windows10 vs2013控制台工程中添加并编译cuda8.0文件操作步骤

一般有两种方法可以在vs2013上添加运行cuda8.0程序：

一、直接新建一个基于CUDA8.0的项目：如下图所示，

点击确定后即可生成test_cuda项目；默认会自动生成一个kernel.cu文件；默认已经配置好Debug/Release, Win32/x64环境，直接编译运行，结果如下图所示：函数执行的是两个数组的加操作。移除kernel.cu文件，加入自己需要的cuda文件即可进行实际操作了，非常方便。

二、实际情况下，多是在已有的项目中添加一些cuda文件，用于加速，下面说下具体的操作步骤：

1、新建一个CUDA_Test x64控制台空工程；

2、新建CUDA_Test.cpp文件；

3、选中CUDA_Test项目，右键单击-->生成依赖项-->生成自定义，勾选CUDA8.0，点击确定，如下图所示：

4、完成第3步后，再次打开工程的属性配置，会多出两项，CUDA C/C++和CUDA Linker，如下图所示：

5、新建或添加几个已有的文件，包括common.hpp、simple.hpp、simple.cpp、simple.cu，各个文件内容如下：

common.hpp:

#ifndef FBC_CUDA_TEST_COMMON_HPP_#define FBC_CUDA_TEST_COMMON_HPP_#define PRINT_ERROR_INFO(info) { \fprintf(stderr, "Error: %s, file: %s, func: %s, line: %d\n", #info, __FILE__, __FUNCTION__, __LINE__); \return -1; }#endif // FBC_CUDA_TEST_COMMON_HPP_

simple.hpp:

#ifndef FBC_CUDA_TEST_SIMPLE_HPP_#define FBC_CUDA_TEST_SIMPLE_HPP_// reference: C:\ProgramData\NVIDIA Corporation\CUDA Samples\v8.0\0_Simpleint test_vectorAdd();int vectorAdd_cpu(const float *A, const float *B, float *C, int numElements);int vectorAdd_gpu(const float *A, const float *B, float *C, int numElements);#endif // FBC_CUDA_TEST_SIMPLE_HPP_

simple.cpp:

#include "simple.hpp"#include <stdlib.h>#include <iostream>#include "common.hpp"// =========================== vector add =============================int test_vectorAdd(){// Vector addition: C = A + B, implements element by element vector additionconst int numElements{ 50000 };float* A = new float[numElements];float* B = new float[numElements];float* C1 = new float[numElements];float* C2 = new float[numElements];// Initialize vectorfor (int i = 0; i < numElements; ++i) {A[i] = rand() / (float)RAND_MAX;B[i] = rand() / (float)RAND_MAX;}int ret = vectorAdd_cpu(A, B, C1, numElements);if (ret != 0) PRINT_ERROR_INFO(vectorAdd_cpu);ret = vectorAdd_gpu(A, B, C2, numElements);if (ret != 0) PRINT_ERROR_INFO(vectorAdd_gpu);for (int i = 0; i < numElements; ++i) {if (fabs(C1[i] - C2[i]) > 1e-5) {fprintf(stderr, "Result verification failed at element %d!\n", i);return -1;}}delete[] A;delete[] B;delete[] C1;delete[] C2;return 0;}int vectorAdd_cpu(const float *A, const float *B, float *C, int numElements){for (int i = 0; i < numElements; ++i) {C[i] = A[i] + B[i];}return 0;}

simple.cu:

