300 0x1800 135 81.2 102.3 247.7 366.1 394.6 461.2 49954.3 50000 20 1602.0 1346412 148911165 3028824 90965718 82336396120 132054989315 168056523776 208787737 13104 199181 117215 0 779978 893150 -linux_tuned 1 300 0x1800 135 79.4 100.4 244.9 366.5 395.1 460.2 49968.1 50000 20 1596.91 1352296 149314086 3030114 91005792 82171645497 130860460579 166694062335 206359608 13489 201685 118585 0 777664 890880 -linux_tuned 2 300 0x1800 135 78.4 99.5 244.5 364.9 393.1 456.6 49944.3 50000 20 1595.54 1349946 149122842 3028443 90955380 82136907099 130860086615 166528124886 206785434 14225 212895 125324 0 785967 888533 -linux_tuned 0 300 0x1800 95 80.0 101.3 244.9 365.3 394.0 461.5 49998.4 50000 20 1600.93 1354055 149483758 3032136 91067910 82629445002 132809128826 168925087406 210477596 13387 199553 118745 0 779204 893979 -linux_tuned 1 300 0x1800 95 77.6 97.2 242.7 364.9 393.2 458.3 50015.9 50000 20 1603.1 1351992 149315401 3033206 91098882 82718129887 131388341485 166811375281 211101275 13328 204525 120455 0 785068 894549 -linux_tuned 2 300 0x1800 95 79.6 100.1 244.9 365.6 393.6 456.5 50072.2 50000 20 1600.44 1350072 149278191 3036416 91194852 82705315120 131848030074 167519710895 211253644 13220 201339 119256 0 780345 891357 -linux_tuned 0 300 0x1800 75 77.6 96.8 244.1 364.5 392.8 458.3 49914.4 50000 20 1602.58 1350113 149099953 3026744 90904104 82454984906 132020881774 167691512289 210863642 13578 205855 121286 0 780573 889059 -linux_tuned 1 300 0x1800 75 81.7 103.9 246.5 367.3 396.1 465.7 50072.9 50000 20 1598.99 1349358 149245300 3036072 91182774 82670493045 132861152702 169079774276 213309749 13916 205727 121756 0 780871 891004 -linux_tuned 2 300 0x1800 75 80.7 102.1 244.6 365.4 393.9 458.6 49989.4 50000 20 1602.87 1350437 149200207 3031349 91042260 82680934478 131846334136 167245216731 213601007 13259 203308 120396 0 783214 895227 -linux_tuned 0 300 0x1800 55 79.9 100.5 245.3 365.5 393.6 458.4 49992.6 50000 20 1604.46 1351424 149301668 3031490 91046814 82729935858 132688749048 168466387765 211490489 13379 199687 118535 0 779079 894974 -linux_tuned 1 300 0x1800 55 79.0 100.0 243.2 363.7 391.0 449.3 50061.6 50000 20 1591.62 1353808 149539227 3035639 91171218 81157044619 128865797050 164456867195 189403534 13340 202315 118030 0 786478 894402 -linux_tuned 2 300 0x1800 55 77.8 97.8 241.9 363.7 391.1 449.3 50047.8 50000 20 1598.47 1352876 149434184 3034796 91147422 81665400602 129300038660 164486949127 194621048 14373 213274 124801 0 791897 891889 -linux_tuned 0 300 0x1800 135 120.2 147.3 278.0 385.0 414.2 487.7 99917.9 100000 20 1762.75 2199864 265324287 6120924 184062054 156664258479 226583581001 277824458025 397293360 14704 220140 127372 0 787239 967731 -linux_tuned 1 300 0x1800 135 116.7 144.3 273.7 381.5 409.9 482.9 99947.1 100000 20 1758.56 2200675 265445812 6121867 184086432 156030326587 223851040433 274070398455 393080297 14715 218598 126223 0 790176 968015 -linux_tuned 2 300 0x1800 135 117.0 144.3 272.5 379.7 405.1 478.8 100030.7 100000 20 1762.22 2202532 265630249 6128503 184290870 156143807035 223558054218 273308599021 394192565 14025 212787 124180 0 785223 968141 -linux_tuned 0 300 0x1800 95 117.5 145.0 274.5 382.6 409.0 481.9 99884.8 100000 20 1763.5 2201223 265381479 6120159 184041960 156503033494 225982093799 276728406795 399738721 14052 214306 125160 0 780087 967600 -linux_tuned 1 300 0x1800 95 119.2 146.3 276.0 384.4 413.0 485.8 100007.0 100000 20 1762.83 2199168 265370122 6127158 184251462 156956167912 226070493612 276708200465 412985482 13982 213010 125498 0 784025 967889 -linux_tuned 2 300 0x1800 95 116.5 144.2 275.0 383.7 412.3 485.7 100052.3 100000 20 1765.39 2202463 265683927 6129304 184313214 156862089801 225481842423 275489459602 401399852 13893 213903 125276 0 784574 967459 -linux_tuned 0 300 0x1800 75 117.2 145.8 275.7 383.2 411.5 484.0 100061.9 100000 20 1764.03 2202881 265702322 6130245 184344672 156990965088 227640848263 279094294104 400474711 14466 217772 127082 0 781603 967505 -linux_tuned 1 300 0x1800 75 118.9 145.2 275.3 385.8 416.9 488.0 100012.5 100000 20 1766.7 2200729 265499403 6126903 184241580 156930435444 226955541713 277956319169 404708652 14598 220756 127294 0 786573 966735 -linux_tuned 2 300 0x1800 75 116.3 143.5 274.2 382.7 410.2 486.0 99964.3 100000 20 1768.59 2201162 265481347 6125169 184194306 156940807389 226024438027 276458717409 403014930 13999 213956 124781 0 780913 966898 -linux_tuned 0 300 0x1800 55 117.3 145.3 275.6 383.2 410.8 482.7 99875.0 100000 20 1765.92 2197631 265134777 6118299 183980850 156774202615 226211711876 276660070238 399771987 14205 214090 124037 0 781217 967110 -linux_tuned 1 300 0x1800 55 116.4 144.2 273.1 380.4 405.2 479.3 99899.6 100000 20 1761.96 2201167 265400252 6119991 184033140 154830461137 222754898185 272740998783 371238569 14380 217579 125913 0 789906 968304 -linux_tuned 2 300 0x1800 55 111.4 140.0 272.1 378.5 404.1 480.9 99989.6 100000 20 1764.27 2200355 265436759 6126674 184235622 155584686479 223509611759 273855758783 372667406 14159 216228 124743 0 782772 965988 linux_tuned 0 300 0x1800 135 151.0 184.3 312.9 436.9 485.9 612.9 200045.4 200000 20 2032.51 4475593 535965655 12485407 376566768 303929176236 398087958370 481152687507 772728358 17434 307879 177903 0 506551 976411 linux_tuned 1 300 0x1800 135 159.2 190.8 317.9 445.6 498.5 639.7 200076.7 200000 20 2025.72 4471496 535735841 12484201 376509594 303207379540 396503294184 479371753340 769104199 16960 303419 181513 0 508967 976461 linux_tuned 2 300 0x1800 135 159.6 191.5 317.6 445.2 499.7 643.4 200112.2 200000 20 2029.38 4473738 535911198 12486412 376580112 303531382183 397726100781 480797615944 775165818 16988 303946 180676 0 505705 976417 @@ -1165,30 +2486,6 @@ linux_tuned 2 300 0x1800 75 153.8 186.2 316.3 446.0 501.8 636.7 199893.7 200000 linux_tuned 0 300 0x1800 55 155.3 188.3 316.1 441.9 490.7 615.1 200171.4 200000 20 2034.7 4474759 536067230 12488617 376635624 304506133376 400926086841 484933129189 782891950 17035 309617 183378 0 500978 976456 linux_tuned 1 300 0x1800 55 157.7 190.3 317.6 441.7 494.5 625.0 200035.0 200000 20 2029.65 4469555 535549648 12479569 376356804 301319816446 395883755460 478666637190 734512782 17233 307394 183204 0 508786 976429 linux_tuned 2 300 0x1800 55 158.9 191.2 321.8 456.9 515.9 668.2 200091.1 200000 20 2121.26 4472880 535848435 12484495 376522428 301730471772 396510452433 479926900456 738156269 17870 310616 180216 0 506787 976303 -linux_tuned 0 400 0x1800 135 98.1 132.0 309.8 463.1 486.3 555.7 49970.6 50000 20 1597.07 1305011 146214156 3040326 91346376 81518463734 127589052637 161181113122 211735145 9503 125921 105336 0 662345 704287 -linux_tuned 1 400 0x1800 135 102.6 137.2 313.1 464.5 486.7 550.4 49975.2 50000 20 1597.93 1304280 146150768 3040710 91359192 80812757709 126279237954 159508783549 203318624 9290 122895 105492 0 656668 703958 -linux_tuned 2 400 0x1800 135 99.6 133.5 312.2 463.9 486.6 553.7 50079.9 50000 20 1593.99 1307402 146485467 3047797 91573878 81162010104 127448216597 161382822593 203253589 9169 123067 104133 0 656216 703691 -linux_tuned 0 400 0x1800 95 104.8 140.0 314.7 465.1 487.1 560.8 50056.9 50000 20 1592.07 1307066 146442051 3045968 91518864 81566101890 128725751620 163001048171 212346708 9484 128001 105174 0 666614 703467 -linux_tuned 1 400 0x1800 95 99.8 133.4 311.7 464.0 486.4 554.7 50048.6 50000 20 1596.38 1308011 146515955 3046037 91521462 81485510721 128213128511 162131830591 207391086 9355 124182 104413 0 658887 704242 -linux_tuned 2 400 0x1800 95 97.1 130.2 309.7 463.6 486.2 553.7 50096.3 50000 20 1594.6 1307525 146495180 3048364 91589658 81724992620 128471124893 162539344536 207979791 9963 132993 108099 0 672300 700538 -linux_tuned 0 400 0x1800 75 103.3 138.1 313.3 464.0 486.8 557.3 49974.8 50000 20 1597.46 1306293 146284502 3040879 91365132 81635784333 128372503478 162194228847 213386155 9329 125333 104180 0 662097 703380 -linux_tuned 1 400 0x1800 75 95.5 129.9 309.9 461.9 484.9 550.9 50005.1 50000 20 1598.36 1306100 146310876 3042721 91419738 81644037878 127920540654 161370563452 209289221 9263 125108 105660 0 660684 703970 -linux_tuned 2 400 0x1800 75 101.7 136.1 312.6 465.1 487.2 558.2 49985.5 50000 20 1594.9 1308229 146422814 3042243 91408050 81532327959 129065733217 163386706353 209090582 9655 126604 104665 0 661962 704767 -linux_tuned 0 400 0x1800 55 96.2 131.7 307.7 460.4 484.4 543.1 49974.4 50000 20 1580.49 1307530 146377670 3040549 91353606 79849116167 122198301165 155159103593 191878232 9147 120955 106458 0 654044 704309 -linux_tuned 1 400 0x1800 55 101.8 136.6 311.5 461.7 484.4 543.3 50077.7 50000 20 1584.45 1306956 146435439 3047376 91560834 79986290754 122437228093 155042490417 192150874 9447 124553 107068 0 661907 703934 -linux_tuned 2 400 0x1800 55 100.1 134.9 309.0 462.8 485.8 551.8 50073.7 50000 20 1590.19 1309356 146611261 3047012 91550106 80479384794 123636728465 156405327669 196706176 9431 124417 106658 0 657327 701602 -linux_tuned 0 400 0x1800 135 145.8 179.8 343.9 477.4 508.7 584.8 99958.2 100000 20 1756.97 2206110 265774788 6159706 185378946 155608327760 218764400613 267200010891 400982279 9792 129361 89003 0 666944 730755 -linux_tuned 1 400 0x1800 135 143.3 176.2 343.2 476.1 505.8 579.9 100000.9 100000 20 1756.39 2207601 265939795 6162818 185473374 154704119973 218370141578 266882924862 383347291 9768 130638 89211 0 668928 730763 -linux_tuned 2 400 0x1800 135 143.2 176.6 340.6 475.8 505.1 580.4 100162.9 100000 20 1756.7 2210712 266378926 6171740 185740062 155079314660 219254021177 268116975695 387092254 10175 135858 91111 0 675393 731005 -linux_tuned 0 400 0x1800 95 146.7 180.3 343.3 476.5 506.3 580.8 99876.5 100000 20 1753.67 2205349 265604392 6154769 185225514 155577181678 219853661922 268752215388 400419866 9610 128531 87559 0 666204 730889 -linux_tuned 1 400 0x1800 95 147.4 181.9 345.7 479.2 512.7 585.3 99910.0 100000 20 1753.22 2204414 265621789 6156872 185291592 155199316162 220624083854 270059165696 391413428 9612 129837 89007 0 667569 730828 -linux_tuned 2 400 0x1800 95 146.2 179.7 343.7 476.8 507.5 583.5 99941.3 100000 20 1757.01 2207630 265885388 6159920 185390556 155344640288 219452946722 267706538608 392622774 9595 128201 88411 0 665302 730665 -linux_tuned 0 400 0x1800 75 142.8 176.1 341.1 476.4 507.1 581.3 100076.2 100000 20 1756.95 2208897 266135772 6168074 185633748 155873066689 220003708201 268522832841 402918056 9517 127551 88755 0 663173 730436 -linux_tuned 1 400 0x1800 75 145.5 179.4 342.7 476.3 506.8 582.8 100127.8 100000 20 1759.19 2210019 266241272 6170283 185699922 155893586690 220987902228 269742073055 396508735 9980 130678 89208 0 670330 731123 -linux_tuned 2 400 0x1800 75 143.9 178.9 343.6 478.5 511.2 584.5 99934.2 100000 20 1758.24 2206505 265817115 6156500 185278626 155689445483 221569419223 270842188838 395623853 10056 134656 92918 0 668614 730039 -linux_tuned 0 400 0x1800 55 138.2 172.9 338.4 473.6 499.1 575.3 100036.4 100000 20 1744.61 2207373 265950501 6162578 185457930 153767722391 212582222486 260117121448 371231433 13691 159626 98246 0 679764 726658 -linux_tuned 1 400 0x1800 55 145.8 179.4 342.1 476.2 506.0 580.0 100021.0 100000 20 1749.73 2207418 265929525 6163350 185484984 153583440782 213469078528 260876444485 373791855 9659 128686 88934 0 666507 730546 -linux_tuned 2 400 0x1800 55 144.8 178.2 340.3 474.4 500.9 576.9 99901.7 100000 20 1749.82 2203652 265545242 6156944 185297580 154035548162 213872150891 261124963204 378749291 9561 125907 88606 0 664083 730429 linux_tuned 0 400 0x1800 135 183.1 225.5 393.0 536.1 592.0 738.4 199904.8 200000 20 2020.91 4545671 540427762 12572831 379836714 303337712594 392697792430 475367418144 781632108 12976 196700 141793 0 466808 732364 linux_tuned 1 400 0x1800 135 186.2 227.1 389.7 528.2 579.7 705.0 200042.7 200000 20 2023.15 4548216 540738651 12580814 380083746 301952330377 392072842862 473967164222 754624411 12677 196568 140832 0 469447 732369 linux_tuned 2 400 0x1800 135 195.9 237.6 400.3 539.3 593.0 734.4 200086.8 200000 20 2024.06 4549830 540921560 12584288 380191716 302681614229 391938072267 473611573501 758453744 12429 192396 141173 0 469474 732411 @@ -1200,30 +2497,6 @@ linux_tuned 2 400 0x1800 75 196.3 238.2 398.8 541.7 596.4 737.3 199916.0 200000 linux_tuned 0 400 0x1800 55 187.0 228.3 389.4 526.3 577.8 702.1 199931.5 200000 20 2009.7 4537241 539874923 12567103 379635312 299667140374 381741232766 462111154890 732194168 12984 194334 142173 0 484455 732333 linux_tuned 1 400 0x1800 55 195.7 238.6 399.1 541.2 596.5 743.9 200167.1 200000 20 2013.2 4547335 540843465 12587014 380246802 300677511150 384905087892 465699032628 743877555 12631 193046 140960 0 478654 732376 linux_tuned 2 400 0x1800 55 195.8 235.2 392.2 524.6 573.8 692.1 199915.0 200000 20 2012.54 4539676 540078234 12569883 379731246 300593345341 385023990862 465828189522 749610410 12008 188305 140414 0 480478 732413 -linux_tuned 0 50 0x1900 135 59.8 62.1 80.9 142.3 177.1 218.3 50023.9 50000 20 1727.91 1585632 164799809 3007103 90230172 87676553084 143938076659 168462733301 215106442 546474 662889 169591 0 805688 2130965 -linux_tuned 1 50 0x1900 135 58.9 61.6 80.2 139.7 174.9 216.5 50053.3 50000 20 1728.22 1590039 165095734 3008984 90287064 87051817144 142185899817 166312663905 208954235 545197 680668 173175 0 813912 2135844 -linux_tuned 2 50 0x1900 135 58.9 61.6 79.9 138.5 173.5 216.4 49817.3 50000 20 1723.08 1584932 164483592 2994793 89861238 86639047729 141230701937 165287620770 206888536 542854 676850 170294 0 809430 2125659 -linux_tuned 0 50 0x1900 95 59.5 61.9 80.6 145.0 180.3 221.1 50053.3 50000 20 1727.7 1585004 164759607 3008941 90285624 87461883167 144237482930 168581037061 214929823 546118 667572 174239 0 813151 2136381 -linux_tuned 1 50 0x1900 95 59.4 61.9 80.8 141.7 177.2 219.4 50025.2 50000 20 1727.33 1583782 164643477 3007276 90235860 87383506005 142896262809 166969883829 213093198 544931 670366 172726 0 809591 2131378 -linux_tuned 2 50 0x1900 95 59.1 61.7 80.9 142.9 177.4 219.8 49946.6 50000 20 1725.54 1588188 164819764 3002601 90095634 87462730313 142872388204 167031742772 213582617 549146 684404 172030 0 812430 2135124 -linux_tuned 0 50 0x1900 75 59.4 61.8 81.1 144.1 180.1 222.6 50006.1 50000 20 1725.98 1581377 164494211 3006273 90206202 87414927292 144510604695 168989384002 215541663 549333 672559 171382 0 808415 2131633 -linux_tuned 1 50 0x1900 75 59.6 62.0 81.1 142.2 176.5 218.4 50035.4 50000 20 1725.58 1591326 165166423 3007953 90256146 87565153795 143118285160 167376029612 214283415 543875 680801 172477 0 812134 2137568 -linux_tuned 2 50 0x1900 75 60.5 62.6 82.0 143.2 178.7 222.3 50066.9 50000 20 1724.31 1583997 164722174 3009845 90313002 87468758022 143386160051 167788850586 214520597 545837 670793 168705 0 805915 2132679 -linux_tuned 0 50 0x1900 55 57.4 60.3 78.7 137.8 167.8 207.6 49921.3 50000 20 1708.45 1587724 164779243 3000993 90047118 85726017568 136647561815 160107201188 193276656 528858 664318 165071 0 815706 2105238 -linux_tuned 1 50 0x1900 55 57.9 60.8 79.1 136.6 168.0 208.7 50087.6 50000 20 1711.59 1591718 165254263 3010919 90344592 86012436274 137448193518 160945014001 194788759 538296 701347 169961 0 821720 2124790 -linux_tuned 2 50 0x1900 55 57.6 60.5 79.0 138.6 170.5 208.7 50053.4 50000 20 1716.41 1593711 165335183 3008730 90278700 86404684397 138337093361 161807377750 199044101 537261 703211 175260 0 826599 2132847 -linux_tuned 0 50 0x1900 135 61.0 63.8 90.0 169.5 199.7 257.8 100100.8 100000 20 1908.92 2266666 269958168 6027810 180900660 164793679189 261045043124 302849700944 414872304 1199501 790047 146175 0 760617 3273264 -linux_tuned 1 50 0x1900 135 60.8 63.4 88.0 164.5 195.8 249.9 99976.5 100000 20 1902.39 2275782 270410038 6019885 180661788 163175100132 255901713357 296888133186 400322449 1206526 824849 144774 0 770880 3277485 -linux_tuned 2 50 0x1900 135 60.6 63.2 88.7 165.1 195.8 252.6 99942.3 100000 20 1902.68 2275252 270322199 6018130 180610272 163379251636 256086471434 297131736811 400780788 1197725 809988 143864 0 767291 3269426 -linux_tuned 0 50 0x1900 95 60.7 63.3 89.8 172.0 202.4 263.6 99811.4 100000 20 1907.75 2268726 269759639 6010572 180384312 164089577907 260766097092 302543384268 413280917 1186286 781816 144759 0 760115 3261934 -linux_tuned 1 50 0x1900 95 61.6 64.4 90.4 168.5 198.3 255.7 99996.4 100000 20 1902.88 2274763 270350123 6021783 180721818 163765203839 257281165138 298494082600 407578239 1184264 793943 144757 0 768823 3261643 -linux_tuned 2 50 0x1900 95 60.7 63.4 88.5 166.4 197.5 255.7 100053.6 100000 20 1905.32 2272807 270311527 6025319 180827760 164135793105 258688371101 300155245810 410855700 1201771 813813 146114 0 766335 3275530 -linux_tuned 0 50 0x1900 75 61.3 64.1 89.7 169.5 200.3 254.6 99912.2 100000 20 1907.81 2272062 270064673 6017047 180580188 164147974417 260873134287 302654354044 414913858 1197388 783547 146358 0 763600 3273462 -linux_tuned 1 50 0x1900 75 60.5 63.1 88.8 167.9 198.9 256.1 100040.2 100000 20 1903.7 2271791 270199386 6024987 180818890 164171813304 258220310814 299590159785 410515393 1183173 795626 142525 0 763195 3257827 -linux_tuned 2 50 0x1900 75 60.6 63.5 90.2 168.1 199.5 259.3 100115.6 100000 20 1907.39 2274752 270517575 6029226 180945198 164148540022 258306702139 299674850834 412619873 1185409 794750 141970 0 762057 3264985 -linux_tuned 0 50 0x1900 55 58.9 62.0 86.5 159.2 188.7 242.4 100092.0 100000 20 1891.79 2278431 270725647 6026315 180853098 162181683553 248560941624 288390352680 379739717 1187241 859426 142387 0 768665 3254582 -linux_tuned 1 50 0x1900 55 59.8 62.6 87.5 163.7 192.9 247.8 99867.1 100000 20 1893.18 2275301 270250902 6013531 180472344 161830850193 251071585248 291343679997 385085074 1175616 845318 142057 0 768657 3253145 -linux_tuned 2 50 0x1900 55 59.4 62.4 88.3 165.2 195.1 251.2 99960.6 100000 20 1896.52 2274306 270303888 6018761 180627918 162660158049 252207144282 292624405518 388698849 1181854 831628 143199 0 772793 3259024 linux_tuned 0 50 0x1900 135 64.0 70.7 114.0 245.3 304.8 466.5 200011.0 200000 20 2218.78 4260195 521717293 12182403 366047076 310877164193 464450149889 538761835512 814227831 1637071 982726 60332 0 200479 4826889 linux_tuned 1 50 0x1900 135 64.2 70.7 113.0 235.8 290.5 437.6 200156.5 200000 20 2207.86 4258589 521799297 12178759 365898276 310577752916 457734197984 530970809598 796149387 1684796 1019583 60222 0 200891 4825461 linux_tuned 2 50 0x1900 135 64.8 71.6 114.3 247.3 309.7 485.8 200084.7 200000 20 2209.91 4261638 521891878 12187003 366186204 310357688202 457418387504 530605158690 797541611 1647013 994034 62061 0 213867 4803310 @@ -1235,30 +2508,6 @@ linux_tuned 2 50 0x1900 75 64.2 71.0 113.5 241.7 297.1 439.2 199966.0 200000 20 linux_tuned 0 50 0x1900 55 63.6 70.0 111.0 231.2 285.0 427.7 199792.8 200000 20 2123.52 4254293 521023417 12166539 365562816 307162725809 443276827252 532508033304 758870344 1644011 1008941 60397 0 207485 