为什么OpenMP版本较慢？ [英] Why OpenMP version is slower?

问题描述

我正在试验OpenMP。我写了一些代码来检查其性能。在使用Kubuntu 11.04的4核单CPU英特尔CPU上，使用OpenMP编译的以下程序比不使用OpenMP编译的程序慢大约20倍。为什么？

I am experimenting with OpenMP. I wrote some code to check its performance. On a 4-core single Intel CPU with Kubuntu 11.04, the following program compiled with OpenMP is around 20 times slower than the program compiled without OpenMP. Why?

我用g ++编译它-g -O2 -funroll-loops -fomit-frame-pointer -march = native -fopenmp

I compiled it by g++ -g -O2 -funroll-loops -fomit-frame-pointer -march=native -fopenmp

#include <math.h>
#include <iostream>

using namespace std;

int main ()
{
  long double i=0;
  long double k=0.7;

  #pragma omp parallel for reduction(+:i)
  for(int t=1; t<300000000; t++){       
    for(int n=1; n<16; n++){
      i=i+pow(k,n);
    }
  }

  cout << i<<"\t";
  return 0;
}

推荐答案

问题是变量k被认为是一个共享变量，因此它必须在线程之间同步。
避免这种情况的一个可能的解决方案是：

The problem is that the variable k is considered to be a shared variable, so it has to be synced between the threads. A possible solution to avoid this is:

#include <math.h>
#include <iostream>

using namespace std;

int main ()
{
  long double i=0;

#pragma omp parallel for reduction(+:i)
  for(int t=1; t<30000000; t++){       
    long double k=0.7;
    for(int n=1; n<16; n++){
      i=i+pow(k,n);
    }
  }

  cout << i<<"\t";
  return 0;
}

按照以下注释中的Martin Beckett的提示，而不是声明k inside循环，你也可以声明k const和外部循环。

Following the hint of Martin Beckett in the comment below, instead of declaring k inside the loop, you can also declare k const and outside the loop.

否则，ejd是正确的 - 这里的问题似乎并不糟糕的并行化，但是代码并行化时的优化不好。记住，gcc的OpenMP实现是相当年轻，远不是最佳。

Otherwise, ejd is correct - the problem here does not seem bad parallelization, but bad optimization when the code is parallelized. Remember that the OpenMP implementation of gcc is pretty young and far from optimal.

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