Thinking parallel gets you enormous speed benefits for any number of arbitrary algorithms: https://mcyoung.xyz/2023/11/27/simd-base64/
I wouldn't be holding my breath though - proper support of high-level portable SIMD abstraction requires quite a lot of compiler complexity due to how wide (heh) the API surface of SIMD extensions is in most ISAs, and because of details necessary to get right to keep data in appropriate (vector and/or mask) registers. This, naturally, goes in the complete opposite direction to the design philosophy of Go's compiler. Instead, you are supposed to write a custom Go ASM syntax, with byte literals used to encode opcodes if they are not natively supported (which is common).
If you're interested in what high-effort SIMD implementation in this kind of language looks like, take a look at C#'s cross-platform Vector API: https://github.com/dotnet/runtime/blob/main/docs/coding-guid...
https://lemire.me/blog/2024/07/05/scan-html-faster-with-simd... (uses platform intrisics, but showcases that you can go one abstraction level lower, retaining the same Vector128<T> type if you need to specialize a particular part of your algorithm for a platform, without having to maintain separate copy for each one)
Here's high-effort vectorized CRC64 implementation that uses these: https://github.com/dotnet/runtime/blob/283de5b5adf08c42d4945... (performs as fast as C++-based mnemonic variant)
Though Haswell seems like a pretty obsolete platform to optimize for at this point. Even Skylake will be a decade old next year.
2) chunks with too many small numbers cannot be processed with just 2 shuffle-adds
3) (not mentioned in the post) HighLoad limits the size of the source code you can submit, so you can't put all possible values in the look-up table
Which would be much faster than the checked add (adc). Does any hardware support such checked SIMD arithmetic already?
Or can you still assume that most arithmetic is still broken in most languages/libraries.
That's terrifying
