[Pandas-dev] [Numpy-discussion] Transonic Vision: unifying Python-Numpy accelerators

Sun Nov 17 14:29:37 EST 2019

On Sat, Nov 16, 2019 at 1:49 AM PIERRE AUGIER <
pierre.augier at univ-grenoble-alpes.fr> wrote:

> Dear Pandas developers,
>
> Ralf Gommers wrote me that there was a discussion on the pandas-dev
> mailing list a couple of weeks ago about adopting Numba as a dependency.
>
> We recently wrote a serious text on this subject:
> http://tiny.cc/transonic-vision.
>
> As a side remark, we also played with Transonic and Pandas in this
> notebook
> https://github.com/fluiddyn/transonic-demos/blob/master/pandas.ipynb (the
> binder link to run the benchmarks:
> https://mybinder.org/v2/gh/fluiddyn/transonic-demos/master)
>

Thanks for sharing Pierre. For those who are interested, here are the
results of running those benchmarks:

Cython: 133 ms ± 11 ms
Numba: 39.3 ms ± 5.52 ms
Pythran: 36.7 ms ± 2.43 ms
Cython + type annotations: 56.3 ms ± 6.56 ms

The ease of switching between Cython, Numba and Pythran with Transonic in
your notebook is very cool.

Cheers,
Ralf

> > Date: Wed, 6 Nov 2019 23:49:08 -0500
> > From: Ralf Gommers <ralf.gommers at gmail.com>
> > To: Discussion of Numerical Python <numpy-discussion at python.org>
> > Subject: Re: [Numpy-discussion] Transonic Vision: unifying
> >        Python-Numpy accelerators
> > Message-ID:
> >        <CABL7CQhr6ps25Yrp_pAF8vWVLS3_2=
> Mw4bCOXb0H1dvU9AjTMw at mail.gmail.com>
> > Content-Type: text/plain; charset="utf-8"
> >
> > On Mon, Nov 4, 2019 at 4:54 PM PIERRE AUGIER <
> > pierre.augier at univ-grenoble-alpes.fr> wrote:
> >
> >> Dear Python-Numpy community,
> >>
> >> Transonic is a pure Python package to easily accelerate modern
> >> Python-Numpy code with different accelerators (currently Cython, Pythran
> >> and Numba).
> >>
> >> I'm trying to get some funding for this project. The related work would
> >> benefit in particular to Cython, Numba, Pythran and Xtensor.
> >>
> >> To obtain this funding, we really need some feedback from some people
> >> knowing the subject of performance with Python-Numpy code.
> >>
> >> That's one of the reason why we wrote this long and serious text on
> >> Transonic Vision: http://tiny.cc/transonic-vision. We describe some
> >> issues (perf for numerical kernels, incompatible accelerators, community
> >> split between experts and simple users, ...) and possible improvements.
> >>
> >
> > Thanks Pierre, that's a very interesting vision paper.
> >
> > In case you haven't seen it, there was a discussion on the pandas-dev
> > mailing list a couple of weeks ago about adopting Numba as a dependency
> > (and issues with that).
> >
> > Your comment on my assessment from 1.5 years ago being a little unfair to
> > Pythran may be true - not sure it was at the time, but Pythran seems to
> > mature nicely.
> >
> > The ability to switch between just-in-time and ahead-of-time compilation
> is
> > nice. One thing I noticed is that this actual switching is not completely
> > fluent: the jit and boost decorators have different signatures, and
> there's
> > no way to globally switch behavior (say with an env var, as for backend
> > selection).
> >
> >
> >> Help would be very much appreciated.
> >>
> >
> > I'd be interested to help think about adoption and/or funding.
> >
> > Cheers,
> > Ralf
> >
> >
> >>
> >> Now a coding riddle:
> >>
> >> import numpy as np
> >> from transonic import jit
> >>
> >> @jit(native=True, xsimd=True)
> >> def fxfy(ft, fn, theta):
> >>     sin_theta = np.sin(theta)
> >>     cos_theta = np.cos(theta)
> >>     fx = cos_theta * ft - sin_theta * fn
> >>     fy = sin_theta * ft + cos_theta * fn
> >>     return fx, fy
> >>
> >> @jit(native=True, xsimd=True)
> >> def fxfy_loops(ft, fn, theta):
> >>     n0 = theta.size
> >>     fx = np.empty_like(ft)
> >>     fy = np.empty_like(fn)
> >>     for index in range(n0):
> >>         sin_theta = np.sin(theta[index])
> >>         cos_theta = np.cos(theta[index])
> >>         fx[index] = cos_theta * ft[index] - sin_theta * fn[index]
> >>         fy[index] = sin_theta * ft[index] + cos_theta * fn[index]
> >>     return fx, fy
> >>
> >> How can be compared the performances of these functions with pure Numpy,
> >> Numba and Pythran ?
> >>
> >> You can find out the answer in our note http://tiny.cc/transonic-vision
> >> :-)
> >>
> >> Pierre
> >>
> >> > Message: 1
> >> > Date: Thu, 31 Oct 2019 21:16:06 +0100 (CET)
> >> > From: PIERRE AUGIER <pierre.augier at univ-grenoble-alpes.fr>
> >> > To: numpy-discussion at python.org
> >> > Subject: [Numpy-discussion] Transonic Vision: unifying Python-Numpy
> >> >       accelerators
> >> > Message-ID:
> >> >       <
> >> 1080118635.5930814.1572552966711.JavaMail.zimbra at univ-grenoble-alpes.fr
> >
> >> >
> >> > Content-Type: text/plain; charset=utf-8
> >> >
> >> > Dear Python-Numpy community,
> >> >
> >> > Few years ago I started to use a lot Python and Numpy for science. I'd
> >> like to
> >> > thanks all people who contribute to this fantastic community.
> >> >
> >> > I used a lot Cython, Pythran and Numba and for the FluidDyn project,
> we
> >> created
> >> > Transonic, a pure Python package to easily accelerate modern
> >> Python-Numpy code
> >> > with different accelerators. We wrote a long and serious text to
> explain
> >> why we
> >> > think Transonic could have a positive impact on the scientific Python
> >> > ecosystem.
> >> >
> >> > Here it is: http://tiny.cc/transonic-vision
> >> >
> >> > Feedback and discussions would be greatly appreciated!
> >> >
> >> > Pierre
> >> >
> >> > --
> >> > Pierre Augier - CR CNRS
> http://www.legi.grenoble-inp.fr
> >> > LEGI (UMR 5519) Laboratoire des Ecoulements Geophysiques et
> Industriels
> >> > BP53, 38041 Grenoble Cedex, France
> tel:+33.4.56.52.86.16
> >> _______________________________________________
> _______________________________________________
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> Pandas-dev at python.org
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>
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