The structure is unlike a traditional company. In a traditional company, the managers decide the priorities and direct the employees what to work on while facilitating that work. While there are people with a more managerial type position working on rust compiler, their job is not to tell the volunteers what to work on (they cannot), but instead to help the volunteers accomplish whatever it is they want to do.

I don't know about std::simd specifically, but for many features, it's simply a case of "none of the very small number of people working on the rust compiler have prioritized it".

I do wish there was a bounty system, where people could say "I really want std::simd so I'll pay $5,000 to the rust foundation if it gets stabilized". If enough people did that I'm sure they could find a way to make it happen. But I think realistically, very few people would be willing to put up even a cent for the features they want. I hear a lot of people wishing for better const generics, but only 27 people have set up a donation to boxy (lead of the const generics group https://github.com/sponsors/BoxyUwU ).

Weren't a bunch of modules deprecated recently as a consequence of intel layoffs?

Corporate-sponsored contributions are probably the majority, but I don't think true volunteers are super-rare. But in both cases they're a "volunteer" from the perspective of the Linux leadership - they're contributing the changes that they want to make, they're not employees of Linux who can be directed to work on the things that that leadership thinks is important.

(And conversely it's the same with Rust - a lot of those volunteer contributors work for employers who want Rust to have some specific functionality, so they employ someone to work on that)

Leaving aside any specific blockers:

- It's a massive hard problem, to build a portable abstraction layer over the SIMD capabilities of various CPUs.

- It's a massive balance between performance and usability, and people care deeply about both.

- It's subject to Rust's stability guarantee for the standard library: once we ship it, we can't fix any API issues.

- There are already portable SIMD libraries in the ecosystem, which aren't subject to that stability guarantee as they can ship new semver-major versions. (One of these days, I hope we have ways to do that for the standard library.)

- Many people already use non-portable SIMD for the 1-3 targets they care about, instead.

This is something a lot of people (myself included) have gotten tripped up by. Non-portable SIMD intrinsics have been stable under std::arch for a long time. Obviously they aren't nearly as nice to hold, but if you're in a place where you need explicit SIMD speed-ups, that probably isn't a killer.

The thing that isn't stable in the standard library is the portable abstraction layer atop those. But several of those exist in the community.

I've written thousands and thousands of lines of Rust SIMD code over the last ~4 years and it's, in my opinion, a pretty nice way of doing SIMD code that is portable.

I don't know about the specific issues in stabilization, but the API has been relatively stable, although there were some breaking changes a few years ago.

Maybe you can't extract 100% of your CPUs capabilities using it, but I don't find that a problem because there's a zero-cost fallback to CPU-specific intrinsics when necessary.

I recently wrote some computer graphics code and I could get really nice performance (~20x my scalar code, 5x from just a naive translation). And the same codebase can be compiled to AVX2, SSE2 and ARM NEON. It uses f32x8's (256b vector width), which are not available on SSE or NEON, but the compiler can split those vectors. The f32x8 version was faster than f32x4 even on 128b hardware. I would've needed to painstakingly port this codebase to each CPU, so it was at least a 3x reduction in lines of code (and more in programmer time).

Absolutely! It seems to work for many people. The point of my comment was not that there's anything necessarily wrong with it. It's a huge API and it's hard and slow to stabilize huge things when you only get one shot.

I'd prefer if std::simd would encurage relative to native SIMD width scaling (and support scalable SIMD ISAs).

Yeah, exceeding native vector width is kinda just adding another round of loop unrolling. Sometimes it helps, sometime it doesn't. This is probably mostly about register pressure.

And architecture specific benchmarking is required if you want to get most performance out of it.

> I'd prefer if std::simd would encurage relative to native SIMD width scaling (and support scalable SIMD ISAs).

It is possible to write width-generic SIMD code (ie. have vector width as generic parameter) in Rust std::simd (or C++ templates and vector extensions) and make it relative to native vector width (albeit you need to explicitly define that).

In my problem domain (computer graphics etc) the vector width is often mandated by the task at hand (e.g. 2d vs 3d). It's often not about doing something on an array of size N. This does not lead to optimal HW utilization, but it's convenient and still a lot faster than scalar code.

