cuda

CUDA

Overview

CUDA is NVIDIA's parallel computing platform and programming model for GPU-accelerated applications. It provides direct access to the GPU's virtual instruction set and parallel compute elements for executing kernels in C, C++, and Fortran.

cuda-samples version: v13.1 (CUDA Toolkit 13.1) CUDALibrarySamples: main (Feb 2025) Language: C/C++ (.cu files) Licenses: BSD-3-Clause (cuda-samples), Apache-2.0 (CUDALibrarySamples)

Quick Start

// Minimal kernel + launch
__global__ void addVectors(float *a, float *b, float *c, int n) {
    int i = blockIdx.x * blockDim.x + threadIdx.x;
    if (i < n) c[i] = a[i] + b[i];
}

int main() {
    int n = 1 << 20;
    float *d_a, *d_b, *d_c;
    cudaMalloc(&d_a, n * sizeof(float));
    cudaMalloc(&d_b, n * sizeof(float));
    cudaMalloc(&d_c, n * sizeof(float));

    int threads = 256;
    int blocks = (n + threads - 1) / threads;
    addVectors<<<blocks, threads>>>(d_a, d_b, d_c, n);
    cudaDeviceSynchronize();

    cudaFree(d_a); cudaFree(d_b); cudaFree(d_c);
    return 0;
}

Core Concepts

• Kernel: __global__ function executed on GPU by many parallel threads
• Grid/Block/Thread: Launch hierarchy — <<<gridDim, blockDim>>> configures parallelism
• Device memory: Must be explicitly allocated with cudaMalloc and freed with cudaFree
• Streams: Async execution queues; default stream is synchronous with host
• Unified Memory (cudaMallocManaged): Automatically migrates data between CPU and GPU

API Reference

Domain	File	Description
CUDA Runtime	api-runtime.md	Device mgmt, memory, streams, events, kernel launch
cuBLAS	api-cublas.md	Dense linear algebra: GEMM, GEMV, TRSM, batched ops
cuFFT	api-cufft.md	1D/2D/3D FFT and batched transforms
cuSPARSE	api-cusparse.md	Sparse matrix ops: SpMM, SpMV, format conversions
cuRAND	api-curand.md	Random number generation on GPU
cuSolver	api-cusolver.md	Dense/sparse solvers: QR, LU, eigenvalue, SVD
Thrust	api-thrust.md	STL-like GPU algorithms: sort, reduce, transform, scan
Cooperative Groups	api-cooperative-groups.md	Flexible thread synchronization beyond blocks
Workflows	workflows.md	Complete working examples

Common Workflows

See references/workflows.md for complete examples.

Quick reference:

• Matrix multiply: see workflows.md — cuBLAS GEMM section
• FFT: see workflows.md — cuFFT 1D section
• Custom kernel: see workflows.md — Custom CUDA Kernel section
• Unified memory: see workflows.md — Unified Memory section
• Error-check macros: see workflows.md — Error-Checked CUDA Boilerplate

Key Considerations

• Error checking: Always check return codes; use CUDA_CHECK(err) macro pattern
• Synchronization: cudaDeviceSynchronize() or stream synchronize before reading results on host
• Memory alignment: 128-byte alignment for coalesced global memory access
• Occupancy: Use cudaOccupancyMaxPotentialBlockSize to tune block dimensions
• Tensor Cores: Available on Volta+ (sm_70+); cuBLAS uses them automatically for GEMM with correct types
• Column-major: cuBLAS and cuSolver use Fortran (column-major) layout — transpose row-major C arrays or swap dimensions
• cuFFT normalization: cuFFT does NOT normalize inverse transforms; divide by N manually
• Streams: Always use cudaStreamNonBlocking when creating non-default streams to avoid implicit synchronization with the null stream