[pypy-dev] Results NBabel benchmark CO2 production versus time: good new for PyPy map-improvements
pierre.augier at univ-grenoble-alpes.fr
Tue Jan 26 15:07:29 EST 2021
----- Mail original -----
> De: "Carl Friedrich Bolz-Tereick" <cfbolz at gmx.de>
> À: "PIERRE AUGIER" <pierre.augier at univ-grenoble-alpes.fr>, "pypy-dev" <pypy-dev at python.org>
> Envoyé: Mardi 26 Janvier 2021 09:10:20
> Objet: Re: [pypy-dev] Results NBabel benchmark CO2 production versus time: good new for PyPy map-improvements
> Hi Pierre,
> wow, those numbers are quite something! I suppose the C++ code could be
> optimized some more?
Yes, of course! The C++ code is really not great. Even the Fortran code could easily be optimized a bit more. However, I think they are representative of many C++ and Fortran codes written by scientists.
> Do you plan to submit that soon? Would it make your story easier if I
> tried to push ahead with getting map-improvements merged?
I do not plan to submit that very soon. However, I plan to contact the editors very soon.
My point of view is the following: I don't think it would be honest to include in a paper the point using map-improvements before this branch is merged so I would rather wait for it...
> Also, would you maybe be interested in (co-?)writing a blog post for the
> PyPy blog?: https://morepypy.blogspot.com/
Yes, I can really help on such blog post! But I'm definitively not the right guy to explain the principle of map-improvements! Also, for me, the possible comment in Nature Astronomy has a higher priority.
> Carl Friedrich
> On 1/25/21 8:44 PM, PIERRE AUGIER wrote:
>> I did some timing and energy consumption measurements with
>> I think the results tend to validate the approach used in the branch
>> (https://foss.heptapod.net/pypy/pypy/-/tree/branch/map-improvements). I attach
>> one of the first figure including a run using an interpreter build with these
>> To be compared with
>> taken from Zwart (2020).
>> The implementation run with PyPy map-improvements is faster than the
>> implementations using Numba, Fortran and C++ (with flags like -Ofast and
>> -march=native activated!) ! It's a great result! Congratulation!
>> For people interested, the code for the benchmarks and the measurement is here
>> pypy-dev mailing list
>> pypy-dev at python.org
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