Question

I am writing simple parallel program in C++ using OpenMP.
I am working on Windows 7 and on Microsoft Visual Studio 2010 Ultimate.
I changed the Language property of the project to “Yes/OpenMP” to support OpenMP

Here I provide the code:

#include <iostream>
#include <omp.h> 

using namespace std;

double sum; 
int i;
int n = 800000000;

int main(int argc, char *argv[])
{               
    omp_set_dynamic(0);
    omp_set_num_threads(4); 

    sum = 0;    
    #pragma omp for reduction(+:sum)
    for (i = 0; i < n; i++)
        sum+= i/(n/10);

    cout<<"sum="<<sum<<endl;        

    return  EXIT_SUCCESS;
}

But, I couldn’t get any acceleration by changing the x in omp_set_num_threads(x);
It doesn’t matter if I use OpenMp or not, the calculating time is the same, about 7 seconds.

Does Someone know what is the problem?

Answer 1

Your pragma statement is missing the parallel specifier:

#include <iostream>
#include <omp.h> 

using namespace std;

double sum; 
int i;
int n = 800000000;

int main(int argc, char *argv[])
{               
    omp_set_dynamic(0);
    omp_set_num_threads(4); 

    sum = 0;    
    #pragma omp parallel for reduction(+:sum)  //  add "parallel"
    for (i = 0; i < n; i++)
        sum+= i/(n/10);

    cout<<"sum="<<sum<<endl;        

    return  EXIT_SUCCESS;
}

Sequential:

sum=3.6e+009
2.30071

Parallel:

sum=3.6e+009
0.618365

---

Here’s a version that some speedup with Hyperthreading. I had to increase the # of iterations by 10x and bump the datatypes to long long:

double sum; 
long long i;
long long n = 8000000000;

int main(int argc, char *argv[])
{               
    omp_set_dynamic(0);
    omp_set_num_threads(8); 

    double start = omp_get_wtime();


    sum = 0;    
    #pragma omp parallel for reduction(+:sum)
    for (i = 0; i < n; i++)
        sum+= i/(n/10);

    cout<<"sum="<<sum<<endl;       

    double end = omp_get_wtime(); 
    cout << end - start << endl;
    system("pause");

    return  EXIT_SUCCESS;
}

Threads: 1

sum=3.6e+014
13.0541

Threads: 2

sum=3.6e+010
6.62345

Threads: 4

sum=3.6e+010
3.85687

Threads: 8

sum=3.6e+010
3.285