并行程序设计 MPI实现矩阵乘法（按行并行，分块并行，Cannon卡农算法）

文章目录

Ubuntu配置环境：不需要看那些乱七八糟教程，只需要一行：
sudo apt-get install mpich2

最简单的实现-按行并行

主进程不参与计算，只负责分发和收集数据。在主进程中，A按行划分为大致相等的部分，然后将部分的A和全部的B传递给子进程。子进程计算部分乘法并返回结果，主进程收集并报告结果。
使用最简单的Send和Recv进行通信。

#include <mpi.h>
#include <iostream>
#include <cstdlib>
#include <ctime>
using namespace std;
const int N = 317; // 随便写的，随机数位于[0, N]之间

void matGene(int *A, int row, int column){
    srand(time(NULL));
    for (int i = 0; i < row; i++)
        for (int j = 0; j < column; j++)
            A[i * row + j] = rand() % N; //A[i][j]
}

void matMulti(int *A, int *B, int*C, int row, int n){
    for (int i = 0; i < row; i++){
        for (int j = 0; j < n; j++){
            C[i*n + j] = 0;
            for (int k = 0; k < n; k++) 
                C[i*n + j] = A[i*n + k] * B[k*n + j];
        }
    }
}

int main(int argc, char *argv[]){
    // Sorry but Only Deal With Square Matrixs

    // To Run: 
    // mpicxx matrix_multi.cpp 
    // mpiexec -n comm_sz ./a.out mat_dim

    // Calculate Parameters Definition
    int n = atoi(argv[1]); // matrix dimension
    int beginRow, endRow; // the range of rows calculating in certain process
    double beginTime, endTime; // time record

    // MPI Common Head
    int my_rank = 0, comm_sz = 0;
    MPI_Init(&argc, &argv);
    MPI_Comm_rank(MPI_COMM_WORLD, &my_rank);
    MPI_Comm_size(MPI_COMM_WORLD, &comm_sz);
    MPI_Status status;

    if (comm_sz == 1){ // no parallel
        // Prepare data
        int* A = new int[n * n];
        int* B = new int[n * n];
        int* C = new int[n * n];
        matGene(A, n, n);
        matGene(B, n, n);

        // Calculate C[i][j] & Time
        beginTime = MPI_Wtime();
        matMulti(A, B, C, n, n);
        endTime = MPI_Wtime();
        cout << "Time: " << endTime - beginTime << endl;

        // End
        delete[] A;
        delete[] B;
        delete[] C;
    }

    else{ // parallel: main process collect the result, others calculate for result of "each_row" rows

        int each_row = n / (comm_sz - 1);
        
        if (my_rank == 0){ // process 0: main process, data distribution & collect calculate results
            // Prepare data
            int* A = new int[n * n];
            int* B = new int[n * n];
            int* C = new int[n * n];
            matGene(A, n, n);
            matGene(B, n, n);  

            beginTime = MPI_Wtime();

            // Send: A[beginRow:endRow, :], whole B
            // beginRow = each_row * (my_rank-1), endRow = each_row * my_rank;
            for (int i = 0; i < comm_sz-1; i++){
                beginRow = each_row * i, endRow = each_row * (i + 1);
                MPI_Send(&A[beginRow * n + 0], each_row * n, MPI_INT, i + 1, 1, MPI_COMM_WORLD);
                MPI_Send(&B[0 * n + 0], n * n, MPI_INT, i + 1, 2, MPI_COMM_WORLD);
                // cout << "I am alive in sending data to process " << i << endl;
            }

            // Recv: C[beginRow:endRow, :]
            for (int i = 0; i < comm_sz-1; i++){
                MPI_Recv(&C[beginRow * n + 0], each_row*n, MPI_INT, i + 1, 3, MPI_COMM_WORLD, &status);
                // cout << "I am alive in receiving data from process " << i << endl;
            }

            endTime = MPI_Wtime();
            cout << "Time: " << endTime - beginTime << endl;
     
            delete[] A;
            delete[] B;
            delete[] C;
        }
     
        if (my_rank != 0){ // other processes: calculate A * B

            // beginRow = each_row * (my_rank-1), endRow = each_row * my_rank;

            int* A = new int[each_row * n]; // A[beginRow:endRow, :]
            int* B = new int[n * n];
            int* C = new int[each_row * n]; // C[beginRow:endRow, :]
            
