The code provided is an experiment to compare the speed of matrix multiplication on different platforms:
Setting up the Data:
Two random tensors, x and y, of sizes (1, 6400) and (6400, 5000) respectively, are created using PyTorch.
GPU Computation:
The code checks if CUDA (used for NVIDIA GPUs) is available with torch.cuda.is_available().
An assertion ensures that the current device is 'cuda', which means that the GPU is being used.
The tensors, x and y, are transferred to the GPU with .to(device).
The %timeit command measures the time it takes to do matrix multiplication of x and y on the GPU using the @ operator.
CPU Computation (with PyTorch):
The tensors are transferred back to the CPU.
The %timeit command measures the time taken for matrix multiplication on the CPU.
CPU Computation (with NumPy):
Two random arrays, x and y, of the same sizes as before, are created using NumPy.
The %timeit command then measures the time taken to multiply these arrays using NumPy's matmul function.
In essence, this code is demonstrating the speed difference between performing matrix multiplications on a GPU versus a CPU, and also between PyTorch and NumPy on a CPU.

Tasks: Deep Learning Fundamentals, Tensors

Task Categories: Deep Learning Fundamentals

Published: 10/11/23