[Numpy-discussion] Objected-oriented SIMD API for Numpy
Wed Oct 21 11:19:02 CDT 2009
On Wed, Oct 21, 2009 at 2:14 PM, Pauli Virtanen <email@example.com<pav%2Bsp@iki.fi>
> Wed, 21 Oct 2009 14:47:02 +0200, Francesc Alted wrote:
> >> Do you have any interest in adding SIMD to some core numpy
> >> (transcendental functions). If so, I would try to go back to the
> >> problem of runtime SSE detection and loading of optimized shared
> >> library in a cross-platform way - that's something which should be done
> >> at some point in numpy, and people requiring it would be a good
> >> incentive.
> > I don't personally have a lot of interest implementing this for numpy.
> > But in case anyone does, I find the next library:
> > http://gruntthepeon.free.fr/ssemath/
> > very interesting. Perhaps there could be other (free)
> > implementations...
> Optimized transcendental functions could be interesting. For example for
> tanh, call overhead is overcome already for ~30-element arrays.
> Since these are ufuncs, I suppose the SSE implementations could just be
> put in a separate module, which is always compiled. Before importing the
> module, we could simply check from Python side that the CPU supports the
> necessary instructions. If everything is OK, the accelerated
> implementations would then just replace the Numpy routines.
> This type of project could probably also be started outside Numpy, and
> just monkey-patch the Numpy routines on import.
> Pauli Virtanen
> NumPy-Discussion mailing list
Anyone seen the corepy numpy gsoc project?
It implements a number of functions with the corepy runtime assembler. The
project showed nice simd speedups for numpy.
I've been following the liborc project... which is a runtime assembler that
uses a generic assembly language and supports many different simd assembly
languages (eg SSE, MMX, ARM, Altivec). It's the replacement for the liboil
library (used in gstreamer etc).
-------------- next part --------------
An HTML attachment was scrubbed...
More information about the NumPy-Discussion