Hi,
More surprises:
shaase@iris:~/code/SwiggedDistOMP: gcc -O3 -c the_lib.c -fPIC -fopenmp
-ffast-math
shaase@iris:~/code/SwiggedDistOMP: gcc -shared -o the_lib.so the_lib.o
-lgomp -lm
shaase@iris:~/code/SwiggedDistOMP: priithon the_python_prog.py
c_threads 0 time 0.000437839031219 # this is now, without
#pragma omp parallel for ...
c_threads 1 time 0.000865449905396
c_threads 2 time 0.000520548820496
c_threads 3 time 0.00033704996109
c_threads 4 time 0.000620169639587
c_threads 5 time 0.000465350151062
c_threads 6 time 0.000696349143982
This correct now the timing of, max OpenMP speed (3 threads) vs. no
OpenMP to speedup of (only!) 1.3x
Not 2.33x (which was the number I got when comparing OpenMP to the
cdist function).
The c code is now:
the_lib.c
------------------------------------------------------------------------------------------
#include <stdio.h>
#include <time.h>
#include <omp.h>
#include <math.h>
void dists2d( double *a_ps, int na,
double *b_ps, int nb,
double *dist, int num_threads)
{
int i, j;
double ax,ay, dif_x, dif_y;
int nx1=2;
int nx2=2;
if(num_threads>0)
{
int dynamic=0;
omp_set_dynamic(dynamic);
omp_set_num_threads(num_threads);
#pragma omp parallel for private(j, i,ax,ay, dif_x, dif_y)
for(i=0;i<na;i++)
{
ax=a_ps[i*nx1];
ay=a_ps[i*nx1+1];
for(j=0;j<nb;j++)
{ dif_x = ax - b_ps[j*nx2];
dif_y = ay - b_ps[j*nx2+1];
dist[2*i+j] = sqrt(dif_x*dif_x+dif_y*dif_y);
}
}
} else {
for(i=0;i<na;i++)
{
ax=a_ps[i*nx1];
ay=a_ps[i*nx1+1];
for(j=0;j<nb;j++)
{ dif_x = ax - b_ps[j*nx2];
dif_y = ay - b_ps[j*nx2+1];
dist[2*i+j] = sqrt(dif_x*dif_x+dif_y*dif_y);
}
}
}
}
------------------------------------------------------------------
$ gcc -O3 -c the_lib.c -fPIC -fopenmp -ffast-math
$ gcc -shared -o the_lib.so the_lib.o -lgomp -lm
So, I guess I found a way of getting rid of the OpenMP overhead when
run with 1 thread,
and found that - if measured correctly, using same compiler settings
and so on - the speedup is so small that there no point in doing
OpenMP - again.
(For my case, having (only) 4 cores)
Cheers,
Sebastian.
On Thu, Feb 17, 2011 at 10:57 AM, Matthieu Brucher
<matthieu.bruc...@gmail.com> wrote:
>
>> Then, where does the overhead come from ? --
>> The call to omp_set_dynamic(dynamic);
>> Or the
>> #pragma omp parallel for private(j, i,ax,ay, dif_x, dif_y)
>
> It may be this. You initialize a thread pool, even if it has only one
> thread, and there is the dynamic part, so OpenMP may create several chunks
> instead of one big chunk.
>
> Matthieu
> --
> Information System Engineer, Ph.D.
> Blog: http://matt.eifelle.com
> LinkedIn: http://www.linkedin.com/in/matthieubrucher
>
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion@scipy.org
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>
>
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