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最近在学cuda，发现自己数学方面的知识不太够，C语言的知识也有待加强。

这里记录个笔记对矩阵求卷积。

逻辑是这样的：

1. 先CUDA生成一个16*16的矩阵；

2. 将这16*16的矩阵，外面包一层0，也就变成18*18的矩阵。

3. 然后再开18*18个线程，进行矩阵的卷积

程序运行截图如下：

源码如下：

#include "cuda_runtime.h"#include "device_launch_parameters.h"#include "curand.h"#include "curand_kernel.h"#include 
   
    #include 
    
     using namespace std;#pragma comment(lib, "cudart.lib")#pragma comment(lib, "curand.lib")#define N 16__global__ void Matrix_convolution(float *a, float *b) {	int x = threadIdx.x;	int y = threadIdx.y;	__shared__ float shared[(N + 2) * (N + 2)];	shared[y * (N + 2) + x] = a[y * (N + 2) + x];	__syncthreads();	int convolution[9] = { 1, 1, 1, 1, -8, 1, 1, 1, 1 };	//对shared进行卷积	int pos = y * (N + 2) + x;	//第一行和最后一行不要卷积	if (pos < N + 2 || (pos >(N + 2) * (N + 1))) {		return;	}	//最左边和最右边一行不要卷积	if (pos % (N + 2) == 0 || pos % (N + 2) == N + 1) {		return;	}	//卷积的9个值	float a00 = shared[(y * (N + 2) + x) - (N + 2) - 1];	float a01 = shared[(y * (N + 2) + x) - (N + 2)];	float a02 = shared[(y * (N + 2) + x) - (N + 2) + 1];	float a10 = shared[(y * (N + 2) + x) - 1];	float a11 = shared[(y * (N + 2) + x)];	float a12 = shared[(y * (N + 2) + x) + 1];	float a20 = shared[(y * (N + 2) + x) + (N + 2) - 1];	float a21 = shared[(y * (N + 2) + x) + (N + 2)];	float a22 = shared[(y * (N + 2) + x) + (N + 2) + 1];	float ret = convolution[0] * a00 + convolution[1] * a01 + convolution[2] * a02		+ convolution[3] * a10 + convolution[4] * a11 + convolution[5] * a12 + convolution[6] * a20		+ convolution[7] * a21 + convolution[8] * a22;	//目前在多少行	int rowCount = (y * (N + 2) + x) / (N + 2);	int posOffset = (N + 2) + (rowCount - 1) * 2 + 1;	b[y * (N + 1) + x - posOffset] = ret;}void Matrix_init_gpu(float *a) {	curandGenerator_t gen;	curandCreateGenerator(&gen, CURAND_RNG_PSEUDO_MRG32K3A);	curandSetPseudoRandomGeneratorSeed(gen, 11ULL);	curandGenerateUniform(gen, a, N * N);}int main() {	float *p_original_gpu;	float *p_original_cpu;	float *p_final_cpu;	float *p_final_gpu;	float *p_ret_gpu;	float *p_ret_cpu;	p_original_cpu = (float*)malloc(N * N * sizeof(float));	p_final_cpu = (float*)malloc((N + 2) * (N + 2) * sizeof(float));	p_ret_cpu = (float*)malloc(N * N * sizeof(float));	cudaMalloc((void**)&p_original_gpu, N * N * sizeof(float));	cudaMalloc((void**)&p_final_gpu, (N + 2) * (N + 2) * sizeof(float));	cudaMalloc((void**)&p_ret_gpu, N * N * sizeof(float));	Matrix_init_gpu(p_original_gpu);	cudaMemcpy(p_original_cpu, p_original_gpu, N * N * sizeof(float), cudaMemcpyDeviceToHost);	for(int i = 0; i < N * N; i++){		if (i % N == 0) printf("\n");		cout << p_original_cpu[i] << " ";	}	//开始填充数据	for (int i = 0; i < (N + 2) * (N + 2); i++) {		p_final_cpu[i] = 0;	}		int pos = 0;	for (int i = N + 2; i < (N + 2) * (N + 1); i++) {		if (i % (N + 2) != 0 && i % (N + 2) != N + 1) {			p_final_cpu[i] = p_original_cpu[pos++];		}	}			cout << "\n\n填充数据:" << endl;	for (int i = 0; i < (N + 2) * (N + 2); i++) {		if (i % (N + 2) == 0) printf("\n");		cout << p_final_cpu[i] << " ";	}	cout << "\n\n最后结果:" << endl;	cudaMemcpy(p_final_gpu, p_final_cpu, (N + 2) * (N + 2) * sizeof(float), cudaMemcpyHostToDevice);	Matrix_convolution << <1, (N + 2) * (N + 2) >> >(p_final_gpu, p_ret_gpu);	cudaMemcpy(p_ret_cpu, p_ret_gpu, N * N * sizeof(float), cudaMemcpyDeviceToHost);	for (int i = 0; i < N * N; i++) {		if (i % N == 0) printf("\n");		cout << p_ret_cpu[i] << " ";	}	cudaFree(p_original_gpu);	cudaFree(p_final_gpu);	cudaFree(p_ret_gpu);	free(p_original_cpu);	free(p_final_cpu);	free(p_ret_cpu);	getchar();	return 0;}
    
   

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