在做项目集成的时候需要用到cpp和cuda文件联调,自己摸索了两种方式实现cpp和cu文件混合编译。
本文环境:
- windows7 64位
- VS2010
- CUDA5.5
- 英伟达显卡Tesla C1060
前言
装好CUDA 5.5 sdk后,默认会自动添加好系统环境变量。
因此不需要额外配置,不过为了保险起见,可以选择性地添加以下环境变量:
CUDA_BIN_PATH %CUDA_PATH%\bin
CUDA_LIB_PATH %CUDA_PATH%\lib\Win32
CUDA_SDK_BIN %CUDA_SDK_PATH%\bin\Win32
CUDA_SDK_LIB %CUDA_SDK_PATH%\common\lib\Win32
CUDA_SDK_PATH C:\cuda\cudasdk\common
这时可以打开CUDA自带的sample运行一下,运行能通过才可以继续下面的内容————cpp和cuda联调。
方法一:先建立cuda工程,再添加cpp文件
1.打开vs2010,新建一个cuda项目,名称CudaCpp。
2.cuda默认建立的工程是如下,实现了两个一维向量的并行相加。kernel函数和执行函数还有main函数全都写在了一个cu文件里。
3.接下来在工程里添加一个空的cpp文件。将原来cu文件里main函数里的内容剪切到cpp文件main函数里。
为了让cpp能够调用cu文件里面的函数,在addWithCuda函数前加上extern "C" 关键字 (注意C大写,为什么addKernel不用加呢?因为cpp里面直接调用的是addWithCuda)
4.在cpp里也要加上addWithCuda函数的完整前向声明。下图就是工程的完整结构
5.可以在cpp里的main函数return之间加入getchar()防止运行后一闪就退出,加上system("pause")或者直接ctrl+F5也行。
运行结果:
下面贴出CudaCpp项目代码。
kernel.cu
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#include "cuda_runtime.h"
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#include "device_launch_parameters.h"
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-
#include <stdio.h>
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__global__ void addKernel(int *c, const int *a, const int *b)
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{
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int i = threadIdx.x;
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c[i] = a[i] + b[i];
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}
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// Helper function for using CUDA to add vectors in parallel.
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extern "C"
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cudaError_t addWithCuda(int *c, const int *a, const int *b, unsigned int size)
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{
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int *dev_a = 0;
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int *dev_b = 0;
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int *dev_c = 0;
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cudaError_t cudaStatus;
-
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// Choose which GPU to run on, change this on a multi-GPU system.
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cudaStatus = cudaSetDevice(0);
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if (cudaStatus != cudaSuccess) {
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fprintf(stderr, "cudaSetDevice failed! Do you have a CUDA-capable GPU installed?");
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goto Error;
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}
-
-
// Allocate GPU buffers for three vectors (two input, one output) .
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cudaStatus = cudaMalloc((void**)&dev_c, size * sizeof(int));
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if (cudaStatus != cudaSuccess) {
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fprintf(stderr, "cudaMalloc failed!");
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goto Error;
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}
-
-
cudaStatus = cudaMalloc((void**)&dev_a, size * sizeof(int));
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if (cudaStatus != cudaSuccess) {
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fprintf(stderr, "cudaMalloc failed!");
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goto Error;
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}
-
-
cudaStatus = cudaMalloc((void**)&dev_b, size * sizeof(int));
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if (cudaStatus != cudaSuccess) {
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fprintf(stderr, "cudaMalloc failed!");
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goto Error;
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}
-
-
// Copy input vectors from host memory to GPU buffers.
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cudaStatus = cudaMemcpy(dev_a, a, size * sizeof(int), cudaMemcpyHostToDevice);
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if (cudaStatus != cudaSuccess) {
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fprintf(stderr, "cudaMemcpy failed!");
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goto Error;
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}
-
-
cudaStatus = cudaMemcpy(dev_b, b, size * sizeof(int), cudaMemcpyHostToDevice);
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if (cudaStatus != cudaSuccess) {
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fprintf(stderr, "cudaMemcpy failed!");
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goto Error;
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}
-
-
// Launch a kernel on the GPU with one thread for each element.
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addKernel<<<1, size>>>(dev_c, dev_a, dev_b);
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// Check for any errors launching the kernel
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cudaStatus = cudaGetLastError();
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if (cudaStatus != cudaSuccess) {
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fprintf(stderr, "addKernel launch failed: %s\n", cudaGetErrorString(cudaStatus));
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goto Error;
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}
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-
// cudaDeviceSynchronize waits for the kernel to finish, and returns
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// any errors encountered during the launch.