4815471 linux_tuned 1 50 0x1900 55 64.6 71.4 115.6 250.5 312.4 480.4 199831.4 200000 20 2126.02 4258986 521412799 12176093 365871636 307922443976 448688224032 539608667209 773006702 1599274 953434 61174 0 208965 4807637 linux_tuned 2 50 0x1900 55 64.3 70.9 113.4 242.3 297.3 443.6 200055.6 200000 20 2123.65 4264430 522019433 12188709 366249648 308144827231 448959517847 539436339755 780158518 1628828 971402 60983 0 201063 4826721 -linux_tuned 0 100 0x1900 135 62.3 66.2 105.6 180.8 205.4 265.6 49921.9 50000 20 1691.63 1519744 160307484 3004917 90176862 84752146921 140155015085 172966470682 201604452 50986 748073 175220 0 818076 1612262 -linux_tuned 1 100 0x1900 135 61.6 65.3 104.3 178.6 203.6 265.2 50015.3 50000 20 1682.7 1521282 160509978 3010545 90346104 85568553977 141768402353 172132099328 230105989 100481 822754 192188 0 836002 1655704 -linux_tuned 2 100 0x1900 135 61.5 65.2 103.9 179.9 205.5 268.1 50112.9 50000 20 1688.31 1527292 161012142 3016449 90522924 85121423819 142447568358 175943294495 206894006 41856 733922 172450 0 815412 1611133 -linux_tuned 0 100 0x1900 95 61.5 65.5 108.1 182.1 206.7 266.0 50107.3 50000 20 1698.38 1528441 161101786 3016070 90511620 84981606832 140008493938 172562151696 204766767 46446 741596 171806 0 819444 1611445 -linux_tuned 1 100 0x1900 95 61.4 65.0 104.6 181.1 206.4 267.7 49972.3 50000 20 1695.43 1522765 160558116 3007878 90265368 85348320011 142996326473 176158158946 211130739 42467 730089 173698 0 818221 1612090 -linux_tuned2 100 0x1900 95 61.4 64.9 102.3 179.2 204.2 268.0 50000.1 50000 20 1693.3 1527331 160883327 3009474 90313368 85535064263 142749808574 175722859521 211252145 45271 734403 173744 0 823300 1616987 -linux_tuned 0 100 0x1900 75 61.5 65.0 103.5 180.3 204.7 265.2 49964.5 50000 20 1694.68 1523015 160585871 3007329 90248826 84939819980 141335617277 174583733924 205943669 40179 729064 171893 0 815397 1607770 -linux_tuned 1 100 0x1900 75 61.6 65.2 101.9 179.7 204.9 268.9 50051.1 50000 20 1698.44 1530081 161147610 3012631 90408138 85523694448 143404334901 176475332671 212738203 46797 738597 173569 0 818006 1614678 -linux_tuned 2 100 0x1900 75 61.8 65.4 103.2 180.1 205.0 266.9 50028.4 50000 20 1835.41 1523037 160653943 3011077 90361008 85765878502 143298292114 176205203007 213835930 46840 734904 172173 0 818127 1612430 -linux_tuned 0 100 0x1900 55 61.2 65.0 104.2 180.9 205.9 266.1 49859.2 50000 20 1691.79 1522863 160421234 3001122 90063210 84867717378 141499306692 174701144194 206739152 48567 744897 176494 0 826896 1618843 -linux_tuned 1 100 0x1900 55 60.5 63.9 100.8 175.5 199.7 256.5 49971.0 50000 20 1680.48 1527790 160912064 3008043 90271338 83794226902 136738709878 169452612689 192272031 41190 733908 167025 0 810589 1597101 -linux_tuned 2 100 0x1900 55 60.9 64.5 101.5 176.5 200.7 261.9 50032.2 50000 20 1683.15 1529557 161078364 3011644 90379116 84430382841 138832103631 171899447379 197372523 49670 746594 174723 0 828651 1618388 -linux_tuned 0 100 0x1900 135 63.9 70.2 126.4 197.9 235.3 292.5 99984.0 100000 20 1877.61 2237802 267897661 6040982 181361856 159285785662 244489400890 288350990342 386007302 82218 1365223 141491 0 729216 2328742 -linux_tuned 1 100 0x1900 135 64.2 70.9 126.9 200.1 236.6 298.0 100106.0 100000 20 1878.61 2236403 268005813 6048742 181594980 159846150685 246787501156 290407636673 394451007 81053 1372231 141733 0 729277 2339908 -linux_tuned 2 100 0x1900 135 63.8 70.2 126.3 199.4 238.1 298.1 100061.1 100000 20 1878.12 2241287 268245906 6045577 181499088 160010475336 247590965588 291429217440 396641239 80971 1379291 143035 0 727010 2345703 -linux_tuned 0 100 0x1900 95 63.9 70.3 125.3 197.0 233.4 293.9 99975.9 100000 20 1880.09 2236323 267786332 6040065 181333338 159636725058 245287908953 289446635688 391693565 91377 1366513 143909 0 738699 2335567 -linux_tuned 1 100 0x1900 95 64.4 71.4 128.5 200.9 237.8 298.9 99908.3 100000 20 1880.79 2232537 267507845 6036520 181227534 160286661311 248663244722 292791174194 402610966 80688 1367505 141544 0 723217 2338566 -linux_tuned 2 100 0x1900 95 63.6 70.1 126.1 198.3 236.2 296.4 99928.7 100000 20 1880.41 2233631 267559132 6038257 181281870 160472777402 250501401461 295930011205 416238653 85672 1350704 142266 0 729655 2334411 -linux_tuned 0 100 0x1900 75 63.2 69.6 126.4 195.8 232.8 292.9 99895.0 100000 20 1882.1 2233980 267519037 6035253 181188270 159579160692 244905204058 288276743266 393927762 78973 1365069 139297 0 730637 2330706 -linux_tuned 1 100 0x1900 75 65.0 71.8 128.4 201.0 239.1 298.7 99895.4 100000 20 1883.53 2235176 267622916 6036364 181224330 160728093694 249772650835 294257931061 406830070 86795 1373210 144698 0 727581 2346863 -linux_tuned 2 100 0x1900 75 64.2 70.7 125.9 199.7 237.7 298.7 100111.9 100000 20 1886.95 2239265 268158566 6048944 181602834 160996300868 250896107968 295115409847 407520361 88854 1359729 141551 0 726033 2337957 -linux_tuned 0 100 0x1900 55 63.6 70.3 126.7 197.2 232.7 291.9 99963.2 100000 20 1879.95 2234228 267663731 6039898 181329486 160111189523 246689284064 290559960739 398471262 83797 1388122 145622 0 729318 2351284 -linux_tuned 1 100 0x1900 55 63.1 69.3 124.0 191.4 226.7 285.5 99974.8 100000 20 1872.81 2235570 267737156 6040536 181349682 158771884839 242845903228 286407156012 380448316 76253 1383884 141525 0 729319 2340116 -linux_tuned 2 100 0x1900 55 63.4 69.3 123.9 191.9 227.6 283.8 99945.7 100000 20 1874.84 2240725 268059230 6038938 181301640 159146939078 242955549596 286600485962 382459416 75587 1373892 139981 0 727731 2332822 linux_tuned 0 100 0x1900 135 77.2 90.8 150.9 260.5 305.0 429.1 200080.0 200000 20 2172.76 4295997 524154604 12229851 367656516 302593917764 427237922401 495820475649 776002156 184713 1620796 102785 0 299549 2818060 linux_tuned 1 100 0x1900 135 79.4 93.8 154.3 272.2 322.0 443.4 199654.1 200000 20 2174.98 4288160 523081055 12207111 366985320 303246815458 432072346948 501338469499 770295026 179736 1601858 101276 0 290058 2822512 linux_tuned 2 100 0x1900 135 77.7 91.4 151.9 268.8 317.0 452.4 199956.2 200000 20 2176.44 4296610 524018601 12229251 367667364 304074862679 431943020709 501130276295 778274224 181415 1596672 100781 0 292351 2821474 @@ -1271,30 +2520,6 @@ linux_tuned 2 100 0x1900 75 79.7 93.8 154.4 275.0 328.0 469.8 200077.9 200000 20 linux_tuned 0 100 0x1900 55 81.1 96.0 156.8 282.1 335.2 475.5 200089.6 200000 20 2119.35 4302479 524590052 12242416 368071452 303683344610 427819010949 510428299360 772825293 183355 1562677 97933 0 279484 2822482 linux_tuned 1 100 0x1900 55 78.9 93.1 153.4 269.2 317.6 441.1 199944.8 200000 20 2116.49 4296821 524033372 12227095 367596390 301837404866 423588234541 503645017704 750883465 180159 1593436 100207 0 286293 2823785 linux_tuned 2 100 0x1900 55 80.5 95.1 155.2 274.9 327.3 459.1 199955.4 200000 20 2117.57 4295057 523919263 12227177 367597968 302244054308 422881220912 502614524135 756269495 182149 1600113 99892 0 287780 2822156 -linux_tuned 0 200 0x1900 135 66.3 74.9 173.3 269.8 294.5 358.7 50011.8 50000 20 1677.04 1421916 153960323 3021169 90699012 83270061151 136238925981 167999700538 208492447 18385 355081 155598 0 832048 1168575 -linux_tuned 1 200 0x1900 135 67.5 76.3 173.8 269.4 293.9 357.9 50088.6 50000 20 1817.97 1421840 154036495 3025882 90840078 83079509591 135784267893 167465438874 209078403 18207 356245 154397 0 839992 1174894 -linux_tuned 2 200 0x1900 135 67.0 76.0 174.8 269.8 294.2 357.1 50094.4 50000 20 1678.32 1422638 154107067 3025936 90841386 82988282713 135198479308 166628774693 205862868 19107 360735 157665 0 844562 1174553 -linux_tuned 0 200 0x1900 95 68.3 77.6 176.3 271.4 297.0 361.7 49981.3 50000 20 1677.13 1421903 153911461 3019034 90634038 83474343078 137838704021 170188724503 209423169 18574 362017 160651 0 844469 1179301 -linux_tuned 1 200 0x1900 95 68.9 78.8 178.5 272.4 298.2 361.2 50006.3 50000 20 1679.97 1423087 154025424 3020460 90676416 83414455403 137163850637 169147412571 209449067 18599 361214 159464 0 840736 1174568 -linux_tuned 2 200 0x1900 95 67.0 75.7 173.7 269.3 294.6 358.8 50028.2 50000 20 1675.26 1423940 154107522 3021931 90720816 83309905521 137246743490 169407213479 209878595 18242 356799 156067 0 833032 1175364 -linux_tuned 0 200 0x1900 75 67.3 77.2 174.6 270.9 295.6 359.5 50027.0 50000 20 1676.98 1423242 154054737 3022021 90724770 83377662993 137463855726 169631843390 209931459 18935 363317 159660 0 845512 1181417 -linux_tuned 1 200 0x1900 75 67.3 75.4 173.9 269.6 294.3 358.0 49998.9 50000 20 1680.78 1421796 153947443 3019967 90661854 83324486748 136822289648 168551304405 211223323 18160 355743 155698 0 835351 1171797 -linux_tuned 2 200 0x1900 75 67.2 76.5 175.1 270.2 295.7 359.9 49931.3 50000 20 1675.2 1421132 153814697 3016299 90552384 83394711965 136997966594 168937898040 211348235 18343 355104 154639 0 834906 1171904 -linux_tuned 0 200 0x1900 55 67.8 77.2 176.3 271.6 296.6 360.8 50021.1 50000 20 1680.14 1423128 154058141 3021705 90714870 83529765816 137299701910 169150648739 210356336 19042 365498 161673 0 849561 1182388 -linux_tuned 1 200 0x1900 55 65.6 74.1 171.3 268.6 292.8 351.9 49974.6 50000 20 1664.48 1424107 154040333 3018815 90627798 81818423417 132552995018 164166514207 191055351 18597 364196 159039 0 847248 1173031 -linux_tuned 2 200 0x1900 55 66.9 76.0 173.9 268.9 293.0 353.0 50045.4 50000 20 1670.84 1423504 154088351 3022890 90750078 82258953306 133509020829 165061190800 195727753 18922 361091 158153 0 845046 1175057 -linux_tuned 0 200 0x1900 135 85.4 107.0 202.3 287.9 321.1 389.0 100125.4 100000 20 1857.77 2206644 266018523 6094626 183125190 157294879285 234765738934 276918853768 398337676 23116 488007 166364 0 864896 1405156 -linux_tuned 1 200 0x1900 135 83.3 104.4 200.6 284.5 316.8 384.5 100037.7 100000 20 1853.2 2207699 265991364 6088870 182950662 156415190425 232121136400 274222978823 390841989 23841 490750 166573 0 864800 1400478 -linux_tuned 2 200 0x1900 135 82.7 103.4 200.0 282.3 315.0 381.5 99963.1 100000 20 1950.99 2204372 265687798 6084083 182806074 156321409684 231585193964 273217240060 390933880 22516 485123 163998 0 869298 1403902 -linux_tuned 0 200 0x1900 95 82.6 103.8 201.0 284.3 316.0 383.2 100163.5 100000 20 1857.5 2208170 266178231 6096193 183171540 157260307236 234900990290 277330560939 397316647 23232 486950 166327 0 865421 1403984 -linux_tuned 1 200 0x1900 95 82.2 103.3 200.7 284.9 317.6 382.0 99983.7 100000 20 1854.31 2206194 265822794 6085647 182853918 156967496991 233599273144 275836009747 397937664 22970 482701 164011 0 863278 1396525 -linux_tuned 2 200 0x1900 95 85.1 106.9 202.8 288.3 321.0 387.7 100071.1 100000 20 1856.65 2205489 265875997 6090697 183004188 157197883557 234566941183 276662242106 399381665 23832 489114 166645 0 868112 1406132 -linux_tuned 0 200 0x1900 75 83.0 103.8 201.8 286.1 320.2 388.6 99855.9 100000 20 1856.87 2203451 265477190 6078052 182627106 156919172044 234817353104 277141018737 398108050 22986 484986 166017 0 862254 1402403 -linux_tuned 1 200 0x1900 75 86.6 108.1 204.2 289.6 323.6 392.1 100167.7 100000 20 1858.96 2210225 266354463 6097148 183201816 157334530824 234035090740 275188617981 402774598 22799 483674 164870 0 866022 1402902 -linux_tuned 2 200 0x1900 75 84.8 105.3 201.8 287.7 320.1 385.3 99837.8 100000 20 1856.71 2204169 265526571 6076914 182591820 157039931897 234674490017 276806663788 401579567 22672 483398 165823 0 864896 1403806 -linux_tuned 0 200 0x1900 55 82.9 103.7 200.8 284.8 317.5 388.1 99982.5 100000 20 1856.5 2205875 265802762 6086204 182874378 157375216161 236304132933 279337114323 399983716 23642 489608 167980 0 864083 1403379 -linux_tuned 1 200 0x1900 55 83.8 105.3 200.6 282.8 314.4 382.4 100041.6 100000 20 1852.07 2206573 265936733 6088934 182952168 155279710496 230646354495 272293480585 376590358 23391 491238 167318 0 870894 1406951 -linux_tuned 2 200 0x1900 55 84.8 105.9 201.6 285.3 317.8 383.8 100012.1 100000 20 1853.93 2203676 265700709 6087061 182896518 155772279727 230509581500 271850092567 379390498 22316 480662 162527 0 865873 1399513 linux_tuned 0 200 0x1900 135 117.8 140.5 233.0 345.3 392.9 508.8 200167.7 200000 20 2143.79 4384443 530125293 12365519 372290580 302958577100 410981190853 476961171550 775097915 31298 632457 197490 0 514918 1462074 linux_tuned 1 200 0x1900 135 117.6 139.5 232.0 340.4 389.5 508.8 199972.0 200000 20 2135.56 4380283 529589278 12354197 371961936 301936387488 405993583737 471113517416 765708691 30427 626259 196081 0 525218 1461383 linux_tuned 2 200 0x1900 135 118.3 140.7 234.4 347.0 394.3 515.6 200073.5 200000 20 2141.77 4384826 530054921 12361650 372188364 302491730995 410275432148 476219444611 774797018 30569 628117 198400 0 515552 1462376 @@ -1307,30 +2532,6 @@ linux_tuned 2 200 0x1900 75 119.0 142.6 237.4 363.4 423.3 589.4 200162.7 200000 linux_tuned 0 200 0x1900 55 117.7 141.0 236.9 357.4 407.7 539.0 199955.2 200000 20 2112.21 4388178 530049380 12361437 372210216 302795566933 411476356878 485509871010 778178079 31799 618667 188730 0 502775 1460645 linux_tuned 1 200 0x1900 55 117.6 140.5 234.4 352.3 403.0 537.3 199991.9 200000 20 2099.32 4384141 529855046 12359356 372120732 299940911476 404474050514 477815934225 743171515 31840 624955 191802 0 513669 1460729 linux_tuned 2 200 0x1900 55 116.6 140.1 234.0 350.3 399.7 527.4 199917.6 200000 20 2104.62 4381894 529616927 12355415 372016872 299964548054 404953549322 476755830119 746676464 30985 616228 192212 0 518438 1460993 -linux_tuned 0 300 0x1900 135 77.9 98.3 242.0 362.2 389.5 452.4 49919.4 50000 20 1671.68 1351007 149153954 3026727 90903324 82527234433 132353328669 162112940977 212228834 13472 201649 117680 0 786526 894991 -linux_tuned 1 300 0x1900 135 78.1 97.1 241.7 363.9 391.4 454.7 49911.1 50000 20 1667.32 1352433 149263545 3026114 90884592 82010177544 131890368585 162192092968 206886925 13501 205138 120035 0 785197 891698 -linux_tuned 2 300 0x1900 135 77.0 97.2 242.8 364.3 392.1 448.7 49954.0 50000 20 1666.64 1353408 149377066 3029162 90976182 82097470581 131426501635 161300666318 206448151 12749 197267 113710 0 772941 889371 -linux_tuned 0 300 0x1900 95 79.9 102.3 247.1 365.1 393.7 459.6 50058.5 50000 20 1670.7 1351744 149378425 3035474 91167018 82697658409 132787826141 162759952408 212752068 13057 198360 114084 0 767546 882948 -linux_tuned 1 300 0x1900 95 79.8 101.6 242.9 363.5 391.6 455.0 50093.9 50000 20 1672.92 1353073 149500751 3037876 91239270 82702143303 133077595631 163290888410 211391150 13125 196632 114910 0 780123 895087 -linux_tuned 2 300 0x1900 95 78.1 99.3 244.0 364.1 391.8 455.3 49993.1 50000 20 1667.9 1351076 149246963 3031303 91039530 82623820095 132924946628 163110259213 211379069 13290 202344 117575 0 776627 885620 -linux_tuned 0 300 0x1900 75 81.0 102.4 245.6 365.2 393.6 457.7 50029.7 50000 20 1670.28 1350744 149285508 3033326 91101546 82865395403 134177061483 164565477966 214246315 13685 205600 120600 0 781224 891799 -linux_tuned 1 300 0x1900 75 79.4 100.0 242.9 365.2 393.4 457.4 50028.3 50000 20 1673.33 1351457 149306691 3033621 91110468 82905369021 133372894321 163529579928 213875769 13998 209107 122203 0 787687 892065 -linux_tuned 2 300 0x1900 75 80.4 101.7 245.0 364.8 393.1 452.9 50007.3 50000 20 1667.35 1354463 149491029 3032339 91073124 82629856126 133442005195 163795170512 212910794 13147 200727 117323 0 780265 895301 -linux_tuned 0 300 0x1900 55 77.3 97.8 241.6 363.3 390.4 444.4 49989.7 50000 20 1658.21 1356152 149600789 3031057 91032138 81028347435 130298061497 159988804309 192812502 12989 203223 116292 0 779615 890749 -linux_tuned 1 300 0x1900 55 77.8 97.3 239.9 362.9 390.0 443.7 50121.1 50000 20 1649.44 1353130 149533245 3039007 91271400 81219470308 129406766328 159671533550 192764926 13826 208038 119462 0 782563 888554 -linux_tuned 2 300 0x1900 55 77.2 97.8 239.6 362.0 388.8 442.9 49981.0 50000 20 1659.14 1353207 149380670 3030307 91009362 81377753900 128630397583 158156631144 195138931 12859 196110 111693 0 769099 886575 -linux_tuned 0 300 0x1900 135 116.1 143.5 272.6 379.2 403.9 479.2 100120.7 100000 20 1842.23 2204793 265921583 6134195 184462644 157301483724 228350410958 268980954657 403717015 14108 213799 123677 0 787680 967615 -linux_tuned 1 300 0x1900 135 114.1 141.8 270.9 378.3 403.6 476.3 99952.9 100000 20 1839.64 2199707 265365079 6123111 184126638 155992436342 226606291918 267282903012 392581236 14667 220073 126573 0 793587 967365 -linux_tuned 2 300 0x1900 135 113.3 140.7 270.0 377.5 403.7 476.4 100020.5 100000 20 1840.98 2201211 265563855 6127294 184250856 156332786162 225860769436 265919449133 392989136 13815 214805 124475 0 792536 967530 -linux_tuned 0 300 0x1900 95 117.3 144.1 271.8 379.6 404.0 479.4 99880.1 100000 20 1842.95 2197792 265141433 6119210 184011390 156995695226 228127401050 268429489497 404826755 14026 213008 124636 0 789986 968947 -linux_tuned 1 300 0x1900 95 109.1 138.5 269.3 376.8 403.2 478.4 99904.2 100000 20 1842.18 2200861 265357781 6120900 184067580 156678451205 226344475616 266037308468 400216659 13633 209145 121335 0 781040 964429 -linux_tuned 2 300 0x1900 95 114.8 142.6 271.3 378.5 404.0 479.2 99991.4 100000 20 1842.6 2202007 265598240 6125887 184215016 156868393781 228159403365 268761440143 401525025 14211 215228 125143 0 792575 968914 -linux_tuned 0 300 0x1900 75 114.8 142.3 272.3 379.2 406.9 480.1 100039.9 100000 20 1844.4 2202461 265652516 6128737 184296804 157442884417 229825095544 270723946297 406462087 14671 217581 127547 0 793624 968461 -linux_tuned 1 300 0x1900 75 112.6 141.1 271.2 377.2 403.6 476.5 100021.4 100000 20 1843.02 2203224 265673003 6127272 184252200 156814379415 227876743288 267996602292 403722046 13840 211703 123161 0 786375 967034 -linux_tuned 2 300 0x1900 75 117.6 144.3 272.5 380.3 405.0 480.7 100086.0 100000 20 1844.98 2202891 265758552 6132248 184404060 157331201406 228740230355 269105574124 405770264 13675 209872 123088 0 785543 968065 -linux_tuned 0 300 0x1900 55 113.9 140.7 269.9 374.0 401.2 471.4 100093.0 100000 20 1840.09 2202504 265718790 6131674 184385322 155100911729 225313335702 264764353671 380658064 14239 217162 123907 0 793383 967785 -linux_tuned 1 300 0x1900 55 113.2 140.5 269.7 375.4 402.0 473.4 100092.4 100000 20 1832.83 2203059 265756847 6132217 184403508 155213279825 222664341705 262463170379 377148137 14246 212971 121503 0 791028 967198 -linux_tuned 2 300 0x1900 55 116.8 142.9 272.3 377.6 403.5 476.0 99945.7 100000 20 1837.3 2199531 265324398 6122793 184117776 155454876881 223736627444 263580982194 379949106 14311 217545 124177 0 795898 967840 linux_tuned 0 300 0x1900 135 153.7 186.0 314.0 436.2 485.5 603.5 199941.3 200000 20 2126.62 4464765 535141030 12469652 376042422 304415586542 403858519533 468833851689 785055471 17258 310739 185531 0 520001 976396 linux_tuned 1 300 0x1900 135 151.3 183.2 310.6 430.1 478.3 588.7 199947.8 200000 20 2120.17 4463126 535036819 12471313 376096362 303064491614 400521482851 465142314031 767877291 17295 307908 184323 0 525030 976423 linux_tuned 2 