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
……→ 库目录,添加以下目录:
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
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#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;
}
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输出结果:
以上就是本文的全部内容,希望对大家的学习有所帮助,也希望大家多多支持服务器之家。
原文链接:https://www.cnblogs.com/wayne793377164/p/8185404.html