Win10+VS2017新CUDA项目配置教程

2020-01-26 13:38:51

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本文记录了CUDA项目配置教程，具有一定的参考价值，感兴趣的小伙伴们可以参考一下

一、新建项目

打开VS2017→ 新建项目→Win32控制台应用程序 → “空项目”打钩

二、调整配置管理器平台类型

右键项目→ 属性→ 配置管理器→ 全改为“x64”

三、配置生成属性

右键项目 → 生成依赖项→ 生成自定义→ 勾选“CUDA 9.0XXX”

四、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

C:/ProgramData/NVIDIA Corporation/CUDA Samples/v9.0/common/inc

C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v9.0/lib/x64

C:/ProgramData/NVIDIA Corporation/CUDA Samples/v9.0/common/lib/x64

五、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：

$(CUDA_PATH_V9_0)/lib/$(Platform)

六、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;cublas_device.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;nppicom.lib;nppidei.lib;nppif.lib;nppig.lib;nppim.lib;nppist.lib;nppisu.lib;nppitc.lib;npps.lib;nvblas.lib;nvcuvid.lib;nvgraph.lib;nvml.lib;nvrtc.lib;OpenCL.lib;

以上为 “第三步” 中添加的库目录 “C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v9.0/lib/x64” 中的库！

注意：

kernel32.lib;user32.lib;gdi32.lib;winspool.lib;comdlg32.lib;advapi32.lib;shell32.lib;ole32.lib;oleaut32.lib;uuid.lib;odbc32.lib;odbccp32.lib;%(AdditionalDependencies)
这些库为原有！

七、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++”

八、测试程序

#include "cuda_runtime.h"#include "device_launch_parameters.h"#include <stdio.h>int main() {  int deviceCount;  cudaGetDeviceCount(&deviceCount);  int dev;  for (dev = 0; dev < deviceCount; dev++)  {    int driver_version(0), runtime_version(0);    cudaDeviceProp deviceProp;    cudaGetDeviceProperties(&deviceProp, dev);    if (dev == 0)      if (deviceProp.minor = 9999 && deviceProp.major == 9999)        printf("/n");    printf("/nDevice%d:/"%s/"/n", dev, deviceProp.name);    cudaDriverGetVersion(&driver_version);    printf("CUDA驱动版本:                  %d.%d/n", driver_version / 1000, (driver_version % 1000) / 10);    cudaRuntimeGetVersion(&runtime_version);    printf("CUDA运行时版本:                 %d.%d/n", runtime_version / 1000, (runtime_version % 1000) / 10);    printf("设备计算能力:                  %d.%d/n", deviceProp.major, deviceProp.minor);    printf("Total amount of Global Memory:         %u bytes/n", deviceProp.totalGlobalMem);    printf("Number of SMs:                 %d/n", deviceProp.multiProcessorCount);    printf("Total amount of Constant Memory:        %u bytes/n", deviceProp.totalConstMem);    printf("Total amount of Shared Memory per block:    %u bytes/n", deviceProp.sharedMemPerBlock);    printf("Total number of registers available per block: %d/n", deviceProp.regsPerBlock);    printf("Warp size:                   %d/n", deviceProp.warpSize);    printf("Maximum number of threads per SM:        %d/n", deviceProp.maxThreadsPerMultiProcessor);    printf("Maximum number of threads per block:      %d/n", deviceProp.maxThreadsPerBlock);    printf("Maximum size of each dimension of a block:   %d x %d x %d/n", deviceProp.maxThreadsDim[0],      deviceProp.maxThreadsDim[1],      deviceProp.maxThreadsDim[2]);    printf("Maximum size of each dimension of a grid:    %d x %d x %d/n", deviceProp.maxGridSize[0], deviceProp.maxGridSize[1], deviceProp.maxGridSize[2]);    printf("Maximum memory pitch:              %u bytes/n", deviceProp.memPitch);    printf("Texture alignmemt:               %u bytes/n", deviceProp.texturePitchAlignment);    printf("Clock rate:                   %.2f GHz/n", deviceProp.clockRate * 1e-6f);    printf("Memory Clock rate:               %.0f MHz/n", deviceProp.memoryClockRate * 1e-3f);    printf("Memory Bus Width:                %d-bit/n", deviceProp.memoryBusWidth);  }  return 0;}

输出结果：