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

[mark florisson]

On 26 June 2013 09:05, Dag Sverre Seljebotn <d.s.seljebotn at astro.uio.no> wrote:
> On 06/25/2013 04:21 PM, Frédéric Bastien wrote:
>>
>> Hi,
>>
>> I wasn't able to attend this year Scipy Conference. My tutorial proposal
>> was rejected and other deadline intefered with this conference date.
>>
>> Will the presentation be recorded? If not, can you make the slide
>> available?
>>
>> What is your opinion on this question:
>>
>> - Should other lib like NumPy/Theano/Cython/Numba base their elemwise
>> implemention (or part of it) on dynd or minivect? I know cython and
>> Numba do it, but it was before dynd and I don't know where dynd fit in
>> the big picture. Do dynd reuse minivect itself?
>
>
> Actually, I think the Cython branch with minivect support was in the end not
> merged, due to lack of interest/manpower to maintain support for
> vectorization in the long term (so it was better to not add the feature than
> have a badly supported feature).
>
> My understanding is that Numba is based on minivect and not on dynd, so it's
> more of a competitor.
>
> Perhaps Mark Florisson will be able to comment.
>
> Dag Sverre

Hey Dag,

Indeed, numba uses it for its array expression support, but it will
likely remove the minivect dependency and generate a simple loop nest
for now. I'm working on pykit now
(https://github.com/ContinuumIO/pykit) which similarly to minivect
defines its own intermediate representation, with array expressions in
the form of map/reduce/scan/etc functions. The project has a broader
scope than minivect, to be used by projects like numba, what but a
"minivect baked in".

As such, minivect isn't really maintained any longer, and I wouldn't
recommend anyone using the code at this point (just maybe some of the
ideas :)).