OpenMP并行映射循环 [英] OpenMP Parallelizing for loop with map

问题描述

我正在尝试并行处理扫描std :: map的for循环.下面是我的玩具程序:

I am trying to parallelize a for-loop which scans std::map. Below is my toy program:

#include <iostream>
#include <cstdio>
#include <map>
#include <string>
#include <cassert>
#include <omp.h>

#define NUM 100000

using namespace std;

int main()
{
  omp_set_num_threads(16);
  int realThreads = 0;
  string arr[] = {"0", "1", "2"};
  std::map<int, string> myMap;
  for(int i=0; i<NUM; ++i)
    myMap[i] = arr[i % 3];

  string is[NUM];

  #pragma omp parallel for
  for(map<int, string>::iterator it = myMap.begin(); it != myMap.end(); it++)
  {
    is[it->first] = it->second;
    if(omp_get_thread_num() == 0)
      realThreads = omp_get_num_threads();
  }
  printf("First for-loop with %d threads\n", realThreads);

  realThreads = 0;
  #pragma omp parallel for
  for(int i=0; i<NUM; ++i)
  {
    assert(is[i] == arr[i % 3]);
    if(omp_get_thread_num() == 0)
      realThreads = omp_get_num_threads();
  }
  printf("Second for-loop with %d threads\n", realThreads);
  return 0;
}

编译命令:

icc -fopenmp foo.cpp

上面的代码块的输出是:

The output of the above code block is:

First for-loop with 1 threads
Second for-loop with 16 threads

为什么我不能并行化第一个for循环?

Why am I not able to parallelize the first for-loop?

推荐答案

std::map不提供随机访问迭代器，仅提供常规的双向迭代器. OpenMP要求并行循环中的迭代器为随机访问类型.对于其他类型的迭代器，应改为使用显式任务:

std::map does not provide random-access iterators, only the usual bi-directional iterator. OpenMP requires that the iterators in parallel loops are of random-access type. With other kind of iterators explicit tasks should be used instead:

#pragma omp parallel
{
  #pragma omp master
  realThreads = omp_get_num_threads();

  #pragma omp single
  for(map<int, string>::iterator it = myMap.begin(); it != myMap.end(); it++)
  {
    #pragma omp task
    is[it->first] = it->second;
  }
}

请注意，在这种情况下，将为地图的每个成员创建一个单独的任务.由于任务主体在计算上非常简单，因此在特定情况下OpenMP开销将相对较高.

Note in that case a separate task is created for each member of the map. Since the task body is very computationally simple, the OpenMP overhead will be relatively high in that particular case.

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