On Tue, Jan 26, 2021 at 02:07:43PM +0100, PIERRE AUGIER wrote:
Hi,
Here are some preliminary results of an experiment on energy consumption
measurement at Grid'5000
(https://www.grid5000.fr/w/Energy_consumption_monitoring_tutorial). The goal
is to have enough to be able to submit a serious comment to a recent article
published in Nature Astronomy (Zwart, 2020) which recommends to stop using
and teaching Python because of the ecological impact of computing.
2 figures are attached (the code is here https://github.com/paugier/nbabel):
1. fig_bench_nbabel.png: CO2 production versus elapsed time for different
implementations. This figure can be compared with
https://raw.githubusercontent.com/paugier/nbabel/master/py/fig/fig_ecolo_impact_transonic.png
taken from Zwart (2020).
The implementations labeled "nbabel.org" have been found on
https://www.nbabel.org/ and have recently been used in the article by Zwart ;
(2020). Note that these C++, Fortran and Julia implementations are not well
optimized. However, I think they are representative of many C++, Fortran and
Julia codes written by scientists.
There is one simple Pythran implementation which is really fast (slightly
slower than the fastest implementation in Julia but it is not a big deal).
Note that of course these results do not show that Python is faster that
C++!! We just show here that it's easy to write in Python **very** efficient
implementations of numerically intensive problems.
2. fig_bench_nbabel_parallel_julia.png shows similar results as a function of
the number of threads for a parallel Julia implementation.
The scaling is not very good but parallelization can of course decrease
elapsed time and CO2 production.