Scalable SIMD ISAs are kind of a new thing, so not sure how well current std::simd or C vector extensions (or LLVM IR SIMD ops) map to the HW. Maybe they would be better served by another kind of API? I don't really know, haven't had the privilege of writing any scalable vector code yet.

What I'm trying to say is IMO std::simd works well enough and should probably be stabilized (almost) as is, barring any show stopper issues. It's already useful and has been for many years.

Can’t APIs be fixed between editions?

1. A high bar for quality in std

2. Dependencies on other unstable features

3. Known bugs

4. Conflicts with other unstable features

It seems anything that affects trait solving is very complicated and is more likely to have bugs or combine non-trivially with other trait-solving features.

I think there is also some sampling bias. Tons of features get stabilized, but you are much more likely to notice a nightly feature that is unstable for a long time and complex enough to be excited about.

Yep and this is why many features die or linger on forever. Getting the trait solving working correctly across types and soundly across lifetimes is complicated enough to have killed several features previously (like specialization/min_specialization). It was the reason async trait took so long and why GAT were so important.

AFAIK that’s not a blocker for Rust - the std library is allowed to use unstable at all times.

Too many promises have been made.

Rust needs more unsafe opt outs. Ironically simd has this so it does not bother me.

There is nothing blocking high quality SIMD libraries on stable in Rust today. The bar for inclusion in std is just much higher than the rest of the ecosystem.

However, like other commenters I assume it’s because it’s hard, not all that many users of Rust really need it, and the compiler team is small and only consists of volunteers.

Most interesting performance optimizations from vector ISAs can't be done by the compiler.

Not disagreeing with this statement in general, but with std::simd I can get 80% of the performance with 20% of the effort compared to intrinsics.

For the last 20%, there's a zero cost fallback to intrinsics when you need it.

A common example is executing the equivalent of a runtime SQL WHERE clause on arbitrary data structures of mixed types. Clever idioms allow surprisingly complex unrelated constraint operators to be evaluated in parallel with SIMD. It would be cool if a compiler could take a large pile of fussy, branchy scalar code that evaluates ad hoc constraints on data structures and converts it to an equivalent SIMD constraint engine but that doesn't seem likely anytime soon. So we roll them by hand.

It's the exact opposite. It's the portable SIMD abstraction that isn't stable yet. But the vendor specific SIMD intrinsics have been stable for quite some time already (x86-64 for many years for example). And indeed, those are necessary for some cases.

ripgrep wouldn't be as fast as it was if it weren't possible to use SIMD on stable Rust.

And since Rust basically sells itself on high standards (zero-cost abstractions, etc.) the devs go back and forth until it feels like the solution is handed down from the heavens.

1. It is highly depending on LLVM intrinsics which itself can change quite a lot. Sometimes the intrinsic would even fail to instantiate and crashed the entire compilation. I for example met chronic ICE crashes for the same code in different nightly Rust version. Then I realize it is because the SIMD operation was too complicated and I need to simplify it, and sometimes need to stop recursing and expanding too much to prevent stack spilling and exhausting register allocation.

This happens from time to time especially when using std::simd with embedded target where registers are scarcity.

2. Some hardware design decisions making SIMD itself not ergonomic and hard to generalize, this is also reflected on the design of std::simd as well.

Recall that SIMD techniques stems from vector processors in supercomputers from the likes of Cray and IBM, that is from the 70s and back then computation and hardware design was primitive and simple, so they have fixed vector size.

The ancient design is very stable, and is still kept till this day, even with the likes of AVX2, AVX512, VFP and NEON, so this influenced the design of things like lane count (https://doc.rust-lang.org/std/simd/struct.LaneCount.html).

But the plot twist: as time goes on, it turns out that modern SIMD is now capable of doing variable sizes; RISC-V's SIMD extension is one such implementation for example.

So now we come to a dilemma on to keep the existing fixed lane count design, or allow it to extend further. If we allow it to extend further to cater for things like variable-SIMD vector length, then we need to wait for generic_const_exprs to be stable, and right now it is not only not stable but incomplete too (https://github.com/rust-lang/portable-simd/issues/416).

This is a hard design philosophical change and is not easy to deal with. Time will tell.

