OpenMp C++ algorithms for min, max, median, average

I was searching Google for a page offering some simple OpenMp algorithms. Probably there is an example to calculate min, max, median, average from a huge data array but I am not capable to find it.

我正在谷歌搜索一个提供一些简单的OpenMp算法的页面。可能有一个例子来计算巨大数据阵列的最小值，最大值，中值，平均值，但我无法找到它。

At least I would normally try to divide the array into one chunk for each core and do some boundary calculation afterwards to get the result for the complete array.

至少我通常会尝试将数组划分为每个核心的一个块，然后进行一些边界计算以获得完整数组的结果。

I just didn't want to reinvent the wheel.

我只是不想重新发明*。

Additional Remark: I know that there are thousands of examples that work with simple reduction. e.g. Calculating PI.

补充说明：我知道有成千上万的例子可以简单地减少。例如计算PI。

const int num_steps = 100000; 
double x, sum = 0.0; 
const double step = 1.0/double(num_steps); 
#pragma omp parallel for reduction(+:sum) private(x) 
for (int i=1;i<= num_steps; i++){ 
  x = double(i-0.5)*step; 
  sum += 4.0/(1.0+x*x); 
} 
const double pi = step * sum;

but when these kind of algorithms aren't usable there are almost no examples left for reducing algorithms.

但是当这些算法不可用时，几乎没有留下用于减少算法的例子。

4 个解决方案

#1

OpenMP (at least 2.0) supports reduction for some simple operations, but not for max and min.

OpenMP（至少2.0）支持减少一些简单的操作，但不支持max和min。

In the following example the reduction clause is used to make a sum and a critical section is used to update a shared variable using a thread-local one without conflicts.

在以下示例中，reduction子句用于生成总和，而临界部分用于使用线程本地更新共享变量而不会发生冲突。

#include <iostream>
#include <cmath>

int main()
{
  double sum = 0;
  uint64_t ii;
  uint64_t maxii = 0;
  uint64_t maxii_shared = 0;
#pragma omp parallel shared(maxii_shared) private(ii) firstprivate(maxii)
  {
#pragma omp for reduction(+:sum) nowait
    for(ii=0; ii<10000000000; ++ii)
      {
        sum += std::pow((double)ii, 2.0);
        if(ii > maxii) maxii = ii;
      }
#pragma omp critical 
    {
      if(maxii > maxii_shared) maxii_shared = maxii;
    }
  }
  std::cerr << "Sum: " << sum << " (" << maxii_shared << ")" << std::endl;
}

EDIT: a cleaner implementation:

编辑：更清洁的实现：

#include <cmath>
#include <limits>
#include <vector>
#include <iostream>
#include <algorithm>
#include <tr1/random>

// sum the elements of v
double sum(const std::vector<double>& v)
{
  double sum = 0.0;
#pragma omp parallel for reduction(+:sum)
  for(size_t ii=0; ii< v.size(); ++ii)
    {
      sum += v[ii];
    }
  return sum;
}

// extract the minimum of v
double min(const std::vector<double>& v)
{
  double shared_min;
#pragma omp parallel 
  {
    double min = std::numeric_limits<double>::max();
#pragma omp for nowait
    for(size_t ii=0; ii<v.size(); ++ii)
      {
        min = std::min(v[ii], min);
      }
#pragma omp critical 
    {
      shared_min = std::min(shared_min, min);
    }
  }
  return shared_min;
}

// generate a random vector and use sum and min functions.
int main()
{
  using namespace std;
  using namespace std::tr1;

  std::tr1::mt19937 engine(time(0));
  std::tr1::uniform_real<> unigen(-1000.0,1000.0);
  std::tr1::variate_generator<std::tr1::mt19937, 
    std::tr1::uniform_real<> >gen(engine, unigen);

  std::vector<double> random(1000000);
  std::generate(random.begin(), random.end(), gen);

  cout << "Sum: " << sum(random) << " Mean:" << sum(random)/random.size()
       << " Min:" << min(random) << endl;
}

#2

in OpenMP 3.1 onwards one can implement for min, max through reduction clause, you can have a look at detailed example covering this in this link.

在OpenMP 3.1中可以通过简化子句实现min，max，你可以在这个链接中查看详细的例子。

#3

OpenMP doesn't support these reduction operations. Consider Intel Threading Building Blocks' parallel_reduce algorithm, where you can implement arbitrary algorithm.

OpenMP不支持这些还原操作。考虑英特尔线程构建模块的parallel_reduce算法，您可以在其中实现任意算法。

Here an example. It uses summation of partial results. You may implement any function you want.

这是一个例子。它使用部分结果的总和。您可以实现任何您想要的功能。

#include <stdio.h>
#include <tbb/blocked_range.h>
#include <tbb/parallel_reduce.h>
#include <tbb/task_scheduler_init.h>


///////////////////////////////////////////////////////////////////////////////


class PiCalculation
{
private:
    long num_steps;
    double step;

public:

    // Pi partial value
    double pi;

    // Calculate partial value
    void operator () (const tbb::blocked_range<long> &r) 
    {
        double sum = 0.0;

        long end = r.end();

        for (int i = r.begin(); i != end; i++)
        {
            double x = (i + 0.5) * step;
            sum += 4.0/(1.0 + x * x);
        }

        pi += sum * step;
    }

    // Combine results. Here you can implement any functions
    void join(PiCalculation &p)
    {
        pi += p.pi;
    }

    PiCalculation(PiCalculation &p, tbb::split)
    {
        pi = 0.0;
        num_steps = p.num_steps;
        step = p.step;
    }

    PiCalculation(long steps)
    {
        pi = 0.0;
        num_steps = steps;
        step = 1./(double)num_steps;
    }
};


///////////////////////////////////////////////////////////////////////////////


int main()
{
    tbb::task_scheduler_init init;

    const long steps = 100000000;

    PiCalculation pi(steps);

    tbb::parallel_reduce(tbb::blocked_range<long>(0, steps, 1000000), pi);

    printf ("Pi is %3.20f\n", pi.pi);
}

Please check this link for additional reduction algorithms. http://cache-www.intel.com/cd/00/00/30/11/301132_301132.pdf#page=19 Please look through paragraph 3.3.1. There is an example on finding minimum value in an array.

请检查此链接以获取其他减少算法。 http://cache-www.intel.com/cd/00/00/30/11/301132_301132.pdf#page=19请仔细阅读第3.3.1段。有一个关于在数组中找到最小值的示例。

#4

This are typical reduction problems.

这是典型的减少问题。

Besides the page pointed by Suvesh, you might have a look at the documentation for the reduction clause.

除了Suvesh指出的页面，您可以查看还原子句的文档。

#1