            MPI_Recv(&A[0 * n + 0], each_row * n, MPI_INT, 0, 1, MPI_COMM_WORLD, &status);//从A[0][0]和B[0][0]开始接受
            MPI_Recv(&B[0 * n + 0], n * n, MPI_INT, 0, 2, MPI_COMM_WORLD, &status);

            matMulti(A, B, C, each_row, n);


            // cout << "Hello and I am process " << my_rank << endl;

            MPI_Send(&C[0 * n + 0], each_row*n, MPI_INT, 0, 3, MPI_COMM_WORLD);//起始地址是C[my_rank-1][0],大小是each_row*n

            delete[] A;
            delete[] B;
            delete[] C;
        }
    }

    MPI_Finalize();
    return 0;
}

第一次改进-用Scatter, Gather, Bcast分发数据

A平均分配给各个子进程，用Scatter分发更合适；B要传递给每个子进程，用Broadcast(Bcast)通信更合适。收集计算结果使用Gather.
注意：
1.改用Scatter分配数据后，每个进程分配的部分矩阵具有完全相等的规模。因此。记comm_sz为进程数，矩阵的维度需要是comm_sz的倍数。我们将矩阵的维度扩展到comm_sz的倍数，多余的部分用0填充，保证正确性。

if(n % comm_sz != 0){
    n -= n % comm_sz;
    n += comm_sz;
}

2.改用Scatter分配数据后，计算任务平均地分配给每一个进程，所以主进程不仅要分发收集结果，也要参与计算。起初我没有注意到这个情况，而将矩阵按(comm_sz-1)的倍数填充维度，导致分发的不平均。如果分发的行数不够，就不能保证结果正确；如果分发的行数超出，就会出现很多不同类型的内存错误（大多都源于free时内存泄漏等原因）。调这个bug很有意思，在调整指令执行顺序（比如delete的先后），调整进程数和矩阵维度时都会报不同类型的错误信息，使人误入歧途。

#include <mpi.h>
#include <iostream>
#include <cstdlib>
#include <ctime>
using namespace std;

// To Run: 
// mpicxx matrix_multi_improvement1.cpp 
// mpiexec -n 4 ./a.out 64

// Improvement 1: 
// Matrix A: different processes deal with different components, so Scatter instead of Send/Recv
// Matrix B: Shared by all processes, so Bcast instead of Send/Recv
// Matrix C: receive different components from different processes, so Gather instead of Send/Recv
// Main process should also involve in calculation

void matGene(int *A, int size, int actual_size){
    // actual size: the matrix we use may have a larger dimension than n * n
    for (int i = 0; i < actual_size; i++){
        for (int j = 0; j < actual_size; j++){
            if(i < size && j < size) A[i * actual_size + j] = rand() % 5; //A[i][j]
            else A[i * actual_size + j] = 0;
        }
    }
}

void matMulti(int *A, int *B, int*C, int row, int n){
    for (int i = 0; i < row; i++){
        for (int j = 0; j < n; j++){
            C[i*n + j] = 0;
            for (int k = 0; k < n; k++) 
                C[i*n + j] += A[i*n + k] * B[k*n + j];
        }
    }
}

int main(int argc, char *argv[]){
    // Only Deal With Square Matrixs

    // Calculate Parameters Definition
    int n = atoi(argv[1]); // matrix dimension
    // int beginRow, endRow; // the range of rows calculating in certain process
    double beginTime, endTime; // time record
    srand(time(NULL));

    // MPI Common Head
    int my_rank = 0, comm_sz = 0;
    MPI_Init(&argc, &argv);
    MPI_Comm_rank(MPI_COMM_WORLD, &my_rank);
    MPI_Comm_size(MPI_COMM_WORLD, &comm_sz);
    MPI_Status status;

    if (comm_sz == 1){ // no parallel
        // Prepare data
        int* A = new int[n * n + 2];
        int* B = new int[n * n + 2];
        int* C = new int[n * n + 2];
        int saveN = n;
        matGene(A, saveN, n);
        matGene(B, saveN, n);

        // Calculate C[i][j] & Time
        beginTime = MPI_Wtime();
        matMulti(A, B, C, n, n);
        endTime = MPI_Wtime();
        cout << "Time: " << endTime - beginTime << endl;

