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转载自https://blog.csdn.net/krian_a/article/details/116031532 由于CUDA自11.6版本不会将Samples集成在toolkit安装包中，原文中介绍的方法需要做一些小的改动，本文针对的是CUDA11.6之后版本的环境配置 CUDA11.6之前的版本需要按照原文中的步骤实现环境配置
Windows10+VisualStudio2022+CUDA11.7环境配置
一、调整配置管理器平台类型二、配置生成属性三、配置基本库目录四、配置CUDA静态链接库路径五、选用CUDA静态链接库六、 配置源码文件风格七、测试程序代码运行结果
一、调整配置管理器平台类型
右键项目→ 属性→ 平台“x64”
 
二、配置生成属性
右键项目 → 生成依赖项→ 生成自定义→ 勾选“CUDA XXX”  
三、配置基本库目录
注意：后续步骤中出现的目录地址需取决于你当前的CUDA版本及安装路径
右键项目→属性→ 配置属性→ VC++目录→ 包含目录，添加以下目录： C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\include ……→ 库目录，添加以下目录： C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\lib\x64   
 
四、配置CUDA静态链接库路径
右键项目→ 属性→ 配置属性→ 链接器→ 常规→ 附加库目录，添加以下目录：
\$(CUDA_PATH_V11_7)\lib$(Platform) （查看环境变量改变CUDA_PATH_V11_7，v11_7指的安装的cuda版本）
 
五、选用CUDA静态链接库
右键项目→ 属性→ 配置属性→ 链接器→ 输入→ 附加依赖项，添加以下库：
cublas.lib cuda.lib cudadevrt.lib cudart.lib cudart_static.lib cufft.lib cufftw.lib curand.lib cusolver.lib cusparse.lib nppc.lib nppial.lib nppicc.lib nppidei.lib nppif.lib nppig.lib nppim.lib nppist.lib nppisu.lib nppitc.lib npps.lib nvblas.lib nvml.lib nvrtc.lib OpenCL.lib
 
六、 配置源码文件风格
右键源文件→ 添加→ 新建项→ 选择 “CUDA C/C++ File” 右键 “xxx.cu" 源文件→ 属性→ 配置属性→ 常规→ 项类型→ 设置为“CUDA C/C++”    
七、测试程序
代码
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include
cudaError_t addWithCuda(int *c, const int *a, const int *b, unsigned int size);
__global__ void addKernel(int *c, const int *a, const int *b)
{
int i = threadIdx.x;
c[i] = a[i] + b[i];
}
int main()
{
const int arraySize = 5;
const int a[arraySize] = { 1, 2, 3, 4, 5 };
const int b[arraySize] = { 10, 20, 30, 40, 50 };
int c[arraySize] = { 0 };
// Add vectors in parallel.
cudaError_t cudaStatus = addWithCuda(c, a, b, arraySize);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "addWithCuda failed!");
return 1;
}
printf("{1,2,3,4,5} + {10,20,30,40,50} = {%d,%d,%d,%d,%d}\n",
c[0], c[1], c[2], c[3], c[4]);
// cudaDeviceReset must be called before exiting in order for profiling and
// tracing tools such as Nsight and Visual Profiler to show complete traces.
cudaStatus = cudaDeviceReset();
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaDeviceReset failed!");
return 1;
}
return 0;
}
// Helper function for using CUDA to add vectors in parallel.
cudaError_t addWithCuda(int *c, const int *a, const int *b, unsigned int size)
{
int *dev_a = 0;
int *dev_b = 0;
int *dev_c = 0;
cudaError_t cudaStatus;
// Choose which GPU to run on, change this on a multi-GPU system.
cudaStatus = cudaSetDevice(0);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaSetDevice failed! Do you have a CUDA-capable GPU installed?");
goto Error;
}
// Allocate GPU buffers for three vectors (two input, one output) .
cudaStatus = cudaMalloc((void**)&dev_c, size * sizeof(int));
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMalloc failed!");
goto Error;
}
cudaStatus = cudaMalloc((void**)&dev_a, size * sizeof(int));
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMalloc failed!");
goto Error;
}
cudaStatus = cudaMalloc((void**)&dev_b, size * sizeof(int));
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMalloc failed!");
goto Error;
}
// Copy input vectors from host memory to GPU buffers.
cudaStatus = cudaMemcpy(dev_a, a, size * sizeof(int), cudaMemcpyHostToDevice);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
cudaStatus = cudaMemcpy(dev_b, b, size * sizeof(int), cudaMemcpyHostToDevice);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
// Launch a kernel on the GPU with one thread for each element.
addKernel(dev_c, dev_a, dev_b);
// Check for any errors launching the kernel
cudaStatus = cudaGetLastError();
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "addKernel launch failed: %s\n", cudaGetErrorString(cudaStatus));
goto Error;
}
// cudaDeviceSynchronize waits for the kernel to finish, and returns
// any errors encountered during the launch.
cudaStatus = cudaDeviceSynchronize();
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaDeviceSynchronize returned error code %d after launching addKernel!\n", cudaStatus);
goto Error;
}
// Copy output vector from GPU buffer to host memory.
cudaStatus = cudaMemcpy(c, dev_c, size * sizeof(int), cudaMemcpyDeviceToHost);
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaMemcpy failed!");
goto Error;
}
Error:
cudaFree(dev_c);
cudaFree(dev_a);
cudaFree(dev_b);
return cudaStatus;
}
运行结果
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