CUDA编程--并行矩阵向量乘法【80+行代码】

简述

矩阵向量乘法。

• 读取文件data.txt
• 并输入到output.txt文件中
• 用typedef方便的修改数据类型（要是写成模板也是可以的）

代码

#include \"cuda_runtime.h\"
#include \"device_launch_parameters.h\"
#include <iostream>
#include <fstream>
#include <iomanip>
#include <stdio.h>

typedef double DATA;

// Kernal:
__global__ void MatrixMultiply(DATA *a, DATA * b, DATA *c, int N) {
	int tx = threadIdx.x + blockIdx.x * blockDim.x;
	if (tx < N) {
		DATA sum = 0;
		for (int k = 0; k < N; ++k) {
			sum += a[tx * N + k] * b[k];
		}
		c[tx] = sum;
	}
}

cudaError_t matrixMultiplyWithCuda(DATA *a, DATA *b, DATA *c, size_t size);

int main()
{
	std::ifstream in(\"data.txt\");
	int N;
	in >> N;
	if (in.fail()) {
		printf(\"Something wrong\\n\");
	}
	else {
		printf(\"Success read\\n\");
	}
	// host initial
	DATA *a = new DATA[N * N];
	DATA *b = new DATA[N];
	DATA *c = new DATA[N];

	// read 
	for (int i = 0; i < N; ++i)
		for (int j = 0; j < N; ++j) in >> a[i * N + j];

	for (int i = 0; i < N; ++i) in >> b[i];
		
	cudaError_t cudaStatus = matrixMultiplyWithCuda(a, b, c, N);

	std::ofstream out(\"output.txt\");
	for (int i = 0; i < N; ++i) {
		out << std::setiosflags(std::ios::fixed) << c[i] << \" \";
		out << std::endl;
	}
	cudaStatus = cudaThreadExit();

	// host free 
	delete[] a;
	delete[] b;
	delete[] c;
	return 0;
}
cudaError_t matrixMultiplyWithCuda(DATA *a, DATA *b, DATA *c, size_t N) {
	DATA *dev_a = 0;
	DATA *dev_b = 0;
	DATA *dev_c = 0;
	cudaError_t cudaStatus;
	cudaStatus = cudaMalloc((void**)&dev_a, N * N * sizeof(DATA));
	cudaStatus = cudaMalloc((void**)&dev_b, N * sizeof(DATA));
	cudaStatus = cudaMalloc((void**)&dev_c, N * sizeof(DATA));
	cudaStatus = cudaMemcpy(dev_a, a, N * N * sizeof(DATA), cudaMemcpyHostToDevice);
	cudaStatus = cudaMemcpy(dev_b, b, N * sizeof(DATA), cudaMemcpyHostToDevice);
	if (cudaStatus != cudaSuccess) {
		printf(\"Something wrong\\n\");
		goto Error;
	}
	// kernal invocation 
	dim3 threadPerBlock(500, 1, 1);
	dim3 numBlocks(N / threadPerBlock.x+1, 1, 1);
	MatrixMultiply<<<numBlocks, threadPerBlock>>>(dev_a, dev_b, dev_c, N);
	if (cudaStatus != cudaSuccess) {
		printf( \"Calculate wrong\\n\");
		goto Error;
	}
	cudaStatus = cudaMemcpy(c, dev_c, N * sizeof(DATA), cudaMemcpyDeviceToHost);
Error:
	cudaFree(dev_a);
	cudaFree(dev_b);
	cudaFree(dev_c);
	return cudaStatus;
}