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CUDA從入門到精通（二）：第一個CUDA程序
書接上回，我們既然直接運行例程成功了，接下來就是了解如何實現例程中的每個環節。當然，我們先從簡單的做起，一般編程語言都會找個helloworld例子，而我們的顯卡是不會說話的，隻能做一些簡單的加減乘除運算。所以，CUDA程序的helloworld，我想應該最合適不過的就是向量加了。
打開VS2008，選擇File->New->Project，彈出下麵對話框，設置如下：
之後點OK，直接進入工程界麵。
工程中，我們看到隻有一個.cu文件，內容如下：
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
cudaError_t addWithCuda(int *c, const int *a, const int *b, size_t 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]);
// cudaThreadExit must be called before exiting in order for profiling and
// tracing tools such as Nsight and Visual Profiler to show complete traces.
cudaStatus = cudaThreadExit();
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaThreadExit failed!");
return 1;
}
return 0;
}
// Helper function for using CUDA to add vectors in parallel.
cudaError_t addWithCuda(int *c, const int *a, const int *b, size_t 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);
// cudaThreadSynchronize waits for the kernel to finish, and returns
// any errors encountered during the launch.
cudaStatus = cudaThreadSynchronize();
if (cudaStatus != cudaSuccess) {
fprintf(stderr, "cudaThreadSynchronize 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;
}
可以看出，CUDA程序和C程序並無區別，隻是多了一些以"cuda"開頭的一些庫函數和一個特殊聲明的函數：
__global__ void addKernel(int *c, const int *a, const int *b)
{
int i = threadIdx.x;
c[i] = a[i] + b[i];
}
這個函數就是在GPU上運行的函數，稱之為核函數，英文名Kernel Function，注意要和操作係統內核函數區分開來。
我們直接按F7編譯，可以得到如下輸出：
1>------ Build started: Project: cuda_helloworld, Configuration: Debug Win32 ------ 1>Compiling with CUDA Build Rule... 1>"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v5.0\\bin\nvcc.exe" -G -gencode=arch=compute_10,code=\"sm_10,compute_10\" -gencode=arch=compute_20,code=\"sm_20,compute_20\" --machine 32 -ccbin "C:\Program Files (x86)\Microsoft Visual Studio 9.0\VC\bin" -Xcompiler "/EHsc /W3 /nologo /O2 /Zi /MT " -I"C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v5.0\\include" -maxrregcount=0 --compile -o "Debug/kernel.cu.obj" kernel.cu 1>tmpxft_000000ec_00000000-8_kernel.compute_10.cudafe1.gpu 1>tmpxft_000000ec_00000000-14_kernel.compute_10.cudafe2.gpu 1>tmpxft_000000ec_00000000-5_kernel.compute_20.cudafe1.gpu 1>tmpxft_000000ec_00000000-17_kernel.compute_20.cudafe2.gpu 1>kernel.cu 1>kernel.cu 1>tmpxft_000000ec_00000000-8_kernel.compute_10.cudafe1.cpp 1>tmpxft_000000ec_00000000-24_kernel.compute_10.ii 1>Linking... 1>Embedding manifest... 1>Performing Post-Build Event... 1>copy "C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v5.0\\bin\cudart*.dll" "C:\Users\DongXiaoman\Documents\Visual Studio 2008\Projects\cuda_helloworld\Debug" 1>C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v5.0\\bin\cudart32_50_35.dll 1>C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v5.0\\bin\cudart64_50_35.dll 1>已複製 2 個文件。 1>Build log was saved at "file://c:\Users\DongXiaoman\Documents\Visual Studio 2008\Projects\cuda_helloworld\cuda_helloworld\Debug\BuildLog.htm" 1>cuda_helloworld - 0 error(s), 105 warning(s) ========== Build: 1 succeeded, 0 failed, 0 up-to-date, 0 skipped ==========
可見，編譯.cu文件需要利用nvcc工具。該工具的詳細使用見後麵博客。
直接運行，可以得到結果圖如下：
如果顯示正確，那麼我們的第一個程序宣告成功！
最後更新：2017-04-03 16:48:43