【MPI程序】向量乘法，向量点积（高性能计算）

末蓝、 2022-04-18 00:28 276阅读 0赞

## 简述 ##

假设，调用的节点数量整除向量的秩。

## 高性能算法 ##

让0节点来读取文件数据  
所有的节点都负责计算，然后，这里使用的是块分配法。  
其他的都是接受到数据之后，再进行计算。  
而0节点由于需要负责传输和调度，所以它会在发送数据之后，再进行计算本地的块。之后，再来接受和保存数据。

## 运行效果 ##

![在这里插入图片描述][20181106002536704.png]

输入的数据为：

10
    1 2 3 4 5 6 7 8 9 10
    2 3 4 5 6 7 8 9 10 11
    8

## 代码 ##

#include<stdio.h>
    #include<string.h>
    #include<mpi.h>
    #pragma warning(disable : 4996)
    #define MAX_STRING 100
    using namespace std;
    #include <fstream>
    #include <iostream>
    
    int Find_bin(double x, double *bin_maxes, int bin_count) { 
    	for (int i = 0; i < bin_count; ++i) { 
    		if (x < bin_maxes[i]) return i;
    	}
    	return bin_count; // 越界
    }
    
    int main(void) { 
    	double *local_vec1, *local_vec2;
    	int len;
    	double scalar, local_sum = 0;
    	int comm_sz;
    	int my_rank;
    	int divided_len;
    	MPI_Init(NULL, NULL);
    	MPI_Comm_size(MPI_COMM_WORLD, &comm_sz);
    	MPI_Comm_rank(MPI_COMM_WORLD, &my_rank);
    	// 假设 n % comm_sz = 0 整除
    	// 只有一个线程的时候不操作
    	if (comm_sz <= 1) { 
    		MPI_Finalize();
    		return 0;
    	}
    
    	if (my_rank == 0) { 
    		ifstream cin("D:\\C++\\VS\\repo\\MPI-DEMO\\MPI-DEMO\\input.txt");
    		cin >> len; // 输入数据长度
    		local_vec1 = new double[len];
    		local_vec2 = new double[len];
    
    		for (int i = 0; i < len; ++i) cin >> local_vec1[i];
    		for (int i = 0; i < len; ++i) cin >> local_vec2[i];
    		cin >> scalar;
    
    		divided_len = len / comm_sz; 
    		double * sub_vec = new double[divided_len];
    		// 发送子串长度
    		for (int i = 1; i < comm_sz; ++i) { 
    			MPI_Send(&divided_len, 1, MPI_INT, i, 3, MPI_COMM_WORLD);
    		}
    
    		// 发送第一个串
    		int target = 1;
    		for (int i = divided_len; i < len; i += divided_len) { 
    			for (int j = 0; j < divided_len; ++j) { 
    				sub_vec[j] = local_vec1[j + i];
    			}
    			MPI_Send(sub_vec, divided_len, MPI_DOUBLE, target, 0, MPI_COMM_WORLD);
    			target++;
    		}
    		// 发送第二个串
    		target = 1;
    		for (int i = divided_len; i < len; i += divided_len) { 
    			for (int j = 0; j < divided_len; ++j) { 
    				sub_vec[j] = local_vec2[j + i];
    			}
    			MPI_Send(sub_vec, divided_len, MPI_DOUBLE, target, 1, MPI_COMM_WORLD);
    			target++;
    		}
    		// 发送因子
    		for (int i = 1; i < target; ++i) { 
    			MPI_Send(&scalar, 1, MPI_DOUBLE, i, 2, MPI_COMM_WORLD);
    		}
    		
    		// 计算本地的数据
    		for (int i = 0; i < divided_len; ++i) { 
    			local_sum += (local_vec1[i] * local_vec2[i]);
    			local_vec1[i] *= scalar;
    			local_vec2[i] *= scalar;
    		}
    
    		// 接受第一个串
    		target = 1;
    		for (int i = divided_len; i < len; i += divided_len) { 
    			MPI_Recv(sub_vec, divided_len, MPI_DOUBLE, target, 0, MPI_COMM_WORLD, MPI_STATUSES_IGNORE);
    			for (int j = 0; j < divided_len; ++j) { 
    				 local_vec1[j + i] = sub_vec[j];
    			}
    			target++;
    		}
    		// 接受第二个串
    		target = 1;
    		for (int i = divided_len; i < len; i += divided_len) { 
    			MPI_Recv(sub_vec, divided_len, MPI_DOUBLE, target, 1, MPI_COMM_WORLD, MPI_STATUSES_IGNORE);
    			for (int j = 0; j < divided_len; ++j) { 
    				local_vec2[j + i] = sub_vec[j];
    			}
    			target++;
    		}
    		// 接受部分和
    		for (int i = 1; i < target; ++i) { 
    			MPI_Recv(&scalar, 1, MPI_DOUBLE, i, 2, MPI_COMM_WORLD, MPI_STATUSES_IGNORE);
    			local_sum += scalar;
    		}
    
    		// 输出
    
    		for (int i = 0; i < len; ++i) { 
    			cout << local_vec1[i] << " ";
    		}cout << endl;
    
    		for (int i = 0; i < len; ++i) { 
    			cout << local_vec2[i] << " ";
    		}cout << endl;
    
    		cout << local_sum << endl;
    
    		delete[] sub_vec;
    		delete[] local_vec1;
    		delete[] local_vec2;
    	} // 运算的子节点
    	else { 
    		MPI_Recv(&divided_len, 1, MPI_INT, 0, 3, MPI_COMM_WORLD, MPI_STATUSES_IGNORE);
    		local_vec1 = new double[divided_len];
    		local_vec2 = new double[divided_len];
    		MPI_Recv(local_vec1, divided_len, MPI_DOUBLE, 0, 0, MPI_COMM_WORLD, MPI_STATUSES_IGNORE);
    		MPI_Recv(local_vec2, divided_len, MPI_DOUBLE, 0, 1, MPI_COMM_WORLD, MPI_STATUSES_IGNORE);
    		MPI_Recv(&scalar, 1, MPI_DOUBLE, 0, 2, MPI_COMM_WORLD, MPI_STATUSES_IGNORE);
    
    		for (int i = 0; i < divided_len; ++i)
    		{ 
    			local_sum += local_vec1[i] * local_vec2[i];
    			local_vec1[i] *= scalar;
    			local_vec2[i] *= scalar;
    		}
    		MPI_Send(local_vec1, divided_len, MPI_DOUBLE, 0, 0, MPI_COMM_WORLD);
    		MPI_Send(local_vec2, divided_len, MPI_DOUBLE, 0, 1, MPI_COMM_WORLD);
    		MPI_Send(&local_sum, 1, MPI_DOUBLE, 0, 2, MPI_COMM_WORLD);
    		delete[] local_vec1;
    		delete[] local_vec2;
    	}
    	MPI_Finalize();
    
    	return 0;
    }

[20181106002536704.png]: /images/20220418/3fb58eefc3ed48dc810a70bccd6c34af.png