        // Output

        cout << "A" << endl;
        for(int i = 0; i < saveN; i++){
            for(int j = 0; j < saveN; j++)
                cout << A[i * n + j] << " ";
            cout << endl;
        }

        cout << "B" << endl;
        for(int i = 0; i < saveN; i++){
            for(int j = 0; j < saveN; j++)
                cout << B[i * n + j] << " ";
            cout << endl;
        }

        cout << "C" << endl;
        for(int i = 0; i < saveN; i++){
            for(int j = 0; j < saveN; j++)
                cout << C[i * n + j] << " ";
            cout << endl;
        }

        delete[] A;
        delete[] B;
        delete[] C;
    }

    else{ // parallel: main process collect the result and also involve in calculation

        int saveN = n;

        // must equal scatter: actual n is bigger than input
        if(n % comm_sz != 0){
            n -= n % comm_sz;
            n += comm_sz;
        }

        int each_row = n / comm_sz;

        // Matrixs
        int* A = new int[n * n + 2];
        int* B = new int[n * n + 2];
        int* C = new int[n * n + 2];
        // beginRow = each_row * (my_rank-1), endRow = each_row * my_rank;
        int* partA = new int[each_row * n + 2]; // A[beginRow:endRow, :]
        int* partC = new int[each_row * n + 2]; // C[beginRow:endRow, :]
        
        if (my_rank == 0){

            // Prepare data
            cout << "n = " << n << endl;
            matGene(A, saveN, n);
            matGene(B, saveN, n);  

            beginTime = MPI_Wtime();

        }

        // data distribution & calculate results & collect 

        // Send: Scatter A, Bcast whole B
        MPI_Scatter(&A[0 * n + 0], each_row * n, MPI_INT, &partA[0 * n + 0], each_row * n, MPI_INT, 0, MPI_COMM_WORLD);
        MPI_Bcast(&B[0 * n + 0], n * n, MPI_INT, 0, MPI_COMM_WORLD);

        // All processes involve calculation
        matMulti(partA, B, partC, each_row, n);

        // Recv: Gather C
        MPI_Gather(&partC[0 * n + 0], each_row * n, MPI_INT, &C[0 * n + 0], each_row * n, MPI_INT, 0, MPI_COMM_WORLD);

        if (my_rank == 0){

            endTime = MPI_Wtime();
            cout << "Time: " << endTime - beginTime << endl;

            // Output
            
            cout << "A" << endl;
            for(int i = 0; i < saveN; i++){
                for(int j = 0; j < saveN; j++)
                    cout << A[i * n + j] << " ";
                cout << endl;
            }   

            cout << "B" << endl;
            for(int i = 0; i < saveN; i++){
                for(int j = 0; j < saveN; j++)
                    cout << B[i * n + j] << " ";
                cout << endl;
            }   

            cout << "C" << endl;
            for(int i = 0; i < saveN; i++){
                for(int j = 0; j < saveN; j++)
                    cout << C[i * n + j] << " ";
                cout << endl;
            }
        
        }

        // if(my_rank != 0)
        if(true){
            delete[] A;
            delete[] B;
            delete[] C;
            delete[] partA;
            delete[] partC;
        }
        else{
            delete[] A;
            cout << "Delete A in " << my_rank << endl;
            delete[] B;
            cout << "Delete B in " << my_rank << endl;
            delete[] C;
            cout << "Delete C in " << my_rank << endl;
            delete[] partA;
            cout << "Delete partA in " << my_rank << endl;
            delete[] partC; 
            cout << "Delete partC in " << my_rank << endl;
        }

    }

    MPI_Finalize();
    return 0;
}

第二次改进-实现分块乘法

将可用于计算的进程数（comm_sz-1）分解为a*b，然后将全体行划分为a个部分，全体列划分为b个部分，从而将整个矩阵划分为size相同的（comm_sz-1）个块。每个子进程负责计算最终结果的一块，只需要接收A对应范围的行和B对应范围的列，而不需要把整个矩阵传过去。主进程负责分发和汇总结果。
注意：
1.为了保证平均划分，矩阵仍需要扩展，具体操作同第一次改进。
2.第二次改进继承的是最初版本，通信接口仍然使用Send/Recv，所以进程编号要管理好。另外，因为主进程不能和自己通信，所以主进程没有参与局部计算，这点和第一次改进不同。

#include <mpi.h>
#include <iostream>
#include <cstdlib>
#include <ctime>
#include <cmath>
using namespace std;

