cuda-c-api

CUDA C memory qualifiers like __shared__ and __constant__ define variable storage in GPU memory hierarchy. This reference details their function signatures and memory management calls to implement efficient device-side kernel operations.

Kernel launch syntax in CUDA C executes __global__ functions on the GPU by specifying grid and block dimensions. This guide covers the launch configuration parameters needed to dispatch kernels and manage thread synchronization.

Managing device memory and atomic operations when porting CPU computations to GPU requires specific CUDA C API calls. This reference provides the function signatures for memory allocation, thread synchronization, and atomic operations.

Yes, this CUDA C API reference covers warp operations and PyTorch integration. It consolidates function modifiers, math utilities, and framework integration details for developers optimizing CUDA-based applications across hardware architectures.

In CUDA C, __global__, __device__, and __host__ function modifiers define execution scope. This reference explains how these modifiers dictate whether functions run on the CPU, GPU, or can be called from the host.

Yes, you can use this reference to implement extern __shared__ memory for dynamic shared memory allocation. It documents the necessary CUDA C API syntax and memory management techniques for optimizing kernel performance.