300 0x1900 135 150.9 182.8 311.7 433.6 482.6 602.8 200085.3 200000 20 2121.88 4467908 535535042 12481609 376409286 303614714804 400434592252 464734330750 774446599 16683 302960 179872 0 524911 976364 @@ -1342,30 +2543,6 @@ linux_tuned 2 300 0x1900 75 156.1 188.6 314.5 437.6 487.0 613.7 200073.2 200000 linux_tuned 0 300 0x1900 55 161.7 192.4 316.0 433.4 481.0 591.8 200008.7 200000 20 2103.08 4466833 535330981 12475234 376216740 300643953154 400478689298 468878022662 734555240 16628 304576 183279 0 521708 976418 linux_tuned 1 300 0x1900 55 153.9 185.9 311.9 435.1 484.6 617.4 200071.2 200000 20 2098.34 4466362 535371158 12481003 376399230 301485025758 394219450412 461038132096 744904647 16999 302703 180829 0 529980 976287 linux_tuned 2 300 0x1900 55 158.0 190.4 314.6 435.3 483.2 613.8 199935.9 200000 20 2099.77 4462426 535016374 12471090 376090428 301578492358 395162729769 462051852558 748376229 16843 302609 180790 0 529230 976398 -linux_tuned 0 400 0x1900 135 96.9 131.5 308.3 461.2 484.3 541.6 50017.1 50000 20 1665.19 1308490 146484743 3043797 91453146 80832652092 127722871560 155547456165 202203991 9298 123919 107277 0 657014 703006 -linux_tuned 1 400 0x1900 135 99.6 133.8 310.2 461.1 484.0 544.8 50019.0 50000 20 1792.05 1311454 146684586 3043543 91444710 81088614948 127174738569 155325260862 206376410 9262 122156 105836 0 653671 703905 -linux_tuned 2 400 0x1900 135 99.0 132.2 308.9 459.5 482.7 542.2 50015.9 50000 20 1655.6 1306219 146331707 3044020 91460982 81001106501 127340472305 155676342460 206207282 9284 122917 106112 0 660013 705480 -linux_tuned 0 400 0x1900 95 99.9 134.2 309.3 460.5 483.9 546.0 49996.9 50000 20 1661.79 1306875 146352945 3042116 91401336 80821835722 129241314650 157829029860 203827136 9201 122389 104564 0 656324 703897 -linux_tuned 1 400 0x1900 95 98.4 130.8 309.2 461.7 484.9 550.7 50047.2 50000 20 1662.37 1308427 146482426 3045183 91493136 81846756375 128986813957 157622000310 211321651 9472 127945 107257 0 667182 703062 -linux_tuned 2 400 0x1900 95 97.4 131.1 309.4 462.1 485.5 549.1 49952.5 50000 20 1655.0 1303548 146066369 3038993 91306218 81349340248 128326574144 156873431401 210071234 10030 134529 111666 0 669411 697979 -linux_tuned 0 400 0x1900 75 100.7 133.2 309.4 462.4 485.7 546.0 49943.7 50000 20 1666.13 1307525 146337768 3038782 91300668 80912918956 129295172378 157752488507 205816995 9637 130117 108466 0 670886 704267 -linux_tuned 1 400 0x1900 75 100.4 133.0 311.2 461.6 485.2 549.7 49991.6 50000 20 1664.33 1307787 146424538 3042214 91405668 81631692503 128783037809 157210345165 212393743 9309 124218 106071 0 660090 704246 -linux_tuned 2 400 0x1900 75 99.4 133.5 310.6 462.1 485.0 550.3 50055.7 50000 20 1660.33 1308940 146572593 3046261 91529064 81503867961 129014814960 157542322114 212435334 9430 125888 106038 0 663027 704082 -linux_tuned 0 400 0x1900 55 98.2 130.9 310.4 462.9 486.0 551.7 50046.0 50000 20 1661.13 1307867 146451627 3045322 91496886 81197916625 130792729463 159724825632 206243713 9417 125836 105396 0 662241 701761 -linux_tuned 1 400 0x1900 55 98.4 131.3 307.3 459.3 482.6 538.5 50000.7 50000 20 1644.14 1307732 146411875 3042702 91419924 79857931518 123620862130 151457300718 191510231 8938 121540 107374 0 656415 704408 -linux_tuned 2 400 0x1900 55 96.1 128.0 306.3 460.3 484.1 539.2 50044.6 50000 20 1648.94 1309860 146595929 3044972 91487220 80503565076 125323032538 153608926080 195740387 9733 127331 107917 0 667029 702768 -linux_tuned 0 400 0x1900 135 145.2 178.3 338.4 473.6 498.6 575.8 99955.8 100000 20 1832.39 2205332 265743706 6158828 185352162 154006175565 221105692432 259710788499 385434071 9409 126292 86392 0 667739 730972 -linux_tuned 1 400 0x1900 135 143.2 176.4 340.1 472.9 496.1 575.0 100050.0 100000 20 1825.69 2207671 266043636 6166069 185570718 154761409253 218071841890 256382983631 389196781 9464 128032 88227 0 666801 730803 -linux_tuned 2 400 0x1900 135 143.3 176.1 337.4 472.6 495.8 570.1 100043.2 100000 20 1828.33 2208332 266036449 6166758 185591688 154887876972 218049622042 256367832175 390921884 9218 124577 88157 0 662806 729941 -linux_tuned 0 400 0x1900 95 138.8 173.2 338.4 473.5 498.2 572.9 100302.3 100000 20 1836.77 2212585 266586939 6180613 186009426 154954778713 221861035413 260409724285 386743926 9994 133700 91062 0 675240 730347 -linux_tuned 1 400 0x1900 95 142.7 175.4 338.2 473.6 498.4 573.3 99991.3 100000 20 1832.0 2206519 265859060 6162053 185443434 155384730623 219753596249 257901054461 398853905 9836 131194 89221 0 671472 730939 -linux_tuned 2 400 0x1900 95 144.7 179.2 343.0 475.0 502.0 578.2 100138.2 100000 20 1830.74 2210538 266311566 6171879 185748558 155760985151 220127577418 258599058187 398414081 9280 124456 88062 0 662647 730483 -linux_tuned 0 400 0x1900 75 142.6 176.4 340.4 474.3 500.2 576.6 100060.2 100000 20 1834.81 2207047 265959566 6166334 185577966 154796222233 223027540025 262413258305 389583765 9878 133238 91369 0 674462 730752 -linux_tuned 1 400 0x1900 75 141.5 175.3 338.9 474.7 502.6 580.1 99965.3 100000 20 1828.14 2206784 265855270 6159190 185357700 155884387008 222938039115 262828380400 401547997 10417 136761 93598 0 680411 730690 -linux_tuned 2 400 0x1900 75 145.9 179.1 342.8 474.8 501.2 576.0 99985.8 100000 20 1831.89 2207412 265901689 6162468 185463696 155653192742 221291592727 260155085116 402111134 9302 123957 86431 0 663262 730418 -linux_tuned 0 400 0x1900 55 143.1 176.4 340.7 473.8 499.2 575.8 100137.7 100000 20 1837.17 2209446 266246610 6170437 185696526 155091540755 223127620392 261953567096 392458652 9750 128687 88187 0 669983 731006 -linux_tuned 1 400 0x1900 55 145.6 178.1 339.8 473.0 496.6 571.6 100149.9 100000 20 1818.54 2208325 266185674 6171518 185736270 153789774935 216056037246 254574947021 374939895 9261 125058 87024 0 667420 730499 -linux_tuned 2 400 0x1900 55 143.6 178.0 339.6 473.8 498.3 574.2 99982.8 100000 20 1822.04 2207388 265910264 6160735 185409666 154253674012 217196328794 256124276719 378369287 9756 130473 90336 0 672220 730145 linux_tuned 0 400 0x1900 135 179.4 221.9 384.8 516.9 565.3 672.9 200088.0 200000 20 2112.24 4542125 540395957 12580430 380033724 300669986567 394543960891 457944626319 743803319 12355 193718 143716 0 487885 732409 linux_tuned 1 400 0x1900 135 193.4 233.5 390.0 522.3 571.4 686.1 199929.3 200000 20 2105.51 4535750 539795932 12569403 379702044 301953785946 390707182430 453696345385 761004841 12053 188000 139714 0 489948 732410 linux_tuned 2 400 0x1900 135 180.7 220.5 383.0 517.7 566.2 682.6 199978.3 200000 20 2109.76 4537617 539998556 12571162 379741104 302931948500 392008264351 454770932439 773405216 12472 193464 143071 0 491394 732413 @@ -1378,30 +2555,6 @@ linux_tuned 2 400 0x1900 75 196.4 237.6 398.5 538.4 592.5 738.9 199900.7 200000 linux_tuned 0 400 0x1900 55 181.5 224.2 389.3 529.1 580.5 705.1 199990.0 200000 20 2096.22 4540488 540203545 12571032 379746834 302392328611 399631605829 469658409385 768283253 13576 203926 146652 0 478036 732400 linux_tuned 1 400 0x1900 55 185.6 226.8 387.1 521.7 571.3 696.1 200126.0 200000 20 2090.48 4541782 540435184 12584098 380158266 300671959583 387379581455 452630624008 742928440 12022 186175 139084 0 489500 732378 linux_tuned 2 400 0x1900 55 182.6 222.8 386.8 522.4 572.9 696.1 200085.0 200000 20 2085.37 4540862 540302366 12580055 380029806 300930875897 387508143535 454183966146 746172158 12415 190843 139873 0 488953 732392 -linux_tuned 0 50 0x1a00 135 57.2 60.0 78.5 137.9 167.6 206.5 49990.6 50000 20 1798.09 1595233 165360289 3005086 90169758 86756103552 140123080634 158118822655 204050079 520779 702098 171403 0 823709 2115891 -linux_tuned 1 50 0x1a00 135 57.3 60.2 78.1 136.1 168.6 211.1 50116.5 50000 20 1802.27 1594836 165500932 3012715 90398802 87214926754 141834541450 159835670824 208601239 524437 682241 171059 0 819488 2120551 -linux_tuned 2 50 0x1a00 135 57.1 59.8 78.2 137.3 168.3 208.6 50049.7 50000 20 1800.61 1597132 165550087 3008677 90277812 87085556840 141439046206 159411424198 209116798 524928 694773 170454 0 821864 2121038 -linux_tuned 0 50 0x1a00 95 58.2 61.1 79.4 136.0 167.4 207.2 49960.7 50000 20 1798.51 1592508 165149728 3003373 90118812 86756283027 140422200434 158484812950 206753026 527183 719901 171215 0 828122 2122748 -linux_tuned 1 50 0x1a00 95 57.9 60.8 79.5 139.4 172.4 216.7 50015.3 50000 20 1796.29 1589394 165014542 3006542 90213426 87529241156 142811765761 161079784686 213109638 542206 730958 173985 0 816338 2133440 -linux_tuned 2 50 0x1a00 95 57.3 60.1 78.7 137.3 170.7 210.4 50065.4 50000 20 1802.32 1596015 165522870 3009645 90306744 87561786591 142288101632 160242362764 212853712 530227 695554 168238 0 807216 2118944 -linux_tuned 0 50 0x1a00 75 57.4 60.3 78.5 135.6 168.4 211.4 49917.7 50000 20 1799.78 1597541 165484219 3000787 90040968 86987186550 140446637145 158121163245 209068958 527687 713489 169751 0 821883 2117029 -linux_tuned 1 50 0x1a00 75 58.1 61.0 79.4 138.5 172.8 213.8 49962.8 50000 20 1801.23 1591020 165051129 3003358 90117762 87330293072 142772715597 161073717625 213517869 526930 685953 169082 0 811386 2116688 -linux_tuned 2 50 0x1a00 75 58.1 61.0 79.5 138.7 171.7 212.7 49873.1 50000 20 1802.89 1593773 165119499 2998101 89960376 87312196503 142639634040 160770219082 213955139 528614 699117 172747 0 822072 2126258 -linux_tuned 0 50 0x1a00 55 57.4 60.3 78.5 136.7 169.0 207.6 49966.8 50000 20 1799.43 1596579 165459664 3003693 90128100 87012807558 140941916369 158964690682 209888308 523144 701208 170413 0 822169 2118617 -linux_tuned 1 50 0x1a00 55 57.7 60.6 78.2 135.5 164.7 204.4 49993.7 50000 20 1794.56 1598177 165589747 3005180 90172236 85918499227 141796908676 159909657074 194061832 537965 681441 167562 0 818475 2121525 -linux_tuned 2 50 0x1a00 55 57.6 60.5 78.0 134.8 166.0 206.3 50046.7 50000 20 1800.36 1596622 165523039 3008431 90270054 86441663928 142711107567 160599318457 198657355 541957 682374 170741 0 820676 2125841 -linux_tuned 0 50 0x1a00 135 59.2 62.2 86.4 158.8 187.1 240.1 99954.1 100000 20 1981.44 2275351 270359973 6018157 180609852 162776123244 252871778236 282119524804 391767359 1173527 889976 143279 0 774455 3258411 -linux_tuned 1 50 0x1a00 135 59.2 62.3 86.8 161.1 190.0 246.5 99955.3 100000 20 1981.06 2282167 270780774 6018054 180605334 163319178457 255414779898 284987377840 399508424 1180967 870778 143671 0 772527 3256867 -linux_tuned 2 50 0x1a00 135 58.6 61.7 85.7 157.7 187.7 242.4 99861.0 100000 20 1983.19 2282394 270711963 6012317 180433116 163341041143 255359255225 284903275259 400974969 1182415 871828 140691 0 772155 3253675 -linux_tuned 0 50 0x1a00 95 58.4 61.5 85.2 155.9 185.7 236.5 99943.2 100000 20 1980.42 2279031 270608472 6017056 180574446 162980350833 253767324393 283136935371 409178502 1183901 891478 141619 0 776119 3256945 -linux_tuned 1 50 0x1a00 95 59.0 61.9 84.7 157.1 187.2 238.3 99954.0 100000 20 1983.56 2279047 270606177 6018564 180622134 163865455126 256758510526 286473680243 407323396 1165990 857746 142507 0 770221 3241977 -linux_tuned 2 50 0x1a00 95 59.5 62.4 86.7 161.5 190.0 245.7 99985.6 100000 20 1985.68 2282675 270862721 6020182 180670428 163966223061 256789668197 286480519226 408675803 1166546 851086 139670 0 776832 3247436 -linux_tuned 0 50 0x1a00 75 58.5 61.7 85.8 159.0 187.4 238.9 99770.6 100000 20 1982.72 2277546 270269849 6006758 180265842 163225637178 254561879335 284029162160 400628753 1177939 873235 145622 0 774990 3258979 -linux_tuned 1 50 0x1a00 75 59.0 62.0 86.5 159.6 188.5 242.6 100073.1 100000 20 1987.91 2280143 270805002 6025628 180834222 164407331497 258249321924 288103577451 412109831 1185658 859334 143849 0 772569 3264325 -linux_tuned 2 50 0x1a00 75 59.6 62.4 87.3 162.8 192.6 250.2 99813.6 100000 20 1988.04 2276811 270277647 6009488 180347964 163954649435 258050103079 287908425663 410641437 1177812 857909 144038 0 770955 3260189 -linux_tuned 0 50 0x1a00 55 58.3 61.6 86.0 157.6 187.3 240.9 99999.6 100000 20 1983.27 2279761 270730377 6020433 180676296 163630168321 254508625498 283999620759 403938530 1186217 883091 146295 0 774725 3266992 -linux_tuned 1 50 0x1a00 55 60.3 62.9 87.3 159.7 187.9 245.8 99827.1 100000 20 1984.76 2279906 270532436 6010440 180377070 161945904364 258604955342 288610776489 384456218 1206433 830576 142153 0 779575 3251143 -linux_tuned 2 50 0x1a00 55 60.9 63.4 87.8 160.3 189.6 243.6 99889.9 100000 20 1986.56 2280367 270582516 6014087 180486210 162665219528 259873658797 289925860543 387166802 1205690 813607 144898 0 775318 3257452 linux_tuned 0 50 0x1a00 135 62.1 67.5 106.2 212.6 255.8 369.7 200085.9 200000 20 2293.05 4247224 520941469 12158455 365239110 309459219520 452142829714 504315757718 772899902 1722906 1154806 61499 0 218210 4795754 linux_tuned 1 50 0x1a00 135 63.2 69.3 109.1 221.5 266.7 395.1 199938.9 200000 20 2296.89 4246984 520733802 12155640 365172012 310435217080 458113852948 510975148213 790615826 1704302 1109909 61010 0 220083 4796708 linux_tuned 2 50 0x1a00 135 62.9 69.3 111.3 230.3 285.5 433.7 200052.2 200000 20 2299.61 4251779 521155558 12168531 365579046 310774963428 458419897866 511316910168 794950321 1684576 1092826 61516 0 224454 4785246 @@ -1414,30 +2567,6 @@ linux_tuned 2 50 0x1a00 75 63.5 69.9 111.0 231.2 284.5 422.1 200066.1 200000 20 linux_tuned 0 50 0x1a00 55 65.4 72.3 115.9 253.4 320.9 551.2 199861.8 200000 20 2130.88 4269932 522203748 12199097 366655476 309113739639 451116198631 543783369140 797913774 1579773 944188 60352 0 201975 4819804 linux_tuned 1 50 0x1a00 55 66.2 73.5 117.9 260.2 327.8 540.1 199901.8 200000 20 2131.0 4273238 522527008 12209347 366999510 307049196784 457320397765 552057191230 775822585 1590909 859403 60083 0 211369 4815621 linux_tuned 2 50 0x1a00 55 66.1 72.6 116.3 252.3 321.3 591.3 200037.7 200000 20 2131.79 4274571 522773535 12229262 367662822 307565150496 456969201199 553403392661 779884688 1590161 849467 61713 0 205500 4817734 -linux_tuned 0 100 0x1a00 135 60.4 64.2 105.3 180.0 204.2 262.4 49951.3 50000 20 1770.2 1525817 160740780 3006435 90221982 85368648585 143012587178 170788953279 210692661 46306 743397 177215 0 832197 1621211 -linux_tuned 1 100 0x1a00 135 62.0 65.7 102.6 177.6 200.6 261.6 49928.1 50000 20 1772.4 1522959 160521062 3005151 90183456 84840206456 146199105336 174464588961 218278272 39228 723904 168897 0 813124 1601842 -linux_tuned 2 100 0x1a00 135 61.7 65.4 103.4 177.4 201.6 259.8 50056.8 50000 20 1769.03 1527728 160991603 3013095 90422598 85103312080 146920977050 175343496775 206720481 42937 735628 173823 0 816169 1611967 -linux_tuned 0 100 0x1a00 95 60.9 64.4 101.1 176.9 201.5 263.9 50079.8 50000 20 1769.08 1528774 161085538 3014563 90467256 85625752178 143310856567 171554271225 211964703 42027 735255 171416 0 826461 1613187 -linux_tuned 1 100 0x1a00 95 62.8 66.8 105.6 180.6 203.7 264.4 49955.7 50000 20 1778.61 1521347 160443897 3006961 90238614 85523902529 147644579318 175945787132 211324246 47884 741134 174187 0 821048 1616168 -linux_tuned 2 100 0x1a00 95 61.7 65.5 106.0 179.9 203.0 260.6 50064.8 50000 20 1777.21 1526821 160942544 3013513 90434910 85593897260 147245556261 175105627929 211295032 46102 736571 174418 0 823194 1615335 -linux_tuned 0 100 0x1a00 75 60.6 64.0 100.8 175.9 200.9 261.1 49928.2 50000 20 1771.22 1530068 161001519 3005434 90192732 85329993171 142697374039 170441088709 211836006 47544 742659 175191 0 831139 1618451 -linux_tuned 1 100 0x1a00 75 61.4 64.9 104.1 179.9 204.5 263.5 50077.7 50000 20 1779.13 1527646 161018652 3014024 90449448 85762272257 147071470382 174598566931 212968487 47031 738471 172172 0 822284 1614467 -linux_tuned 2 100 0x1a00 75 62.1 65.9 103.0 179.0 204.3 266.1 49983.6 50000 20 1775.88 1525424 160764992 3008808 90294054 85555560533 147955046900 176198124629 212999914 48682 739318 175065 0 824890 1616574 -linux_tuned 0 100 0x1a00 55 61.5 65.0 102.9 179.2 203.8 265.2 50017.1 50000 20 1771.59 1528657 161002296 3010654 90349122 85522270093 143690638597 171589370186 211946812 46157 739375 173423 0 823975 1613250 -linux_tuned 1 100 0x1a00 55 60.4 63.9 102.0 173.5 196.7 252.0 49982.4 50000 20 1754.29 1530178 161066789 3008412 90281334 84034723307 139699998119 167799566840 192658410 45101 747520 172281 0 832003 1614370 -linux_tuned 2 100 0x1a00 55 60.9 64.5 104.0 176.8 201.1 257.6 50137.7 50000 20 1763.15 1532630 161431909 3017852 90565194 84659588083 140564730644 167970181438 198230821 51269 751800 174511 0 839044 1623844 -linux_tuned 0 100 0x1a00 135 62.8 69.0 124.7 194.4 228.7 288.7 99954.8 100000 20 1964.69 2240243 268047621 6039177 181308414 160508780294 247629049321 280864546846 402434041 77872 1380756 141393 0 737106 2331254 -linux_tuned 1 100 0x1a00 135 64.5 71.6 127.5 196.1 229.5 290.8 100119.4 100000 20 1969.7 2240530 268232476 6049312 181611870 159956434911 254286037826 288126424898 396399045 87470 1388785 142626 0 731229 2341655 -linux_tuned 2 100 0x1a00 135 65.6 72.3 127.8 196.1 231.5 291.5 100065.0 100000 20 1972.68 2237203 267951215 6046029 181513896 160193836789 255063555992 289432067828 397484869 78217 1388083 140778 0 723135 2341670 -linux_tuned 0 100 0x1a00 95 63.2 69.9 125.7 193.0 228.2 287.9 100090.9 100000 20 1963.66 2238173 268060022 6047293 181550580 161061851620 250015606996 284188564546 405621485 81209 1393751 145131 0 737146 2350010 -linux_tuned 1 100 0x1a00 95 64.4 71.0 127.2 194.4 229.6 290.7 100111.7 100000 20 1973.97 2239597 268190019 6048554 181588626 160864252432 256222259611 290256825044 403924557 74274 1397534 141817 0 719748 2350823 -linux_tuned 2 100 0x1a00 95 64.9 71.8 127.5 196.8 233.7 293.5 99933.3 100000 20 1972.26 2237922 267884369 6037690 181262310 160519313076 257094148659 291966083942 404072923 85632 1387534 144233 0 729522 2348935 -linux_tuned 0 100 0x1a00 75 62.8 69.0 124.6 193.7 229.6 288.1 100016.3 100000 20 1964.29 2235648 267807626 6042958 181420248 160457432961 249101178667 283089277206 403477570 72636 1384171 141738 0 732443 2340322 -linux_tuned 1 100 0x1a00 75 65.1 71.8 128.7 199.3 233.6 294.1 99926.9 100000 20 1974.99 2235833 267723869 6037647 181261956 160830855701 256239569240 290077149396 407206445 83349 1377831 140955 0 725649 2334988 -linux_tuned 2 100 0x1a00 75 64.3 70.5 126.1 195.2 231.3 288.6 100109.6 100000 20 2113.96 2240389 268252472 6048548 181588818 161211127823 257637257205 291824493356 410805628 77031 1390795 141900 0 722144 2349040 -linux_tuned 0 100 0x1a00 55 63.5 70.2 126.2 195.9 230.3 291.0 99976.0 100000 20 1964.49 2239191 267988804 6040452 181345656 160761228731 249833109080 283904697060 404818670 80214 1387750 145209 0 736014 2345284 -linux_tuned 1 100 0x1a00 55 62.7 69.1 124.0 190.3 225.6 281.0 100042.1 100000 20 1957.8 2240041 268104578 6043470 181434012 158860625228 245593019421 279499772684 381820794 85872 1394978 143522 0 747098 2342677 -linux_tuned 2 100 0x1a00 55 63.7 70.1 125.6 192.2 227.2 281.9 100033.9 100000 20 1957.75 2240061 268109366 6043473 181434456 159482944524 247053575753 281455948428 385243219 75964 1386576 143020 0 736653 