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

Question

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.

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.

I just didn't want to reinvent the wheel.

---

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

Answer 1

This page contains some examples (not exactly the ones you asked for, but sorting et al.)

Should get you started.

http://msdn.microsoft.com/en-us/magazine/cc163717.aspx

Answer 2

This are typical reduction problems.

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

Answer 3

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

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

#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.

Answer 4

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

#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:

#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;
}

Answer 5

It is indeed hard to find examples but keep on searching, the examples exist out there

This https://computing.llnl.gov/tutorials/openMP/ general tutorial is a good place to start with openMP