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

Charles R Harris charlesr.harris@gmail....
Mon May 25 09:51:26 CDT 2009

On Mon, May 25, 2009 at 4:59 AM, Andrew Friedley <afriedle@indiana.edu>wrote:

> 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.
> > Another interesting approach is to implement costly operations (from the
> point
> > of view of CPU resources), namely, transcendental functions like sin, cos
> or
> > tan, but also others like sqrt or pow) in a parallel way.  If besides,
> you can
> > combine this with vectorized versions of them (by using the well spread
> SSE2
> > instruction set, see [1] for an example), then you would be able to
> achieve
> > really good results for sure (at least Intel did with its VML library ;)
> >
> > [1] http://gruntthepeon.free.fr/ssemath/
> 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.

Numpy uses the C library version. If long double and float aren't available
the double version is used with number conversions, but that shouldn't give
a factor of 100x. Something else is going on.

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