Tensor Addition with PyTorch and CUDA

Code Example with PyTorch and CUDA

import torch

# Check if CUDA is available and set the device
if torch.cuda.is_available():
    device = torch.device("cuda")
    print(f"Using CUDA device: {torch.cuda.get_device_name(0)}")
else:
    device = torch.device("cpu")
    print("CUDA not available, using CPU")

# Define two tensors of the same shape
tensor_a = torch.tensor([[1, 2, 3], [4, 5, 6]], dtype=torch.float32).to(device)
tensor_b = torch.tensor([[7, 8, 9], [10, 11, 12]], dtype=torch.float32).to(device)

# Perform tensor addition
tensor_sum = torch.add(tensor_a, tensor_b)
# Alternatively, you can use the '+' operator:
# tensor_sum = tensor_a + tensor_b

# Print the result
print("Tensor A:\n", tensor_a)
print("Tensor B:\n", tensor_b)
print("Sum of Tensor A and Tensor B:\n", tensor_sum.cpu().numpy())
            

Code Explanation:

• import torch: Imports the PyTorch library.
• if torch.cuda.is_available(): ... else: ...: Checks for CUDA availability and sets the device accordingly.
• tensor_a = torch.tensor(...) and tensor_b = torch.tensor(...): Create two tensors and move them to the specified device.
• tensor_sum = torch.add(tensor_a, tensor_b): Performs element-wise addition.
• The print() statements display the original tensors and the result.

How CUDA Accelerates Tensor Addition

When these tensors are on the GPU, CUDA enables parallel computation of the addition operation. Each element-wise addition can be performed by a different thread running on the GPU’s numerous cores. This massive parallelism allows for significantly faster computation, especially for very large tensors, compared to performing the additions sequentially on a CPU.