上一篇文章，小王是直接複製圖片到編輯框中，經過審覈後，不知道爲什麼圖片全部消失了，這次小王採取上傳圖片的方式，希望圖片不會再消失掉。
言歸正傳，剛剛測試了同事安裝的項目運行沒有問題，現在需要我自己建立一個簡單項目進行測量，先測量一個簡單的加法吧。
一 打開VS2010，文件-->新建-->項目，取名cudaAdd，點擊“確定”按鈕，如下圖，
二 運行，看看有沒有問題
1.運行前發現，這個項目自動創建了一個kernel.cu文件，並且添加了許多代碼，如下所示
2.按F5直接運行，發現編譯失敗
3.查看原因
首先這個問題，可能原因是配置哪裏出現了問題，如下配置即可解決問題
4. 運行成功
三 分析
整個項目已經完成了加法，所以小王在這裏就不獻醜了，咱們一起來看看這個代碼吧。這個文件以 .cu 結尾，代表cuda文件，類似於C++的.cpp含義
1.最開始，聲明cuda頭文件和C++標準頭文件
#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
2.聲明cuda加法函數
cudaError_t addWithCuda(int *c, const int *a, const int *b, unsigned int size);
3.定義內核函數（cuda函數稱爲kernal函數或者device函數；cpu函數稱爲host函數），從這裏可以看到cuda的kernal函數是以__global__ 開頭聲明的，當然還有其他的聲明方式，小王接觸到哪個武器，咱們再一起學習使用哪個武器。
__global__ void addKernel(int *c, const int *a, const int *b)
{
int i = threadIdx.x;
c[i] = a[i] + b[i];
}
4.項目的入口函數 main函數
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;
}
return 0;
}
5.cuda加法的核心函數，進行 內存分配 / 加法計算 / 內存釋放 操作.
// 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;
}
好了，這篇文章就到這裏咯，寫的不好，小王會繼續努力的
