[Theory] How to speed up python code execution / pypy vs GPU

[Steve D'Aprano]

On Wed, 9 Nov 2016 06:35 pm, John Ladasky wrote:

[...]
> I work a lot with a package called GROMACS, which does highly iterative
> calculations to simulate the motions of atoms in complex molecules. 
> GROMACS can be built to run on a pure-CPU platform (taking advantage of
> multiple cores, if you want), a pure-GPU platform (leaving your CPU cores
> free), or a blended platform, where certain parts of the algorithm run on
> CPUs and other parts on GPUs.  This latter configuration is the most
> powerful, because only some parts of the simulation algorithm are optimal
> for GPUs.  GROMACS only supports NVidia hardware with CUDA 2.0+.
> 
> Because of the iterative nature of these calculations, small discrepancies
> in the arithmetic algorithms can rapidly lead to a completely
> different-looking result.  In order to verify the integrity of GROMACS,
> the developers run simulations with all three supported hardware
> configurations, and verify that the results are identical.  Now, I don't
> know that every last function and corner case in the IEEE-754 suite gets
> exercised by GROMACS, but that's a strong vote of confidence.

That is really good, and I'm very pleased to learn about it.

But I don't think that the average scientist writes code of that quality.
(Nor should they: replicating work is the job of the scientific community
as a whole, not a single scientist.)

Thanks for the update on the state of art for GPU numeric computing. I'll
agree that things are better than I feared.




-- 
Steve
“Cheer up,” they said, “things could be worse.” So I cheered up, and sure
enough, things got worse.