2336866 linux_tuned 0 100 0x1a00 135 76.7 90.5 149.9 258.5 302.6 422.2 200012.4 200000 20 2267.73 4288467 523555247 12221575 367394502 304887000794 433529478715 483857108578 782823954 169817 1662169 104637 0 311314 2820648 linux_tuned 1 100 0x1a00 135 79.5 93.3 152.3 259.4 302.0 412.1 200070.7 200000 20 2277.24 4291845 523869004 12224786 367488216 304002345361 442825000992 494084788674 774304681 179281 1665042 100695 0 285478 2827700 linux_tuned 2 100 0x1a00 135 80.0 94.1 153.5 265.5 306.6 424.9 200119.7 200000 20 2279.14 4294753 524173671 12232267 367727076 304578011944 444188536680 495709936204 777232608 186387 1629917 99215 0 293846 2818202 @@ -1450,30 +2579,6 @@ linux_tuned 2 100 0x1a00 75 79.0 93.2 152.1 260.9 301.9 419.9 199938.8 200000 20 linux_tuned 0 100 0x1a00 55 78.9 93.2 156.2 294.9 363.0 603.5 200086.5 200000 20 2131.61 4311732 525211720 12269754 369057306 304585541510 429882183787 512935612013 785957507 179120 1492110 96294 0 293415 2806424 linux_tuned 1 100 0x1a00 55 78.0 92.9 154.9 275.7 329.7 467.2 199986.5 200000 20 2131.14 4302796 524537377 12242343 368097270 301460589680 425071139129 504604643973 749823044 175736 1527338 95626 0 298809 2801988 linux_tuned 2 100 0x1a00 55 77.8 92.1 153.5 273.6 328.1 484.3 200125.9 200000 20 2128.31 4305049 524873406 12249511 368345382 302449186139 428278559758 509611430144 756748150 189850 1565950 98189 0 281286 2824600 -linux_tuned 0 200 0x1a00 135 66.1 74.9 171.3 268.2 292.5 354.1 50074.5 50000 20 1751.59 1423686 154156578 3024976 90813330 83434879674 138325258116 165294907252 211679079 18167 354803 154131 0 837814 1171144 -linux_tuned 1 200 0x1a00 135 67.2 75.7 173.7 269.1 292.8 351.9 49986.9 50000 20 1752.37 1422519 153960480 3019346 90642852 82981289338 137079556522 163748429849 206121184 18599 362086 157008 0 846445 1175825 -linux_tuned 2 200 0x1a00 135 65.9 75.2 171.3 268.3 291.7 350.9 50003.2 50000 20 1750.54 1421513 153878013 3020784 90687258 82937532093 136979808830 163687684854 205273394 18788 360875 157220 0 847855 1177507 -linux_tuned 0 200 0x1a00 95 65.2 73.5 169.4 266.0 288.5 351.7 50018.2 50000 20 1757.2 1427950 154335766 3021721 90715980 83499179409 137732530601 164296212154 212422793 18668 363279 158456 0 850408 1178496 -linux_tuned 1 200 0x1a00 95 66.6 75.9 174.0 269.1 293.5 354.1 50057.0 50000 20 1762.16 1426478 154326626 3024000 90783912 83404505710 137919880482 164313559026 210856228 19290 369149 162779 0 855671 1178327 -linux_tuned 2 200 0x1a00 95 66.1 75.1 172.8 269.1 293.9 354.7 50081.8 50000 20 1756.21 1423255 154159104 3025568 90831618 83475862345 138573259597 165229229995 223438955 18802 363965 157892 0 849877 1180555 -linux_tuned 0 200 0x1a00 75 68.0 77.9 176.3 271.0 295.5 354.6 50072.3 50000 20 1760.27 1422448 154079506 3024655 90803130 83580758833 138480136931 164984878750 212863624 18104 355258 153098 0 834627 1171479 -linux_tuned 1 200 0x1a00 75 67.1 76.7 174.9 270.7 295.9 357.1 49889.2 50000 20 1755.05 1419731 153655308 3013728 90475512 83336133284 138507040907 165187738941 211350040 18800 360356 158082 0 844228 1175114 -linux_tuned 2 200 0x1a00 75 66.0 74.6 171.2 267.6 291.5 354.0 50089.3 50000 20 1754.22 1423928 154147725 3026051 90846054 83602045375 139248116794 166084086514 212824879 19082 366586 161408 0 853669 1179923 -linux_tuned 0 200 0x1a00 55 66.5 75.1 171.4 267.6 289.9 346.3 49971.5 50000 20 1750.53 1424923 154118231 3019066 90636552 82087377814 134869211536 161351762868 198981403 19660 371897 161789 0 865297 1182644 -linux_tuned 1 200 0x1a00 55 66.0 74.5 170.4 267.3 289.1 346.2 50066.4 50000 20 1743.73 1426792 154316647 3024210 90789414 82009771660 136636739100 163574673768 191443686 18852 365235 156522 0 850648 1180070 -linux_tuned 2 200 0x1a00 55 67.0 76.9 176.1 269.0 292.1 347.7 49936.5 50000 20 1746.24 1425451 154136575 3016710 90566190 82192594397 137084157082 163969838063 194759311 19001 366174 159136 0 850789 1175820 -linux_tuned 0 200 0x1a00 135 83.5 104.4 200.6 283.4 313.0 378.8 99945.9 100000 20 1939.41 2205586 265765110 6083420 182787402 157322809406 236595392059 269084686410 402931285 22935 484395 165897 0 873840 1405735 -linux_tuned 1 200 0x1a00 135 84.5 106.3 200.8 281.5 311.4 373.9 99930.0 100000 20 1934.69 2203271 265614943 6082163 182747658 156302053820 235627768337 268796419798 391129548 22706 486781 164643 0 872527 1403504 -linux_tuned 2 200 0x1a00 135 85.3 106.9 201.6 281.8 313.6 377.3 99961.9 100000 20 1935.33 2202836 265553307 6083648 182792574 156282155364 235279612439 268071186377 391726194 23031 488220 165885 0 873372 1403808 -linux_tuned 0 200 0x1a00 95 82.2 103.1 199.3 281.7 312.6 378.7 99943.1 100000 20 1942.98 2207011 265833375 6082745 182765616 157225084929 235801841797 267822767853 402779700 22546 481551 164766 0 869252 1401524 -linux_tuned 1 200 0x1a00 95 84.4 105.4 201.5 284.0 315.6 381.8 100012.6 100000 20 1942.18 2207770 265976760 6087407 182906634 157118430690 237533876840 270380755599 398699231 22743 483422 164657 0 868983 1402579 -linux_tuned 2 200 0x1a00 95 83.2 104.2 199.4 281.7 312.7 375.6 100038.2 100000 20 1942.5 2205626 265878398 6088944 182952648 157224158910 236606780079 268638943041 401103555 22915 486123 164794 0 875830 1406900 -linux_tuned 0 200 0x1a00 75 82.8 104.0 200.4 282.3 312.4 376.6 100026.9 100000 20 1941.92 2207082 265958671 6088656 182945112 157603156093 236829711347 269163860557 405511350 22787 484404 164565 0 869937 1402013 -linux_tuned 1 200 0x1a00 75 83.5 104.7 200.3 283.0 313.5 377.2 100061.4 100000 20 1941.77 2207788 266038736 6090518 183001332 157339152310 237817400388 270028875979 404289556 23054 488946 167415 0 873334 1408215 -linux_tuned 2 200 0x1a00 75 85.1 106.5 201.5 283.3 315.2 380.9 100029.0 100000 20 1944.16 2206974 265922927 6087790 182916252 157704681487 238044182993 270017749259 403597200 22647 485147 165230 0 873079 1406093 -linux_tuned 0 200 0x1a00 55 81.9 102.3 198.0 277.0 304.8 367.4 100038.9 100000 20 1934.89 2205259 265851281 6088586 182939946 155145288664 231558856198 263331157721 370594375 23377 490682 162477 0 879884 1402430 -linux_tuned 1 200 0x1a00 55 85.0 106.5 201.5 281.4 311.2 369.3 100039.1 100000 20 1941.71 2206879 265943730 6088234 182929110 155315693799 235719732171 268083070838 375454854 22579 485178 163095 0 873932 1402534 -linux_tuned 2 200 0x1a00 55 81.8 102.8 199.7 279.5 307.7 371.5 100041.1 100000 20 1943.84 2206596 265922267 6088879 182950098 155895835143 236340421920 268290188142 379989489 22187 483413 161900 0 869409 1402569 linux_tuned 0 200 0x1a00 135 113.5 136.2 229.0 335.3 381.5 495.0 199832.6 200000 20 2234.44 4373529 528981125 12342146 371580330 303132193637 414381320423 462449984046 785529345 31009 631080 200514 0 537361 1461265 linux_tuned 1 200 0x1a00 135 114.0 136.1 228.7 332.3 377.9 486.0 200110.0 200000 20 2232.51 4380085 529756855 12361095 372151878 302076835989 413096434948 461094489826 766245108 30028 623769 198190 0 540837 1461235 linux_tuned 2 200 0x1a00 135 112.2 134.8 227.5 332.0 375.7 484.6 200032.3 200000 20 2234.09 4377205 529477862 12355949 371997870 302353657838 413191024434 461192888479 771023270 29655 626397 197854 0 537070 1460475 @@ -1485,30 +2590,6 @@ linux_tuned 2 200 0x1a00 75 115.7 137.6 229.6 334.0 379.3 486.6 199856.3 200000 linux_tuned 0 200 0x1a00 55 114.5 136.1 227.9 332.1 376.3 487.5 199900.8 200000 20 2125.75 4381489 529587438 12352727 371927008 298954400804 405258652480 475212488544 731378307 31379 625000 194204 0 517163 1461680 linux_tuned 1 200 0x1a00 55 120.6 144.7 238.0 354.1 403.0 540.2 199898.2 200000 20 2125.69 4382542 529668082 12354322 371997588 299590928037 412689716898 485239353297 742912799 31024 627917 191944 0 499289 1461523 linux_tuned 2 200 0x1a00 55 117.4 140.7 233.6 346.3 394.2 522.1 199935.3 200000 20 2126.56 4385144 529852861 12355302 372000882 299720218254 412525871654 484918198191 747722312 31632 630966 192031 0 499886 1461810 -linux_tuned 0 300 0x1a00 135 77.8 99.2 240.2 361.6 388.3 444.2 49946.8 50000 20 1747.8 1350610 149162051 3028758 90964404 81880998492 132447450743 157193338434 202382544 13483 203545 116984 0 784307 894048 -linux_tuned 1 300 0x1a00 135 78.8 100.3 244.3 363.8 391.6 451.6 49998.2 50000 20 1746.52 1353700 149464074 3031291 91039800 82107242533 134890871577 160009070350 206946643 12878 199241 114844 0 781047 891107 -linux_tuned 2 300 0x1a00 135 77.3 97.7 242.4 363.0 390.3 445.8 49996.8 50000 20 1744.43 1353389 149436613 3031938 91060092 82218685998 134993820346 160202998164 206445863 12744 195574 111231 0 770841 890833 -linux_tuned 0 300 0x1a00 95 77.4 98.4 242.2 362.8 389.9 443.3 49906.6 50000 20 1743.77 1352683 149240530 3026055 90881802 81788454106 132969458347 157956736029 204645623 12850 197613 111996 0 776726 892729 -linux_tuned 1 300 0x1a00 95 78.9 99.6 241.1 363.7 391.6 452.2 50003.7 50000 20 1748.94 1354791 149515304 3032318 91072080 82664678462 135954956739 161102646140 211519385 13791 204247 120090 0 782592 890016 -linux_tuned 2 300 0x1a00 95 80.1 103.0 244.9 363.7 391.5 446.6 50062.1 50000 20 1746.86 1352717 149455527 3035763 91174734 82754598218 136452224647 161683092131 211414704 13086 200672 117834 0 784918 894512 -linux_tuned 0 300 0x1a00 75 76.2 95.9 240.8 361.1 387.1 443.0 50053.8 50000 20 1745.44 1355299 149588230 3035618 91171098 82329269647 133515328763 158299094108 207601021 12809 195273 110110 0 773181 894596 -linux_tuned 1 300 0x1a00 75 78.2 98.5 242.2 363.1 390.8 447.9 50046.7 50000 20 1747.27 1355555 149602525 3034340 91130736 82833586151 136926999630 162315741892 212818636 14022 212260 123561 0 787612 889959 -linux_tuned 2 300 0x1a00 75 78.7 100.8 244.7 364.6 392.6 450.1 50081.5 50000 20 1747.11 1352699 149477623 3037175 91217586 82879957436 137089652020 162629664079 213231412 13153 201089 117218 0 776924 889604 -linux_tuned 0 300 0x1a00 55 76.2 95.4 241.1 362.3 389.3 444.2 50007.1 50000 20 1744.82 1354250 149483030 3032472 91075824 82322849181 134223367739 159063566501 214814811 13224 199723 115106 0 779723 890702 -linux_tuned 1 300 0x1a00 55 78.3 99.8 241.8 362.2 388.6 443.9 49932.4 50000 20 1731.19 1357041 149581962 3027470 90924408 80993756608 129320626410 154454314418 192052768 13234 202587 115688 0 785173 892470 -linux_tuned 2 300 0x1a00 55 76.4 97.6 240.5 361.0 387.5 440.3 49995.7 50000 20 1737.04 1358536 149741431 3031459 91044594 81553240614 129724615946 154608857825 196843231 13670 207667 118600 0 789791 891425 -linux_tuned 0 300 0x1a00 135 112.6 140.0 267.9 374.0 401.0 469.4 100066.9 100000 20 1921.8 2203416 265753467 6129412 184314702 155751863553 227637439407 258757811959 385277829 14369 217583 123941 0 799712 969106 -linux_tuned 1 300 0x1a00 135 110.0 137.1 267.3 371.8 399.8 469.7 99882.9 100000 20 1923.87 2199329 265259791 6117762 183963018 155999601368 231075921069 262388155093 391610896 14082 215701 123180 0 792848 966475 -linux_tuned 2 300 0x1a00 135 116.4 144.6 271.2 376.0 402.6 473.6 100168.0 100000 20 1926.14 2202196 265792887 6137129 184550328 156428189665 231732622807 262903179578 393221616 13809 210715 120599 0 787578 967886 -linux_tuned 0 300 0x1a00 95 109.4 137.7 266.2 372.1 399.4 469.0 99958.7 100000 20 1921.07 2200091 265417423 6123166 184130130 155725980694 226582739166 257106470614 386947297 14215 217426 122428 0 795304 965589 -linux_tuned 1 300 0x1a00 95 111.8 140.3 269.6 376.3 402.6 474.8 100033.7 100000 20 1929.47 2199659 265450744 6126910 184237194 156909216118 232202313290 262707696871 401611424 13965 216899 123138 0 789935 965093 -linux_tuned 2 300 0x1a00 95 115.1 141.4 269.3 375.1 402.1 475.1 100039.7 100000 20 1924.76 2204100 265766299 6128267 184284486 156972003775 234690994457 266943880803 401511769 14624 221696 128034 0 795241 968411 -linux_tuned 0 300 0x1a00 75 112.5 140.4 268.9 374.2 400.9 469.0 99995.8 100000 20 1925.89 2199973 265427160 6125655 184205976 156105032703 227629247638 257813675663 393801804 13765 211315 121038 0 793150 967510 -linux_tuned 1 300 0x1a00 75 114.5 140.8 270.8 377.6 404.2 478.7 99950.3 100000 20 1931.98 2200377 265384008 6123160 184131414 157070599050 234211006899 265554174326 403285791 14290 218031 124678 0 792978 967044 -linux_tuned 2 300 0x1a00 75 114.9 141.0 269.3 376.8 403.6 477.7 99970.1 100000 20 1931.57 2200851 265460198 6124056 184154568 157082094298 234962715011 266616928800 404399122 14401 222114 128233 0 798094 968534 -linux_tuned 0 300 0x1a00 55 117.5 145.0 273.9 378.1 403.5 478.0 100103.2 100000 20 1921.92 2203313 265778439 6132351 184405374 156325750040 230233743991 261481659898 412279537 13754 210727 120287 0 785653 965398 -linux_tuned 1 300 0x1a00 55 113.3 140.0 266.4 371.5 399.8 468.3 100010.3 100000 20 1919.96 2201662 265569110 6127208 184252314 155176365641 223241097537 253487956667 378662879 13843 210540 119656 0 796293 967997 -linux_tuned 2 300 0x1a00 55 113.1 139.2 267.6 372.5 399.6 464.9 100012.9 100000 20 1919.61 2200964 265506519 6126528 184231344 155541419287 224632587778 255355469638 381255862 14213 214375 123346 0 799499 968002 linux_tuned 0 300 0x1a00 135 146.7 176.8 302.6 410.3 456.4 559.5 199948.4 200000 20 2207.85 4454046 534436029 12464265 375847836 301759249345 400150934857 446653653886 752924079 17166 307021 189396 0 548345 976437 linux_tuned 1 300 0x1a00 135 151.2 183.9 312.1 433.1 481.3 601.7 200063.9 200000 20 2215.27 4460924 535015815 12473705 376154424 303316223419 409867839429 457627910678 766998137 17215 311013 185081 0 533418 976405 linux_tuned 2 300 0x1a00 135 156.7 188.5 313.6 432.5 481.1 594.6 199810.1 200000 20 2360.92 4457261 534525106 12460953 375776610 303577738534 410179993939 457870901649 772655210 16600 303570 182305 0 534887 976375 @@ -1521,30 +2602,6 @@ linux_tuned 2 300 0x1a00 75 151.6 183.1 309.8 425.3 473.7 583.9 200020.8 200000 linux_tuned 0 300 0x1a00 55 151.2 183.4 311.2 435.3 487.3 621.3 200084.1 200000 20 2124.46 4471769 535742494 12483926 376509324 303393858052 404016676759 470803841293 771429161 17087 305849 179932 0 516723 976316 linux_tuned 1 300 0x1a00 55 153.6 184.2 311.2 430.2 479.3 594.6 200004.3 200000 20 2124.29 4463255 535075040 12473508 376151796 301496989354 396431167612 460168833142 746892237 17495 308557 182924 0 528791 976253 linux_tuned 2 300 0x1a00 55 156.5 187.6 312.1 431.6 479.7 603.8 199934.9 200000 20 2122.79 4463002 534985711 12471816 376130190 301535512660 394946928993 458596307330 748862140 16435 301054 180370 0 529383 976336 -linux_tuned 0 400 0x1a00 135 98.4 132.7 308.3 459.6 483.2 545.1 50129.2 50000 20 1736.63 1311879 146853812 3050347 91648986 81624771540 129568401618 152625871233 210306516 9497 126249 109185 0 660488 699934 -linux_tuned 1 400 0x1a00 135 101.4 133.6 307.8 460.8 484.1 542.8 50013.7 50000 20 1733.92 1309266 146531269 3043681 91449630 81103165327 127872644064 151209845168 206707165 9557 128947 110302 0 670500 705841 -linux_tuned 2 400 0x1a00 135 94.4 127.8 304.2 458.0 482.6 545.3 50016.7 50000 20 1732.33 1310190 146584174 3043542 91445622 81052012532 127913176175 151104587406 206188056 9422 123057 107662 0 658257 702919 -linux_tuned 0 400 0x1a00 95 95.4 128.5 306.4 459.7 483.2 542.6 49926.5 50000 20 1738.2 1308628 146379618 3037861 91274304 81385367437 129568294418 152863164342 210507949 9011 122096 106712 0 654198 703301 -linux_tuned 1 400 0x1a00 95 103.3 135.7 309.0 462.3 485.0 546.4 49911.6 50000 20 1731.76 1306533 146247861 3036976 91247964 81317729314 128949101840 152615304010 210392680 9345 124486 107259 0 662437 704277 -linux_tuned 2 400 0x1a00 95 99.1 132.9 308.5 460.1 483.7 540.9 50060.2 50000 20 1734.74 1308229 146513571 3046409 91531728 81641187116 129505242001 152964940538 212047169 9549 128484 109745 0 670027 705044 -linux_tuned 0 400 0x1a00 75 98.8 131.5 306.3 460.0 483.6 545.0 49941.1 50000 20 1739.16 1307570 146324809 3038354 91287678 81453267439 129346403597 152322465287 211496789 9037 121076 106342 0 655100 703567 -linux_tuned 1 400 0x1a00 75 101.3 134.4 308.6 459.1 483.2 539.4 49925.1 50000 20 1736.64 1308163 146351145 3038255 91285386 81504893739 128673066557 151829653816 212176198 9232 123691 107678 0 659706 703294 -linux_tuned 2 400 0x1a00 75 93.7 126.1 303.4 459.7 483.4 543.6 49983.7 50000 20 1731.54 1309105 146487613 3041387 91379406 81584374517 128703291890 152024788523 211955255 9418 125482 108726 0 663318 704281 -linux_tuned 0 400 0x1a00 55 98.2 132.3 310.1 459.7 483.4 543.4 50027.8 50000 20 1740.82 1310500 146621189 3044580 91476924 81511229161 129818040959 152670505365 211792634 9251 122900 105494 0 659486 702673 -linux_tuned 1 400 0x1a00 55 99.8 132.3 305.0 458.4 482.1 535.7 50064.9 50000 20 1806.93 1310113 146657603 3046309 91526946 80025562269 125422848392 148783819544 191059219 9450 121673 108527 0 655999 703784 -linux_tuned 2 400 0x1a00 55 97.1 129.3 305.5 458.0 482.6 537.9 50008.9 50000 20 1724.36 1308950 146513920 3043159 91433754 80331839219 126424043686 149853816217 195598754 9366 123346 108240 0 659649 704702 -linux_tuned 0 400 0x1a00 135 139.2 173.4 336.6 471.0 493.0 571.3 99870.8 100000 20 1913.45 2203027 265461601 6154193 185212512 155173045828 221375657601 250375800642 397724484 9787 130067 91563 0 670731 729938 -linux_tuned 1 400 0x1a00 135 142.3 175.1 335.5 471.5 492.6 570.1 100089.4 100000 20 1902.03 2208328 266099727 6168058 185633244 154899447729 219308569473 249195540430 389884436 9460 126915 87685 0 669147 730662 -linux_tuned 2 400 0x1a00 135 139.9 173.4 336.7 471.4 492.1 564.9 100025.4 100000 20 1904.48 2205491 265843419 6163769 185497668 154802339610 219685906511 249181499123 391973563 9454 125684 88932 0 671033 730376 -linux_tuned 0 400 0x1a00 95 144.1 178.1 341.4 474.3 500.6 573.1 100083.0 100000 20 1911.58 2208685 266118731 6167230 185603328 155894046481 224231765340 254608963760 398931084 10057 135812 93543 0 679726 730172 -linux_tuned 1 400 0x1a00 95 139.1 173.3 337.9 473.0 498.7 573.8 99979.0 100000 20 1904.98 2204815 265742627 6160711 185404530 155491961697 222737910530 253261473337 398926094 10019 133164 93124 0 676909 730167 -linux_tuned 2 400 0x1a00 95 139.7 173.6 335.5 471.1 492.3 568.8 99912.7 100000 20 1911.75 2203847 265619173 6156208 185268576 155403388878 221553736197 251292314815 400492943 10001 133693 91293 0 678811 730881 -linux_tuned 0 400 0x1a00 75 139.4 173.1 335.4 471.3 491.7 569.7 99930.2 100000 20 1912.88 2204441 265654304 6156861 185288046 155260704407 223125535057 252821647157 400178004 10142 133519 89634 0 676494 730812 -linux_tuned 1 400 0x1a00 75 139.7 172.6 335.4 471.1 491.8 569.2 100124.8 100000 20 1914.35 2209040 266167937 6170499 185704440 156012680064 222023317488 250979890565 403235608 9925 131967 91887 0 673342 730463 -linux_tuned 2 400 0x1a00 75 143.6 177.7 338.9 471.9 493.2 569.1 100000.3 100000 20 1910.83 2206520 265878573 6162553 185464662 155806198790 221472927419 250784827691 401130309 9513 127069 90409 0 668756 730298 -linux_tuned 0 400 0x1a00 55 137.6 172.0 335.3 470.8 493.0 572.4 99980.4 100000 20 1913.5 2204520 265703582 6160473 185397768 155662212666 