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cudaStatus = cudaDeviceSynchronize();
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if (cudaStatus != cudaSuccess) {
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fprintf(stderr, "cudaDeviceSynchronize returned error code %d after launching addKernel!\n", cudaStatus);
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goto Error;
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}
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// Copy output vector from GPU buffer to host memory.
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cudaStatus = cudaMemcpy(c, dev_c, size * sizeof(int), cudaMemcpyDeviceToHost);
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if (cudaStatus != cudaSuccess) {
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fprintf(stderr, "cudaMemcpy failed!");
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goto Error;
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}
-
-
Error:
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cudaFree(dev_c);
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cudaFree(dev_a);
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cudaFree(dev_b);
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-
return cudaStatus;
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}
main.cpp
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#include <stdio.h>
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#include "cuda_runtime.h"
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#include "device_launch_parameters.h"
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-
extern "C"
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cudaError_t addWithCuda(int *c, const int *a, const int *b, unsigned int size);
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int main()
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{
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const int arraySize = 5;
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const int a[arraySize] = { 1, 2, 3, 4, 5 };
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const int b[arraySize] = { 10, 20, 30, 40, 50 };
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int c[arraySize] = { 0 };
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// Add vectors in parallel.
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cudaError_t cudaStatus = addWithCuda(c, a, b, arraySize);
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if (cudaStatus != cudaSuccess) {
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fprintf(stderr, "addWithCuda failed!");
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return 1;
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}
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printf("{1,2,3,4,5} + {10,20,30,40,50} = {%d,%d,%d,%d,%d}\n",
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c[0], c[1], c[2], c[3], c[4]);
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printf("cuda工程中调用cpp成功!\n");
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-
// cudaDeviceReset must be called before exiting in order for profiling and
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// tracing tools such as Nsight and Visual Profiler to show complete traces.
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cudaStatus = cudaDeviceReset();
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if (cudaStatus != cudaSuccess) {
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fprintf(stderr, "cudaDeviceReset failed!");
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return 1;
-
}
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getchar(); //here we want the console to hold for a while
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return 0;
-
}
方法二:先建立cpp工程,再添加cu文件
方法一由于是cuda工程是自动建立的,所以比较简单,不需要多少额外的配置。而在cpp工程里面添加cu就要复杂一些。为了简单起见,这里采用console程序讲解,至于MFC或者Direct3D程序同理。
1.建立一个空的win32控制台工程,名称CppCuda。
2.然后右键工程-->添加一个cu文件
3.将方法一中cu和cpp文件的代码分别拷贝到这个工程里来(做了少许修改,extern "C"关键字和某些头文件不要忘了加),工程结构如图:
这个时候编译是通不过的,需要作一些配置。
4.关键的一步,右键工程-->生成自定义 ,将对话框中CUDA5.5前面的勾打上。
这时点击 工程-->属性,会发现多了CUDA链接器这一项。
5.关键的一步,右键kernel.cu文件-->属性,在 常规-->项类型 里面选择CUDA C/C++(由于cu文件是由nvcc编译的,这里要修改编译链接属性)
6.工程-->属性-->链接器-->附加依赖项,加入cudart.lib
7.工具-->选项-->文本编辑器-->文件扩展名 添加cu \cuh两个文件扩展名
8.至此配置成功。运行一下:
9.为了更加确信cuda中的函数确实被调用,在main.cpp里面调用cuda函数的地方加入了一个断点。
单步执行一下。
可以看到程序跳到了cu文件里去执行了,说明cpp调用cuda函数成功。
贴上代码(其实跟方式一基本一样,没怎么改),工程CppCuda
kernel.cu
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#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];
-
}
-
// Helper function for using CUDA to add vectors in parallel.
-
extern "C"
-
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;
-
}
main.cpp
-
#include <iostream>
-
#include "cuda_runtime.h"
-
#include "device_launch_parameters.h"
-
using namespace std;
-
-
extern "C"
-
cudaError_t addWithCuda(int *c, const int *a, const int *b, unsigned int size);
-
int main(int argc,char **argv)
-
{
-
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;
-
}
-
-
cout<<"{1,2,3,4,5} + {10,20,30,40,50} = {"<<c[0]<<','<<c[1]<<','<<c[2]<<','<<c[3]<<'}'<<endl;
-
printf("cpp工程中调用cu成功!\n");
-
-
// 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;
-
}
-
system("pause"); //here we want the console to hold for a while
-
return 0;
-
}
注意有时候编译出问题,把 "device_launch_parameters.h" 这个头文件去掉就好了(去掉之后就不能调里面的函数或变量了),至于为什么,还不是很清楚。