3. As an extension to #2, the way that thinking in SIMD is hard in the very first place, and to use it in production you even have to think about different situations. This come in the form of dynamic dispatch, and it is a pain to dealt with, although we have great helpers such as multiversion...it is still very hard to design an interface that scales. Take Google's highway (https://github.com/google/highway/blob/master/g3doc/quick_re...) for example, it is the library to write portable SIMD code with dynamic dispatch in C++, but in an esotheric and not so ergonomic way. How we could do better with std::simd is still a myth. How do you abstract the idea of scatter-gather operation? What the heck is swizzle? Why do we call it shuffle and not permutation. Lots of stuff to learn, that means lots of pain to go through.

4. Plus, when you think in SIMD, there could be multiple instructions and multiple ways to do the same thing, one maybe more efficient than the other.

For example, as I have to touch some finite field stuff in GF(2^8), there are few ways to do finite field multiplication:

a. Precomputed table lookup

b. Russian Peasant Multiplication (basically carryless Karatsuba multiplication, but oftenly reduce to the form of table lookups as well, can also seen as ripple counter with modulo arithmetic except carry has to be delivered in a different way)

c. Do an inner product and then do Barrett reduction (https://www.esat.kuleuven.be/cosic/publications/article-1115...)

d. Or just treat it as multiplcation over a polynominal power series but this essentially mean we treat it as a finite field convolution, which I suspect is highly related to fourier transform. (https://arxiv.org/pdf/1102.4772)

e. Use the somewhat new GF2P8AFFINEQB (https://www.felixcloutier.com/x86/gf2p8affineqb) from GFNI which, contrary to most people who think it is available for AVX512 only, but is actually available for SSE/AVX/AVX2 as well (this is called GFNI-SSE in gcc), so it works on my 13600KF too (except obviously I cannot use ZMM registers or I just get illegal instruction for any instructions that touches ZMM or uses the EVEX encoding). I have an internal implementation of finite field multiplication using just that, but I need to use the polynomial of 0x11D rather than 0x11B so GF2P8MULB (https://www.felixcloutier.com/x86/gf2p8mulb) is out of question (which is supposed to be the fastest in the world theoretically if we can use arbitary polynomial), but this is rather hard to understand and explain in the first place. (by the way I used SIMDE for that: https://github.com/simd-everywhere/simde)

All of these can be done in SIMD, but each one of these methods have its pros and cons. Table lookup maybe fast and seemingly O(1) but you actually need to keep the table in cache, meaning we trade time with space, and SIMD would amplify the cache thrashing from multiple access. This could slow down the CPU pipeline although modern CPU are clever enough on cache management. If you want to do Russian Peasant Multiplication then you need a bunch of loops to go through the division and XOR chunk by chunk.

If you want Barrett reduction then you need to have efficient carryless multiplication such as PCLMULQDQ (https://www.felixcloutier.com/x86/pclmulqdq), to do the inner product and reduce the polynomial. Or a more primitive way find ways to do finite field Horner's method in SIMD...

How to think in SIMD is already hard as said in #3. How to balance in SIMD like this is even harder.

Unless you want to have a certain edge, or want to shatter the benchmark, I would say SIMD is not a good investment. You need to use SIMD at the right scenario at the right time. SIMD is useful, but also kind of niche, and modern CPU is optimized well enough that the performance of general solutions without using SIMD, is good enough too, since all of which will eventually dump right down to the uops anyway, with deep pipeline, branch predictor, superscalar and speculative execution doing their magics altogether, and most of the time if you want to use SIMD, using the easiest SIMD methods is generally enough.

*: I myself used std::simd intensively in my own project, well it got refused that the paper was actually severely lacking in literature studies, but that I shouldn't have used LLM too much to generate the paper.

However, the code was here (https://github.com/stevefan1999-personal/sigmah). Now I have a new approach to this problem that is derived from my current work with finite field, error correction, divide and conquer and polynominal multiplication, and I plan to resubmit the paper once I have time to clear it, with a more careful approach next time too, although the problem of string matching with don't care can be seen as convolution and I doubt my approach would ended up something like that...making the paper still unworthy for acceptance.

A quick comment on this one point (personal opinion): from a hyperscalar perspective, scalar code is most certainly not enough. The energy cost from scheduling a MUL instruction is something like 10x of the actual operation it performs. It is important to amortize that cost over many elements (i.e. SIMD).