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
matrix math
gpu vs cpu
torch vs numpy