CUDA Support

PyTorch provides comprehensive CUDA support for GPU-accelerated tensor operations and neural network training. The CUDA API allows you to manage GPU devices, streams for asynchronous execution, and memory efficiently.

When to use CUDA APIs:

• When you need explicit control over which GPU device to use
• When implementing custom CUDA kernels or operations
• When optimizing performance with asynchronous stream execution
• When managing multi-GPU workloads

Basic usage:

#include <torch/torch.h>
#include <c10/cuda/CUDAGuard.h>

// Check if CUDA is available
if (torch::cuda::is_available()) {
    // Create tensor on GPU
    auto tensor = torch::randn({2, 3}, torch::device(torch::kCUDA));

    // Switch to a specific GPU
    c10::cuda::CUDAGuard guard(0);  // Use GPU 0

    // Get the current CUDA stream
    auto stream = c10::cuda::getCurrentCUDAStream();

    // Move model to GPU
    model->to(torch::kCUDA);
}

Header Files

• c10/cuda/CUDAStream.h - CUDA stream management
• c10/cuda/CUDAGuard.h - CUDA device guards
• ATen/cuda/CUDAContext.h - CUDA context management
• ATen/cudnn/Descriptors.h - cuDNN tensor descriptors

CUDA Categories

:maxdepth: 1

streams
guards
utilities