[Numpy-discussion] low level optimization in NumPy and minivect

Julian Taylor jtaylor.debian at googlemail.com
Mon Jun 17 17:03:00 EDT 2013

On 17.06.2013 17:11, Frédéric Bastien wrote:
> Hi,
> I saw that recently Julian Taylor is doing many low level optimization
> like using SSE instruction. I think it is great.
> Last year, Mark Florisson released the minivect[1] project that he
> worked on during is master thesis. minivect is a compiler for
> element-wise expression that do some of the same low level optimization
> that Julian is doing in NumPy right now.
> Mark did minivect in a way that allow it to be reused by other project.
> It is used now by Cython and Numba I think. I had plan to reuse it in
> Theano, but I didn't got the time to integrate it up to now.
> What about reusing it in NumPy? I think that some of Julian optimization
> aren't in minivect (I didn't check to confirm). But from I heard,
> minivect don't implement reduction and there is a pull request to
> optimize this in NumPy.

what I vectorized is just the really easy cases of unit stride
continuous operations, so the min/max reductions which is now in numpy
is in essence pretty trivial.
minivect goes much further in optimizing general strided access and
broadcasting via loop optimizations (it seems to have a lot of overlap
with the graphite loop optimizer available in GCC [0]) so my code is
probably not of very much use to minivect.

The most interesting part in minivect for numpy is probably the
optimization of broadcasting loops which seem to be pretty inefficient
in numpy [0].

Concerning the rest I'm not sure how much of a bottleneck general
strided operations really are in common numpy using code.

I guess a similar discussion about adding an expression compiler to
numpy has already happened when numexpr was released?
If yes what was the outcome of that?

[0] http://gcc.gnu.org/wiki/Graphite
[1] ones((5000,100)) - ones((100,) spends about 40% of its time copying
stuff around in buffers

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