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CUDA从入门到放弃(三):Hello World 之第一个CUDA程序


vs2015中打开新建一个CUDA程序

CUDA从入门到放弃(三):Hello World 之第一个CUDA程序_CUDA


拿个CUDA中的 hello world 示例程序过来跑一下吧!

示例来源于:​​CUDA Samples​​ 计算两个数组间的加法

#include "cuda_runtime.h"
#include "device_launch_parameters.h"

#include <stdio.h>

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;
}

getchar();

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<<<1, size>>>(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;
}

编译信息:

1>------ 已启动全部重新生成: 项目: TestCUDA, 配置: Release x64 ------
1>
1> E:\code\cppcode\TestCUDA\TestCUDA>"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin\nvcc.exe" -ccbin "C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin\x86_amd64" -x cu -I"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\include" -I"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\include" --keep-dir x64\Release -maxrregcount=0 --machine 64 --compile -DWIN32 -DWIN64 -DNDEBUG -D_CONSOLE -D_MBCS -Xcompiler "/EHsc /W0 /nologo /O2 /FS /Zi /MD " -o x64\Release\kernel.cu.obj "E:\code\cppcode\TestCUDA\TestCUDA\kernel.cu" -clean
1> kernel.cu
1> Compiling CUDA source file kernel.cu...
1>
1> E:\code\cppcode\TestCUDA\TestCUDA>"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin\nvcc.exe" -gencode=arch=compute_30,code=\"sm_30,compute_30\" --use-local-env --cl-version 2015 -ccbin "C:\Program Files (x86)\Microsoft Visual Studio 14.0\VC\bin\x86_amd64" -x cu -I"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\include" -I"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\include" --keep-dir x64\Release -maxrregcount=0 --machine 64 --compile -cudart static -DWIN32 -DWIN64 -DNDEBUG -D_CONSOLE -D_MBCS -Xcompiler "/EHsc /W0 /nologo /O2 /FS /Zi /MD " -o x64\Release\kernel.cu.obj "E:\code\cppcode\TestCUDA\TestCUDA\kernel.cu"
1> kernel.cu
1> LINK : 已指定 /LTCG,但不需要生成代码;从链接命令行中移除 /LTCG 以提高链接器性能
1> TestCUDA.vcxproj -> E:\code\cppcode\TestCUDA\x64\Release\TestCUDA.exe
1> TestCUDA.vcxproj -> E:\code\cppcode\TestCUDA\x64\Release\TestCUDA.pdb (Full PDB)
1> copy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin\cudart*.dll" "E:\code\cppcode\TestCUDA\x64\Release\"
1> C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin\cudart32_90.dll
1> C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v9.0\bin\cudart64_90.dll
1> 已复制 2 个文件。
========== 全部重新生成: 成功 1 个,失败 0 个,跳过 0 个 ==========

显示结果:

CUDA从入门到放弃(三):Hello World 之第一个CUDA程序_#include_02


参考资料

1 ​​CUDA编程入门​​​ 2 ​​Nvidia官方教程​​

3 ​​CUDA编程入门极简教程​​

4 ​​CUDA Toolkit Documentation v9.0.176​​

5 ​​NVIDIA CUDA初级教程视频​​

6 CUDA专家手册 [GPU编程权威指南]

7 CUDA并行程序设计:GPU编程指南

8 CUDA C编程权威指南



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