cuda笔记-一个Block多线程求卷积

ゝ一纸荒年。 2022-12-28 07:39 40阅读 0赞

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：

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxNzg0NDI3NjE_size_16_color_FFFFFF_t_70][]

![watermark_type_ZmFuZ3poZW5naGVpdGk_shadow_10_text_aHR0cHM6Ly9ibG9nLmNzZG4ubmV0L3FxNzg0NDI3NjE_size_16_color_FFFFFF_t_70 1][]

源码如下：

#include "cuda_runtime.h"
    #include "device_launch_parameters.h"
    #include "curand.h"
    #include "curand_kernel.h"
    #include <stdio.h>
    #include <iostream>
    
    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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