【发布时间】:2015-01-26 05:33:10
【问题描述】:
我正在尝试使用 OpenMPI 1.6.4 版编写一个 3D 并行计算泊松求解器。
以下部分是我使用阻塞发送接收进行并行计算的代码。
以下变量在另一个文件中声明。
int px = lx*meshx; //which is meshing point in x axis.
int py = ly*meshy;
int pz = lz*meshz;
int L = px * py * pz
以下代码运行良好,而
lx=ly=lz=10;
meshx=meshy=2, meshz=any int number.
meshx 和 meshy 大于 4 时发送 recv 部分失败。
挂在那里等待发送或接收数据的程序。
但如果我只将数据从一个处理器发送到另一个处理器,而不是交换数据,它就可以工作。 (即:从等级 0 发送到 1,但不要从 1 发送到 0)
当 meshx 和 meshy 很小但网格数 x y 很大时,我无法理解这段代码是如何工作的。
阻塞发送接收进程是否会自行中断,或者我在代码中混淆了处理器?这与我的数组大小有关系吗?
#include "MPI-practice.h"
# include <iostream>
# include <math.h>
# include <string.h>
# include <time.h>
# include <sstream>
# include <string>
# include "mpi.h"
using namespace std;
extern int px,py,pz;
extern int L;
extern double simTOL_phi;
extern vector<double> phi;
int main(int argc, char *argv[]){
int numtasks, taskid, offset_A, offset_B, DD_loop,s,e;
double errPhi(0),errPhi_sum(0);
MPI_Init(&argc, &argv);
MPI_Comm_size(MPI_COMM_WORLD, &numtasks);
MPI_Comm_rank(MPI_COMM_WORLD, &taskid);
MPI_Status status;
if((pz-1)%numtasks!=0){
//cerr << "can not properly divide meshing points."<<endl;
exit(0);
}
offset_A=(pz-1)/numtasks*px*py;
offset_B=((pz-1)/numtasks+1)*px*py;
s=offset_A*taskid;
e=offset_A*taskid+offset_B;
int pz_offset_A=(pz-1)/numtasks;
int pz_offset_B=(pz-1)/numtasks+1;
stringstream name1;
string name2;
Setup_structure();
Initialize();
Build_structure();
if (taskid==0){
//master processor
ofstream output;
output.open("time", fstream::out | fstream::app);
output.precision(6);
clock_t start,end;
start=clock();
do{
errPhi_sum=0;
errPhi=Poisson_inner(taskid,numtasks,pz_offset_A,pz_offset_B);
//Right exchange
MPI_Send(&phi[e-px*py], px*py, MPI_DOUBLE, taskid+1, 1, MPI_COMM_WORLD);
MPI_Recv(&phi[e], px*py, MPI_DOUBLE, taskid+1, 1, MPI_COMM_WORLD, &status);
MPI_Allreduce ( &errPhi, &errPhi_sum, 1, MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD );
}while(errPhi_sum>simTOL_phi);
end=clock();
output << "task "<< 0 <<" = "<< (end-start)/CLOCKS_PER_SEC <<endl<<endl;
Print_to_file("0.txt");
//recv from slave
for (int i=1;i<numtasks;i++){
MPI_Recv(&phi[offset_A*i], offset_B, MPI_DOUBLE, i, 1, MPI_COMM_WORLD, &status);
}
Print_to_file("sum.txt");
}
else{
//slave processor
do{
errPhi=Poisson_inner(taskid,numtasks,pz_offset_A,pz_offset_B);
//Left exchange
MPI_Send(&phi[s+px*py], px*py, MPI_DOUBLE, taskid-1, 1, MPI_COMM_WORLD);
MPI_Recv(&phi[s], px*py, MPI_DOUBLE, taskid-1, 1, MPI_COMM_WORLD, &status);
//Right exchange
if(taskid!=numtasks-1){
MPI_Send(&phi[e-px*py], px*py, MPI_DOUBLE, taskid+1, 1, MPI_COMM_WORLD);
MPI_Recv(&phi[e], px*py, MPI_DOUBLE, taskid+1, 1, MPI_COMM_WORLD, &status);
}
MPI_Allreduce ( &errPhi, &errPhi_sum, 1, MPI_DOUBLE, MPI_SUM, MPI_COMM_WORLD );
}while(errPhi_sum>simTOL_phi);
//send back master
MPI_Send(&phi[s], offset_B, MPI_DOUBLE, 0, 1, MPI_COMM_WORLD);
name1<<taskid<<".txt";
name2=name1.str();
Print_to_file(name2.c_str());
}
MPI_Finalize();
}
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标签: c++ parallel-processing mpi simulator