cuda入门学习

最近接触cuda 编程，记录一下。

1 工作实现一个【0-100）的加法

如果用python

sum = 0
for i in range(200):sum+=i
print(sum)

2 cuda 的一些简单的概念

一维情况下大概是这样的
(1个grid * 2个blocks * 4个thread)
在这里插入图片描述

3 代码直接上代码

我把100分为20个blocks ,每个block 有5个threads。

int num_blocks = 20;
int block_size = data_len/num_blocks // 100/20 = 5;
sum_kernel << <num_blocks, block_size >> > (sum, dev_c, data_len); //将其送入到内核中去

内核函数计算加法

 int tid = blockIdx.x * blockDim.x + threadIdx.x; // blockDim.x =5

原子相加,相当加了一个锁，保证运算的正确性。

atomicAdd(sum, data[tid]);

3 完整代码

#include <stdio.h>    
#include <stdlib.h>   
#include <cuda_runtime.h>  
__global__ void sum_kernel(int* sum, int* data, int n) {int tid = blockIdx.x * blockDim.x + threadIdx.x;int stride = gridDim.x * blockDim.x;printf("stride=%d, blockIdx.x blockDim.x threadIdx.x [%d, %d, %d] \n", stride,blockIdx.x,blockDim.x,threadIdx.x);atomicAdd(sum, data[tid]);printf("data[%d] = %d  sum  in kernel:  %d\n",tid,data[tid],*sum);
}
int main() {const int data_len = 100;int* dev_c = 0;int *sum=0;cudaError_t cudaStatus;cudaStatus = cudaSetDevice(0);if (cudaStatus != cudaSuccess) {fprintf(stderr, "选择GPU失败，您的电脑上没有GPU");return 0;}cudaStatus = cudaMalloc((void**)&dev_c, data_len * sizeof(int));cudaStatus = cudaMalloc((void**)&sum, data_len * sizeof(int));//cudaMalloc(&sum, sizeof(int));int data_cpu[data_len];for (int i = 0; i < data_len; ++i){data_cpu[i] = i;}cudaStatus =cudaMemcpy(dev_c, data_cpu,sizeof(int)* data_len, cudaMemcpyHostToDevice);//cudaMemcpy(dev_histo, &threadSum, sizeof(int), cudaMemcpyHostToDevice);if (cudaStatus != cudaSuccess) {fprintf(stderr, "dev_b复制失败");}int num_blocks = 20;int block_size = data_len/num_blocks;sum_kernel << <num_blocks, block_size >> > (sum, dev_c, data_len);int result;cudaMemcpy(&result, sum, sizeof(int), cudaMemcpyDeviceToHost);printf("sum = %d\n", result);
}

