sleef VS version2

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.

Vector class library - 938 GH stars

Most of the time is spent parsing, but this problem lends itself nicely to a SIMD formulation, which using vectorclass doesn't even require detailed knowledge of the intrinsics. Hot runs take ~14 µs on a Core i9-12900K, including I/O. Full code is (here)[https://github.com/ahans/aoc2022/blob/main/cpp/day04.cc], the interesting part is this, where we process 32 elements at once: