[Numpy-discussion] Openmp support (was numpy's future (1.1 and beyond): which direction(s) ?)
Charles R Harris
Sat Mar 22 14:04:25 CDT 2008
On Sat, Mar 22, 2008 at 12:54 PM, Anne Archibald <email@example.com>
> On 22/03/2008, Travis E. Oliphant <firstname.lastname@example.org> wrote:
> > James Philbin wrote:
> > > Personally, I think that the time would be better spent optimizing
> > > routines for single-threaded code and relying on BLAS and LAPACK
> > > libraries to use multiple cores for more complex calculations. In
> > > particular, doing some basic loop unrolling and SSE versions of the
> > > ufuncs would be beneficial. I have some experience writing SSE code
> > > using intrinsics and would be happy to give it a shot if people tell
> > > me what functions I should focus on.
> > Fabulous! This is on my Project List of todo items for NumPy. See
> > http://projects.scipy.org/scipy/numpy/wiki/ProjectIdeas I should spend
> > some time refactoring the ufunc loops so that the templating does not
> > get in the way of doing this on a case by case basis.
> > 1) You should focus on the math operations: add, subtract, multiply,
> > divide, and so forth.
> > 2) Then for "combined operations" we should expose the functionality at
> > a high-level. So, that somebody could write code to take advantage of
> > It would be easiest to use intrinsics which would then work for AMD,
> > Intel, on multiple compilers.
> I think even heavier use of code generation would be a good idea here.
> There are so many different versions of each loop, and the fastest way
> to run each one is going to be different for different versions and
> different platforms, that a routine that assembled the code from
> chunks and picked the fastest combination for each instance might make
> a big difference - this is roughly what FFTW and ATLAS do.
Maybe it's time to revisit the template subsystem I pulled out of Django.
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