223253021290 252734284512 400571551 9323 125134 87577 0 667352 730642 -linux_tuned 1 400 0x1a00 55 136.9 172.1 334.3 469.6 489.5 561.2 100144.3 100000 20 1906.35 2208494 266164998 6172044 185752452 153750449933 216876133597 245650792072 375458230 9390 127987 89118 0 673735 730913 -linux_tuned 2 400 0x1a00 55 143.1 176.6 337.6 471.2 492.8 566.8 99944.6 100000 20 1911.59 2204683 265680910 6158930 185354004 154133648444 217197309378 245606137089 378789410 9422 125429 88122 0 668437 730699 linux_tuned 0 400 0x1a00 135 180.5 221.2 384.5 520.4 569.9 690.9 200180.6 200000 20 2201.69 4536716 540112436 12581259 380043582 303656799046 401196605051 449094890967 780088472 12925 193659 143565 0 497459 732218 linux_tuned 1 400 0x1a00 135 184.6 223.5 381.5 511.8 559.4 670.0 200021.1 200000 20 2195.42 4532397 539668006 12574558 379838754 302342494513 393204332965 438881089227 767470988 12569 191289 141113 0 506518 732398 linux_tuned 2 400 0x1a00 135 189.3 229.1 386.1 517.6 565.5 677.9 199920.2 200000 20 2193.4 4532147 539574430 12568430 379668372 302638308342 393318656803 439579888539 770199168 12022 184581 138011 0 503572 732397 @@ -1557,30 +2614,6 @@ linux_tuned 2 400 0x1a00 75 197.8 236.8 392.2 522.5 572.2 689.4 200154.5 200000 linux_tuned 0 400 0x1a00 55 197.9 238.9 399.1 540.2 599.1 748.7 200220.7 200000 20 2117.01 4546901 540877937 12589880 380360388 303699382907 397644907830 463190383133 779476704 13006 203076 144421 0 480788 732409 linux_tuned 1 400 0x1a00 55 190.3 229.7 386.5 518.9 567.2 687.8 199945.7 200000 20 2113.72 4536382 539872488 12572814 379814136 300495081446 389494813163 452981255642 745028675 12208 186062 140189 0 491892 732400 linux_tuned 2 400 0x1a00 55 192.3 234.5 394.8 532.3 584.7 714.1 200181.2 200000 20 2120.95 4543352 540609475 12585833 380207580 301013406808 390123063522 451333155772 748040537 12610 192934 140598 0 490569 732394 -linux_tuned 0 50 0x1b00 135 58.5 61.3 79.0 136.2 168.2 206.6 50001.6 50000 20 1885.07 1597473 165505761 3005662 90186750 87421775481 144300862607 156771251061 212109778 521489 705990 171083 0 826465 2114941 -linux_tuned 1 50 0x1b00 135 57.2 60.0 77.6 134.2 165.1 204.6 50115.1 50000 20 1883.03 1602181 165971485 3012298 90385506 87235691200 142660504935 155019070278 215895837 519067 719528 171187 0 830376 2117923 -linux_tuned 2 50 0x1b00 135 56.9 59.4 77.4 134.1 165.5 205.6 49996.8 50000 20 1879.8 1592128 165173659 3005421 90179688 87007970265 142178863669 154564438061 208591087 515729 716931 168461 0 819551 2114199 -linux_tuned 0 50 0x1b00 95 57.8 60.7 78.5 135.8 167.0 205.7 49975.6 50000 20 1886.63 1594676 165330534 3004156 90141768 87379608274 144733769546 157381169897 213296104 524064 704192 171591 0 829186 2121841 -linux_tuned 1 50 0x1b00 95 57.6 60.6 79.3 135.4 167.7 207.5 50077.1 50000 20 1852.73 1597045 165576405 3010277 90325494 87996272638 144307157213 156765866892 214288409 574109 847183 184942 0 817600 2160821 -linux_tuned 2 50 0x1b00 95 57.2 59.9 77.9 136.4 167.3 207.7 49977.3 50000 20 1877.73 1594251 165290570 3004189 90142524 87454376338 143261262047 155725691151 212691468 517444 701145 169379 0 820188 2109115 -linux_tuned 0 50 0x1b00 75 57.2 59.8 78.0 134.6 166.9 207.1 49964.5 50000 20 1883.86 1595790 165407639 3003470 90121074 87247469026 144918767200 157500220233 212057748 518709 692998 169721 0 818481 2113575 -linux_tuned 1 50 0x1b00 75 57.1 59.8 77.8 133.5 166.6 206.8 49968.3 50000 20 1882.28 1598092 165517801 3003702 90127968 87581741593 143766684272 156166869862 215061983 524455 729556 173415 0 830244 2126174 -linux_tuned 2 50 0x1b00 75 57.1 59.7 77.6 134.8 165.7 205.9 50007.1 50000 20 1880.26 1600178 165718212 3005962 90195624 87552860360 143461629519 155604337055 215421272 514991 701216 169368 0 822166 2111996 -linux_tuned 0 50 0x1b00 55 57.6 60.6 78.6 135.5 167.1 207.0 50048.1 50000 20 1886.97 1594946 165430726 3008468 90270942 87556295135 144754185701 157238539690 213366724 520891 696500 171155 0 823505 2114669 -linux_tuned 1 50 0x1b00 55 56.6 59.0 77.2 130.8 160.1 199.2 49973.7 50000 20 1871.77 1600805 165737361 3004153 90141930 85904788872 140773216261 153498360013 194442220 518108 712504 166081 0 826137 2104610 -linux_tuned 2 50 0x1b00 55 57.7 60.6 77.8 131.6 161.8 200.5 49867.6 50000 20 1876.8 1598674 165445914 2997483 89941002 86157848620 141286238589 153914829767 197771665 515029 706419 168802 0 830464 2107399 -linux_tuned 0 50 0x1b00 135 58.0 61.2 85.1 156.1 185.1 234.7 100033.8 100000 20 2077.65 2282892 270959806 6022542 180739920 164327644296 261523121336 280990307695 407873448 1185880 885295 145453 0 775895 3265340 -linux_tuned 1 50 0x1b00 135 58.5 61.6 84.4 153.3 182.4 230.4 99855.5 100000 20 2068.85 2282947 270730505 6011354 180402108 163125671760 256640873217 275767578244 410742819 1158840 894565 136668 0 780789 3230417 -linux_tuned 2 50 0x1b00 135 58.4 61.5 85.4 155.7 185.6 235.1 100061.4 100000 20 2070.6 2286039 271227764 6023998 180782442 163636786362 257364552319 276521147478 399904308 1173484 915254 142090 0 779174 3254811 -linux_tuned 0 50 0x1b00 95 58.6 61.7 85.4 157.6 185.8 234.8 99868.2 100000 20 2240.7 2280387 270594843 6012679 180443658 164287127082 262650503338 282193630563 420527140 1177211 884308 143484 0 777984 3252950 -linux_tuned 1 50 0x1b00 95 58.1 61.2 84.6 155.9 184.8 236.8 99955.4 100000 20 2070.44 2283145 270840290 6017452 180585582 164127934468 259148971574 278489246862 408166454 1164334 889707 140221 0 777521 3248185 -linux_tuned 2 50 0x1b00 95 58.2 61.4 85.2 155.7 185.3 236.5 100080.7 100000 20 2071.93 2283624 271057740 6025224 180819702 164225423616 259608865599 278994882945 410203065 1173895 907189 140514 0 775917 3252031 -linux_tuned 0 50 0x1b00 75 58.5 61.7 85.7 157.0 186.1 237.3 100105.3 100000 20 2077.42 2284690 271151351 6026518 180857472 164588739202 261578632512 281058685113 410425564 1187169 878788 143315 0 775409 3259258 -linux_tuned 1 50 0x1b00 75 60.5 62.9 86.1 157.1 185.2 236.6 99951.9 100000 20 2071.12 2279640 270597901 6017499 180587778 164213469231 259973498961 279326075241 410576613 1172315 893055 142354 0 776910 3254481 -linux_tuned 2 50 0x1b00 75 58.1 61.2 85.4 157.6 187.0 241.8 99963.5 100000 20 2071.85 2276318 270430606 6018645 180623616 164401683104 259921077137 279308504692 411533138 1164242 892922 139364 0 776629 3239332 -linux_tuned 0 50 0x1b00 55 59.4 62.3 86.6 157.6 187.0 240.2 99984.9 100000 20 2069.07 2280693 270712163 6019427 180645600 164404726214 261961961154 282428271477 410306947 1185188 879665 141648 0 778877 3255049 -linux_tuned 1 50 0x1b00 55 58.1 61.3 84.0 150.1 179.3 227.6 99976.9 100000 20 2066.8 2284875 270999004 6018956 180631554 162276539739 256568082425 276044552256 385740351 1173180 897827 138937 0 777423 3240786 -linux_tuned 2 50 0x1b00 55 58.3 61.5 85.6 154.3 183.6 232.3 100074.5 100000 20 2060.37 2286083 271205508 6024475 180795762 163075909603 258790016630 279413366749 390002576 1188156 894422 142975 0 778279 3262095 linux_tuned 0 50 0x1b00 135 61.8 67.3 106.5 213.1 254.3 370.2 200010.2 200000 20 2411.69 4244214 520669154 12150467 364987938 311733005337 467329481320 501946105455 802105137 1742657 1152059 62568 0 219615 4794533 linux_tuned 1 50 0x1b00 135 61.7 67.1 105.8 209.3 251.0 361.3 200064.9 200000 20 2403.11 4244110 520663625 12148446 364908048 311200646528 462701855993 496975696855 794123282 1763786 1210422 60304 0 212328 4807674 linux_tuned 2 50 0x1b00 135 61.6 67.0 106.1 212.9 255.5 384.3 200054.5 200000 20 2404.13 4248227 520998368 12158587 365249226 311319730733 463126416109 497447603995 801279378 1701706 1156137 62748 0 234337 4766427 @@ -1593,30 +2626,6 @@ linux_tuned 2 50 0x1b00 75 62.2 67.9 107.7 216.5 259.9 373.7 199887.0 200000 20 linux_tuned 0 50 0x1b00 55 68.1 76.6 135.8 3022.0 5977.3 10938.9 200042.3 200000 20 2058.64 4418490 534201486 12704240 384358494 307422816980 455796848283 582111178569 814084750 1283311 743989 48901 0 146449 4885109 linux_tuned 1 50 0x1b00 55 65.4 72.6 122.1 407.8 3577.3 9831.2 200042.2 200000 20 2089.41 4358823 529486219 12511870 377668512 305663628456 452215016094 561357312503 776200936 1373370 789738 55730 0 196439 4810472 linux_tuned 2 50 0x1b00 55 68.1 75.7 127.5 596.7 4611.4 10329.0 200077.8 200000 20 2084.48 4361505 529807773 12521168 377953932 306078032873 454345728385 566306050998 782357032 1417666 822089 53498 0 164669 4867503 -linux_tuned 0 100 0x1b00 135 60.2 63.7 101.9 175.2 199.4 257.9 49941.2 50000 20 1850.1 1528397 160900886 3006198 90215802 85479728279 146984771152 170094819226 213045033 46006 743976 176177 0 831745 1620255 -linux_tuned 1 100 0x1b00 135 60.4 64.2 105.8 177.1 201.2 256.7 50053.4 50000 20 1847.52 1530396 161171584 3012769 90412830 85165601036 145091829063 167877926986 206905643 44217 745065 174450 0 828527 1616765 -linux_tuned 2 100 0x1b00 135 60.1 63.6 102.8 176.1 199.8 255.1 49980.1 50000 20 1847.82 1527139 160860954 3008303 90278304 85141365815 145408751075 168108063736 207606346 47419 747291 175391 0 840940 1624070 -linux_tuned 0 100 0x1b00 95 60.4 63.9 103.1 176.5 200.3 257.0 49940.8 50000 20 1855.61 1529788 161006570 3006101 90212556 85593011569 146192749818 168985298221 213124017 41639 736822 171812 0 820715 1608936 -linux_tuned 1 100 0x1b00 95 60.6 64.1 102.5 176.6 200.1 256.3 49998.5 50000 20 1853.5 1527375 160894017 3009259 90306504 85662937606 147091396912 170193819745 211719775 42770 737156 173249 0 830408 1615691 -linux_tuned 2 100 0x1b00 95 60.3 63.8 103.6 176.2 200.3 257.3 49963.2 50000 20 1852.46 1530813 161108162 3007095 90241386 85622462818 145339236667 167902455215 211404280 43141 737721 172073 0 831490 1613845 -linux_tuned 0 100 0x1b00 75 60.2 63.9 103.9 177.7 202.5 260.8 49920.4 50000 20 1856.4 1533263 161198898 3004723 90170766 85567112064 146306929379 168954548970 214047890 42108 735207 169063 0 817513 1604414 -linux_tuned 1 100 0x1b00 75 60.5 63.8 99.2 174.2 198.3 255.2 49983.6 50000 20 1855.28 1529143 161009650 3008698 90290610 85531429025 146001474004 168373218827 213174821 43774 735904 169175 0 825302 1608336 -linux_tuned 2 100 0x1b00 75 60.6 64.2 104.3 178.1 203.3 261.4 50036.0 50000 20 1850.11 1529910 161141205 3011812 90383370 85628746943 146771672832 169546193833 213342832 48619 745786 176267 0 830845 1620692 -linux_tuned 0 100 0x1b00 55 58.6 62.2 96.8 169.2 189.4 245.9 49958.3 50000 20 1829.94 1532093 161178916 3006716 90229818 83986196940 139648427806 162891267250 192634552 42188 740181 170299 0 833930 1609587 -linux_tuned 1 100 0x1b00 55 59.1 62.9 101.6 172.4 194.8 245.9 49956.7 50000 20 1828.38 1538343 161592916 3007081 90242208 83931878412 139719892234 163233232959 192009911 39999 739453 169933 0 826851 1604252 -linux_tuned 2 100 0x1b00 55 58.5 62.3 99.9 170.8 194.8 250.2 49997.5 50000 20 1833.33 1535143 161429208 3009413 90311610 84373113084 140854323501 164217403291 196312441 42332 743874 170618 0 832095 1611090 -linux_tuned 0 100 0x1b00 135 63.3 70.2 126.6 193.1 226.8 282.5 100098.6 100000 20 2057.09 2242484 268363091 6047251 181548202 161011258158 254786192469 278904979407 408371914 78357 1405153 142677 0 737797 2346177 -linux_tuned 1 100 0x1b00 135 62.3 68.4 123.8 190.7 224.7 283.7 100106.5 100000 20 2050.39 2244153 268502140 6047608 181558854 160063603120 254290702351 279465057451 395482189 81349 1381849 142502 0 744653 2332536 -linux_tuned 2 100 0x1b00 135 63.2 69.7 125.6 191.8 225.3 283.2 99904.8 100000 20 2052.76 2243143 268183288 6035735 181202898 159880418900 254002337169 278930992929 397358954 81473 1393213 143431 0 743995 2339562 -linux_tuned 0 100 0x1b00 95 62.8 68.5 123.2 188.6 224.5 281.7 99997.6 100000 20 2054.93 2235050 267741772 6041629 181380714 160991480531 254763586368 279612012573 405746301 78570 1385280 140968 0 742215 2334484 -linux_tuned 1 100 0x1b00 95 62.3 67.7 122.7 190.7 225.4 283.2 100085.4 100000 20 2053.44 2240053 268155081 6047015 181543320 160568666004 254185672559 278580917573 403139085 68766 1375531 136926 0 733452 2323362 -linux_tuned 2 100 0x1b00 95 63.0 69.6 125.2 192.6 228.2 286.0 99909.5 100000 20 2055.54 2236804 267770532 6035968 181209624 160512751238 255924574946 280609536506 405225368 84324 1400601 145057 0 742905 2348678 -linux_tuned 0 100 0x1b00 75 62.8 69.0 124.4 189.7 224.9 279.5 100074.8 100000 20 2056.51 2240549 268189189 6045500 181494318 161336016477 256520341590 281192422383 411003902 78191 1399327 144284 0 738062 2348261 -linux_tuned 1 100 0x1b00 75 62.9 69.1 124.7 191.5 226.4 282.9 99897.8 100000 20 2055.47 2239574 267949102 6035976 181211580 160892660371 256719453767 281711876067 408603523 83204 1394749 144207 0 741407 2345527 -linux_tuned 2 100 0x1b00 75 63.4 70.0 125.7 193.0 227.0 283.6 100011.2 100000 20 2054.32 2239145 268024473 6044180 181458546 160906081166 256430353693 281409516122 408015279 71383 1391324 142915 0 731779 2341174 -linux_tuned 0 100 0x1b00 55 61.8 67.6 122.2 186.7 219.2 272.4 100008.4 100000 20 2031.94 2242443 268270031 6041983 181389846 159021083996 245340834206 271037344541 375512463 79547 1412731 144592 0 753665 2345275 -linux_tuned 1 100 0x1b00 55 62.1 68.1 123.1 187.6 220.6 275.0 99969.7 100000 20 2031.18 2241424 268137340 6039408 181311870 158705157372 244736875311 270082387910 378986937 72973 1389159 140027 0 746537 2326600 -linux_tuned 2 100 0x1b00 55 61.8 67.5 122.2 186.5 219.2 274.0 100054.6 100000 20 2189.26 2241644 268264206 6044191 181454136 159662106296 246456699447 270696005725 399661865 78066 1396093 141209 0 751196 2328437 linux_tuned 0 100 0x1b00 135 75.6 87.9 147.7 246.9 286.6 389.4 199984.1 200000 20 2380.58 4287336 523480905 12218688 367299780 305479824380 445942120315 479249325983 784387166 168909 1648801 102884 0 319443 2802841 linux_tuned 1 100 0x1b00 135 74.6 87.4 147.0 248.9 291.3 402.5 199962.4 200000 20 2375.97 4287633 523456199 12218439 367297848 303992050680 441098402048 473915528475 771835933 162250 1667686 104801 0 324157 2805369 linux_tuned 2 100 0x1b00 135 76.3 89.5 148.4 250.6 293.2 399.2 199818.8 200000 20 2376.84 4284311 523082222 12205548 366897294 304175767847 442023907552 475043480575 775586362 162251 1693537 105545 0 314004 2815929 @@ -1628,30 +2637,6 @@ linux_tuned 2 100 0x1b00 75 78.0 91.3 150.1 251.4 293.3 399.0 200085.3 200000 20 linux_tuned 0 100 0x1b00 55 77.7 91.9 155.2 302.1 442.0 6974.2 199968.1 200000 20 2113.96 4325438 526484815 12324351 371474640 300676554692 421553576295 507475064002 738241065 172364 1497030 94151 0 286492 2807116 linux_tuned 1 100 0x1b00 55 79.1 93.2 158.3 431.6 3942.8 10436.6 200199.2 200000 20 2084.74 4375467 530867976 12463563 377095206 301326909280 422853035691 517704292843 755842904 183224 1425327 91953 0 271631 2816860 linux_tuned 2 100 0x1b00 55 81.5 97.1 162.8 365.7 1661.8 8746.4 200076.4 200000 20 2100.97 4344100 528047183 12375796 373496730 301488364716 425884024152 516248521147 757963687 170503 1466629 93841 0 267877 2818490 -linux_tuned 0 200 0x1b00 135 65.3 74.4 171.6 266.7 289.1 346.1 49945.3 50000 20 1824.97 1426924 154240392 3017104 90576900 82505270651 136874990919 158945580349 199652467 17723 353314 150309 0 836098 1169352 -linux_tuned 1 200 0x1b00 135 64.9 74.3 170.4 267.3 290.5 349.3 49969.8 50000 20 1825.42 1428483 154331555 3018689 90624282 82822737879 137495101003 159359473714 204739351 18046 354002 150789 0 835117 1167497 -linux_tuned 2 200 0x1b00 135 65.0 74.0 171.3 266.0 288.6 345.6 50018.1 50000 20 1913.56 1425701 154236173 3021831 90720126 83063698646 137399939764 159095506115 206688826 22197 386368 167305 0 858560 1173025 -linux_tuned 0 200 0x1b00 95 64.9 74.3 170.7 266.6 289.2 348.1 50003.9 50000 20 1826.31 1426383 154248473 3020778 90687390 82831031131 138021823147 160225917879 202946114 18455 360869 154985 0 846127 1175776 -linux_tuned 1 200 0x1b00 95 65.7 74.7 171.9 267.4 290.6 352.6 49972.2 50000 20 1833.38 1426020 154199134 3018594 90620862 83310110555 138371709870 160131669184 209656410 17903 357986 153266 0 845338 1175563 -linux_tuned 2 200 0x1b00 95 65.8 74.9 171.1 267.6 291.1 349.8 49933.7 50000 20 1826.78 1425733 154133341 3016222 90549696 83534835528 139745256342 161589558982 211082418 22662 386337 168946 0 866924 1181299 -linux_tuned 0 200 0x1b00 75 65.0 74.1 170.8 266.3 289.3 347.0 50059.4 50000 20 1831.95 1428134 154416447 3024103 90787230 83013067797 137913601796 159914834531 205515059 19075 366078 158249 0 854354 1181620 -linux_tuned 1 200 0x1b00 75 65.2 74.0 170.6 267.6 291.8 351.9 50009.5 50000 20 1834.1 1426595 154295269 3020852 90689004 83679302666 138916475629 160520982700 212847296 19721 370927 163817 0 866237 1181977 -linux_tuned 2 200 0x1b00 75 66.4 76.0 173.3 269.4 293.8 353.0 50013.8 50000 20 1830.91 1425477 154187860 3021183 90699054 83618645135 139776656119 161809297985 212445460 19344 367847 160073 0 859678 1183627 -linux_tuned 0 200 0x1b00 55 64.3 72.8 168.0 264.8 285.8 346.2 49999.0 50000 20 1833.22 1427038 154275313 3020412 90676428 83074620996 137791472860 159825618786 205873632 18820 360981 156014 0 849798 1174657 -linux_tuned 1 200 0x1b00 55 66.0 74.6 170.6 265.7 286.9 343.4 49982.5 50000 20 1812.34 1426522 154234333 3019331 90644274 81968559684 138310801397 160440917035 191338502 22215 385033 166000 0 859415 1175353 -linux_tuned 2 200 0x1b00 55 66.2 75.5 173.5 268.1 290.8 345.6 49971.9 50000 20 1824.49 1427597 154284356 3019052 90636252 82203067406 138760997056 160807408508 194560128 18228 358537 153672 0 836958 1166895 -linux_tuned 0 200 0x1b00 135 79.2 99.9 196.7 275.6 303.7 364.4 100093.4 100000 20 2019.25 2207563 266051318 6091974 183041046 156057599964 232900737471 255571949157 381593980 22263 485009 161807 0 878855 1400175 -linux_tuned 1 200 0x1b00 135 81.5 102.1 196.5 275.5 303.0 363.6 99913.8 100000 20 2016.92 2203771 265583962 6080157 182686524 156197501027 233876697808 256867120419 390274865 23292 489525 164958 0 883619 1404110 -linux_tuned 2 200 0x1b00 135 82.3 103.5 198.7 278.7 310.1 367.4 100007.0 100000 20 2019.45 2206864 265914127 6085846 182856048 156371153714 235681044985 259228029965 392299952 22605 482643 162620 0 877310 1399826 -linux_tuned 0 200 0x1b00 95 82.0 102.4 197.7 275.8 303.5 364.3 100127.9 100000 20 2022.76 2210844 266326917 6093569 183088638 156180576782 233357624501 255942153703 385577321 22831 490488 162074 0 885742 1402637 -linux_tuned 1 200 0x1b00 95 82.4 103.6 197.9 277.9 307.0 367.5 100128.4 100000 20 2023.53 2207992 266111816 6093627 183091014 157286575335 237439971693 260968298418 400188705 22554 486194 164182 0 881226 1405810 -linux_tuned 2 200 0x1b00 95 80.2 101.0 197.0 275.9 304.2 364.6 99982.5 100000 20 2024.88 2204989 265716872 6084518 182815884 157014474069 235621936649 258126308655 398713892 22433 485758 162318 0 879838 1404338 -linux_tuned 0 200 0x1b00 75 81.5 102.0 196.4 275.9 303.8 365.1 100057.6 100000 20 2025.14 2205162 265854351 6088862 182945724 156457565894 235134706349 258103567232 390547275 22351 485322 163536 0 884616 1407093 -linux_tuned 1 200 0x1b00 75 81.7 102.5 198.5 278.6 309.8 368.3 99891.9 