diff --git a/021_array_sum.py b/021_array_sum.py
index e9b35e1..c70f7b0 100644
--- a/021_array_sum.py
+++ b/021_array_sum.py
@@ -36,10 +36,10 @@
 cl_arrays = [cl_a, cl_b, cl_c]
 
 for x in range(3):
-    cl.enqueue_copy(queue, cl_arrays[x], np_arrays[x])
+    cl.enqueue_copy(queue, np_arrays[x], cl_arrays[x])
 queue.finish()
 # Copy the data for array c back to the host
 
 for x in np_arrays:
 	print(x)
-# Print all three host arrays, to show sum() worked
\ No newline at end of file
+# Print all three host arrays, to show sum() worked
diff --git a/030_timing.py b/030_timing.py
index 19c361d..f628854 100644
--- a/030_timing.py
+++ b/030_timing.py
@@ -39,10 +39,10 @@ def gpu_array_sum(a, b):
     elapsed = 1e-9*(event.profile.end - event.profile.start)  # Calculate the time it took to execute the kernel
     print("GPU Kernel Time: {0} s".format(elapsed))  # Print the time it took to execute the kernel
     c_gpu = np.empty_like(a)  # Create an empty array the same size as array a
-    cl.enqueue_read_buffer(queue, c_buffer, c_gpu).wait()  # Read back the data from GPU memory into array c_gpu
+    cl.enqueue_copy(queue, c_buffer, c_gpu).wait()  # Read back the data from GPU memory into array c_gpu
     gpu_end_time = time()  # Get the GPU end time
     print("GPU Time: {0} s".format(gpu_end_time - gpu_start_time))  # Print the time the GPU program took, including both memory copies
     return c_gpu  # Return the sum of the two arrays
 
 cpu_array_sum(a, b)  # Call the function that sums two arrays on the CPU
-gpu_array_sum(a, b)  # Call the function that sums two arrays on the GPU
\ No newline at end of file
+gpu_array_sum(a, b)  # Call the function that sums two arrays on the GPU
diff --git a/040_elementwise.py b/040_elementwise.py
index 358051c..5f6124d 100644
--- a/040_elementwise.py
+++ b/040_elementwise.py
@@ -7,14 +7,14 @@
 context = cl.create_some_context()  # Initialize the Context
 queue = cl.CommandQueue(context)  # Instantiate a Queue
 
-a = cl_array.to_device(queue, numpy.random.randn(10).astype(numpy.float32))  # Create a random pyopencl array
-b = cl_array.to_device(queue, numpy.random.randn(10).astype(numpy.float32))  # Create a random pyopencl array
+a = cl_array.to_device(queue, numpy.random.randn(50000).astype(numpy.float32))  # Create a random pyopencl array
+b = cl_array.to_device(queue, numpy.random.randn(50000).astype(numpy.float32))  # Create a random pyopencl array
 c = cl_array.empty_like(a)  # Create an empty pyopencl destination array
 
 sum = cl.elementwise.ElementwiseKernel(context, "float *a, float *b, float *c", "c[i] = a[i] + b[i]", "sum")
 # Create an elementwise kernel object
 #  - Arguments: a string formatted as a C argument list
-#  - Operation: a snippet of C that carries out the desired map operatino
+#  - Operation: a snippet of C that carries out the desired map operation
 #  - Name: the fuction name as which the kernel is compiled
 
 sum(a, b, c)  # Call the elementwise kernel
@@ -22,4 +22,4 @@
 print("a: {}".format(a))
 print("b: {}".format(b))
 print("c: {}".format(c))
-# Print all three arrays, to show sum() worked
\ No newline at end of file
+# Print all three arrays, to show sum() worked
diff --git a/README.md b/README.md
index 5345fb7..6c985e8 100644
--- a/README.md
+++ b/README.md
@@ -11,3 +11,5 @@ PyOpenCL is a tool that is worth learning.  Python allows exceptional clarity-of
 - 010 Introspection - Find out about your computer's OpenCL situation
 - 020 Array Sum - Use OpenCL To Add Two Large Random Arrays - Hiding Details
 - 021 Array Sum - Use OpenCL To Add Two Large Random Arrays - Showing Details
+- 030 Timing - Compare performance of a loop in pure Python versus OpenCL
+- 040 Elementwise - Use PyOpenCL arrays and elementwise to add two large random arrays