// To Run: 
// mpicxx matrix_multi_improvement2.cpp 
// mpiexec -n 4 ./a.out 64

// Improvement 2: Block Multiplication, based on Original
// Main process: process 0, data distribution & collect calculate results, no calculation
// Others: calculate for a block of A * B

void matGene(int *A, int size, int actual_size){
    // actual size: the matrix we use may have a larger dimension than n * n
    for (int i = 0; i < actual_size; i++){
        for (int j = 0; j < actual_size; j++){
            if(i < size && j < size) A[i * actual_size + j] = 1; //A[i][j]
            else A[i * actual_size + j] = 0;
        }
    }
}

void matMulti(int *A, int *B, int*C, int m, int n, int p){
    for (int i = 0; i < m; i++){
        for (int j = 0; j < p; j++){
            C[i*p + j] = 0;
            for (int k = 0; k < n; k++) 
                C[i*p + j] += A[i*n + k] * B[k*p + j];
        }
    }
}

int factor(int n){
    // return the largest factor that is not larger than sqrt(n)
    double sqr_root = sqrt(n);
    for(int i = sqr_root; i >= 1; i--){
        if(n % i == 0) return i;
    } 
}

int main(int argc, char *argv[]){
    // Only Deal With Square Matrixs

    // Calculate Parameters Definition
    int n = atoi(argv[1]); // matrix dimension
    // int beginRow, endRow; // the range of rows calculating in certain process
    double beginTime, endTime; // time record
    srand(time(NULL));

    // MPI Common Head
    int my_rank = 0, comm_sz = 0;
    MPI_Init(&argc, &argv);
    MPI_Comm_rank(MPI_COMM_WORLD, &my_rank);
    MPI_Comm_size(MPI_COMM_WORLD, &comm_sz);
    MPI_Status status;

    if (comm_sz == 1){ // no parallel
        // Prepare data
        int* A = new int[n * n + 2];
        int* B = new int[n * n + 2];
        int* C = new int[n * n + 2];
        int saveN = n;
        matGene(A, saveN, n);
        matGene(B, saveN, n);

        // Calculate C[i][j] & Time
        beginTime = MPI_Wtime();
        matMulti(A, B, C, n, n, n);
        endTime = MPI_Wtime();
        cout << "Time: " << endTime - beginTime << endl;

        // Output

        cout << "A" << endl;
        for(int i = 0; i < saveN; i++){
            for(int j = 0; j < saveN; j++)
                cout << A[i * n + j] << " ";
            cout << endl;
        }

        cout << "B" << endl;
        for(int i = 0; i < saveN; i++){
            for(int j = 0; j < saveN; j++)
                cout << B[i * n + j] << " ";
            cout << endl;
        }

        cout << "C" << endl;
        for(int i = 0; i < saveN; i++){
            for(int j = 0; j < saveN; j++)
                cout << C[i * n + j] << " ";
            cout << endl;
        }

        delete[] A;
        delete[] B;
        delete[] C;
    }

    else{ // parallel: main process collect the result and also involve in calculation

        int saveN = n;

        // must equal scatter: actual n is bigger than input
        if(n % (comm_sz - 1) != 0){
            n -= n % (comm_sz - 1);
            n += (comm_sz - 1);
        }

        int a = (comm_sz - 1) / factor(comm_sz - 1);
        int b = factor(comm_sz - 1);
        int each_row = n / a;
        int each_column = n / b;
        if(my_rank == 0) cout << each_row << "\t" << each_column << endl;
        
        if (my_rank == 0){

            int* A = new int[n * n + 2];
            int* B = new int[n * n + 2];
            int* C = new int[n * n + 2];

            // Prepare data
            cout << "n = " << n << endl;
            matGene(A, saveN, n);
            matGene(B, saveN, n);  

            beginTime = MPI_Wtime(); 

            // include begin not end
            // beginRow = ((my_rank - 1) / b) * each_row, endRow = beginRow + each_row;
            // beginColumn = ((my_rank - 1) % b) * each_column, endColumn = beginColumn + each_column;
            
