Hello,
Does this require a MATLAB install, or are these equivalent routines?
Thanks,
Eric
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Hello,
For most people who want to be doing amazing things through python with
little fuss, you'd probably be better off downloading a comprehensive
distribution that includes many useful modules. Some examples of several -
For windows:
Pythonxy - http://code.google.com/p/pythonxy/wiki/Downloads
return the_result
else:
return reshape(the_result,[the_shape[0],the_shape[1],2])
fxy = lambda x,y: sin(x*y) #just a little test
x,y=mgrid[0:5,0:4]
the_gradient = gradient2D_vect(fxy, x,y)
Cheers,
Eric Carlson
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I'm not sure if I am addressing your question on vectorizing directly,
but consider the following code, which does (maybe?) what your asking.
import scipy
from numpy import reshape,ones, zeros, arange, array
A=reshape(arange(100),[10,10])
nr,nc=A.shape
B=zeros(A.shape) #initialize array
#calcu
For 4 cores, on your system, your conclusion makes some sense. That
said, I played around with this on both a core 2 duo and the 12 core
system. For the 12-core system, on my tests the 0 case ran extremely
close to the 2-thread case for all my sizes.
The core 2 duo runs windows 7, and after dow
Hello Francesc,
The problem appears to related to my lack of optimization in the
compilation. If I use
gcc -O3 -c my_lib.c -fPIC -fopenmp -ffast-math
the C executable and ctypes/python versions behave almost identically.
Getting decent behavior takes some thought, though, far from the
incredi
Sebastian,
Optimization appears to be important here. I used no optimization in my
previous post, so you could try the -O3 compile option:
gcc -O3 -c my_lib.c -fPIC -fopenmp -ffast-math
for na=329 and nb=340 I get (about 7.5 speedup)
c_threads 1 time 0.00103106021881
c_threads 2 time 0.000
I don't have the slightest idea what I'm doing, but
file name - the_lib.c
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#include
#include
#include
#include
void dists2d( double *a_ps, int na,
double *b_ps, int nb,
double *dist, int num_threads)
{
int i, j;
int dynamic=0;
Hello All,
I have been toying with OpenMP through f2py and ctypes. On the whole,
the results of my efforts have been very encouraging. That said, some
results are a bit perplexing.
I have written identical routines that I run directly as a C-derived
executable, and through ctypes as a shared li
last until at least 12/8/2012,
with a good chance for extension for a few months after that.
Regards,
Eric Carlson, Associate Professor
University of Alabama
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>On 1/23/2011 2:57 PM, Vladimir Voznesensky wrote:
>
> Sure, I will give you my code, but who will "follow this up"?
>
Hey Vladimir,
A good question. At this point, I am most curious about the difficulties
of using this as a standard built into numpy.
EC
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On 1/23/2011 10:36 AM, Vladimir Voznesensky wrote:
> My computer has 12 hyperthreaded cores.
> My application uses dot multiplication from Intel MKL, that accelerated
> it by ~ 5 times.
> After OpenMP-fication of loops.c.src, my app was accelerated by ~12-15
> times.
>
I was greatly disappointed i
As a user, I am very interested. That said, do you any tests or examples
or benchmarks that give a ballpark estimate of performance improvements?
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