[Numpy-discussion] Objected-oriented SIMD API for Numpy

Francesc Alted faltet@pytables....
Wed Oct 21 07:47:02 CDT 2009

A Wednesday 21 October 2009 14:27:46 David Cournapeau escrigué:
> > This is because numpy is a package that works mainly with arrays in an
> > element-wise way, and in this scenario, the time to transmit data to CPU
> > dominates, by and large, over the time to perform operations.
> Is it general, or just for simple operations in numpy and ufunc ? I
> remember that for music softwares, SIMD used to matter a lot, even for
> simple bus mixing (which is basically a ax+by with a, b scalars and x
> y the input arrays).

This is general, as long as the dataset has to be brought from memory to CPU, 
and operations to be done are element-wise and simple (i.e. not 
transcendental).  SIMD does matter in general when the dataset:

1) is already in cache
2) you have to perform costly operations (mainly transcendental)
3) a combination of the above

I don't know the case for music software, but if you say that ax+by are 
accelerated by SIMD, I'd say that case 1) is happening.

> 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:


very interesting.  Perhaps there could be other (free) implementations...

Francesc Alted

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