            // Send: A[beginRow:endRow, :], B[:, beginColumn:endColumn]
            for(int i = 1; i < comm_sz; i++){
                int beginRow = ((i - 1) / b) * each_row;
                int beginColumn = ((i - 1) % b) * each_column;
                // A: beginRow ~ endRow
                MPI_Send(&A[beginRow * n + 0], each_row * n, MPI_INT, i, i, MPI_COMM_WORLD);
                // B: n times, once a row
                for(int j = 0; j < n; j++){
                    MPI_Send(&B[j * n + beginColumn], each_column, MPI_INT, i, i * n + j + comm_sz + 2, MPI_COMM_WORLD);;
                } 
            }

            // Recv: C[beginRow:endRow, beginColumn:endColumn]
            for (int i = 1; i < comm_sz; i++){
                int beginRow = ((i - 1) / b) * each_row;
                int endRow = beginRow + each_row;
                int beginColumn = ((i - 1) % b) * each_column;
                for(int j = beginRow; j < endRow; j++){
                    MPI_Recv(&C[j * n + beginColumn], each_column, MPI_INT, i, each_row * i + (j - beginRow), MPI_COMM_WORLD, &status);
                }
            }

            endTime = MPI_Wtime();
            cout << "Time: " << endTime - beginTime << endl;

            // Output
            
            cout << "A" << endl;
            for(int i = 0; i < saveN; i++){
                for(int j = 0; j < saveN; j++)
                    cout << A[i * n + j] << " ";
                cout << endl;
            }   

            cout << "B" << endl;
            for(int i = 0; i < saveN; i++){
                for(int j = 0; j < saveN; j++)
                    cout << B[i * n + j] << " ";
                cout << endl;
            }   

            cout << "C" << endl;
            for(int i = 0; i < saveN; i++){
                for(int j = 0; j < saveN; j++)
                    cout << C[i * n + j] << " ";
                cout << endl;
            }

            delete[] A;
            delete[] B;
            delete[] C;
        
        }

        else{

            int* partA = new int[each_row * n + 2]; // A[beginRow:endRow, :]
            int* partB = new int[n * each_column + 2]; // B[:, beginColumn:endColumn]
            int* partC = new int[each_row * each_column + 2]; // C[beginRow:endRow, beginColumn:endColumn]

            // include begin not end
            // beginRow = ((my_rank - 1) / b) * each_row, endRow = beginRow + each_row;
            // beginColumn = ((my_rank - 1) % b) * each_column, endColumn = beginColumn + each_column;

            // Recv: partA, partB
            MPI_Recv(&partA[0 * n + 0], each_row * n, MPI_INT, 0, my_rank, MPI_COMM_WORLD, &status);
            for(int j = 0; j < n; j++){
                MPI_Recv(&partB[j * each_column + 0], each_column, MPI_INT, 0, my_rank * n + j + comm_sz + 2, MPI_COMM_WORLD, &status);
            }
            
            matMulti(partA, partB, partC, each_row, n, each_column);    

            // Send: partC
            for(int j = 0; j < each_row; j++){
                MPI_Send(&partC[j * each_column + 0], each_column, MPI_INT, 0, each_row * my_rank + j, MPI_COMM_WORLD);
            } 

            delete[] partA;
            delete[] partB;
            delete[] partC;
        }

    }

    MPI_Finalize();
    return 0;
}

第三次改进-实现Cannon卡农算法

针对最后的实验要求，修改两个小问题：
1.主进程需要参与计算：针对此，主进程也需要有一套partABC，又因为我使用Send和Recv通信（进程不能用Send和Recv和自己通信），主进程partA/partB/C的数据直接从A/B/partC里面复制，针对此要做条件判断。
2.实现Cannon算法：Cannon算法实际上和之前的分块乘法相同，每个进程负责矩阵其中一块的计算；但partA, partB每次只传入一块（而非整行整列），虽然增加了Send和Recv的次数，但是节省了内存，用于存储partA, partB的内存减少到原来的

\frac{1}{\sqrt{comm\_sz}}

$\frac{1}{co mm _ sz}$ .
注意：
1.这里的实现比较简单，要求进程数comm_sz必须是平方数（程序中记comm_sz = a * a），同时矩阵的维度需要扩展到能被a整除。
2.通信接口使用Send/Recv，进程编号要管理好。

#include <mpi.h>
#include <iostream>
#include <cstdlib>
#include <ctime>
#include <cmath>
using namespace std;

