Question

I have written this test application: it goes through iterations from 0 to 9999, for each integer in the range it calculates some useless but calculation-intensive function. As a result the program outputs the sum of function values. To make it run on several threads I’m using InterlockedIncrement – if after increment the iteration number is <10000 then a thread processes this iteration, otherwise it terminates.

I am wondering why it is not scaling as well as I would like it to. With 5 threads it runs 8s versus 36s with a single thread. This gives ~4.5 scalability. During my experiments with OpenMP (on slightly different problems) I was getting much better scalability.

The source code is shown below.

I am running Windows7 OS on a Phenom II X6 desktop. Don’t know what other parameters might be relevant.

Could you please help me explain this sub-optimal scalability?
Many thanks.

#include <boost/thread.hpp>
#include <boost/shared_ptr.hpp>
#include <boost/make_shared.hpp>
#include <vector>
#include <windows.h>
#include <iostream>
#include <cmath>

using namespace std;
using namespace boost;

struct sThreadData
{
  sThreadData() : iterCount(0), value( 0.0 ) {}
  unsigned iterCount;
  double value;
};

volatile LONG g_globalCounter;
const LONG g_maxIter = 10000;

void ThreadProc( shared_ptr<sThreadData> data )
{
  double threadValue = 0.0;
  unsigned threadCount = 0;

  while( true )
  {
    LONG iterIndex = InterlockedIncrement( &g_globalCounter );
    if( iterIndex >= g_maxIter )
      break;

    ++threadCount;

    double value = iterIndex * 0.12345777;
    for( unsigned i = 0; i < 100000; ++i )
      value = sqrt( value * log(1.0 + value) );

    threadValue += value;
  }

  data->value = threadValue;
  data->iterCount = threadCount;
}

int main()
{
  const unsigned threadCount = 1;

  vector< shared_ptr<sThreadData> > threadData;
  for( unsigned i = 0; i < threadCount; ++i )
    threadData.push_back( make_shared<sThreadData>() );

  g_globalCounter = 0;

  DWORD t1 = GetTickCount();
  vector< shared_ptr<thread> > threads;
  for( unsigned i = 0; i < threadCount; ++i )
    threads.push_back( make_shared<thread>( &ThreadProc, threadData[i] ) );

  double sum = 0.0;
  for( unsigned i = 0; i < threadData.size(); ++i )
  {
    threads[i]->join();
    sum += threadData[i]->value;
  }

  DWORD t2 = GetTickCount();
  cout << "T=" << static_cast<double>(t2 - t1) / 1000.0 << "s\n";

  cout << "Sum= " << sum << "\n";
  for( unsigned i = 0; i < threadData.size(); ++i )
    cout << threadData[i]->iterCount << "\n";

  return 0;
}

Edit:
Attaching sample output of this test program (1 and 5 threads):

Answer 1

It turned out the the results can be explained by the fact that my CPU supports the AMD Turbo Core technology.

While in Turbo CORE mode, the AMD Phenom™ II X6 1090T shifts frequency
speed from 3.2GHz on six cores, to 3.6GHz on three cores

So the clock frequencies were not the same in single-threaded mode and multi-threaded mode. I was used to playing aroung with multithreading on CPUs that don’t support TurboCore. Below is an image that shows results of

• AMD OverDrive utility window (a thing that allows to toggle TurboCore on/off)
• a run with 1 threads with TurboCore ON
• a run with 1 threads with TurboCore OFF
• a run with 5 threads
  

Many thanks to people who tried to help.