Eric,
this is amazing !! Thanks very much, I have rarely seen such a compact
source example that just worked.
The timings I get are:
c_threads 1 time 0.00155731916428
c_threads 2 time 0.000829789638519
c_threads 3 time 0.000616888999939
c_threads 4 time 0.000704760551453
c_threads 5 time 0.000933599472046
c_threads 6 time 0.000809240341187
c_threads 7 time 0.000837240219116
c_threads 8 time 0.000817658901215
c_threads 9 time 0.000843930244446
c_threads 10 time 0.000861320495605
c_threads 11 time 0.000936930179596
c_threads 12 time 0.000847370624542
The optimum for my
Intel(R) Core(TM)2 Quad CPU Q9550 @ 2.83GHz
seems to be at 3 threads with .6 ms for the given test case.
I just reran my normal non-OpenMP C based code, and it takes
.84 ms (1.35x slower).
from scipy.spatial import distance
distance.cdist(a,b) takes
.9 ms -- which includes the allocation of the output array, because
there is no `out` option available.
So I'm happy that OpenMP works,
but apparently on my CPU the speed increase is not overwhelming (yet)...
Thanks,
-- Sebastian
On Wed, Feb 16, 2011 at 4:50 AM, Eric Carlson <ecarl...@eng.ua.edu> wrote:
> I don't have the slightest idea what I'm doing, but....
>
>
>
> ____
> file name - 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;
> int dynamic=0;
> omp_set_dynamic(dynamic);
> omp_set_num_threads(num_threads);
> double ax,ay, dif_x, dif_y;
> int nx1=2;
> int nx2=2;
>
>
> #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);
> }
> }
> }
>
>
> ________
>
>
> COMPILE:
> __________
> gcc -c the_lib.c -fPIC -fopenmp -ffast-math
> gcc -shared -o the_lib.so the_lib.o -lgomp -lm
>
>
> ____
>
> the_python_prog.py
> _____________
>
> from ctypes import *
> my_lib=CDLL('the_lib.so') #or full path to lib
> import numpy as np
> import time
>
> na=329
> nb=340
> a=np.random.rand(na,2)
> b=np.random.rand(nb,2)
> c=np.zeros(na*nb)
> trials=100
> max_threads = 24
> for k in range(1,max_threads):
> n_threads =c_int(k)
> na2=c_int(na)
> nb2=c_int(nb)
>
> start = time.time()
> for k1 in range(trials):
> ret =
> my_lib.dists2d(a.ctypes.data_as(c_void_p),na2,b.ctypes.data_as(c_void_p),nb2,c.ctypes.data_as(c_void_p),n_threads)
> print "c_threads",k, " time ", (time.time()-start)/trials
>
>
>
> ____
> Results on my machine, dual xeon, 12 cores
> na=329
> nb=340
> ____
>
> 100 trials each:
> c_threads 1 time 0.00109949827194
> c_threads 2 time 0.0005726313591
> c_threads 3 time 0.000429179668427
> c_threads 4 time 0.000349278450012
> c_threads 5 time 0.000287139415741
> c_threads 6 time 0.000252468585968
> c_threads 7 time 0.000222821235657
> c_threads 8 time 0.000206289291382
> c_threads 9 time 0.000187981128693
> c_threads 10 time 0.000172770023346
> c_threads 11 time 0.000164999961853
> c_threads 12 time 0.000157740116119
>
> ____
> ____
> Results on my machine, dual xeon, 12 cores
> na=3290
> nb=3400
> ______
> 100 trials each:
> c_threads 1 time 0.10744508028
> c_threads 2 time 0.0542239999771
> c_threads 3 time 0.037127559185
> c_threads 4 time 0.0280736112595
> c_threads 5 time 0.0228648614883
> c_threads 6 time 0.0194904088974
> c_threads 7 time 0.0165715909004
> c_threads 8 time 0.0145838689804
> c_threads 9 time 0.0130002498627
> c_threads 10 time 0.0116940999031
> c_threads 11 time 0.0107557415962
> c_threads 12 time 0.00990005016327 (speedup almost 11)
>
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