Hi Sebastian, Could you clarify whether there are now varying code paths, depending on the CPU features available? As mentioned on the skimage issue, if results differ but errors are reduced across the board, I'd be happy to fix the test suite. But if this simply jiggers results, I'm less sure it is worth it. You also mentioned a potential middle ground, where the approximating polynomial could be expanded by another term? Overall, I feel this is a rather invasive change to NumPy that affects results that have been stable for many years, so it warrants careful consideration--perhaps even postponing until 2.0? Best regards, Stéfan On Tue, May 30, 2023, at 22:55, Sebastian Berg 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.