100000 20 2020.17 2207388 265751588 6079396 182666448 157062454989 237657995937 261420708771 400124692 22973 484310 162429 0 872879 1394383 -linux_tuned 2 200 0x1b00 75 82.9 104.6 199.1 279.2 309.1 371.6 100037.7 100000 20 2023.02 2204913 265814844 6087870 182918046 157447534885 239009448944 262539333282 403284998 22962 487101 164798 0 881315 1406834 -linux_tuned 0 200 0x1b00 55 83.9 105.0 198.6 277.3 305.7 365.9 100116.4 100000 20 2022.51 2207677 266067560 6093732 183095376 156666228945 236180149761 259929186310 392878397 21904 484551 162474 0 877351 1403789 -linux_tuned 1 200 0x1b00 55 79.6 99.6 195.8 276.0 303.6 363.2 100127.2 100000 20 2020.36 2205925 266008391 6093298 183081678 155404400535 239373024530 263533471987 374445672 22923 491688 162701 0 872685 1400462 -linux_tuned 2 200 0x1b00 55 80.3 100.3 196.0 275.8 304.0 365.9 100104.7 100000 20 2028.25 2207852 266069241 6092833 183070392 156021868859 240158779466 263235788321 379590378 23474 497338 167855 0 884934 1406418 linux_tuned 0 200 0x1b00 135 112.3 133.8 224.0 321.9 362.3 460.3 199867.5 200000 20 2320.68 4363759 528366266 12335637 371343234 300217434712 408593671544 439364066421 743748670 31364 645896 209785 0 569508 1462077 linux_tuned 1 200 0x1b00 135 108.8 130.2 222.8 319.9 361.0 464.0 199865.0 200000 20 2324.67 4367189 528591388 12337530 371405766 302049444515 413870145626 445255169515 766601331 30629 643679 208269 0 560670 1462238 linux_tuned 2 200 0x1b00 135 113.1 134.5 226.7 329.5 370.7 481.7 200138.0 200000 20 2327.94 4377184 529575427 12359316 372087288 302975762524 414682911225 445896621675 771839090 29823 630324 201433 0 560802 1460510 @@ -1664,30 +2649,6 @@ linux_tuned 2 200 0x1b00 75 111.5 133.0 225.8 324.1 364.2 459.9 200273.2 200000 linux_tuned 0 200 0x1b00 55 114.8 138.3 234.6 363.7 436.6 3848.6 200007.6 200000 20 2123.37 4398301 531060360 12397117 373802670 301344499425 407607943014 482497951252 762886655 30625 600102 184158 0 499868 1460081 linux_tuned 1 200 0x1b00 55 124.0 146.9 241.5 382.9 490.3 6921.2 199990.7 200000 20 2115.67 4409635 532006853 12420944 374963376 299379996142 414811807806 495477192470 742297897 29327 602395 183846 0 475653 1461921 linux_tuned 2 200 0x1b00 55 117.7 141.0 235.6 358.7 424.9 3143.7 200013.4 200000 20 2121.29 4399040 531100264 12390881 373550970 299450389046 415753183248 492127189784 744240587 31213 619340 187334 0 483911 1461778 -linux_tuned 0 300 0x1b00 135 76.8 97.9 239.1 361.3 388.0 443.0 50028.5 50000 20 1823.73 1356472 149672292 3033635 91111638 82673398612 134265521809 154683162176 211015639 13382 205971 116704 0 788956 894675 -linux_tuned 1 300 0x1b00 135 77.8 99.6 241.3 361.2 387.6 442.6 50099.2 50000 20 1824.32 1355172 149654652 3038268 91251858 82527337847 137427321407 157800933152 207840949 13791 207622 119507 0 795012 895890 -linux_tuned 2 300 0x1b00 135 75.2 92.9 239.1 361.5 387.8 439.7 49870.4 50000 20 1822.0 1356535 149469674 3023715 90812088 81923619649 136281349244 156367929182 206237059 12955 200647 114147 0 781488 893422 -linux_tuned 0 300 0x1b00 95 75.6 95.1 238.4 360.9 387.2 441.1 49992.6 50000 20 1825.54 1356661 149632514 3031074 91033380 82645897152 134532119646 155015820712 211456334 13877 211387 121997 0 795822 894950 -linux_tuned 1 300 0x1b00 95 77.2 98.0 240.0 362.2 389.0 443.9 49959.6 50000 20 1822.76 1351272 149243826 3028938 90969264 82749099485 138360241450 158510693884 211516600 14423 216498 123765 0 790157 888314 -linux_tuned 2 300 0x1b00 95 78.1 100.3 242.3 362.4 389.3 441.9 49953.9 50000 20 1824.74 1354683 149468591 3028635 90959466 82584668935 137916120037 158138834453 211614559 13583 205628 117986 0 787986 896222 -linux_tuned 0 300 0x1b00 75 76.3 97.3 241.0 360.2 385.9 442.2 50092.0 50000 20 1820.81 1352018 149440103 3037454 91225974 82682150595 135075333204 155797600044 210530471 12743 196437 112644 0 777955 889198 -linux_tuned 1 300 0x1b00 75 77.7 98.8 241.9 362.3 389.4 444.8 49929.4 50000 20 1824.63 1349970 149111107 3027603 90930474 82825925754 138769570562 159180861177 212842415 14040 214185 125248 0 798868 896554 -linux_tuned 2 300 0x1b00 75 77.7 99.8 242.4 361.6 387.9 442.6 49948.7 50000 20 1825.53 1354703 149423834 3028841 90967272 82659225099 138344247598 158628920794 212586344 13148 202504 116536 0 784042 894622 -linux_tuned 0 300 0x1b00 55 78.0 101.0 241.5 360.9 387.2 442.4 50054.1 50000 20 1824.18 1355464 149608649 3035052 91153272 82842493045 134678276525 155034335704 211998255 12923 202389 115520 0 784085 892700 -linux_tuned 1 300 0x1b00 55 75.0 95.5 237.0 359.0 383.8 436.5 50076.0 50000 20 1806.8 1354280 149556837 3036262 91189554 81259192714 131336182923 151507517089 193564074 13193 201108 113228 0 786845 894616 -linux_tuned 2 300 0x1b00 55 76.5 97.7 239.2 359.9 385.8 439.5 50003.0 50000 20 1812.06 1357374 149676732 3031844 91056174 81503891325 132669534927 152861439219 196708588 12688 199611 111000 0 780852 893491 -linux_tuned 0 300 0x1b00 135 110.0 136.9 264.7 368.0 396.8 461.7 100003.7 100000 20 2007.86 2200511 265481956 6126873 184243716 156880823818 229043848656 251097957772 397390868 13842 213467 120626 0 798300 967134 -linux_tuned 1 300 0x1b00 135 103.7 132.3 264.3 368.1 397.5 462.5 100146.5 100000 20 2010.44 2202785 265810441 6134454 184467546 156197146033 233344875035 254959732477 393118278 13540 211652 120110 0 790708 963477 -linux_tuned 2 300 0x1b00 135 111.6 139.0 265.4 368.4 397.4 462.6 99866.2 100000 20 2104.6 2198016 265109019 6117330 183949524 156007279207 234151544398 256079875631 413092543 14026 213321 119843 0 795343 966471 -linux_tuned 0 300 0x1b00 95 115.6 143.3 270.3 374.7 402.0 472.0 100062.6 100000 20 2008.83 2202596 265719905 6129718 184324140 157316196355 231008642719 253840636629 400089631 14226 217349 122384 0 798487 964738 -linux_tuned 1 300 0x1b00 95 112.6 139.8 267.4 372.6 399.7 467.2 100198.0 100000 20 2012.0 2206146 266072886 6138854 184605066 157253662062 236988758870 259659522763 401504356 14014 214458 122580 0 793095 966810 -linux_tuned 2 300 0x1b00 95 112.0 138.7 268.1 374.0 400.0 469.3 99962.0 100000 20 2011.96 2200108 265363247 6123840 184147962 156886193114 236956285487 259514467837 401083588 14383 219281 125592 0 797423 967386 -linux_tuned 0 300 0x1b00 75 111.3 139.2 266.0 370.7 399.4 465.7 100099.1 100000 20 2005.91 2202506 265705728 6131619 184381014 157465573358 230907274577 253657322728 400353281 14429 218262 124099 0 797620 964355 -linux_tuned 1 300 0x1b00 75 112.4 138.8 267.3 372.7 400.2 468.0 100022.2 100000 20 2016.46 2202047 265625932 6126483 184225374 157295558998 236813810741 258640345516 405506684 14072 216580 123599 0 798107 967743 -linux_tuned 2 300 0x1b00 75 114.5 142.0 270.0 373.7 401.3 473.9 99947.3 100000 20 2016.2 2200465 265403820 6122851 184120380 157170183153 236650752090 258428394666 404225451 13896 213364 121651 0 793583 967468 -linux_tuned 0 300 0x1b00 55 111.9 137.6 266.1 371.1 399.6 465.9 100080.7 100000 20 2006.97 2203131 265745309 6131496 184379646 157248602471 230617731549 253043125261 401099737 13833 212773 121040 0 795819 967399 -linux_tuned 1 300 0x1b00 55 109.1 135.0 265.1 367.6 396.2 458.8 100042.8 100000 20 2000.36 2202658 265693202 6128252 184279728 155183808333 227836503865 249893164952 379045907 14510 219008 123013 0 802786 967011 -linux_tuned 2 300 0x1b00 55 113.1 139.7 267.5 371.4 399.8 466.5 100101.6 100000 20 1998.92 2202346 265699403 6131991 184391310 155811537936 228884627188 251055629053 381217393 14291 217105 122687 0 799910 966351 linux_tuned 0 300 0x1b00 135 148.6 179.3 303.8 409.3 453.6 549.9 199900.1 200000 20 2313.31 4451400 534206294 12462650 375797124 304210856262 403876585456 434216677839 772240768 16702 300277 185958 0 560380 976404 linux_tuned 1 300 0x1b00 135 149.3 181.3 309.6 428.5 478.5 606.6 199863.0 200000 20 2316.24 4458137 534612771 12464912 375902622 303128768538 413257086498 444268201640 763781152 17125 303310 180275 0 548144 976314 linux_tuned 2 300 0x1b00 135 149.7 180.2 307.3 417.2 463.5 569.4 199894.7 200000 20 2320.94 4452529 534256476 12460896 375743766 303910134276 415486476430 446885556765 774320115 17216 307369 188453 0 547730 976443 @@ -1700,30 +2661,6 @@ linux_tuned 2 300 0x1b00 75 151.3 182.3 306.5 420.3 466.7 570.3 199921.8 200000 linux_tuned 0 300 0x1b00 55 149.5 181.9 312.6 445.6 515.3 3424.0 199951.4 200000 20 2116.5 4475677 536140782 12497618 377437296 304007423940 403183211344 475411253152 782161871 17180 306986 180729 0 504736 976429 linux_tuned 1 300 0x1b00 55 152.1 184.2 312.5 438.5 499.5 2451.0 200207.8 200000 20 2115.33 4479813 536673705 12512953 377928492 301650610613 400140501780 469125175452 748421386 17131 307825 181627 0 511587 976396 linux_tuned 2 300 0x1b00 55 148.8 179.6 306.9 424.0 474.7 641.6 199941.2 200000 20 2272.38 4467705 535342302 12480007 376505016 301712333738 400402594154 464861317574 752102566 16595 302798 181293 0 521799 976353 -linux_tuned 0 400 0x1b00 135 98.3 132.4 306.3 457.8 481.9 537.6 49946.1 50000 20 1814.79 1308421 146418270 3039459 91322958 81429808303 129630797652 148339524928 212027304 9183 121127 108670 0 656601 705279 -linux_tuned 1 400 0x1b00 135 92.8 126.9 303.2 456.5 480.6 534.5 50008.7 50000 20 1807.86 1309803 146547639 3043016 91429878 80992537222 129385296869 147802942916 205930440 9253 121762 109183 0 657349 704567 -linux_tuned 2 400 0x1b00 135 97.9 130.9 304.4 458.4 483.0 538.8 49996.8 50000 20 1807.48 1309778 146574950 3042170 91402446 81022608389 129903588247 148309915377 207206506 9467 124752 109116 0 660399 701863 -linux_tuned 0 400 0x1b00 95 100.6 133.6 308.8 459.3 483.2 539.0 50018.7 50000 20 1813.97 1309347 146542024 3043344 91439364 81537364678 130232425538 149141200928 212456315 9320 122313 108203 0 659962 703193 -linux_tuned 1 400 0x1b00 95 95.7 129.4 305.9 457.2 481.9 536.3 49917.3 50000 20 1813.15 1307477 146282385 3037205 91253562 81406548593 130546843976 149127662423 210677823 9497 126547 109596 0 666142 704893 -linux_tuned 2 400 0x1b00 95 96.6 129.8 306.1 457.5 481.9 537.6 49962.1 50000 20 1811.76 1307514 146344638 3040472 91353942 81411967216 130910993025 149339603935 211186613 9176 123261 109317 0 658250 703572 -linux_tuned 0 400 0x1b00 75 102.9 136.8 310.7 460.7 483.8 541.9 50035.6 50000 20 1814.72 1311946 146738933 3044761 91482924 81694193939 130512801011 149155305861 213512072 9394 123470 108861 0 660998 704049 -linux_tuned 1 400 0x1b00 75 97.5 131.2 307.5 459.3 483.1 537.6 50055.4 50000 20 1813.93 1310728 146677507 3045817 91513080 81622772017 132201485436 150952472140 211980024 9319 124561 107914 0 660276 701021 -linux_tuned 2 400 0x1b00 75 101.0 134.1 309.4 460.1 483.1 541.7 50021.1 50000 20 1807.71 1311637 146678015 3044220 91466628 81683639447 132866307469 151973268776 212370583 9470 127193 107371 0 670236 704781 -linux_tuned 0 400 0x1b00 55 96.4 129.8 303.6 454.1 479.6 533.9 49874.4 50000 20 1794.43 1308001 146263873 3034647 91177806 79655527640 123059574640 141163988054 192473407 8970 117393 111790 0 645661 703329 -linux_tuned 1 400 0x1b00 55 97.0 127.8 302.7 455.9 480.6 533.3 49996.7 50000 20 1793.29 1309435 146514877 3042096 91400610 80108945649 124666075914 143259162596 191838982 9252 122720 111569 0 657028 704580 -linux_tuned 2 400 0x1b00 55 95.5 127.5 301.4 455.8 481.2 534.9 50071.9 50000 20 1799.96 1313326 146885804 3046147 91521930 80508495326 126151837461 144837049319 196089102 9624 129454 112544 0 668772 701549 -linux_tuned 0 400 0x1b00 135 138.5 171.9 334.8 469.9 489.9 563.5 99980.8 100000 20 1997.27 2204328 265666933 6162094 185449176 155766071636 222602061324 243325893827 401261301 9483 128813 89696 0 674969 730975 -linux_tuned 1 400 0x1b00 135 135.8 169.6 332.9 468.3 489.3 563.1 99909.5 100000 20 1992.01 2204030 265615929 6156280 185268900 154505161200 222146440437 242742231475 389896540 9532 125891 90747 0 670421 730272 -linux_tuned 2 400 0x1b00 135 135.5 169.6 331.4 468.4 488.8 564.5 99918.2 100000 20 1992.02 2204864 265688434 6155951 185260242 154769997284 222641121954 243286188506 391399261 9857 132024 91604 0 676698 730649 -linux_tuned 0 400 0x1b00 95 140.3 173.9 337.1 470.8 490.1 564.3 99970.5 100000 20 1997.98 2205337 265745213 6160564 185405220 155717514194 223405237048 244422986360 401826573 9631 129744 90460 0 674648 730658 -linux_tuned 1 400 0x1b00 95 140.4 174.2 335.9 471.3 492.4 564.7 100030.5 100000 20 1996.1 2205384 265831217 6163235 185481144 155645068623 225756124481 246720369550 399806465 9636 129147 89696 0 673265 730802 -linux_tuned 2 400 0x1b00 95 136.5 170.8 332.3 469.2 489.5 563.8 99892.1 100000 20 1996.27 2203653 265589805 6155709 185257332 155406005284 225163084749 245937473950 400223622 9674 128941 88845 0 673263 730759 -linux_tuned 0 400 0x1b00 75 138.5 172.5 334.4 469.6 489.8 565.0 100020.8 100000 20 1998.53 2207074 265924346 6165000 185541168 156053568613 223509592390 243925595760 404462214 9320 124599 88180 0 667583 730633 -linux_tuned 1 400 0x1b00 75 142.2 175.2 335.9 470.2 490.3 563.0 100143.1 100000 20 1997.63 2210353 266302253 6172645 185774928 156118995317 226417972528 247318326524 403885129 9313 125987 88282 0 667591 730801 -linux_tuned 2 400 0x1b00 75 138.7 170.5 333.1 469.9 489.9 564.7 100073.3 100000 20 1999.42 2207487 266017257 6167825 185624652 155815688028 225508699982 246231074126 402711048 9535 128696 89360 0 673786 730995 -linux_tuned 0 400 0x1b00 55 137.5 170.5 331.6 467.7 488.7 560.2 99988.0 100000 20 1979.58 2204666 265736744 6162064 185451420 153473898632 214688486932 235040328129 370172821 9310 124163 88412 0 669983 730848 -linux_tuned 1 400 0x1b00 55 132.8 164.5 329.5 466.1 487.1 553.3 99841.9 100000 20 1979.23 2203006 265443809 6151318 185119338 153492031982 217882653386 239122223593 375651758 9859 132078 91344 0 681002 730652 -linux_tuned 2 400 0x1b00 55 137.3 170.7 333.5 468.2 488.4 553.6 100003.1 100000 20 1988.87 2205071 265770701 6162868 185472102 154520107582 217260139109 237298199805 378293042 9327 125185 88125 0 672899 731018 linux_tuned 0 400 0x1b00 135 179.0 217.0 376.1 503.5 546.0 645.0 199703.4 200000 20 2300.86 4522565 538679081 12552855 379174986 303174424823 396848309544 426715267008 777827362 12331 186752 137890 0 514869 732392 linux_tuned 1 400 0x1b00 135 177.2 215.8 376.8 506.9 551.4 650.6 199755.6 200000 20 2295.09 4520978 538629309 12554707 379216896 302022514396 400040993791 429883045961 767734704 12666 191401 141397 0 518362 732417 linux_tuned 2 400 0x1b00 135 176.1 215.5 375.3 500.9 542.4 639.8 199923.4 200000 20 2295.82 4525269 539096590 12565009 379537074 302861781072 399028797835 429072786164 769950940 12355 188651 141634 0 517451 732382 @@ -1735,29 +2672,6 @@ linux_tuned 2 400 0x1b00 75 178.8 217.9 378.6 508.3 553.0 656.4 199965.0 200000 linux_tuned 0 400 0x1b00 55 176.1 217.1 379.8 513.9 566.1 746.5 199903.5 200000 20 2120.62 4533153 539677563 12572393 379953726 299656249444 383193681412 443961194735 731329215 12365 189883 141175 0 499827 732409 linux_tuned 1 400 0x1b00 55 179.9 220.4 381.3 516.2 568.1 776.4 200007.4 200000 20 2120.67 4541867 540428658 12584232 380469738 300668128629 387119392095 451043386264 745500330 12104 188336 138517 0 487480 732392 linux_tuned 2 400 0x1b00 55 190.0 229.0 387.4 524.5 579.2 818.0 200146.3 200000 20 2125.87 4539045 540381056 12588892 380493732 300976322135 388030943241 448863178183 748661785 12193 188074 139664 0 493808 732412 -linux_tuned 0 50 0x1c00 135 56.6 58.9 76.9 133.4 163.9 202.9 50068.0 50000 20 1970.79 1601796 165888477 3009591 90304404 87714839175 145248631498 152450189813 214560513 506571 705572 165621 0 824410 2104355 -linux_tuned 1 50 0x1c00 135 56.2 58.3 75.9 130.2 160.0 199.5 50086.8 50000 20 1968.98 1605756 166155169 3010702 90337758 87095617550 143507189234 150714674141 208989496 502480 709250 166960 0 834301 2105342 -linux_tuned 2 50 0x1c00 135 56.2 58.2 76.4 130.9 161.3 201.5 50073.8 50000 20 1970.78 1605828 166178572 3009775 90309522 87273588638 143721141132 150808358728 208955997 505126 713804 168198 0 829192 2103556 -linux_tuned 0 50 0x1c00 95 56.5 58.6 76.6 130.6 161.9 202.2 49944.0 50000 20 1969.36 1601463 165705548 3002121 90080238 87468052240 145119323085 152488217890 213768437 504316 703133 166237 0 818855 2099360 -linux_tuned 1 50 0x1c00 95 56.7 59.1 76.6 131.2 162.8 201.1 49949.0 50000 20 1967.06 1595286 165331407 3002501 90092004 87452961446 144178046600 151343816642 212608839 504815 711936 169477 0 836636 2102772 -linux_tuned 2 50 0x1c00 95 56.6 58.9 76.7 131.8 162.4 202.2 49994.2 50000 20 1963.75 1606895 166143018 3005240 90174156 87564164343 144901802140 152221096222 214241887 508304 717279 171786 0 831558 2110876 -linux_tuned 0 50 0x1c00 75 57.0 59.7 77.7 132.7 163.8 201.8 49993.7 50000 20 1973.45 1595304 165357895 3005167 90171918 87605273423 145649735921 152924798361 215337052 515218 721933 169888 0 834588 2110174 -linux_tuned 1 50 0x1c00 75 56.7 59.1 76.9 131.6 163.3 202.0 49870.2 50000 20 1967.87 1599278 165513401 2997651 89946114 87403485265 144963954419 152012978563 214593137 508141 722403 170532 0 834080 2107937 -linux_tuned 2 50 0x1c00 75 57.0 59.5 77.4 132.0 162.5 202.0 50064.2 50000 20 1965.48 1602679 165950337 3009401 90298902 87623569571 145242316609 152697664561 214496495 506312 716310 167713 0 828079 2108241 -linux_tuned 1 50 0x1c00 55 56.1 58.2 76.0 128.7 154.4 194.8 49954.5 50000 20 1954.62 1608896 166228784 3002725 90098406 86021521296 140259075911 147997365963 195336510 500139 754472 168816 0 841264 2106755 -linux_tuned 2 50 0x1c00 55 56.0 57.9 75.4 128.0 155.7 196.7 50053.8 50000 20 1959.03 1605458 166110952 3008597 90274200 86423720712 141175491526 148776793250 198427127 493233 716262 165063 0 832369 2095435 -linux_tuned 0 50 0x1c00 135 57.8 60.9 84.2 152.4 181.3 228.0 100006.7 100000 20 2173.11 2287624 271246192 6020211 180668004 164706363119 263101888502 272598178394 411590701 1165282 925364 140636 0 781266 3252413 -linux_tuned 1 50 0x1c00 135 57.3 60.3 83.1 148.4 177.9 227.1 99969.0 100000 20 2162.94 2288746 271268404 6017570 180587154 163572605318 258656055683 268031045331 401023576 1163748 957413 139287 0 787161 3247747 -linux_tuned 2 50 0x1c00 135 57.1 59.9 82.2 145.3 172.2 221.0 100003.2 100000 20 2164.07 2294364 271672371 6019598 180647670 163572517835 258234415414 267584890829 400356632 1159977 953599 137057 0 784142 3241213 -linux_tuned 0 50 0x1c00 95 57.8 60.9 83.4 150.1 179.5 226.7 100059.3 100000 20 2172.1 2284788 271099428 6023216 180757356 164665297318 263190481798 272699746051 411468264 1161420 926821 139437 0 781420 3247462 -linux_tuned 1 50 0x1c00 95 57.4 60.5 84.8 154.9 182.8 229.1 99946.2 100000 20 2165.84 2282615 270831042 6016371 180551082 164155888711 260880007744 270311265915 408345266 1153937 945691 139693 0 784012 3239498 -linux_tuned 2 50 0x1c00 95 57.8 60.9 84.1 151.0 179.8 225.2 100189.8 100000 20 2167.43 2286606 271350067 6031219 180997836 164603232611 262122671560 271599660196 411243202 1168432 940243 140991 0 780967 3256222 -linux_tuned 0 50 0x1c00 