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Getting Started with PyGPUkit

PyGPUkit is a lightweight GPU runtime for Python that provides NumPy-like array operations with CUDA acceleration.

Installation

pip install pygpukit

Requirements

  • Python 3.10+
  • NVIDIA GPU with drivers installed (RTX 30XX or newer)
  • Optional: CUDA Toolkit (for JIT compilation of custom kernels)

Supported GPUs

GPU Series Support
RTX 40XX (Ada) Full
RTX 30XX (Ampere) Full
A100, H100 Full
RTX 20XX, GTX 10XX Not supported (SM < 80)

Quick Start

Basic Array Operations

import pygpukit as gpk
import numpy as np

# Create GPU arrays
a = gpk.zeros((1024, 1024), dtype="float32")
b = gpk.ones((1024, 1024), dtype="float32")

# Element-wise operations
c = gpk.add(a, b)
d = gpk.mul(a, b)

# Or use operator overloads
c = a + b
d = a * b
e = a - b
f = a / b

# Matrix multiplication
g = a @ b  # or gpk.matmul(a, b)

# Transfer to/from NumPy
np_array = c.to_numpy()
gpu_array = gpk.from_numpy(np_array)

Data Types

import pygpukit as gpk

# Available data types
gpk.float32   # 32-bit float (default)
gpk.float64   # 64-bit float
gpk.float16   # 16-bit float (half precision)
gpk.bfloat16  # Brain float16
gpk.int32     # 32-bit integer
gpk.int64     # 64-bit integer

# Create arrays with specific dtype
a = gpk.zeros((1024, 1024), dtype=gpk.float16)
b = gpk.ones((1024, 1024), dtype=gpk.bfloat16)

# Convert between types
c = a.astype(gpk.float32)

Math Operations

import pygpukit as gpk
import numpy as np

a = gpk.from_numpy(np.random.randn(1024, 1024).astype(np.float32))

# Unary operations
b = gpk.exp(a)
c = gpk.log(a)

# Activation functions
d = gpk.relu(a)
e = gpk.gelu(a)

# Reductions
total = gpk.sum(a)      # Sum of all elements
avg = gpk.mean(a)       # Mean of all elements
maximum = gpk.max(a)    # Maximum element

Matrix Operations

import pygpukit as gpk
import numpy as np

# Matrix multiplication (uses CUTLASS TensorCore)
a = gpk.from_numpy(np.random.randn(4096, 4096).astype(np.float32))
b = gpk.from_numpy(np.random.randn(4096, 4096).astype(np.float32))
c = a @ b  # ~30 TFLOPS on RTX 3090 Ti

# Transpose
d = gpk.transpose(a)

Neural Network Operations

import pygpukit as gpk
import numpy as np

# LayerNorm
batch, features = 32, 768
x = gpk.from_numpy(np.random.randn(batch, features).astype(np.float32))
gamma = gpk.ones(features)
beta = gpk.zeros(features)
normalized = gpk.layernorm(x, gamma, beta, eps=1e-5)

# Fused Linear + Bias + GELU (uses CUTLASS epilogue fusion)
input = gpk.from_numpy(np.random.randn(512, 768).astype(np.float32))
weight = gpk.from_numpy(np.random.randn(3072, 768).astype(np.float32))
bias = gpk.from_numpy(np.random.randn(3072).astype(np.float32))
output = gpk.linear_bias_gelu(input, weight, bias)

Checking GPU Status

import pygpukit as gpk

# Check if CUDA is available
print(f"CUDA available: {gpk.is_cuda_available()}")

# Check if JIT compilation is available
print(f"NVRTC available: {gpk.is_nvrtc_available()}")

# Get device info
info = gpk.get_device_info()
print(f"Device: {info.name}")
print(f"Compute capability: {info.compute_capability}")
print(f"Total memory: {info.total_memory / 1e9:.1f} GB")

Next Steps