On Fri, 2021-07-30 at 11:04 -0700, Jerry Morrison wrote:
> On Tue, Jul 27, 2021 at 4:55 PM Sebastian Berg <
> sebast...@sipsolutions.net>
> wrote:
>
> > Hi all,
> >
> > there is a proposal to add some Intel specific fast math routine to
> > NumPy:
> >
> > https://github.com/numpy/numpy/pull/19478
> >
> > part of numerical algorithms is that there is always a speed vs.
> > precision trade-off, giving a more precise result is slower.
> >
<snip>
I have to make a correction: I linked the SVML, which is distinct from
VML (which the PR proposes), the actual table for precision is here:
https://github.com/numpy/numpy/pull/19485#issuecomment-887995864
> "Close enough" depends on the application but non-linear models can
> get the
> "butterfly effect" where the results diverge if they aren't
> identical.
Right, so my hope was to gauge what the general expectation is. I take
it you expect a high accuracy.
The error for the computations itself is seems low on first sight, but
of course they can explode quickly in non-linear settings...
(In the chaotic systems I worked with, the shadowing theorem would
usually alleviate such worries. And testing the integration would be
more important. But I am sure for certain questions things may be far
more tricky.)
>
> For a certain class of scientific programming applications,
> reproducibility
> is paramount.
>
> Development teams may use a variety of development laptops,
> workstations,
> scientific computing clusters, and cloud computing platforms. If the
> tests
> pass on your machine but fail in CI, you have a debugging problem.
>
> If your published scientific article links to source code that
> replicates
> your computation, scientists will expect to be able to run that code,
> now
> or in a couple decades, and replicate the same outputs. They'll be
> using
> different OS releases and maybe different CPU + accelerator
> architectures.
>
> Reproducible Science is good. Replicated Science is better.
> <http://rescience.github.io/>
>
> Clearly there are other applications where it's easy to trade
> reproducibility and some precision for speed.
Agreed, although there are so many factors, often out of our control,
that I am not sure that true replicability is achievable without
containers :(.
It would be amazing if NumPy could have a "replicable" mode, but I am
not sure how that could be done, or if the "ground work" in the math
and linear algebra libraries even exists.
However, even if it is practically impossible to make things
replicable, there is an argument for improving reproducibility and
replicability, e.g. by choosing the high-accuracy version here. Even
if it is impossible to actually ensure.
Cheers,
Sebastian
> _______________________________________________
> NumPy-Discussion mailing list
> NumPy-Discussion@python.org
> https://mail.python.org/mailman/listinfo/numpy-discussion
_______________________________________________
NumPy-Discussion mailing list
NumPy-Discussion@python.org
https://mail.python.org/mailman/listinfo/numpy-discussion
