argminmax VS simde

Fast: leverages the optimized argminmax crate which is SIMD accelerated with runtime feature detection (matches or even outperforms numpy's speed)

tsdownsample brings highly optimized time series downsampling to Python, by using the SIMD optimized argminmax crate - which matches or even outperforms numpy's speed (300x faster for f16). Through using runtime feature set detection, this crate selects the most optimal SIMD implementation for the current CPU (supports SSE, AVX(2), AVX512, NEON).

Code: https://github.com/jvdd/argminmax

I was curious about these libraries a few weeks ago and did some searching. Is there one that's got a clearly dominating set of users or contributors?

I don't know what a good way to compare these might be, other than perhaps activity/contributor count.

[1] https://github.com/simd-everywhere/simde

[2] https://github.com/ermig1979/Simd

[3] https://github.com/google/highway

[4] https://gitlab.com/libeigen/eigen

[5] https://github.com/shibatch/sleef

I note that SIMDe doesn't have RISC-V support yet (but it does support Loongson LoongArch):

https://github.com/simd-everywhere/simde/

There are still a ton of things to do to get the Debian riscv64 port going too:

https://wiki.debian.org/PortsDocs/New

SIMDe is everything you're after: https://github.com/simd-everywhere/simde

> low level code that can run on multiple hardware architectures

I thought SIMD Everywhere was a pretty interesting project for that, lets you write x86 SSE/AVX code and run it on non-x86 architectures:

https://github.com/simd-everywhere/simde

> architecture-specific features such as SSE/AVX which is not portable.

I don’t have hands-on experience, but somewhere on HN I saw this: https://github.com/simd-everywhere/simde If starting a new cross-platform project today, I would try that library first, before doing the usual intrinsics.