c++ - Openmp:无法正确计算并行 for 循环内的作业状态

标签 c++ openmp parallel.for

我正在尝试在并行 for 循环中实现任务状态报告功能。正在使用“OPENMP”执行 for 循环的这种并行化。

我希望像这样执行状态报告:

Work done 70%; estimated time left 3:30:05 hour.

当然，我可以通过计算“开始时间”和“当前时间”之间的差来计算“预计剩余时间”。但是，即使使用“静态”声明，我似乎也无法在 for 循环内准确计算“完成的工作”。

一些指导将不胜感激。

我的代码输出:

Values of cores : 8
Outer loop =================================
Thread 0  iCount0   
 % of work done 10
Outer loop ================================= 
Thread 0  iCount1
Outer loop ================================= 
Thread 2  iCount2
Outer loop ================================= 
Thread 7  iCount3
 % of work done 40
Outer loop =================================
Thread 5  iCount4
 % of work done 50
Outer loop =================================
Thread 3  iCount5
 % of work done 60
Outer loop =================================
Thread 4  iCount6 
 % of work done 70
Outer loop =================================
Thread 1  iCount7
 % of work done 20
 % of work done 80
Outer loop ================================= 
Thread 6  iCount8 
 % of work done 90
Outer loop ================================= 
Thread 1  iCount9  
 % of work done 100
 % of work done 30

正如您从最后两行输出中看到的，我无法正确计算作业的状态。

这是我的代码:

注意:我有意使用“std::endl”而不是“\n”，因为以某种方式刷新输出缓冲区会扰乱我的 work% 计算。我确信如果我在内部并行执行真正的计算，也会出现类似的情况

#include "stdafx.h"
#include <iostream>     // std::cout, std::endl
#include <iomanip>      // std::setfill, std::setw
#include <math.h>       /* pow */
#include <omp.h>

int main(int argc, char** argv)
  {
    // Get the number of processors in this system
    int iCPU = omp_get_num_procs();

    // Now set the number of threads
    omp_set_num_threads(iCPU);
    std::cout << "Values of cores : " << iCPU <<" \n";

    int x = 0; 
    int iTotalOuter = 10;
    static int iCount = 0;

    #pragma omp parallel for private(x) 
    for(int y = 0; y < iTotalOuter; y++) 
    { 
        std::cout << "Outer loop =================================\n" ;     
        std::cout <<"\nThread "<<omp_get_thread_num()<<"  iCount" << iCount<<std::endl;

        for(x = 0; x< 5; x++) 
        { 
            //std::cout << "Inner loop \n" ;        
        } 
        iCount = iCount + 1;        
        std::cout <<"\n % of work done " << (double)100*((double)iCount/(double)iTotalOuter)<<std::endl;
    }

  std::cin.ignore(); //Wait for user to hit enter
  return 0;
  }

更新: 根据“Avi Ginsburg”的回答，我正在尝试这样做:

#include "stdafx.h"
#include <iostream>     // std::cout, std::endl
#include <iomanip>      // std::setfill, std::setw
#include <math.h>       /* pow */
#include <omp.h>
void ReportJobStatus(int , int );

int main(int argc, char** argv)
  {   
    // Get the number of processors in this system
    int iCPU = omp_get_num_procs();

    // Now set the number of threads
    omp_set_num_threads(iCPU);
    std::cout << "Values of cores : " << iCPU <<" \n";

    int x = 0; 
    int iTotalOuter = 100;
    static int iCount = 0;

    #pragma omp parallel for private(x) 
    for(int y = 0; y < iTotalOuter; y++) 
    { 
        std::cout << "Outer loop =================================\n" ;     

        for(x = 0; x< 5; x++) 
        { 
            //std::cout << "Inner loop \n" ;        
        } 
        #pragma omp atomic
        iCount++;   

        std::cout<< " omp_get_thread_num(): " << omp_get_thread_num() <<"\n";
        if (omp_get_thread_num() == 0){
            ReportJobStatus(iCount, iTotalOuter);
        }

    }

  std::cin.ignore(); //Wait for user to hit enter
  return 0;
  }

问题(已更新):问题是同一线程正用于并发执行。因此，“工作完成”报告变得严重受限。如何根据数据将作业分配到不同的核心。

这是我的代码的当前输出:

Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 1
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 2
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 3
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 4
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 5
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 6
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 7
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 8
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 9
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 10
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 11
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 12
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 13
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 14
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 15
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 16
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 17
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 18
Outer loop =================================
 omp_get_thread_num(): 0
Outer loop =================================
 omp_get_thread_num(): 3
Outer loop =================================
 omp_get_thread_num(): 3
Outer loop =================================
 omp_get_thread_num(): 3
Outer loop =================================
 omp_get_thread_num(): 3
Outer loop =================================
 omp_get_thread_num(): 3
Outer loop =================================
 omp_get_thread_num(): 3
Outer loop =================================
 omp_get_thread_num(): 3
Outer loop =================================
 omp_get_thread_num(): 3
Outer loop =================================
 omp_get_thread_num(): 3
Outer loop =================================
 omp_get_thread_num(): 3
Outer loop =================================
 omp_get_thread_num(): 3
Outer loop =================================
 omp_get_thread_num(): 3
Outer loop =================================
 omp_get_thread_num(): 3
Outer loop =================================
 omp_get_thread_num(): 3
Outer loop =================================
 omp_get_thread_num(): 3
Outer loop =================================
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 1

 % of work done 19
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 54
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 55
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 56
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 57
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 58
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 59
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 60
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 61
Outer loop =================================
 omp_get_thread_num(): 0

 % of work done 62
Outer loop =================================
 omp_get_thread_num(): 6
Outer loop =================================
 omp_get_thread_num(): 6
Outer loop =================================
 omp_get_thread_num(): 6
Outer loop =================================
 omp_get_thread_num(): 6
Outer loop =================================
 omp_get_thread_num(): 6
 omp_get_thread_num(): 3
Outer loop =================================
 omp_get_thread_num(): 3
Outer loop =================================
 omp_get_thread_num(): 1
Outer loop =================================
 omp_get_thread_num(): 5
Outer loop =================================
 omp_get_thread_num(): 5
Outer loop =================================
 omp_get_thread_num(): 5
Outer loop =================================
 omp_get_thread_num(): 5
Outer loop =================================
 omp_get_thread_num(): 5
Outer loop =================================
 omp_get_thread_num(): 5
Outer loop =================================
 omp_get_thread_num(): 5
Outer loop =================================
 omp_get_thread_num(): 5
Outer loop =================================
Outer loop =================================
 omp_get_thread_num(): 4
Outer loop =================================
 omp_get_thread_num(): 4
Outer loop =================================
 omp_get_thread_num(): 4
Outer loop =================================
 omp_get_thread_num(): 4
Outer loop =================================
 omp_get_thread_num(): 4
Outer loop =================================
 omp_get_thread_num(): 4
Outer loop =================================
 omp_get_thread_num(): 4
Outer loop =================================
 omp_get_thread_num(): 4
Outer loop =================================
 omp_get_thread_num(): 4
Outer loop =================================
 omp_get_thread_num(): 4
 omp_get_thread_num(): 7
Outer loop =================================
 omp_get_thread_num(): 7
Outer loop =================================
 omp_get_thread_num(): 7
Outer loop =================================
 omp_get_thread_num(): 7
Outer loop =================================
 omp_get_thread_num(): 7
Outer loop =================================
 omp_get_thread_num(): 7
Outer loop =================================
 omp_get_thread_num(): 2
Outer loop =================================
 omp_get_thread_num(): 2
Outer loop =================================
 omp_get_thread_num(): 2
Outer loop =================================
 omp_get_thread_num(): 2
Outer loop =================================
 omp_get_thread_num(): 2
Outer loop =================================
 omp_get_thread_num(): 2
Outer loop =================================
 omp_get_thread_num(): 2

最佳答案

在循环中使用critical 或atomic:

#pragma omp critical
    {
        (++prog);
    }

或更好:

#pragma omp atomic
(++prog);

并考虑只让主线程打印进度。

if(omp_get_thread_num() == 0)
{
  cout << "Progress: " << float(prog)/totalNumber;
}

关于c++ - Openmp:无法正确计算并行 for 循环内的作业状态，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/28275795/

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