You've got a point but are overstating it considerably. There is a big gap between just autovectorization and the portable primitives a library like Highway or Fearless SIMD will give you. For example, I haven't seen autovectorization do select or swizzle.

But there's another point in the tradeoff space. One of the explicit design decisions in Fearless SIMD is to support "downcasting," or specialization to a specific microarchitecture. At least for the kind of problems I've worked on, even when you're doing something fancy with arch-specific permutations or what not, the majority of the operations will be pretty vanilla, and can be expressed well in the portable subset.

So you can think of a library like Fearless SIMD as enabling your extreme optimization use case, just more ergonomically.

Of course, this depends on LLVM compiling intrinsics to assembly efficiently. That hasn't always been the case, and is not perfect now (a number of issues have been filed against rustc and LLVM while developing Fearless SIMD), but is pretty good.

As always, though, you do have to measure performance, and I frequently look at the assembler output to double-check that it's doing the right thing. The day of "fire and forget" portable SIMD has not yet arrived.

Getting 2x or 4x performance in your inner loops using a reasonable SIMD library is infinitely better than theoretically getting 8x performance with hand-coded nonportable intrinsics, because the latter is never going to happen in most programs, so the actual point of comparison is scalar code, or autovectorized code at best.

I agree with that historically auto-vertorization does not seem to work reliably. I'm not sure about your broad claim.

Thoughts on an abstraction over ARM and x86, at 128, 256, and 512-bit widths which, either in a manual or automatic way (The latter more challenging) makes your floating point computations 4-16x faster with minimal restructuring? I think that's doable, and a nice goal of SIMD.

Sure, in the same way that there is no such thing as portable code at all. The result will be suboptimal, but it will still be better than not having it.

> there is no portable SIMD

Except in languages with a JIT compiler

It's a continuum. Some things basically all SIMD implementations support. Want to add 2 4xf32 vectors together? That's pretty easy to do portably.

But yeah to be fair if you are at that point, you probably want to go fully non-portable anyway. Especially with AI.

Has anyone even figured out how to do vector stuff (SVE/RVV) without assembly?

What about numpy, numba, and torch.compile?