// To Run: 
// mpicxx matrix_multi_improvement2.cpp 
// mpiexec -n 4 ./a.out 64

// Improvement 3: Cannon Algorithm + Block Multiplication, based on Improvement 2
// Main process: process 0, data distribution & collect calculate results, no calculation
// Others: calculate for a block of A * B

void matGene(int *A, int size, int actual_size){
    // actual size: the matrix we use may have a larger dimension than n * n
    for (int i = 0; i < actual_size; i++){
        for (int j = 0; j < actual_size; j++){
            if(i < size && j < size) A[i * actual_size + j] = 1; //A[i][j]
            else A[i * actual_size + j] = 0;
        }
    }
}

void matMulti(int *A, int *B, int*C, int m, int n, int p){
    for (int i = 0; i < m; i++){
        for (int j = 0; j < p; j++){
            C[i*p + j] = 0;
            for (int k = 0; k < n; k++) 
                C[i*p + j] += A[i*n + k] * B[k*p + j];
        }
    }
}

int factor(int n){
    // return the largest factor that is not larger than sqrt(n)
    double sqr_root = sqrt(n);
    for(int i = sqr_root; i >= 1; i--){
        if(n % i == 0) return i;
    } 
}

int main(int argc, char *argv[]){
    // Only Deal With Square Matrixs

    // Calculate Parameters Definition
    int n = atoi(argv[1]); // matrix dimension
    // int beginRow, endRow; // the range of rows calculating in certain process
    double beginTime, endTime; // time record
    srand(time(NULL));

    // MPI Common Head
    int my_rank = 0, comm_sz = 0;
    MPI_Init(&argc, &argv);
    MPI_Comm_rank(MPI_COMM_WORLD, &my_rank);
    MPI_Comm_size(MPI_COMM_WORLD, &comm_sz);
    MPI_Status status;

    if (comm_sz == 1){ // no parallel
        // Prepare data
        int* A = new int[n * n + 2];
        int* B = new int[n * n + 2];
        int* C = new int[n * n + 2];
        int saveN = n;
        matGene(A, saveN, n);
        matGene(B, saveN, n);

        // Calculate C[i][j] & Time
        beginTime = MPI_Wtime();
        matMulti(A, B, C, n, n, n);
        endTime = MPI_Wtime();
        cout << "Time: " << endTime - beginTime << endl;

        // Output
        /*
        cout << "A" << endl;
        for(int i = 0; i < saveN; i++){
            for(int j = 0; j < saveN; j++)
                cout << A[i * n + j] << " ";
            cout << endl;
        }

        cout << "B" << endl;
        for(int i = 0; i < saveN; i++){
            for(int j = 0; j < saveN; j++)
                cout << B[i * n + j] << " ";
            cout << endl;
        }

        cout << "C" << endl;
        for(int i = 0; i < saveN; i++){
            for(int j = 0; j < saveN; j++)
                cout << C[i * n + j] << " ";
            cout << endl;
        }
        */
        delete[] A;
        delete[] B;
        delete[] C;
    }

    else{ // parallel: main process collect the result and also involve in calculation

        int a = sqrt(comm_sz);
        if(my_rank == 0 && comm_sz != a * a){
            cout << "Not Full Square" << endl;
            MPI_Finalize();
            return 0;
        }


        int saveN = n;
        // must equal scatter: actual n is bigger than input
        if(n % a != 0){
            n -= n % a;
            n += a;
        }   

        int each_row = n / a;
        int* partA = new int[each_row * each_row + 2]; // A[beginRow:endRow, :]
        int* partB = new int[each_row * each_row + 2]; // B[:, beginColumn:endColumn]
        int* partC = new int[each_row * each_row + 2]; // C[beginRow:endRow, beginColumn:endColumn]
        int beginRow, beginColumn;
            
        // Data generation
        if (my_rank == 0){  
            // Prepare data
            cout << "n = " << n << endl;
            int* A = new int[n * n + 2];
            int* B = new int[n * n + 2];
            int* C = new int[n * n + 2];
            matGene(A, saveN, n);
            matGene(B, saveN, n); 
            for(int ii = 0; ii < n; ii++){
                for(int jj = 0; jj < n; jj++){
                    C[ii * n + jj] = 0;
                }
            }  
            beginTime = MPI_Wtime();   
        }