#include "simple.hpp"#include <iostream>#include <cuda_runtime.h> // For the CUDA runtime routines (prefixed with "cuda_")#include <device_launch_parameters.h>// reference: C:\ProgramData\NVIDIA Corporation\CUDA Samples\v8.0\0_Simple// =========================== vector add =============================__global__ void vectorAdd(const float *A, const float *B, float *C, int numElements){int i = blockDim.x * blockIdx.x + threadIdx.x;if (i < numElements) {C[i] = A[i] + B[i];}}int vectorAdd_gpu(const float *A, const float *B, float *C, int numElements){// Error code to check return values for CUDA callscudaError_t err{ cudaSuccess };size_t length{ numElements * sizeof(float) };fprintf(stderr, "Length: %d\n", length);float* d_A{ nullptr };float* d_B{ nullptr };float* d_C{ nullptr };err = cudaMalloc(&d_A, length);if (err != cudaSuccess) {fprintf(stderr, "Failed to allocate device vector A (error code %s)!\n", cudaGetErrorString(err));return -1;}err = cudaMalloc(&d_B, length);if (err != cudaSuccess) {fprintf(stderr, "Failed to allocate device vector B (error code %s)!\n", cudaGetErrorString(err));return -1;}err = cudaMalloc(&d_C, length);if (err != cudaSuccess) {fprintf(stderr, "Failed to allocate device vector C (error code %s)!\n", cudaGetErrorString(err));return -1;}err = cudaMemcpy(d_A, A, length, cudaMemcpyHostToDevice);if (err != cudaSuccess) {fprintf(stderr, "Failed to copy vector A from host to device (error code %s)!\n", cudaGetErrorString(err));return -1;}err = cudaMemcpy(d_B, B, length, cudaMemcpyHostToDevice);if (err != cudaSuccess) {fprintf(stderr, "Failed to copy vector B from host to device (error code %s)!\n", cudaGetErrorString(err));return -1;}// Launch the Vector Add CUDA kernelint threadsPerBlock = 256;int blocksPerGrid = (numElements + threadsPerBlock - 1) / threadsPerBlock;fprintf(stderr, "CUDA kernel launch with %d blocks of %d threads\n", blocksPerGrid, threadsPerBlock);vectorAdd << <blocksPerGrid, threadsPerBlock >> >(d_A, d_B, d_C, numElements);err = cudaGetLastError();if (err != cudaSuccess) {fprintf(stderr, "Failed to launch vectorAdd kernel (error code %s)!\n", cudaGetErrorString(err));return -1;}// Copy the device result vector in device memory to the host result vector in host memory.err = cudaMemcpy(C, d_C, length, cudaMemcpyDeviceToHost);if (err != cudaSuccess) {fprintf(stderr, "Failed to copy vector C from device to host (error code %s)!\n", cudaGetErrorString(err));return -1;}err = cudaFree(d_A);if (err != cudaSuccess) {fprintf(stderr, "Failed to free device vector A (error code %s)!\n", cudaGetErrorString(err));return -1;}err = cudaFree(d_B);if (err != cudaSuccess) {fprintf(stderr, "Failed to free device vector B (error code %s)!\n", cudaGetErrorString(err));return -1;}err = cudaFree(d_C);if (err != cudaSuccess) {fprintf(stderr, "Failed to free device vector C (error code %s)!\n", cudaGetErrorString(err));return -1;}return err;}

CUDA_Test.cpp:

#include <iostream>#include "simple.hpp"int main(){int ret = test_vectorAdd();if (ret == 0) fprintf(stderr, "***** test success *****\n");else fprintf(stderr, "===== test fail =====\n");return 0;}

6、调整属性配置项：

(1)、CUDA C/C++-->Common中Target Machine Platform中默认是32-bit(--machine32)，因为是x64，所以将其调整为64-bit(--machine 64)；

(2)、添加附加库：链接器-->输入-->附加依赖项：cudart.lib；

(3)、消除nvcc warning: The 'compute_20', 'sm_20', and'sm_21' architectures are deprecated, and may be removed in a future release：CUDA C/C++-->Device: Code Generation:由compute_20,sm_20修改为compute_30,sm_30; compute_35,sm_35; compute_37,sm_37;compute_50,sm_50; compute_52,sm_52; compute_60,sm_60

以上code是参考NVIDIA Corporation\CUDA Samples\v8.0\0_Simple中vectorAdd例子进行的改写，输出结果如下：

GitHub：https://github.com/fengbingchun/CUDA_Test