Hi Sebastian, I had a quick look at the PR and it looks like you re-implemented the sin-cos function using SIMD. I wonder how it compares with SLEEF (header only library, CPU-architecture agnostic SIMD implementation of transcendental functions with precision validation). SLEEF is close to the Intel SVML library in spirit but extended to multi-architecture (tested on PowerPC and ARM for example). This is just curiosity ... Like Juan, I am afraid of this change since my code, which depends on numpy for sin/cos used for rotation is likely to see large change of behavior. Cheers, Jerome On Wed, 31 May 2023 07:55:34 +0200 Sebastian Berg <sebastian@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
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