Hi,
I recently took a bit of time to study the comment "The ecological impact of high-performance computing in astrophysics" published in Nature Astronomy (Zwart, 2020,
https://www.nature.com/articles/s41550-020-1208-y,
https://arxiv.org/pdf/2009.11295.pdf), where it is stated that "Best however, for the environment is to abandon Python for a more environmentally friendly (compiled) programming language.".
I wrote a simple Python-Numpy implementation of the problem used for this study (
https://www.nbabel.org) and, accelerated by Transonic-Pythran, it's very efficient. Here are some numbers (elapsed times in s, smaller is better):
| # particles | Py | C++ | Fortran | Julia |
|-------------|-----|-----|---------|-------|
| 1024 | 29 | 55 | 41 | 45 |
| 2048 | 123 | 231 | 166 | 173 |
The code and a modified figure are here:
https://github.com/paugier/nbabel (There is no check on the results for
https://www.nbabel.org, so one still has to be very careful.)
I think that the Numpy community should spend a bit of energy to show what can be done with the existing tools to get very high performance (and low CO2 production) with Python. This work could be the basis of a serious reply to the comment by Zwart (2020).
Unfortunately the Python solution in
https://www.nbabel.org is very bad in terms of performance (and therefore CO2 production). It is also true for most of the Python solutions for the Computer Language Benchmarks Game in
https://benchmarksgame-team.pages.debian.net/benchmarksgame/ (codes here
https://salsa.debian.org/benchmarksgame-team/benchmarksgame#what-else).
We could try to fix this so that people see that in many cases, it is not necessary to "abandon Python for a more environmentally friendly (compiled) programming language". One of the longest and hardest task would be to implement the different cases of the Computer Language Benchmarks Game in standard and modern Python-Numpy. Then, optimizing and accelerating such code should be doable and we should be able to get very good performance at least for some cases. Good news for this project, (i) the first point can be done by anyone with good knowledge in Python-Numpy (many potential workers), (ii) for some cases, there are already good Python implementations and (iii) the work can easily be parallelized.
It is not a criticism, but the (beautiful and very nice) new Numpy website
https://numpy.org/ is not very convincing in terms of performance. It's written "Performant The core of NumPy is well-optimized C code. Enjoy the flexibility of Python with the speed of compiled code." It's true that the core of Numpy is well-optimized C code but to seriously compete with C++, Fortran or Julia in terms of numerical performance, one needs to use other tools to move the compiled-interpreted boundary outside the hot loops. So it could be reasonable to mention such tools (in particular Numba, Pythran, Cython and Transonic).
Is there already something planned to answer to Zwart (2020)?
Any opinions or suggestions on this potential project?
Pierre
PS: Of course, alternative Python interpreters (PyPy, GraalPython, Pyjion, Pyston, etc.) could also be used, especially if HPy (
https://github.com/hpyproject/hpy) is successful (C core of Numpy written in HPy, Cython able to produce HPy code, etc.). However, I tend to be a bit skeptical in the ability of such technologies to reach very high performance for low-level Numpy code (performance that can be reached by replacing whole Python functions with optimized compiled code). Of course, I hope I'm wrong! IMHO, it does not remove the need for a successful HPy!
--
Pierre Augier - CR CNRS
http://www.legi.grenoble-inp.frLEGI (UMR 5519) Laboratoire des Ecoulements Geophysiques et Industriels
BP53, 38041 Grenoble Cedex, France
tel:+33.4.56.52.86.16_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@python.orghttps://mail.python.org/mailman/listinfo/numpy-discussion