Win10+VS2017新CUDA項目配置教程

2020-05-23 13:26:18

字體：大中小

來源：轉載

供稿：網友

本文記錄了CUDA項目配置教程，具有一定的參考價值，感興趣的小伙伴們可以參考一下

一、新建項目

打開VS2017→ 新建項目→Win32控制臺應用程序 → “空項目”打鉤

二、調整配置管理器平臺類型

右鍵項目→ 屬性→ 配置管理器→ 全改為“x64”

Win10,VS2017,CUDA

三、配置生成屬性

右鍵項目 → 生成依賴項→ 生成自定義→ 勾選“CUDA 9.0XXX”

Win10,VS2017,CUDA

四、配置基本庫目錄

注意：后續步驟中出現的目錄地址需取決于你當前的CUDA版本及安裝路徑

右鍵項目→屬性→ 配置屬性→ VC++目錄→ 包含目錄，添加以下目錄：

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

Win10,VS2017,CUDA

五、配置CUDA靜態鏈接庫路徑

右鍵項目→ 屬性→ 配置屬性→ 鏈接器→ 常規→ 附加庫目錄，添加以下目錄：

$(CUDA_PATH_V9_0)/lib/$(Platform)

Win10,VS2017,CUDA

六、選用CUDA靜態鏈接庫

右鍵項目→ 屬性→ 配置屬性→ 鏈接器→ 輸入→ 附加依賴項，添加以下庫：

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;

以上為 “第三步” 中添加的庫目錄 “C:/Program Files/NVIDIA GPU Computing Toolkit/CUDA/v9.0/lib/x64” 中的庫！

Win10,VS2017,CUDA

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)
這些庫為原有！

七、配置源碼文件風格

右鍵源文件→ 添加→ 新建項→ 選擇 “CUDA C/C++ File”

右鍵 “xxx.cu" 源文件→ 屬性→ 配置屬性→ 常規→ 項類型→ 設置為“CUDA C/C++”

Win10,VS2017,CUDA

八、測試程序

#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驅動版本:                  %d.%d/n", driver_version / 1000, (driver_version % 1000) / 10);    cudaRuntimeGetVersion(&runtime_version);    printf("CUDA運行時版本:                 %d.%d/n", runtime_version / 1000, (runtime_version % 1000) / 10);    printf("設備計算能力:                  %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;}

輸出結果：

Win10,VS2017,CUDA