        for(int k = 0; k < a; k++){
            int begin_part = k * each_row;
            // k th
            // Data Distributing
            if (my_rank == 0){
                for(int i = 0; i < comm_sz; i++){
                    // A[beginRow:beginRow+each_row, begin_part:begin_part+each_row]
                    // B[begin_part:begin_part+each_row, beginColumn:beginColumn+each_row]
                    beginRow = (i / a) * each_row;
                    beginColumn = (i % a) * each_row;
                    if(i == 0){
                        // Copy Straightly
                        for(int ii = 0; ii < each_row; ii++){
                            for(int jj = 0; jj < each_row; jj++){
                                partA[ii * each_row + jj] = A[(beginRow + ii) * n + (begin_part + jj)];
                                partB[ii * each_row + jj] = A[(begin_part + ii) * n + (beginColumn + jj)];
                            }
                        }
                    }
                    else{
                        for(int ii = 0; ii < each_row; ii++){
                            MPI_Send(&A[(beginRow + ii) * n + begin_part], each_row, MPI_INT, i, i * each_row + ii, MPI_COMM_WORLD);
                            MPI_Send(&B[(begin_part + ii) * n + beginColumn], each_row, MPI_INT, i, (i + comm_sz) * each_row + ii, MPI_COMM_WORLD);
                        }
                    }
                }
            }
            // Data Receive
            if (my_rank != 0){
                for(int ii = 0; ii < each_row; ii++){
                    MPI_Recv(&partA[ii * each_row + 0], each_row, MPI_INT, 0, my_rank * each_row + ii, MPI_COMM_WORLD, &status);
                    MPI_Recv(&partB[ii * each_row + 0], each_row, MPI_INT, 0, (my_rank + comm_sz) * each_row + ii, MPI_COMM_WORLD, &status);
                }
            }
            // Calculation
            matMulti(partA, partB, partC, each_row, each_row, each_row);
            // Return Result
            if (my_rank != 0){
                for(int ii = 0; ii < each_row; ii++){
                    MPI_Send(&partC[ii * each_row + 0], each_row, MPI_INT, 0, (my_rank + 2 * comm_sz) * each_row + ii, MPI_COMM_WORLD);
                }
            }
            // Data Collection & add
            if (my_rank == 0){
                // C[beginRow:beginRow+each_row, beginColumn:beginColumn+each_row]
                for(int i = 0; i < comm_sz; i++){
                    beginRow = (i / a) * each_row;
                    beginColumn = (i % a) * each_row;
                    if(i == 0){
                        // Copy Straightly
                        for(int ii = 0; ii < each_row; ii++){
                            for(int jj = 0; jj < each_row; jj++){
                                C[(beginRow + ii) * n + (beginColumn + jj)] += partC[ii * each_row + jj];
                            }
                        }
                    }
                    else{  
                        for(int ii = 0; ii < each_row; ii++){
                            int* tmp_partC = new int[each_row + 2];
                            MPI_Recv(&tmp_partC[0], each_row, MPI_INT, i, (i + 2 * comm_sz) * each_row + ii, MPI_COMM_WORLD, &status);
                            for(int jj = 0; jj < each_row; jj++){
                                C[(beginRow + ii) * n + (beginColumn + jj)] += tmp_partC[jj];
                            }
                            delete[] tmp_partC;
                        }
                    }
                }
            }
        }

        if (my_rank == 0){
            endTime = MPI_Wtime();
            cout << "Time: " << endTime - beginTime << endl; 
            // Output   
            /*
            cout << "A" << endl;
            for(int i = 0; i < saveN; i++){
                for(int j = 0; j < saveN; j++)
                    cout << A[i * n + j] << " ";
                cout << endl;
            }       
            cout << "B" << endl;
            for(int i = 0; i < saveN; i++){
                for(int j = 0; j < saveN; j++)
                    cout << B[i * n + j] << " ";
                cout << endl;
            }       
            cout << "C" << endl;
            for(int i = 0; i < saveN; i++){
                for(int j = 0; j < saveN; j++)
                    cout << C[i * n + j] << " ";
                cout << endl;
            }
            */  
            delete[] A;
            delete[] B;
            delete[] C;
        }
        delete[] partA;
        delete[] partB;
        delete[] partC;
    }           
    MPI_Finalize();
    return 0;
}

原文链接：https://blog.csdn.net/WinterShiver/article/details/88987484

文章目录

最简单的实现-按行并行

第一次改进-用Scatter, Gather, Bcast分发数据

第二次改进-实现分块乘法

第三次改进-实现Cannon卡农算法

你可能也喜欢