Cuda Toolkit [upd] Site

int main() int n = 1000000; size_t bytes = n * sizeof(float);

std::cout << (correct ? "SUCCESS" : "FAILURE") << std::endl; cuda toolkit

return 0; # Compile nvcc -o vector_add vector_add.cu Run ./vector_add Makefile for larger projects CUDA_PATH ?= /usr/local/cuda NVCC = $(CUDA_PATH)/bin/nvcc NVCC_FLAGS = -arch=sm_75 -O3 -std=c++17 TARGET = vector_add SOURCES = vector_add.cu int main() int n = 1000000; size_t bytes

// Allocate host memory float *h_a = new float[n]; float *h_b = new float[n]; float *h_c = new float[n]; int main() int n = 1000000

// Allocate device memory float *d_a, *d_b, *d_c; cudaMalloc(&d_a, bytes); cudaMalloc(&d_b, bytes); cudaMalloc(&d_c, bytes);

// Launch kernel int threadsPerBlock = 256; int blocksPerGrid = (n + threadsPerBlock - 1) / threadsPerBlock; vectorAdd<<<blocksPerGrid, threadsPerBlock>>>(d_a, d_b, d_c, n);

.PHONY: all run clean | Operation | Function | |-----------|----------| | Allocate GPU memory | cudaMalloc(&ptr, size) | | Free GPU memory | cudaFree(ptr) | | Copy to GPU | cudaMemcpy(dst, src, size, cudaMemcpyHostToDevice) | | Copy to CPU | cudaMemcpy(dst, src, size, cudaMemcpyDeviceToHost) | | Get GPU count | cudaGetDeviceCount(&count) | | Set active GPU | cudaSetDevice(device_id) | | Synchronize | cudaDeviceSynchronize() | | Error checking | cudaGetLastError() | Installation Check # Check CUDA version nvcc --version Check GPU driver & CUDA capability nvidia-smi Check available GPUs nvidia-smi -L This gives you a working starting point. Need a specific CUDA library example (cuBLAS for matrix multiplication, cuFFT for FFTs, or multi-GPU programming)?