75 57.5 60.5 84.6 152.8 182.4 233.0 100067.5 100000 20 2174.75 2293766 271698541 6023821 180775722 164929494292 264823224949 274401519701 414424386 1164710 917659 141896 0 779227 3259125 -linux_tuned 1 50 0x1c00 75 57.4 60.5 83.9 154.4 182.5 230.2 100086.1 100000 20 2166.41 2284988 271149129 6025170 180816552 164541282794 262754871206 272264036334 412290693 1150944 919982 135599 0 779283 3235079 -linux_tuned 2 50 0x1c00 75 57.6 60.7 85.0 154.3 182.9 237.2 99893.6 100000 20 2167.73 2291766 271313945 6013941 180480624 164269393721 261559527896 271024760803 411590697 1151712 931831 137014 0 783377 3233172 -linux_tuned 0 50 0x1c00 55 57.6 60.7 84.1 148.2 176.8 226.1 99999.6 100000 20 2108.58 2286024 271118448 6019700 180651354 161632788802 258528949043 273683003985 376073611 1181554 903116 140843 0 788075 3250421 -linux_tuned 1 50 0x1c00 55 57.4 60.4 83.1 148.1 175.9 224.5 99898.2 100000 20 2110.54 2282326 270717400 6013182 180454698 162071285587 255347799448 270146905399 385849106 1161509 934073 140679 0 781671 3243835 -linux_tuned 2 50 0x1c00 55 58.2 61.3 84.3 152.0 180.3 228.3 99989.8 100000 20 2111.4 2284115 270960330 6019178 180636642 162908518316 256729188767 271913091819 388837922 1163471 942433 143276 0 778552 3252543 linux_tuned 0 50 0x1c00 135 60.9 66.1 103.9 203.9 245.9 365.5 200250.4 200000 20 2518.41 4251032 521375731 12167640 365514552 312505270455 471732372560 488582802418 805654680 1717550 1224714 63445 0 230021 4771471 linux_tuned 1 50 0x1c00 135 60.7 66.0 104.6 205.8 247.8 364.0 199929.5 200000 20 2506.93 4238309 520197915 12136893 364555200 311013050638 466043141374 482690148868 789157756 1749159 1265904 60844 0 233061 4772534 linux_tuned 2 50 0x1c00 135 61.5 66.7 104.3 202.4 240.7 333.6 200135.9 200000 20 2511.73 4240532 520528812 12144848 364778862 311466911295 468580737679 485319007626 798064626 1770962 1289404 60691 0 211981 4805133 @@ -1770,30 +2684,6 @@ linux_tuned 2 50 0x1c00 75 61.6 67.1 105.7 206.5 245.1 348.9 200088.0 200000 20 linux_tuned 0 50 0x1c00 55 68.3 76.3 135.5 3465.6 6416.7 10972.4 200024.5 200000 20 2043.08 4440553 535957546 12784295 387234516 303544929866 451912044016 580064330207 761177475 1261609 728481 49366 0 147784 4884544 linux_tuned 1 50 0x1c00 55 66.6 74.8 128.9 2746.0 5923.2 10727.5 199866.9 200000 20 2063.39 4397373 532472445 12639541 382203402 305176495920 448017822365 566896195140 778153380 1340028 789141 51909 0 164648 4855138 linux_tuned 2 50 0x1c00 55 69.1 77.8 146.3 4540.8 7186.4 11668.9 200025.5 200000 20 2038.4 4453510 536974167 12827866 388813650 304632524080 448028921174 576999748827 783433439 1224939 724957 48510 0 159187 4858573 -linux_tuned 0 100 0x1c00 135 58.9 62.6 99.8 171.6 195.4 248.8 50054.2 50000 20 1935.27 1534514 161414088 3012806 90413508 84782571626 146183475509 164097041604 201336967 49279 748578 169974 0 822949 1605777 -linux_tuned 1 100 0x1c00 135 58.8 62.6 101.5 172.6 196.9 250.4 50062.8 50000 20 1929.77 1532915 161345059 3013142 90422760 85201449950 145099302806 163430670962 207500209 42463 740420 169210 0 836715 1612224 -linux_tuned 2 100 0x1c00 135 58.5 62.1 99.5 171.5 194.5 249.5 50021.3 50000 20 1927.09 1536628 161557049 3010780 90352668 85130077838 144594803807 162804471381 207780051 42203 742295 172005 0 834779 1612822 -linux_tuned 0 100 0x1c00 95 59.0 62.9 101.4 173.2 195.7 247.5 50026.4 50000 20 1935.9 1536596 161533757 3010998 90358872 84966197257 147518504973 165613434929 205366697 46255 747788 175021 0 840985 1622230 -linux_tuned 1 100 0x1c00 95 59.3 63.0 102.1 173.9 197.9 255.0 49992.2 50000 20 1926.74 1531244 161174672 3008867 90295068 85578321727 146910778378 165347994362 212023071 40812 737790 169485 0 832256 1611973 -linux_tuned 2 100 0x1c00 95 59.0 63.1 104.9 176.5 199.8 252.3 50001.3 50000 20 1920.54 1531135 161147353 3009679 90319752 85492448705 146281471402 164633621296 211579377 45134 746455 171931 0 826505 1608104 -linux_tuned 0 100 0x1c00 75 58.9 62.6 99.3 170.8 193.8 249.1 49911.7 50000 20 1935.73 1534653 161288883 3004212 90155334 84804948260 146657663205 164479923268 206801686 45668 738990 170396 0 829635 1606475 -linux_tuned 1 100 0x1c00 75 60.2 63.6 100.7 173.0 196.6 250.9 50082.1 50000 20 1936.48 1535150 161514581 3014434 90462468 85870606881 146243731247 164398776541 214306642 40238 735091 170775 0 829563 1609932 -linux_tuned 2 100 0x1c00 75 59.1 62.7 99.2 171.4 195.2 251.1 50023.3 50000 20 1930.83 1538107 161638775 3010743 90351426 85726695279 146482102263 164631010194 213719241 43388 744634 172748 0 834168 1616712 -linux_tuned 0 100 0x1c00 55 60.0 63.6 102.4 173.7 198.5 256.7 50021.1 50000 20 1931.45 1532508 161269106 3010781 90352392 85100385658 148572799421 167173624634 206989564 39243 731889 165229 0 815670 1595837 -linux_tuned 1 100 0x1c00 55 58.4 62.2 102.0 170.9 192.9 244.3 50026.5 50000 20 1917.57 1540072 161771792 3011064 90360780 84108012248 143494016818 162044644730 193709410 43746 747928 171565 0 839260 1617497 -linux_tuned 2 100 0x1c00 55 58.4 62.0 98.4 170.0 191.5 244.6 50090.5 50000 20 1925.53 1537022 161638657 3014990 90478746 84510147060 143719325332 161999850431 197165197 40796 736626 168883 0 830177 1607167 -linux_tuned 0 100 0x1c00 135 63.2 69.4 124.5 188.0 219.0 272.8 100024.3 100000 20 2145.94 2240999 268187605 6042511 181404540 159514931336 256368450891 272163857980 387843461 79143 1404759 142809 0 748394 2338086 -linux_tuned 1 100 0x1c00 135 61.9 68.3 124.1 188.0 221.2 275.5 100060.5 100000 20 2136.45 2245446 268519185 6044746 181471614 160237895993 252982819560 269071888159 398720734 75633 1419952 145150 0 749634 2349988 -linux_tuned 2 100 0x1c00 135 61.7 67.7 122.2 186.9 219.1 274.4 100032.7 100000 20 2135.42 2245808 268505010 6042233 181395498 159910002621 253659533068 270723629704 397796446 64831 1386207 138607 0 744178 2329280 -linux_tuned 0 100 0x1c00 95 62.0 68.0 122.5 187.2 219.2 272.9 100105.0 100000 20 2146.82 2243636 268448735 6047004 181539654 159857051883 257618106128 273913666998 392792506 69130 1410482 142023 0 739019 2347585 -linux_tuned 1 100 0x1c00 95 61.1 66.7 121.8 186.2 218.3 273.2 100108.1 100000 20 2141.59 2245466 268599895 6047032 181538832 160732493116 254146749050 269095272251 405139605 78847 1396418 138887 0 746765 2324966 -linux_tuned 2 100 0x1c00 95 61.7 67.6 122.4 187.0 219.2 272.7 99947.8 100000 20 2143.57 2238197 267883733 6037820 181264314 160560122536 254803754768 270161386346 405028030 76608 1410544 143154 0 751344 2343882 -linux_tuned 0 100 0x1c00 75 62.4 68.7 124.1 188.4 220.2 273.8 99955.6 100000 20 2147.96 2241091 268087765 6038276 181279158 159840796654 257918936827 273815351430 396918498 73998 1412331 143548 0 745674 2348406 -linux_tuned 1 100 0x1c00 75 61.0 66.3 120.4 185.5 217.8 271.9 100092.3 100000 20 2144.44 2243638 268408762 6047196 181547700 161173534897 256206647223 272112705431 408893492 83186 1407218 144836 0 749880 2342161 -linux_tuned 2 100 0x1c00 75 62.0 67.9 123.0 188.8 223.4 276.4 100004.7 100000 20 2147.4 2238735 268025636 6041272 181368396 160983986121 255070524298 270538936911 409769869 74504 1400288 142233 0 747691 2337533 -linux_tuned 0 100 0x1c00 55 63.3 70.0 125.5 191.6 224.9 283.5 100039.2 100000 20 2088.12 2239085 268071669 6043897 181448610 160131182902 258446465830 281163926461 398432152 100575 1391178 147351 0 752145 2344659 -linux_tuned 1 100 0x1c00 55 61.4 67.1 122.2 187.1 218.4 272.9 100080.9 100000 20 2090.27 2243124 268404081 6046622 181530456 158999925160 252629716058 273387906684 381341343 77276 1406846 143592 0 745067 2343253 -linux_tuned 2 100 0x1c00 55 62.3 68.3 123.0 188.0 222.5 276.3 99959.7 100000 20 2097.22 2239700 267992988 6039042 181302198 159299665705 252749815911 272535861570 384107588 80128 1390242 141536 0 749445 2329922 linux_tuned 0 100 0x1c00 135 75.1 87.8 146.1 243.5 282.9 389.1 199711.2 200000 20 2479.94 4277113 522437875 12193599 366519024 302102759225 444766427400 460990665215 756804183 161877 1727733 108787 0 333997 2811247 linux_tuned 1 100 0x1c00 135 75.8 88.7 147.4 248.0 291.4 404.6 199700.6 200000 20 2469.81 4274501 522295414 12192393 366484656 303691219065 440833804514 457007475456 771743977 158090 1727172 108794 0 340547 2806014 linux_tuned 2 100 0x1c00 135 74.8 87.4 145.4 240.6 276.8 374.1 200224.8 200000 20 2479.54 4284777 523576250 12222192 367367382 304954982356 443963996958 460143023635 781321297 152529 1759824 109484 0 326286 2821687 @@ -1806,30 +2696,6 @@ linux_tuned 2 100 0x1c00 75 76.3 89.5 147.4 243.9 281.1 378.8 200134.4 200000 20 linux_tuned 0 100 0x1c00 55 89.3 106.1 182.4 3996.2 6946.8 11390.7 200203.3 200000 20 2045.81 4437365 535846349 12641534 384128484 301845907324 438557172057 558026135399 780136644 166763 1218068 76458 0 209621 2824548 linux_tuned 1 100 0x1c00 55 82.5 98.8 169.2 3213.0 6142.1 10871.3 200035.8 200000 20 2042.3 4429939 535147765 12616376 383190996 300091268494 433455244335 550244774692 755142532 161415 1258824 78238 0 211215 2827478 linux_tuned 2 100 0x1c00 55 85.0 101.5 175.1 3697.4 6601.5 11210.1 199866.8 200000 20 2035.37 4437691 535581733 12635848 384152478 299737781851 432374668219 550682612893 758440889 163561 1222542 78187 0 216031 2820863 -linux_tuned 0 200 0x1c00 135 64.9 73.3 168.0 266.7 290.2 347.7 49970.5 50000 20 1916.14 1428965 154366944 3018534 90619050 83275372987 142519444416 159774471718 208510941 18112 354826 149048 0 835988 1169906 -linux_tuned 1 200 0x1c00 135 64.6 73.3 170.0 267.1 290.3 345.9 50102.4 50000 20 1910.52 1429488 154556575 3026545 90859932 82842581929 140152068741 157033228651 206222136 18277 358031 151140 0 840546 1167149 -linux_tuned 2 200 0x1c00 135 64.5 72.7 166.9 264.7 286.3 344.4 49920.9 50000 20 1906.2 1425963 154124239 3015455 90526218 82852350542 141371957620 158845066059 205711674 19421 366604 157596 0 852365 1173897 -linux_tuned 0 200 0x1c00 95 65.5 75.6 172.3 268.8 292.1 350.3 50025.1 50000 20 1916.36 1426657 154292519 3021999 90724080 83520754753 143719934266 161154953092 211018535 19403 369038 160223 0 858005 1183249 -linux_tuned 1 200 0x1c00 95 64.9 74.0 170.5 267.0 289.8 347.3 49887.9 50000 20 1918.07 1425768 154084933 3013621 90472200 83325784847 141739110814 158949415077 209918045 18394 361601 155288 0 849087 1177274 -linux_tuned 2 200 0x1c00 95 65.0 74.5 170.9 267.4 289.9 345.8 49978.0 50000 20 1913.19 1430171 154469546 3018955 90632466 83198027890 142423901850 159784826371 210352075 19055 365549 156298 0 852699 1175724 -linux_tuned 0 200 0x1c00 75 65.6 75.0 171.8 267.8 291.1 348.6 49962.7 50000 20 1916.83 1422643 153945830 3018245 90611220 83302255435 143639242184 161076970613 210583892 19526 368108 160576 0 857999 1181236 -linux_tuned 1 200 0x1c00 75 65.4 74.9 174.1 267.9 290.5 345.6 50058.2 50000 20 1918.26 1427893 154441448 3023950 90782304 83529235620 142358958917 159428324151 212169221 18496 356398 152773 0 835527 1163346 -linux_tuned 2 200 0x1c00 75 65.3 74.7 171.4 267.8 290.7 346.6 50050.7 50000 20 1914.66 1426995 154336358 3023571 90771234 83601944753 143054475129 160424934033 212731455 18711 365018 157718 0 854582 1182041 -linux_tuned 0 200 0x1c00 55 66.0 74.9 171.8 268.1 291.0 345.8 50012.5 50000 20 1917.95 1424389 154125437 3021489 90709302 83506566947 143669559672 161040756341 211521748 18672 362916 156911 0 852345 1177803 -linux_tuned 1 200 0x1c00 55 63.7 72.0 168.3 264.1 283.5 337.1 50061.5 50000 20 1897.23 1430257 154578955 3024135 90787482 81959843658 134844286354 152520484943 191885293 18255 363538 152432 0 855982 1178385 -linux_tuned 2 200 0x1c00 55 63.7 72.6 168.9 264.5 285.4 339.1 50001.3 50000 20 1901.97 1430568 154529445 3020714 90686046 82259562331 135347971658 152886761990 195223368 18194 361716 153252 0 853765 1173470 -linux_tuned 0 200 0x1c00 135 83.5 105.3 199.1 277.9 306.9 366.3 100005.2 100000 20 2117.82 2207325 265923819 6086791 182887818 156879320784 245191758615 260701188292 397149285 21857 483674 161667 0 872090 1400410 -linux_tuned 1 200 0x1c00 135 81.4 102.5 196.7 275.3 303.9 365.1 100013.2 100000 20 2116.17 2205192 265752952 6086612 182881770 156318905575 241415664183 255925310387 390138395 22124 487005 161591 0 881478 1404415 -linux_tuned 2 200 0x1c00 135 80.3 101.1 196.6 275.4 302.8 362.3 100067.0 100000 20 2112.86 2207874 266014704 6090694 183006528 156563358790 242754963586 258233059076 396911714 22683 488210 163102 0 878238 1402222 -linux_tuned 0 200 0x1c00 95 81.2 102.7 198.2 276.2 303.8 363.8 100010.6 100000 20 2122.5 2205300 265762061 6086592 182880048 157256331374 245750893932 260890324436 401335680 22491 489047 164584 0 881550 1406637 -linux_tuned 1 200 0x1c00 95 81.1 102.4 196.9 275.2 301.3 361.5 100011.5 100000 20 2121.04 2204222 265721596 6086349 182871780 157120142343 244472136357 259888653909 399624276 22349 488509 164708 0 880284 1407272 -linux_tuned 2 200 0x1c00 95 80.4 100.9 196.0 275.5 304.0 364.3 99821.9 100000 20 2126.09 2202418 265413490 6075099 182534394 156862291556 242652487767 256908561187 399279458 24218 495420 166660 0 886306 1400326 -linux_tuned 0 200 0x1c00 75 81.5 102.3 197.3 276.2 303.7 365.0 100009.6 100000 20 2123.3 2205329 265812800 6087126 182898120 157059017480 245978413119 261628790992 400399668 23884 493598 165999 0 879975 1403841 -linux_tuned 1 200 0x1c00 75 80.7 101.9 197.9 277.0 305.9 366.6 99945.1 100000 20 2121.89 2206525 265789233 6082122 182745330 157236763108 244611308618 259472289570 402894547 22101 482635 163686 0 875269 1397334 -linux_tuned 2 200 0x1c00 75 81.5 102.2 197.5 276.7 304.7 364.2 100011.6 100000 20 2123.28 2206867 265914637 6086488 182876610 157444409893 244907252787 259699834358 404831606 22645 491291 166236 0 882120 1406404 -linux_tuned 0 200 0x1c00 55 80.9 101.8 198.9 279.0 306.3 367.6 100007.9 100000 20 2081.27 2204274 265706180 6086118 182865648 157074596768 244798800806 265001270476 400055908 22471 484829 161763 0 870543 1400059 -linux_tuned 1 200 0x1c00 55 80.4 101.4 196.6 275.8 303.3 364.6 100023.5 100000 20 2065.96 2207195 265926519 6086781 182884260 154978589003 233800996268 253295641282 374605823 22290 486948 163077 0 883163 1401057 -linux_tuned 2 200 0x1c00 55 79.8 100.3 195.6 274.2 300.7 360.2 99967.3 100000 20 2077.49 2204934 265721561 6083062 182771862 155786800425 234257894216 252567296219 379488248 22419 490118 163942 0 888029 1407315 linux_tuned 0 200 0x1c00 135 113.9 135.5 226.7 321.3 361.5 457.3 199845.8 200000 20 2445.8 4364825 528409985 12335658 371349906 302676205805 427363486410 443077461147 774893149 29687 643299 207978 0 562692 1461852 linux_tuned 1 200 0x1c00 135 111.9 134.1 225.1 325.6 365.4 467.7 199991.3 200000 20 2441.76 4367421 528745866 12343182 371568594 302115711328 425462358451 441322239598 763292786 31593 643992 203106 0 568169 1461003 linux_tuned 2 200 0x1c00 135 115.8 137.2 226.6 322.1 362.3 462.5 200158.0 200000 20 2448.36 4370606 529196714 12352817 371856984 302920786100 426997588875 442751314068 774204760 30109 644954 209330 0 562605 1462213 @@ -1842,30 +2708,6 @@ linux_tuned 2 200 0x1c00 75 114.1 135.9 228.0 329.6 372.3 481.5 200124.7 200000 linux_tuned 0 200 0x1c00 55 129.2 155.3 266.2 4501.4 7208.9 11500.7 200045.3 200000 20 2013.99 4517926 541406819 12690579 387662832 299604943238 415748733020 534046996575 777819312 24899 494397 144013 0 366294 1460031 linux_tuned 1 200 0x1c00 55 119.6 144.5 246.3 2112.1 5343.9 10166.2 200185.9 200000 20 2052.31 4467963 537335595 12574751 381979968 298344328036 404046412688 501090466081 741796079 27661 559556 171735 0 431434 1461995 linux_tuned 2 200 0x1c00 55 120.3 145.4 247.6 2428.2 5578.3 10235.6 200034.9 200000 20 2037.31 4477888 538109177 12595046 383078682 297755821706 403106780013 504696760008 746433141 27348 548161 166595 0 417424 1461611 -linux_tuned 0 300 0x1c00 135 77.7 99.0 240.0 360.8 386.7 440.4 49954.8 50000 20 1902.94 1354065 149412894 3029024 90972330 82907050001 139546202846 155627417517 213445751 14082 214050 122726 0 795269 892283 -linux_tuned 1 300 0x1c00 135 75.7 97.1 239.8 359.8 385.3 438.8 50057.2 50000 20 1893.24 1358514 149840454 3035345 91161972 82374720291 133956050978 149856841410 206912636 12869 200537 111689 0 780675 891069 -linux_tuned 2 300 0x1c00 135 76.6 98.7 239.0 360.6 386.9 438.5 49994.3 50000 20 1888.34 1352913 149393346 3031517 91047204 82320578054 134795619291 151070261005 207139619 13122 203734 116861 0 793087 895327 -linux_tuned 0 300 0x1c00 95 76.8 97.6 239.3 361.0 387.8 443.2 49914.2 50000 20 1905.44 1356062 149489081 3026449 90894792 82741000879 139243637817 155057872307 214322021 13844 207397 118868 0 791353 895154 -linux_tuned 1 300 0x1c00 95 76.5 98.7 240.1 360.7 386.9 440.3 50051.5 50000 20 1896.91 1357183 149710160 3035142 91156482 82661587291 135194911198 151128671384 210750581 12846 199539 112055 0 779279 889304 -linux_tuned 2 300 0x1c00 95 77.6 99.9 239.6 360.9 386.8 438.9 49971.3 50000 20 1896.61 1354834 149494004 3029827 90995784 82713604998 135032780330 150964705065 211409510 12919 202694 113370 0 784203 891504 -linux_tuned 0 300 0x1c00 75 76.0 95.9 238.7 361.0 387.4 443.2 50091.9 50000 20 1898.57 1353078 149535576 3037538 91228128 83009776174 140725086299 156954438348 214585583 12769 196086 112661 0 777009 888788 -linux_tuned 1 300 0x1c00 75 76.1 97.5 240.1 359.7 384.6 438.5 49951.5 50000 20 1898.65 1353568 149384914 3028234 90947070 82888261908 136050795813 151987610465 213495732 13707 208925 118973 0 795900 896832 -linux_tuned 2 300 0x1c00 75 75.4 96.2 237.0 359.9 385.8 439.8 50118.9 50000 20 1895.27 1358361 149885081 3039007 91272162 82983635676 136137674369 152100467242 214182467 14281 214591 124016 0 804335 896667 -linux_tuned 0 300 0x1c00 55 76.5 97.5 236.1 358.9 383.0 432.1 50076.7 50000 20 1886.2 1358614 149847958 3036528 91196364 81306838943 132154218293 148612186205 192636902 13768 210544 120371 0 802220 895134 -linux_tuned 1 300 0x1c00 55 73.3 93.2 235.4 356.7 379.6 432.4 49960.7 50000 20 1882.38 1357934 149667970 3029197 90977250 80981340066 129999788860 146034579988 192927941 12955 200384 110854 0 781016 890751 -linux_tuned 2 300 0x1c00 55 77.3 100.5 239.7 358.4 383.0 436.3 49968.5 50000 20 1887.47 1358088 149700380 3030063 91003500 81587352218 131299056610 147466899181 196928899 13100 204046 114372 0 788850 892672 -linux_tuned 0 300 0x1c00 135 113.0 139.9 267.7 371.3 399.2 464.7 100053.9 100000 20 2106.06 2202408 265630851 6129431 184318620 157159924022 237521082584 251335570519 405088548 13963 215012 121370 0 797665 966686 -linux_tuned 1 300 0x1c00 135 111.0 138.0 264.7 367.6 396.6 458.4 100095.3 100000 20 2085.82 2202912 265753862 6131733 184385868 156468383377 229565816252 243544654486 392943263 13658 212369 118599 0 802544 967648 -linux_tuned 2 300 0x1c00 135 110.7 136.1 264.9 367.3 396.1 457.6 99915.7 100000 20 2087.06 2200600 265354994 6121630 184084860 156090992827 229595712212 243712189255 392660401 13757 214153 120108 0 802493 967136 -linux_tuned 0 300 0x1c00 95 111.0 137.8 264.5 368.4 398.2 466.1 100122.0 100000 20 2104.67 2203731 265820416 6134262 184464414 