What is the "nasty surprise" of Zen 4 AVX512? Sure, it's not quite the twice as fast you might initially assume, but (unlike Intel's downclocking) it's still a strict upgrade over AVX2, is it not?

Every Rust SIMD article should mention the .chunks_exact() auto vectorization trick by law.

Lazy man's "kinda good enough for some cases SIMD in pure Rust" is to simply target x86-64-v3 (RUSTFLAGS=-Ctarget-cpu=x86-64-v3), which is supported by all AMD Zen and Intel CPUs since Haswell; and for floating point code, which cannot be auto-vectorized due to the accuracy implications, "simply" write it with explicit four or eight-way lanes, and LLVM will do the rest. Usually. Loops may need explicit handling of head or tail to auto-vectorize (chunks_exact helps with this, it hands you the tail).

The benchmarks on Zen 5 are absolutely insane for just a bit of extra work. I really hope the portable SIMD module stabilizes soon, so we do not have to keep rewriting the same logic for NEON and AVX every time we want to optimize something. That example about implementing ChaCha20 twice really hit home for me.

"Intel CPUs were downclocking their frequency when using AVX-512 instructions due to excessive energy usage (and thus heat generation) which led to performance worse than when not using AVX-512 acceleration."

This is an overstatement so gross that it can be considered false. On Skylake-X, for mixed workloads that only had a few AVX-512 instructions, a net performance loss could have happened. On Ice Lake and later this statement was not true in any way. For code like ChaCha20 it was not true even on Skylake-X.

> For example, NEON ... can hold up to 32 128-bit vectors to perform your operations without having to touch the "slow" memory.

Something I recently learnt: the actual number of physical registers in modern x86 CPUs are significantly larger, even for 512-bit SIMD. Zen 5 CPUs actually have 384 vectors registers, 384*512b = 24KB!