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  1 /*  2 * Copyright 1993-2010 NVIDIA Corporation.  All rights reserved.  3 *  4 * NVIDIA Corporation and its licensors retain all intellectual property and  5 * proprietary rights in and to this software and related documentation.  6 * Any use, reproduction, disclosure, or distribution of this software  7 * and related documentation without an express license agreement from  8 * NVIDIA Corporation is strictly prohibited.  9 * 10 * Please refer to the applicable NVIDIA end user license agreement (EULA) 11 * associated with this source code for terms and conditions that govern 12 * your use of this NVIDIA software. 13 * 14 */ 15  16  17 #include "../common/book.h" 18 #include "cuda.h" 19 #include "cuda_runtime.h" 20 #include "device_launch_parameters.h" 21 #include "device_functions.h" 22 #define imin(a,b) (a<b?a:b) 23  24 const int N = 33 * 1024; 25 const int threadsPerBlock = 256;//每个线程块启动256个线程 26 const int blocksPerGrid = imin(32, (N + threadsPerBlock - 1) / threadsPerBlock); 27  28 /* 29 内核函数 30 */ 31 __global__ void dot(float *a, float *b, float *c) { 32     //设备上的共享内存，在每个线程块中都有 33     __shared__ float cache[threadsPerBlock]; 34     int tid = threadIdx.x + blockIdx.x * blockDim.x; 35     //线程块中的线程索引赋值给缓冲索引 36     int cacheIndex = threadIdx.x; 37  38     float   temp = 0; 39     //当前索引小于总共的数据量时 40     while (tid < N) { 41         temp += a[tid] * b[tid]; 42         //步长为活动的线程数 43         tid += blockDim.x * gridDim.x; 44     }//如果再次在这个线程上执行时，temp中存放的是上次计算的值，也就是再次计算的结果是加上上次计算的值 45  46     // set the cache values 47     //将结果存放在共享存储中，每个线程对应一个共享存储 48     cache[cacheIndex] = temp; 49  50     /* 51     synchronize threads in this block 52     同步操作，使得每个线程都计算完毕，再继续后面的操作 53     */ 54     __syncthreads(); 55   56  57     // for reductions, threadsPerBlock must be a power of 2 58     // because of the following code 59     /* 60     归约操作 61     blockDim.x / 2块中的线程个数除以2，相当于取中间值 62     因为这个blockDim是2的倍数，所以不会有除不尽的情况 63     */ 64     int i = blockDim.x / 2; 65     while (i != 0) { 66         if (cacheIndex < i) 67             /* 68             前半部分和后半部分对应的第一个相加，以此类推 69             */ 70             cache[cacheIndex] += cache[cacheIndex + i]; 71         /* 72         同步使得所有线程完成了第一次归约在进行下一次归约 73         */ 74         __syncthreads(); 75         //下次归约的中间值 76         i /= 2; 77     } 78     //最终结果存放在cache[0]中，所以将cache[0]赋给以块索引为下标的数组中 79     if (cacheIndex == 0) 80         c[blockIdx.x] = cache[0]; 81 } 82  83  84 int main(void) { 85     float   *a, *b, c, *partial_c; 86     float   *dev_a, *dev_b, *dev_partial_c; 87  88     // allocate memory on the cpu side 89     a = (float*)malloc(N*sizeof(float)); 90     b = (float*)malloc(N*sizeof(float)); 91     partial_c = (float*)malloc(blocksPerGrid*sizeof(float)); 92  93     // allocate the memory on the GPU 94     HANDLE_ERROR(cudaMalloc((void**)&dev_a, 95         N*sizeof(float))); 96     HANDLE_ERROR(cudaMalloc((void**)&dev_b, 97         N*sizeof(float))); 98     HANDLE_ERROR(cudaMalloc((void**)&dev_partial_c, 99         blocksPerGrid*sizeof(float)));100 101     // fill in the host memory with data102     for (int i = 0; i<N; i++) {103         a[i] = i;104         b[i] = i * 2;105     }106 107     // copy the arrays ‘a‘ and ‘b‘ to the GPU108     HANDLE_ERROR(cudaMemcpy(dev_a, a, N*sizeof(float),109         cudaMemcpyHostToDevice));110     HANDLE_ERROR(cudaMemcpy(dev_b, b, N*sizeof(float),111         cudaMemcpyHostToDevice));112 113     dot << <blocksPerGrid, threadsPerBlock > >>(dev_a, dev_b, dev_partial_c);114 115     // copy the array ‘c‘ back from the GPU to the CPU116     HANDLE_ERROR(cudaMemcpy(partial_c, dev_partial_c,117         blocksPerGrid*sizeof(float),118         cudaMemcpyDeviceToHost));119 120     /* 在主机上完成最后的相加工作121     这样是为了避免简单的工作在GPU上造成的资源浪费122     因为好多资源处于空闲状态123     */124     c = 0;125     for (int i = 0; i<blocksPerGrid; i++) {126         c += partial_c[i];127     }128 129 #define sum_squares(x)  (x*(x+1)*(2*x+1)/6)130     printf("Does GPU value %.6g = %.6g?\n", c, 2 * sum_squares((float)(N - 1)));131 132     // free memory on the gpu side133     HANDLE_ERROR(cudaFree(dev_a));134     HANDLE_ERROR(cudaFree(dev_b));135     HANDLE_ERROR(cudaFree(dev_partial_c));136 137     // free memory on the cpu side138     free(a);139     free(b);140     free(partial_c);141 }

共享内存实现大规模点积