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多GPU设备处理点积示例

时间:2014-09-28 20:10:25      阅读:223      评论:0      收藏:0      [点我收藏+]

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 多GPU设备处理点积示例,项目打包下载

  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 "device_launch_parameters.h"
 20 #include "device_functions.h"
 21 #include "cuda_runtime.h"
 22 
 23 #define imin(a,b) (a<b?a:b)
 24 
 25 #define     N    (33*1024*1024)
 26 const int threadsPerBlock = 256;
 27 const int blocksPerGrid =
 28 imin(32, (N / 2 + threadsPerBlock - 1) / threadsPerBlock);
 29 
 30 
 31 __global__ void dot(int size, float *a, float *b, float *c) {
 32     __shared__ float cache[threadsPerBlock];
 33     int tid = threadIdx.x + blockIdx.x * blockDim.x;
 34     int cacheIndex = threadIdx.x;
 35 
 36     float   temp = 0;
 37     while (tid < size) {
 38         temp += a[tid] * b[tid];
 39         tid += blockDim.x * gridDim.x;
 40     }
 41 
 42     // set the cache values
 43     cache[cacheIndex] = temp;
 44 
 45     // synchronize threads in this block
 46     __syncthreads();
 47 
 48     //块内归约
 49     int i = blockDim.x / 2;
 50     while (i != 0) {
 51         if (cacheIndex < i)
 52             cache[cacheIndex] += cache[cacheIndex + i];
 53         __syncthreads();
 54         i /= 2;
 55     }
 56 
 57     if (cacheIndex == 0)
 58         c[blockIdx.x] = cache[0];
 59 }
 60 
 61 
 62 struct DataStruct {
 63     int     deviceID;
 64     int     size;
 65     float   *a;
 66     float   *b;
 67     float   returnValue;
 68 };
 69 
 70 unsigned WINAPI routine(void *pvoidData)
 71 //void* routine(void *pvoidData) 
 72 {
 73     DataStruct  *data = (DataStruct*)pvoidData;
 74     HANDLE_ERROR(cudaSetDevice(data->deviceID));
 75 
 76     int     size = data->size;
 77     float   *a, *b, c, *partial_c;
 78     float   *dev_a, *dev_b, *dev_partial_c;
 79 
 80     // allocate memory on the CPU side
 81     a = data->a;
 82     b = data->b;
 83     partial_c = (float*)malloc(blocksPerGrid*sizeof(float));
 84 
 85     // allocate the memory on the GPU
 86     HANDLE_ERROR(cudaMalloc((void**)&dev_a,
 87         size*sizeof(float)));
 88     HANDLE_ERROR(cudaMalloc((void**)&dev_b,
 89         size*sizeof(float)));
 90     HANDLE_ERROR(cudaMalloc((void**)&dev_partial_c,
 91         blocksPerGrid*sizeof(float)));
 92 
 93     // copy the arrays ‘a‘ and ‘b‘ to the GPU
 94     HANDLE_ERROR(cudaMemcpy(dev_a, a, size*sizeof(float),
 95         cudaMemcpyHostToDevice));
 96     HANDLE_ERROR(cudaMemcpy(dev_b, b, size*sizeof(float),
 97         cudaMemcpyHostToDevice));
 98 
 99     dot <<<blocksPerGrid, threadsPerBlock >>>(size, dev_a, dev_b,
100         dev_partial_c);
101     // copy the array ‘c‘ back from the GPU to the CPU
102     HANDLE_ERROR(cudaMemcpy(partial_c, dev_partial_c,
103         blocksPerGrid*sizeof(float),
104         cudaMemcpyDeviceToHost));
105 
106     // finish up on the CPU side
107     c = 0;
108     for (int i = 0; i<blocksPerGrid; i++) {
109         c += partial_c[i];
110     }
111 
112     HANDLE_ERROR(cudaFree(dev_a));
113     HANDLE_ERROR(cudaFree(dev_b));
114     HANDLE_ERROR(cudaFree(dev_partial_c));
115 
116     // free memory on the CPU side
117     free(partial_c);
118 
119     data->returnValue = c;
120     return 0;
121 }
122 
123 
124 int main(void) {
125     int deviceCount;
126     HANDLE_ERROR(cudaGetDeviceCount(&deviceCount));
127     //要求两个设备
128     if (deviceCount < 2) {
129         printf("We need at least two compute 1.0 or greater "
130             "devices, but only found %d\n", deviceCount);
131         return 0;
132     }
133 
134     float   *a = (float*)malloc(sizeof(float)* N);
135     HANDLE_NULL(a);
136     float   *b = (float*)malloc(sizeof(float)* N);
137     HANDLE_NULL(b);
138 
139     // fill in the host memory with data
140     for (int i = 0; i<N; i++) {
141         a[i] = i;
142         b[i] = i * 2;
143     }
144 
145     /*
146     为多线程做准备
147     每个DateStruct都为数据集大小的一半
148     */
149     DataStruct  data[2];
150     data[0].deviceID = 0;
151     data[0].size = N / 2;
152     data[0].a = a;
153     data[0].b = b;
154 
155     data[1].deviceID = 1;
156     data[1].size = N / 2;
157     data[1].a = a + N / 2;
158     data[1].b = b + N / 2;
159 
160     CUTThread   thread = start_thread(routine, &(data[0]));
161     routine(&(data[1]));
162     end_thread(thread);
163 
164 
165     // free memory on the CPU side
166     free(a);
167     free(b);
168 
169     printf("Value calculated:  %f\n",
170         data[0].returnValue + data[1].returnValue);
171 
172     return 0;
173 }

 

多GPU设备处理点积示例

标签:style   blog   http   color   io   os   ar   for   数据   

原文地址:http://www.cnblogs.com/liangliangdetianxia/p/3998729.html

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