[SciPy-Dev] Numba as a dependency for SciPy?

[Juan Luis Cano]

On Thu, Mar 8, 2018 at 5:16 PM, Joshua Wilson <josh.craig.wilson at gmail.com>
wrote:

> > if someone is interested in trying to implement some SciPy functions
> with Numba implementations
>
> When this thread started I actually did just that for a small part of
> scipy.special. Code is here:
>

Now that you mention scipy.special, here is another data point with some
promising benchmarks, wrapping CEPHES with CFFI:

https://github.com/poliastro/pycephes#performance


>
> https://github.com/person142/special
>
> It currently implements `special.loggamma` and some private functions
> in special (sinpi, cospi, polynomial evaluation) that are needed to
> support it. I'm currently seeing a factor of 2 slowdown and trying to
> figure out why, but I'm very interested in figuring out how to close
> the speed gap/porting more functions.
>
> - Josh
>
> On Thu, Mar 8, 2018 at 8:44 AM, Stanley Seibert <sseibert at anaconda.com>
> wrote:
> > TBH, I agree with this general sentiment.  This thread has been very
> > valuable to clarify the Numba team's understanding of what the SciPy
> > community needs from a compilation solution.  Our trajectory is good, but
> > we're not quite there yet for a project that needs to be as conservative
> > about dependencies as SciPy.  We will keep working to get there, though.
> >
> > However, if someone is interested in trying to implement some SciPy
> > functions with Numba implementations, there's nothing blocking that work
> in
> > a separate repository as an experiment.  (One of the Numba developers has
> > already named this hypothetical project "Scumba," which I quite like.)
> If
> > anyone does decide to try this, please make sure to ping the Numba
> > developers on Gitter.  We will learn a great deal from the effort, I
> think.
> >
> > On Thu, Mar 8, 2018 at 4:00 AM, Matthew Brett <matthew.brett at gmail.com>
> > wrote:
> >>
> >> Hi,
> >>
> >> On Thu, Mar 8, 2018 at 9:38 AM, Pauli Virtanen <pav at iki.fi> wrote:
> >> > Ralf Gommers kirjoitti 08.03.2018 klo 08:04:
> >> > [clip]
> >> >>
> >> >> Also, I don't think performance will necessarily be unacceptable.
> There
> >> >> are
> >> >> a bunch of places in the existing code base where we can throw in
> @jit
> >> >> and
> >> >> get speedups basically for free. Performance in the noop case will
> then
> >> >> be
> >> >> what it is today - not great, but apparently also not enough of a
> >> >> problem
> >> >> that someone has attempted to go to Cython.
> >> >
> >> >
> >> > I guess you agree that Numba would regardless be declared a dependency
> >> > in
> >> > setup.py? People on unsupported arches can edit it away manually.
> >> >
> >> > For computational tight loops operating on arrays when Numba is used
> as
> >> > an
> >> > alternative to Cython/C/Fortran, there probably will be a performance
> >> > hit in
> >> > the ballpark of 100x.
> >> >
> >> > If we are planning to use numba features more fully, e.g. numba.cfunc
> >> > e.g.
> >> > to write callback functions, that would also require Numba as a hard
> >> > dependency.
> >>
> >> If we were at the top of the stack, like pystatsmodels, then this
> >> would be reasonable, but, if we make numba a dependency, that makes
> >> numba a dependency for almost anyone doing scientific computing.  I
> >> think we do have to care about people not running on Intel.   If we
> >> make numba an optional dependency, it gives us an additional
> >> maintenance burden, because we'd have to check for each numba segment,
> >> whether it is going to be disabling for a user without numba.
> >>
> >> Is there anything we have at the moment where Cython won't get us into
> >> the ballpark?  If not, my preference would be to wait for a year or
> >> so, to see how things turn out.
> >>
> >> Cheers,
> >>
> >> Matthew
> >> _______________________________________________
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> >
> >
> >
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-- 
Juan Luis Cano
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