Re: JIT compilation with callbacks into Scheme

[Jan Wedekind]

On Fri, 21 Oct 2016, Amirouche Boubekki wrote:

> On 2016-10-21 14:13, Jan Wedekind wrote:
> > On Fri, 21 Oct 2016, Amirouche Boubekki wrote:
> >
> > > Héllo Jan!
> > >
> > > On 2016-10-20 22:14, Jan Wedekind wrote:
> > > > Hi,
> > > >   I managed to implement JIT compilation of method calls into the
> > > > Guile interpreter (AIscm 0.8.2 [1]):
>
> [...]
>
> > > > [1] http://wedesoft.github.io/aiscm/
> > >
> > > How does this compare with numpy?
> > >
> > > TIA!
> > >
> > > -- Amirouche ~ amz3 ~ http://www.hyperdev.fr
> >
> >   First of all numpy supports native floating point operations and
> > this work does not yet do that ;)
> >   In terms of performance I previously posted some benchmarks
> > comparing with C programs [1].
> >   Numpy is statically compiled and uses generic functions and function
> > pointers to implement binary operations for combinations of types [2].
> >
> >     np.array([1,2,3],dtype=np.uint8) + np.array([1,2,3],dtype=np.int16)
> >     # array([2, 4, 6], dtype=int16)
> >
> >   When instead using a JIT (e.g. Python Theano) the resulting software
> > is more generic and composable in terms of operations (tensors,
> > composite operations) and in terms of datatypes (RGB, complex values,
> > arrays, hypercomplex values, ...).
> >   Here's a technical report [3] with more details and motivation (in
> > that case libJIT and Ruby was used).
> >   Please let me know if you have some interesting applications in mind :)
>
> It's a long shot, but I plan to port spacy and gensim and I read they
> use numpy that's why I ask.
>
> > Regards
> > Jan
> >
> > [1] https://lists.gnu.org/archive/html/guile-user/2016-06/msg00019.html
> > [2]
> > https://github.com/numpy/numpy/blob/master/numpy/core/src/multiarray/calculation.c#L534
> > [3] http://www.wedesoft.de/downloads/tensors_libjit.pdf
>
> --
> Amirouche ~ amz3 ~ http://www.hyperdev.fr

Not sure how they use NumPy. However you might be interested in the 
publication "Natural Language Processing (almost) from Scratch" which uses 
machine learning [4].

[4] https://arxiv.org/abs/1103.0398