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
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