CUDA从入门到精通（二）：第一个CUDA程序

书接上回，我们既然直接运行例程成功了，接下来就是了解如何实现例程中的每个环节。当然，我们先从简单的做起，一般编程语言都会找个helloworld例子，而我们的显卡是不会说话的，只能做一些简单的加减乘除运算。所以，CUDA程序的helloworld，我想应该最合适不过的就是向量加了。

打开VS2008，选择File->New->Project，弹出下面对话框，设置如下：

之后点OK，直接进入工程界面。

工程中，我们看到只有一个.cu文件，内容如下：

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

 #include

 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程序并无区别，只是多了一些以"cuda"开头的一些库函数和一个特殊声明的函数：

 __global__ void addKernel(int *c, const int *a, const int *b)
 {
     int i = threadIdx.x;
     c[i] = a[i] + b[i];
 }

这个函数就是在GPU上运行的函数，称之为核函数，英文名Kernel Function，注意要和操作系统内核函数区分开来。

 

我们直接按F7编译，可以得到如下输出：

 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>已复制         2 个文件。
 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 ==========

可见，编译.cu文件需要利用nvcc工具。该工具的详细使用见后面博客。

 

直接运行，可以得到结果图如下：

 

如果显示正确，那么我们的第一个程序宣告成功！