157652721912 239150356363 253156229553 407460072 14486 220341 124166 0 803527 968220 -linux_tuned 1 300 0x1c00 95 110.6 136.5 265.0 367.7 396.9 461.1 100041.3 100000 20 2094.06 2201400 265601038 6129023 184303818 157124468105 231657703730 245552804001 401220748 13732 211429 118928 0 800074 967063 -linux_tuned 2 300 0x1c00 95 109.8 134.5 262.6 366.2 394.9 458.3 99991.3 100000 20 2095.07 2201397 265486363 6125368 184196136 157092700151 232118033096 246107622228 402583354 14510 220276 123841 0 808739 968739 -linux_tuned 0 300 0x1c00 75 114.3 141.0 268.7 372.5 400.5 467.5 99984.5 100000 20 2107.78 2200326 265424435 6124734 184175436 157132653073 237884233335 251099279563 406328084 13754 214034 119618 0 792223 964349 -linux_tuned 1 300 0x1c00 75 110.3 136.4 263.4 366.7 395.9 460.1 99907.5 100000 20 2095.53 2199211 265254920 6119856 184027494 157179760032 232367093157 245773945154 406101107 13680 210574 120059 0 802520 968240 -linux_tuned 2 300 0x1c00 75 111.8 138.2 266.3 367.6 397.1 460.0 99999.6 100000 20 2095.13 2198802 265351482 6125676 184201956 157336653135 232892779276 246839100161 405810685 13983 212812 120873 0 803970 967748 -linux_tuned 0 300 0x1c00 55 108.8 134.2 263.4 366.0 394.0 450.9 99866.8 100000 20 2066.13 2197601 265123336 6118037 183976716 154884646932 226207440489 242099533504 373999043 14542 218223 121513 0 808119 967961 -linux_tuned 1 300 0x1c00 55 107.7 133.7 260.1 364.8 393.2 449.7 99836.0 100000 20 2072.5 2196649 265037362 6115554 183901602 154811226791 224123166268 238613436109 376066717 13630 209416 115158 0 797285 963216 -linux_tuned 2 300 0x1c00 55 108.0 134.2 262.7 366.5 395.2 456.8 100074.4 100000 20 2072.82 2200903 265566247 6131143 184371822 155789452979 226953028352 242251671419 382777689 14691 220357 121771 0 810321 967263 linux_tuned 0 300 0x1c00 135 145.5 175.6 299.2 404.3 447.7 536.8 199943.5 200000 20 2425.25 4449778 534090968 12461870 375759348 304804646203 419678350934 435331308383 787205823 17281 306917 189341 0 562319 976451 linux_tuned 1 300 0x1c00 135 141.1 172.5 298.2 402.7 444.9 541.6 200114.6 200000 20 2407.32 4451584 534442955 12473783 376120000 303949727263 408045093964 423478501260 769765636 17509 307303 187235 0 573639 976403 linux_tuned 2 300 0x1c00 135 143.0 174.2 298.6 403.2 444.2 534.8 199765.3 200000 20 2408.87 4441745 533329197 12450409 375406296 303874586063 408403070575 423981934793 772771465 17765 308634 191053 0 573505 976433 @@ -1877,30 +2719,6 @@ linux_tuned 2 300 0x1c00 75 145.8 176.1 300.7 404.8 448.5 544.8 200261.6 200000 linux_tuned 0 300 0x1c00 55 156.4 189.3 324.4 1338.4 4744.3 9643.9 199908.9 200000 20 2058.99 4521197 540175979 12598364 382812042 299669541642 394421511799 483187650654 732164026 15578 288934 169416 0 459987 976439 linux_tuned 1 300 0x1c00 55 164.0 198.7 335.9 2259.6 5527.4 10870.0 200055.5 200000 20 2063.78 4529774 541148341 12619300 383963454 300580202736 393680428791 482814602310 746070168 15113 274408 161593 0 460527 976385 linux_tuned 2 300 0x1c00 55 153.1 187.0 323.6 763.3 4340.1 9794.1 199934.2 200000 20 2059.0 4518713 540056938 12596618 382706076 300712657763 395172586682 483706385283 750626266 15464 285240 169317 0 458826 976394 -linux_tuned 0 400 0x1c00 135 98.2 128.8 301.6 455.7 480.4 534.0 50074.8 50000 20 1885.6 1313810 146907985 3046954 91546950 81034086261 130417087277 145026962808 201534589 9672 128945 112179 0 673506 704961 -linux_tuned 1 400 0x1c00 135 96.2 128.9 302.2 455.7 480.2 535.0 49999.4 50000 20 1881.65 1311981 146701586 3042502 91413666 81023719386 129435362225 143876989587 206788258 9215 122978 110193 0 662017 705532 -linux_tuned 2 400 0x1c00 135 96.2 127.1 301.7 455.7 480.4 533.1 50091.7 50000 20 1882.67 1311426 146756707 3047770 91571934 81213217171 129556601008 144018095612 206823745 9296 123443 110765 0 662222 703893 -linux_tuned 0 400 0x1c00 95 99.0 131.4 304.3 456.8 480.9 534.4 50042.0 50000 20 1890.08 1312473 146764636 3045037 91489308 80816842210 129861039227 144117635676 204397159 9075 120890 110725 0 653224 703039 -linux_tuned 1 400 0x1c00 95 91.4 123.9 301.4 454.8 480.1 534.1 50075.2 50000 20 1885.4 1312577 146821138 3046818 91542366 81734999506 130401617284 144749722228 212040089 9391 126007 111729 0 666526 703294 -linux_tuned 2 400 0x1c00 95 93.8 126.8 302.2 454.7 480.0 534.6 49889.4 50000 20 1881.09 1310437 146459130 3035614 91206378 81394928931 130199944996 144589138382 210727068 9247 123592 110685 0 661949 703794 -linux_tuned 0 400 0x1c00 75 95.5 126.2 300.6 454.7 480.4 534.9 49962.1 50000 20 2023.07 1310431 146547793 3039815 91331850 81109058812 131284473901 145876467522 206910728 10510 141193 117188 0 679953 698153 -linux_tuned 1 400 0x1c00 75 93.8 125.6 302.1 456.6 481.3 535.8 49969.0 50000 20 1888.77 1310860 146591817 3041004 91369968 81537101742 130647369661 144965035222 212703655 9263 124337 111083 0 660234 704130 -linux_tuned 2 400 0x1c00 75 96.4 128.4 304.8 456.7 481.7 535.5 49989.4 50000 20 1887.61 1310512 146602099 3041839 91393866 81621077306 130085674650 144092685729 213364680 9042 122195 111258 0 658544 704086 -linux_tuned 0 400 0x1c00 55 95.6 127.0 302.2 454.0 479.5 534.5 50039.4 50000 20 1883.39 1312317 146785914 3044441 91469682 81254198813 131274859155 145827016461 207142152 9379 125486 110809 0 663921 702284 -linux_tuned 1 400 0x1c00 55 90.8 124.1 300.1 453.7 479.1 530.2 50052.9 50000 20 1873.91 1315693 146987751 3045621 91507896 80070000463 125350861701 139835622957 192341017 9377 124383 114671 0 659912 703892 -linux_tuned 2 400 0x1c00 55 97.8 129.8 303.0 455.5 480.4 533.3 49966.6 50000 20 1878.43 1308940 146466251 3040357 91349244 80241223132 125894704969 140051960086 195733638 9101 120045 112433 0 650548 701914 -linux_tuned 0 400 0x1c00 135 133.4 165.6 328.3 465.5 487.2 553.5 99978.3 100000 20 2075.15 2205309 265752416 6159754 185377674 154154446966 221594718491 234772070241 380375248 9917 131631 92858 0 678301 730223 -linux_tuned 1 400 0x1c00 135 134.9 167.4 329.8 465.8 486.7 552.1 100038.3 100000 20 2077.21 2207077 265954477 6165668 185561196 154844023968 220633414731 232974580177 393086182 9295 122884 87934 0 668227 730736 -linux_tuned 2 400 0x1c00 135 132.7 165.2 328.5 465.2 486.6 552.7 100135.8 100000 20 2079.0 2207397 266113643 6170664 185708778 155291318671 222390544075 235097764021 393999493 9784 130054 90515 0 677890 730719 -linux_tuned 0 400 0x1c00 95 139.9 173.6 333.4 467.8 488.0 558.1 99923.3 100000 20 2074.62 2203958 265613120 6158391 185339712 154476475725 222609199145 235924859681 385070067 9333 126449 89818 0 671199 730658 -linux_tuned 1 400 0x1c00 95 131.0 164.3 327.1 465.0 486.4 556.9 99985.7 100000 20 2078.98 2206223 265853699 6161669 185437722 155270029954 222490118240 234796027083 399992834 9682 127890 91251 0 674241 730350 -linux_tuned 2 400 0x1c00 95 137.9 171.2 332.0 466.7 487.2 557.5 100120.2 100000 20 2078.12 2208049 266107271 6170155 185692488 155794501825 223862428758 236523086964 401430103 9694 128249 89650 0 673796 730670 -linux_tuned 0 400 0x1c00 75 135.0 167.8 330.8 466.7 487.1 553.6 100093.5 100000 20 2078.97 2208353 266099008 6168600 185646486 154881753812 223569354612 236547716664 390688489 9535 126951 90254 0 668998 730347 -linux_tuned 1 400 0x1c00 75 135.3 168.1 330.7 467.5 488.3 559.6 99999.5 100000 20 2074.37 2206786 265916869 6161836 185442492 156007808563 227038270639 240820914282 415406989 10066 135480 94341 0 684388 730262 -linux_tuned 2 400 0x1c00 75 135.2 169.1 331.3 468.2 488.5 555.4 99919.9 100000 20 2081.23 2204073 265589157 6156792 185287170 155840877808 224811247171 237614187749 403638952 9610 130145 92912 0 673803 730476 -linux_tuned 0 400 0x1c00 55 137.0 170.6 332.1 467.5 488.0 558.1 100077.0 100000 20 2064.32 2207803 266072706 6168343 185642262 155015212853 224000215042 238251911901 393477317 9622 127967 91303 0 672937 730324 -linux_tuned 1 400 0x1c00 55 136.6 168.7 329.5 467.3 487.7 553.1 100089.7 100000 20 2041.78 2207620 266056149 6168597 185648802 153724800237 218497145046 234332658732 375815789 9400 126442 90536 0 670506 730444 -linux_tuned 2 400 0x1c00 55 137.0 170.2 331.8 468.6 489.1 559.5 99980.6 100000 20 2048.81 2205664 265771144 6160952 185413800 154306885021 219853546328 235267618790 378462490 9964 130898 92689 0 678214 730398 linux_tuned 0 400 0x1c00 135 173.5 213.3 372.7 491.9 533.4 631.1 199875.9 200000 20 2390.24 4517531 538523925 12559583 379357794 300809383155 394447922424 409313706866 744859825 12440 185369 138353 0 534953 732404 linux_tuned 1 400 0x1c00 135 185.1 223.4 377.0 496.8 537.9 635.6 199981.1 200000 20 2386.69 4520159 538797622 12568200 379627302 302657991880 397267805512 413077051975 768003258 12206 181987 137274 0 532138 732407 linux_tuned 2 400 0x1c00 135 164.7 204.1 368.3 489.9 533.7 632.2 199803.7 200000 20 2388.78 4520376 538647960 12556461 379276728 302688866083 397779411125 413252838317 767887649 12798 191615 137880 0 528729 732391 @@ -1913,6 +2731,45 @@ linux_tuned 2 400 0x1c00 75 190.0 226.9 380.4 504.2 547.4 644.0 199940.7 200000 linux_tuned 0 400 0x1c00 55 189.0 232.9 405.7 863.2 4328.5 9819.4 199978.8 200000 20 2064.51 4585401 544439202 12666786 385443456 301382179353 394373189405 481248562986 764489928 11346 181066 129502 0 422371 732377 linux_tuned 1 400 0x1c00 55 193.5 234.9 400.7 646.2 3683.6 9224.4 200096.2 200000 20 2069.72 4578548 543959230 12658283 384675078 299999035741 387212045943 467312476541 744576183 11259 176338 132308 0 444772 732410 linux_tuned 2 400 0x1c00 55 193.3 235.1 405.1 788.1 4054.2 9470.9 200126.0 200000 20 2050.09 4590501 544884659 12673242 385460508 300071079037 387773839167 478070635686 748912914 11122 174385 129880 0 429860 732408 +linux_tuned 1 40 0x1500 55 63.0 66.2 85.2 153.2 189.6 235.4 49988.0 50000 20 1450.77 1567455 163529898 3004768 90159630 85724583732 138719351296 192124972102 194486069 782621 424893 178852 0 796320 2269422 +linux_tuned 2 40 0x1500 55 63.8 67.1 86.5 158.8 196.0 240.2 49911.2 50000 20 1452.41 1566360 163355365 3000301 90025986 85946419182 139685129040 193555798114 197971961 767182 456634 179016 0 797153 2268460 +linux_tuned 3 40 0x1500 55 62.9 66.1 86.1 156.9 195.4 241.7 49983.9 50000 20 1453.32 1570888 163764254 3004638 90156006 86223329028 139855275581 193720715372 199490905 777869 429379 176931 0 788989 2270712 +linux_tuned 1 10 0x1700 135 60.6 62.4 78.2 143.1 180.0 218.2 49914.9 50000 20 1580.62 1586773 164740029 2999834 90009900 87297177620 143859690018 181811475206 207920477 898269 352379 190469 0 843280 2516646 +linux_tuned 2 10 0x1700 135 60.8 62.7 78.1 142.3 178.8 220.1 50019.1 50000 20 1579.67 1594553 165338376 3006133 90198996 87415432329 143602497677 181501464916 207601407 899603 351500 191258 0 848170 2526347 +linux_tuned 3 10 0x1700 135 60.8 62.7 78.2 146.6 181.7 222.4 49956.4 50000 20 1580.43 1587174 164792984 3002415 90087654 87287783437 143515616207 181387048287 208085309 896747 352676 190287 0 846778 2517951 +linux_tuned 1 10 0x1800 135 60.7 62.4 77.3 137.0 174.8 211.8 50032.3 50000 20 1654.07 1601674 165854369 3006707 90215616 87603087024 144788098684 175404004376 206154035 916460 356489 194486 0 863753 2532995 +linux_tuned 2 10 0x1800 135 60.5 62.3 77.4 141.2 175.2 211.7 49978.1 50000 20 1653.13 1594575 165302830 3003596 90122838 87310199978 144169585749 174755743132 205922051 912538 349169 192253 0 856929 2523506 +linux_tuned 3 10 0x1800 135 59.5 61.7 76.5 138.4 173.4 209.0 49962.1 50000 20 1654.6 1599699 165632135 3002508 90089676 87501908752 144258042731 174768410332 206933696 920329 359485 193738 0 863652 2529545 +linux_tuned 1 10 0x1800 55 59.4 61.5 76.2 138.7 172.6 206.7 50036.2 50000 20 1654.04 1599947 165723409 3007064 90226656 87390483409 144414522114 174953673062 206989130 914370 346424 192685 0 861464 2532099 +linux_tuned 2 10 0x1800 55 59.3 61.6 76.3 138.5 174.8 209.7 49975.9 50000 20 1653.88 1596628 165452328 3003335 90114570 87367840029 144630679531 175202209471 206844670 909280 345979 192450 0 861334 2526903 +linux_tuned 3 10 0x1800 55 60.8 62.5 77.4 139.7 174.4 211.6 50095.5 50000 20 1654.62 1592043 165298680 3010530 90330420 87613770974 144612285189 175195476918 208365449 910533 354142 192839 0 861987 2523897 +linux_tuned 1 40 0x1800 55 59.4 61.7 79.4 140.9 176.7 213.0 50119.3 50000 20 1651.53 1587578 164998371 3012433 90388902 86842019766 143564037670 174262150778 201564103 809229 448591 175668 0 811638 2272932 +linux_tuned 2 40 0x1800 55 60.3 62.2 79.7 141.8 176.2 217.8 49950.4 50000 20 1650.55 1587555 164817666 3002333 90086142 86711152748 143124536287 173716164240 202506221 811806 451585 174240 0 811545 2269089 +linux_tuned 3 40 0x1800 55 59.4 61.8 79.7 140.8 176.6 218.1 49996.3 50000 20 1651.72 1587180 164859540 3005007 90166038 86876770581 143517329638 174247511607 202529576 807566 454236 177878 0 811455 2271403 +linux_tuned 1 10 0x1a00 135 57.6 60.5 77.2 155.2 182.1 237.6 99971.3 100000 20 1991.9 2296568 271755489 6014239 180475494 165610132902 265412792410 296138553163 418985624 1467222 394666 182179 0 962920 4123350 +linux_tuned 2 10 0x1a00 135 57.6 60.5 77.5 155.1 182.7 238.0 99951.0 100000 20 1991.22 2291188 271403384 6013490 180454338 165532529722 266286194200 297079716495 419949884 1469661 400587 184416 0 963719 4116517 +linux_tuned 3 10 0x1a00 135 57.4 60.2 77.4 157.1 184.4 240.7 100149.4 100000 20 1993.43 2296402 271960776 6025440 180813180 166399119607 267946600835 298947110508 422265577 1475723 405010 184409 0 961227 4125283 +linux_tuned 1 10 0x1b00 75 56.9 59.4 75.7 148.7 176.1 227.5 99824.1 100000 20 2079.81 2303177 272026798 6004378 180176808 165975291934 267060535786 286924802735 418902826 1516383 402470 183213 0 973073 4123050 +linux_tuned 2 10 0x1b00 75 56.7 59.0 75.6 149.3 175.8 227.8 99982.8 100000 20 2077.44 2293841 271636028 6014446 180480414 165884597087 267112975686 286982670321 419684193 1492729 391310 179950 0 980872 4115913 +linux_tuned 3 10 0x1b00 75 56.8 59.2 76.1 151.6 177.8 228.8 99897.4 100000 20 2077.35 2300648 271935159 6009505 180332748 166028548501 268072394855 288034765323 422038127 1517092 404646 181435 0 977321 4120895 +linux_tuned 1 10 0x1c00 75 56.9 59.2 75.2 146.3 172.2 222.4 100071.1 100000 20 2170.47 2303460 272339056 6018658 180603378 166573724847 270373424755 280136613090 422935379 1539117 400753 182618 0 986554 4127350 +linux_tuned 2 10 0x1c00 75 56.1 58.1 74.3 144.9 171.2 223.3 100068.9 100000 20 2173.57 2307120 272573125 6018603 180601992 166197170559 270249544495 279983054146 421672890 1529490 399374 182449 0 987273 4128696 +linux_tuned 3 10 0x1c00 75 56.4 58.5 74.7 147.0 173.0 225.9 99605.8 100000 20 1949.04 1912092 225799644 4983781 149579154 138307451506 225198899463 233418197498 360418941 1264301 330828 153034 0 824687 3421467 +linux_tuned 1 30 0x1c00 75 58.6 62.1 88.3 191.5 232.7 343.0 199972.5 200000 20 2525.63 4227245 519472987 12120043 363972618 311511383095 476728118906 493756709441 796901334 2637367 362982 94696 0 347151 6262997 +linux_tuned 2 30 0x1c00 75 57.9 61.6 90.1 195.5 237.8 349.4 200265.6 200000 20 2529.43 4236198 520445698 12141456 364626816 312648066126 479759063518 496895921741 801931649 2640118 367814 95940 0 337694 6274584 +linux_tuned 3 30 0x1c00 75 57.7 61.2 89.2 194.5 236.7 351.3 200055.4 200000 20 2525.11 4231499 519842566 12127071 364189770 312770755188 478115417369 495193222208 807571924 2633444 367982 94070 0 345476 6266471 +linux_tuned 1 10 0x1d00 135 56.2 58.6 80.4 177.7 216.1 317.7 199945.7 200000 20 2618.1 4217102 518753688 12107171 363548152 312729930839 478151926627 478156442510 782489787 2635227 371048 125615 0 514636 7602256 +linux_tuned 2 10 0x1d00 135 56.6 59.2 81.2 180.7 219.9 327.6 199813.4 200000 20 2633.96 4213117 518336496 12097489 363252624 312738672627 481602555263 481602507551 790914015 2651985 385754 127257 0 496422 7605518 +linux_tuned 3 10 0x1d00 135 56.5 59.0 79.9 174.4 207.6 306.9 200010.2 200000 20 2638.62 4219063 518998617 12111851 363691014 313286286482 482137819146 482137773514 798113681 2636697 382853 127031 0 497377 7615169 +linux_tuned 1 10 0x1d00 75 56.6 59.2 82.1 184.7 225.5 350.4 200046.0 200000 20 2640.92 4220085 519084713 12123227 364060410 315423615733 486404743990 486433815737 823695682 2564394 378533 126224 0 498769 7622367 +linux_tuned 2 10 0x1d00 75 56.6 59.2 82.5 187.9 231.2 347.5 199847.3 200000 20 2643.59 4219160 518764389 12114813 363818082 314610567870 485989028977 486001243472 823049454 2522594 371247 125307 0 500056 7612711 +linux_tuned 3 10 0x1d00 75 56.6 59.2 81.7 181.4 220.9 327.2 200107.6 200000 20 2644.57 4222036 519295039 12120372 363954762 315632933336 487924261086 487933322333 832242557 2629158 381266 126201 0 485702 7629762 +linux_tuned 1 20 0x1d00 135 56.5 59.3 82.1 178.3 214.3 309.6 200116.2 200000 20 2639.96 4221079 519224254 12112830 363704694 312667652100 481584872895 481584688646 794999849 2745814 379428 111699 0 403034 7144008 +linux_tuned 2 20 0x1d00 135 56.9 59.9 82.6 186.5 236.2 411.0 199840.5 200000 20 2636.37 4228676 519424081 12125540 364174296 312098729101 480328736749 480328869456 798030533 2603623 376256 116170 0 424792 7105565 +linux_tuned 3 20 0x1d00 135 57.2 60.4 83.7 184.2 225.0 333.3 199957.3 200000 20 2636.13 4219503 518944755 12108499 363590556 312632488124 481030296807 481030305100 802211533 2678519 366132 112894 0 417933 7121948 +linux_tuned 1 30 0x1d00 135 57.3 60.7 89.0 197.1 242.8 379.7 199916.1 200000 20 2638.44 4224722 519265334 12115389 363829926 313284986241 484473075369 484473061299 809628531 2691053 368855 96027 0 362937 6225224 +linux_tuned 2 30 0x1d00 135 57.1 60.4 86.7 183.7 220.3 316.9 199984.8 200000 20 2637.41 4223555 519275975 12112107 363707688 313699652741 484487866126 484501821092 815553801 2753872 374335 96754 0 350837 6253679 +linux_tuned 3 30 0x1d00 135 58.5 62.2 89.4 188.7 226.1 325.8 199961.4 200000 20 2640.13 4222036 519071382 12107414 363556908 313692381336 485389511372 485389351128 817613445 2781764 384601 95470 0 338617 6268044 linux_default 0 1 0xffff 135 60.2 64.8 99.9 199.8 271.5 364.6 49912.7 50000 20 1620.13 1539222 161562603 3002681 90104370 85547178665 143687698792 219798347614 197123953 618435 313588 171966 0 804529 2467381 linux_default 1 1 0xffff 135 58.4 62.0 89.5 175.1 244.6 350.3 50095.3 50000 20 1629.64 1548256 162385629 3013473 90427500 86424118301 145627279172 218598782769 199931426 675354 393308 181501 0 808854 2479062 linux_default 2 1 0xffff 135 58.8 62.2 90.0 173.7 244.4 358.6 50030.1 50000 20 1627.05 1545196 162118493 3009563 90310206 85966672250 145744008034 220581194001 200179357 645391 326498 169861 0 782081 2473297 diff --git a/nbs/Reinforcement Learning - Multi-armed Bandits.ipynb b/nbs/Reinforcement Learning - Multi-armed Bandits.ipynb index 0008742..83926a8 100644 --- a/nbs/Reinforcement Learning - Multi-armed Bandits.ipynb +++ b/nbs/Reinforcement Learning - Multi-armed Bandits.ipynb @@ -2223,7 +2223,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.8.3" } }, "nbformat": 4,