eigen VS sleef

Is Eigen still alive? There's been no release in 3 years, and no news about it: https://gitlab.com/libeigen/eigen/-/issues/2699

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 am trying to include Eigen in my project via FetchContent. They define/assume-defined PROJECT_IS_TOP_LEVEL on line 19 and, among other locations, on line 607 in their top level list file.

Eigen is a header-only library, thus simply cloning it from the official repository into the FOGGDD folder should be enough.

math library because you should never implement a math library yourself, and you probably want somethign more focused on performance than STL. GLM may work if you just need basic vector support. Eigen may help for a more physics heavy game. But I'd probably find something in-between those two

cmake_minimum_required(VERSION 3.15) project(app) include(FetchContent) FetchContent_Declare(Eigen3 URL https://gitlab.com/libeigen/eigen/-/archive/3.4.0/eigen-3.4.0.tar.gz) FetchContent_MakeAvailable(Eigen3) add_executable(app main.cpp) target_link_libraries(app Eigen3::Eigen)

Today was very easy to do with Eigen

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.