[Numpy-discussion] numpy ufuncs and COREPY - any info?

Francesc Alted faltet@pytables....
Mon May 25 10:10:05 CDT 2009


A Monday 25 May 2009 12:59:31 Andrew Friedley escrigué:
> For some reason the list seems to occasionally drop my messages...
>
> Francesc Alted wrote:
> > A Friday 22 May 2009 13:52:46 Andrew Friedley escrigué:
> >> I'm the student doing the project.  I have a blog here, which contains
> >> some initial performance numbers for a couple test ufuncs I did:
> >>
> >> http://numcorepy.blogspot.com
> >>
> >> Another alternative we've talked about, and I (more and more likely) may
> >> look into is composing multiple operations together into a single ufunc.
> >>   Again the main idea being that memory accesses can be
> >> reduced/eliminated.
> >
> > IMHO, composing multiple operations together is the most promising venue
> > for leveraging current multicore systems.
>
> Agreed -- our concern when considering for the project was to keep the
> scope reasonable so I can complete it in the GSoC timeframe.  If I have
> time I'll definitely be looking into this over the summer; if not later.

You should know that Numexpr has already started this path for some time now.  
The fact that it already can evaluate complex array expressions like 
'a+b*cos(c)' without using temporaries (like NumPy does) should allow it to 
use multiple cores without stressing the memory bus too much.  I'm planning to 
implement such parallelism in Numexpr for some time now, but not there yet.

> I've seen that page before.  Using another source [1] I came up with a
> quick/dirty cos ufunc.  Performance is crazy good compared to NumPy
> (100x); see the latest post on my blog for a little more info.  I'll
> look at the source myself when I get time again, but is NumPy using a
> Python-based cos function, a C implementation, or something else?  As I
> wrote in my blog, the performance gain is almost too good to believe.
>
> [1] http://www.devmaster.net/forums/showthread.php?t=5784

100x?  Uh, sounds really impressing...

-- 
Francesc Alted


More information about the Numpy-discussion mailing list