I would much, much rather have the special functions in the `np.*` namespace be more accurate than fast on all platforms. These would not have been on my list for general purpose speed optimization. How much time is actually spent inside sin/cos even in a trig-heavy numpy program? And most numpy programs aren't trig-heavy, but the precision cost would be paid and noticeable even for those programs. I would want fast-and-inaccurate functions to be strictly opt-in for those times that they really paid off. Probably by providing them in their own module or package rather than a runtime switch, because it's probably only a *part* of my program that needs that kind of speed and can afford that precision loss while there will be other parts that need the precision.
On Wed, May 31, 2023 at 1:59 AM Sebastian Berg <sebast...@sipsolutions.net> wrote: > Hi all, > > there was recently a PR to NumPy to improve the performance of sin/cos > on most platforms (on my laptop it seems to be about 5x on simple > inputs). > This changes the error bounds on platforms that were not previously > accelerated (most users): > > https://github.com/numpy/numpy/pull/23399 > > The new error is <4 ULP similar to what it was before, but only on high > end Intel CPUs which not users would have noticed. > And unfortunately, it is a bit unclear whether this is too disruptive > or not. > > The main surprise is probably that the range of both does not include 1 > (and -1) exactly with this and quite a lot of downstream packages > noticed this and needed test adaptions. > > Now, most of these are harmless: users shouldn't expect exact results > from floating point math and test tolerances need adjustment. OTOH, > sin/cos are practically 1/-1 on a wide range of inputs (they are > basically constant) so it is surprising that they deviate from it and > never reach 1/-1 exactly. > > Since quite a few downstream libs notice this and NumPy users cannot > explicitly opt-in to a different performance/precision trade-off. The > question is how everyone feels about it being better to revert for now > and hope for a better one? > > I doubt we can decide on a very clear cut yes/no, but I am very > interested what everyone thinks whether this precision trade-off is too > surprising to users? > > Cheers, > > Sebastian > > > _______________________________________________ > NumPy-Discussion mailing list -- numpy-discussion@python.org > To unsubscribe send an email to numpy-discussion-le...@python.org > https://mail.python.org/mailman3/lists/numpy-discussion.python.org/ > Member address: robert.k...@gmail.com > -- Robert Kern
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