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Comment by steppi | Hacker News Reader

Another example is writing hand optimized matrix and vector operation routines tailored to specific hardware for BLAS libraries [0].

[0] https://en.m.wikipedia.org/wiki/Basic_Linear_Algebra_Subprog...

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Is this really still a thing?

Do people go further than using instrinsics for let's say AVX?

Sure. You'll see it very often in codec implementations. From rav1e, a fast AV1 encoder mostly written in Rust: https://github.com/xiph/rav1e/tree/master/src/x86

Portions of the algorithm have been translated into assembly for ARM and x86. Shaving even a couple percent off something like motion compensation search will add up to meaningful gains. See also the current reference implementation of JPEG: https://github.com/libjpeg-turbo/libjpeg-turbo/tree/main/sim...

Yeah. I'm going to be helping to work on expanding CI for OpenBLAS and have been diving into this stuff lately. See the discussion in this closed OpenBLAS issue gh-1968 [0] for instance. OpenBLAS’s Skylake kernels do rely heavily on intrinsics [1] for compilers that support them, but there's a wide range of architectures to support, and when hand-tuned assembly kernels work better, that's what are used. For example, [2].

[0] https://github.com/xianyi/OpenBLAS/issues/1968

[1] https://github.com/xianyi/OpenBLAS/blob/develop/kernel/x86_6...

[2] https://github.com/xianyi/OpenBLAS/blob/23693f09a26ffd8b60eb...

interesting stuff. thanks for the links

FWIW I've found that compilers' code generation around intrinsics is often suboptimal in pretty obvious ways, moving data around needlessly, so I resort to assembly. For me this has just been for hobby side projects, but I'm sure people doing it for stuff that matters run into the same issue.

> Is this really still a thing?

Why wouldn’t it be? Compilers haven’t advanced tremendously in the past two decades in terms of optimizations and don’t have much new to add to high performance SIMD numeric kernels.

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