4 运行结果

stride=100, blockIdx.x blockDim.x threadIdx.x [6, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [6, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [6, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [6, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [6, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [18, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [18, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [18, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [18, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [18, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [2, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [2, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [2, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [2, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [2, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [9, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [9, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [9, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [9, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [9, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [8, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [8, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [8, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [8, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [8, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [14, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [14, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [14, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [14, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [14, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [17, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [17, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [17, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [17, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [17, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [11, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [11, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [11, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [11, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [11, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [5, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [5, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [5, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [5, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [5, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [3, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [3, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [3, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [3, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [3, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [15, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [15, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [15, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [15, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [15, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [0, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [0, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [0, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [0, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [0, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [12, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [12, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [12, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [12, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [12, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [7, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [7, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [7, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [7, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [7, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [19, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [19, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [19, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [19, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [19, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [10, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [10, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [10, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [10, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [10, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [4, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [4, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [4, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [4, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [4, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [16, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [16, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [16, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [16, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [16, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [1, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [1, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [1, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [1, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [1, 5, 4]
stride=100, blockIdx.x blockDim.x threadIdx.x [13, 5, 0]
stride=100, blockIdx.x blockDim.x threadIdx.x [13, 5, 1]
stride=100, blockIdx.x blockDim.x threadIdx.x [13, 5, 2]
stride=100, blockIdx.x blockDim.x threadIdx.x [13, 5, 3]
stride=100, blockIdx.x blockDim.x threadIdx.x [13, 5, 4]
data[25] = 25  sum  in kernel:  135
data[26] = 26  sum  in kernel:  135
data[27] = 27  sum  in kernel:  135
data[28] = 28  sum  in kernel:  135
data[29] = 29  sum  in kernel:  135
data[40] = 40  sum  in kernel:  345
data[41] = 41  sum  in kernel:  345
data[42] = 42  sum  in kernel:  345
data[43] = 43  sum  in kernel:  345
data[44] = 44  sum  in kernel:  345
data[45] = 45  sum  in kernel:  740
data[46] = 46  sum  in kernel:  740
data[47] = 47  sum  in kernel:  740
data[48] = 48  sum  in kernel:  740
data[49] = 49  sum  in kernel:  740
data[30] = 30  sum  in kernel:  740
data[31] = 31  sum  in kernel:  740
data[32] = 32  sum  in kernel:  740
data[33] = 33  sum  in kernel:  740
data[34] = 34  sum  in kernel:  740
data[50] = 50  sum  in kernel:  1110
data[51] = 51  sum  in kernel:  1110
data[52] = 52  sum  in kernel:  1110
data[53] = 53  sum  in kernel:  1110
data[54] = 54  sum  in kernel:  1110
data[85] = 85  sum  in kernel:  1545
data[86] = 86  sum  in kernel:  1545
data[87] = 87  sum  in kernel:  1545
data[88] = 88  sum  in kernel:  1545
data[89] = 89  sum  in kernel:  1545
data[55] = 55  sum  in kernel:  1830
data[56] = 56  sum  in kernel:  1830
data[57] = 57  sum  in kernel:  1830
data[58] = 58  sum  in kernel:  1830
data[59] = 59  sum  in kernel:  1830
data[20] = 20  sum  in kernel:  1110
data[21] = 21  sum  in kernel:  1110
data[22] = 22  sum  in kernel:  1110
data[23] = 23  sum  in kernel:  1110
data[24] = 24  sum  in kernel:  1110
data[90] = 90  sum  in kernel:  2290
data[91] = 91  sum  in kernel:  2290
data[92] = 92  sum  in kernel:  2290
data[93] = 93  sum  in kernel:  2290
data[94] = 94  sum  in kernel:  2290
data[10] = 10  sum  in kernel:  3155
data[11] = 11  sum  in kernel:  3155
data[12] = 12  sum  in kernel:  3155
data[13] = 13  sum  in kernel:  3155
data[14] = 14  sum  in kernel:  3155
data[15] = 15  sum  in kernel:  3155
data[16] = 16  sum  in kernel:  3155
data[17] = 17  sum  in kernel:  3155
data[18] = 18  sum  in kernel:  3155
data[19] = 19  sum  in kernel:  3155
data[60] = 60  sum  in kernel:  3155
data[61] = 61  sum  in kernel:  3155
data[62] = 62  sum  in kernel:  3155
data[63] = 63  sum  in kernel:  3155
data[64] = 64  sum  in kernel:  3155
data[80] = 80  sum  in kernel:  2700
data[81] = 81  sum  in kernel:  2700
data[82] = 82  sum  in kernel:  2700
data[83] = 83  sum  in kernel:  2700
data[84] = 84  sum  in kernel:  2700
data[95] = 95  sum  in kernel:  3675
data[96] = 96  sum  in kernel:  3675
data[97] = 97  sum  in kernel:  3675
data[98] = 98  sum  in kernel:  3675
data[99] = 99  sum  in kernel:  3675
data[5] = 5  sum  in kernel:  3190
data[6] = 6  sum  in kernel:  3190
data[7] = 7  sum  in kernel:  3190
data[8] = 8  sum  in kernel:  3190
data[9] = 9  sum  in kernel:  3190
data[70] = 70  sum  in kernel:  4035
data[71] = 71  sum  in kernel:  4035
data[72] = 72  sum  in kernel:  4035
data[73] = 73  sum  in kernel:  4035
data[74] = 74  sum  in kernel:  4035
data[75] = 75  sum  in kernel:  4615
data[76] = 76  sum  in kernel:  4615
data[77] = 77  sum  in kernel:  4615
data[78] = 78  sum  in kernel:  4615
data[79] = 79  sum  in kernel:  4615
data[0] = 0  sum  in kernel:  4615
data[1] = 1  sum  in kernel:  4615
data[2] = 2  sum  in kernel:  4615
data[3] = 3  sum  in kernel:  4615
data[4] = 4  sum  in kernel:  4615
data[35] = 35  sum  in kernel:  4615
data[36] = 36  sum  in kernel:  4615
data[37] = 37  sum  in kernel:  4615
data[38] = 38  sum  in kernel:  4615
data[39] = 39  sum  in kernel:  4615
data[65] = 65  sum  in kernel:  4950
data[66] = 66  sum  in kernel:  4950
data[67] = 67  sum  in kernel:  4950
data[68] = 68  sum  in kernel:  4950
data[69] = 69  sum  in kernel:  4950
sum = 4950

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