[Numpy-discussion] Numpy x Matlab: some synthetic benchmarks

David M. Cooke cookedm at physics.mcmaster.ca
Fri Jan 20 09:48:03 CST 2006

Fernando Perez <Fernando.Perez at colorado.edu> writes:

> David M. Cooke wrote:
>> I've done a little bit of work along these lines. I have a module I
>> call vector3 [*] which has 2- and 3-dimensional immutable vectors,
>> using either ints or doubles. It's as fast as I could make it, while
>> keeping it all written in Pyrex. I find it very convenient for
>> anything vector-related. Konrad Hinsen has something similiar in the
>> development version of his ScientificPython package.
>> [*] http://arbutus.mcmaster.ca/dmc/software/vector3.html
>> Also, I've also done some playing around with a n-dimensional vector
>> type (restricted to doubles). My best attempts make it ~4-5x faster
>> than numpy (and 2x faster than Numeric) for vectors of dimension 10
>> on simple ops like + and *, 2x faster than numpy for dimension 1000,
>> and approaching 1x as you make the vectors larger. Indexing is about
>> 3x faster than numpy, and 1.4x faster than Numeric. So that gives I
>> think some idea of the maximum speed-up possible.
>> I think the speedups mostly come from the utter lack of any
>> polymorphism: it handles vectors of doubles only, and only as
>> contiguous vectors (no strides).
> This is excellent, thanks for the pointer.  I can see uses for vectors
> (still 1-d, no strides, etc) with more than 3 elements, and perhaps
> fixed-size (no reshaping, no striding) 2-d arrays (matrices), but this
> looks like a good starting point.  Sandbox material?

Well, I'd be pleased to donate vector3 to scipy as a sandbox (although
I think it's very useful as a standalone package for others with no
need of full scipy. The general vector stuff is still quite rough, and
at this point is really only meant as a study on how fast I can make
something like that run :-)

|David M. Cooke                      http://arbutus.physics.mcmaster.ca/dmc/
|cookedm at physics.mcmaster.ca

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