simde VS sleef

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.

I'm the main author of Highway, so I have some opinions :D Number of operations/platforms supported are important criteria.

A hopefully unbiased commentary:

Simde allows you to take existing nonportable intrinsics and get them to run on another platform. This is useful when you have a bunch of existing code and tight deadlines. The downside is less than optimal performance - a portable abstraction can be more efficient than forcing one platform to exactly match the semantics of another. Although a ton of effort has gone into Simde, sometimes it also resorts to autovectorization which may or may not work.

Eigen and SLEEF are mostly math-focused projects that also have a portability layer. SLEEF is designed for C and thus has type suffixes which are rather verbose, see https://github.com/shibatch/sleef/blob/master/src/libm/sleef... But it offers a complete (more so than Highway's) libm.

But the other part about SIMD: I'm unsure if mgl-mat uses SIMD for transcendental functions or even for something like element-wise multiplication and division*. SIMD easily provides a speed-boost of 4-8 times which numpy uses. Libraries like sleef have been put to use by many.

Highway and Agner's VectorClass also have math functions. And SLEEF should definitely be mentioned.

"SIMD Library for Evaluating Elementary Functions, vectorized libm and DFT" - https://github.com/shibatch/sleef

Not mentioned in the list of users is SLEEF (https://github.com/shibatch/sleef), which provides fast approximations for various elementary functions. (It generates coefficients for the approximations with mpfr)

SLEEF itself is used by PyTorch.

If you are looking at floats, there's https://sleef.org

It would be interesting to see SLEEF added in the benchmarks.