15 ms·

The state of SIMD in Rust in 2025

237 points by ashvardanian 11mo ago

mdriley 11mo ago
> TL;DR: use std::simd if you don’t mind nightly, wide if you don’t need multiversioning, and otherwise pulp or macerator. This matches the conclusion we reached for Chromium. We were okay with nightly, so we're using `std::simd` but trying to avoid the least stable APIs. More details: https://docs.google.com/document/d/1lh9x43gtqXFh5bP1LeYevWj0EcIRgIu0XGahHY08aeY/edit?tab=t.0#heading=h.7nki9mck5t64 https://docs.google.com/document/d/1lh9x43gtqXFh5bP1LeYevWj0...
- vlovich123 11mo ago
  Do you compile the whole project with nightly or just specific components?
josephg 11mo ago
Why isn’t std::simd in stabile yet? Why do so many great features seem stuck in the same nightly-forever limbo land - like generators? I’m sure more people than ever are working on the compiler. What’s going on?
- the__alchemist 11mo ago
  Would love this. I've heard it's not planned to be in the near future. Maybe "perfect is the enemy of good enough"?
  - CooCooCaCha 11mo ago
    Rust doesn’t have a BDFL so there’s nobody with the power to push things through when they’re good enough. 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.
    - ChadNauseam 11mo ago
      And somehow it has ended up feeling more pleasant and consistent than most languages with a BDFL, even though it was designed by committee. I don't really understand how that happened, but I appreciate the cautious and conservative approach they've taken
- singron 11mo ago
  There is a GitHub issue that details what's blocking stabilization for a each feature. I've read a few recently and noticed some patterns: 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.
  - vlovich123 11mo ago
    > Dependencies on other unstable features AFAIK that’s not a blocker for Rust - the std library is allowed to use unstable at all times.
    - estebank 11mo ago
      I think they meant on unstable features which might yet change their semantics. A stable API relying on unstable implementation is common in Rust (? operator, for example), but that is entirely dependent on having a good idea of what the eventual stable version is going to look like, in such a way that the already stable feature won't break in any way.
  - throwup238 11mo ago
    > 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. 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.
- ChadNauseam 11mo ago
  There really aren't that many people working on the compiler. It's mostly volunteers. 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 https://github.com/sponsors/BoxyUwU ).
  - LtWorf 11mo ago
    > There really aren't that many people working on the compiler. It's mostly volunteers. Seems smart to put the language as a requirement for compiling the linux kernel and a bunch of other core projects then!
    - ChadNauseam 11mo ago
      I think it seems just right. Languages these days are either controlled by volunteers or megacorps. Because linux is about freedom and is not aligned with megacorps, I think they'd prefer a volunteer-driven language like Rust or C++ rather than the corporate ones.
      - FuckButtons 11mo ago
        I’m not sure you can argue that Rust and C++ have anything like a similar story around being volunteer oriented, given the number of places that have C++ compiler groups that contribute papers / implementations.
- Avi-D-coder 11mo ago
  Usually when I go and read the github and zulip threads the reason for paused work comes down to the fact that no one has come up with a design that maintains every existing promise the compiler has made. The most common ones I see are the feature conflicts with safety, semver/encapsulation, interacts weirdly with object safety, causes post post-monomorphization errors, breaks perfect type class coherence (see haskells unsound specialization). Too many promises have been made. Rust needs more unsafe opt outs. Ironically simd has this so it does not bother me.
- IshKebab 11mo ago
  I would love generators too but I think the more features they add the more interactions with existing features they have to deal with, so it's not surprising that its slowing down.
  - estebank 11mo ago
    Generators in particular has been blocked on the AsyncIterator trait. There are also open questions around consuming those (`for await i in stream`, or just keep to `while let Some(i) in stream.next().await`? What about parallel iteration? What about pinning obligations? Do that as part of desugaring or making it explicit?). It is a shame because it is almost orthogonal, but any given decision might not be compatible with different approaches for generators. The good news is that some people are working on it again.
- JoshTriplett 11mo ago
  > Why isn’t std::simd in stable yet? 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.
  - vlovich123 11mo ago
    > we can't fix any API issues. Can’t APIs be fixed between editions?
    - JoshTriplett 11mo ago
      Partially (with upcoming support for renaming things across editions), but it's a pain if the types change (because then they're no longer common vocabulary), and all the old APIs still have to exist.
  - colonial 11mo ago
    > 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.
    - JoshTriplett 11mo ago
      Exactly. Many parts of SIMD are entirely stable, for x86, ARM, WebAssembly... 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.
- duped 11mo ago
  std::arch::* intrinsics for SIMD are stable and you can use them today. The situation is only slightly worse than C/C++ because the rust compilers cares a lot about undefined behavior, so there's some safe-but-technically-unsafe/annoying cfg stuff to make sure the intrinsics are actually emitted as you intend. 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.
- capyba 11mo ago
  Given the “blazingly fast” branding, I too would have thought this would be in stable Rust by now. 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.
  - steveklabnik 11mo ago
    Don’t forget that autovectorization does a lot too. This is only for when you want to ensure you get exactly what you want, for many applications, they just kinda get it for free sometimes.
  - jandrewrogers 11mo ago
    Getting maximum performance out of SIMD requires rolling your own code with intrinsics. It is something a compiler can't do for you at a pretty fundamental level. Most interesting performance optimizations from vector ISAs can't be done by the compiler.
    - capyba 11mo ago
      Interesting, how so? I’ve had really good success with the autovectorization in gcc and the intel c compiler. Often it’s faster than my own instrinsics, though not always. One notable example though is that it seems to struggle with reduction - when I’m updating large arrays ie `A[i] += a` the compiler struggles to use simd for this and I need to do it myself.
      - burntsushi 11mo ago
        There's no optimal portable `movemask` operation. Because aarch64 NEON doesn't have it.
    - exDM69 11mo ago
      > Getting maximum performance out of SIMD requires rolling your own code with intrinsics 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.
- stevefan1999 11mo ago
  As someone who used std::simd in an attempt for submitting to an academic conference CFP*, I have look deep into how std::simd and I would conclude that there are a couple of reasons it isn't stable yet (this is rather long and maybe need 10 minutes to read): 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 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 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_reference.md 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.pdf 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 https://arxiv.org/pdf/1102.4772) e. Use the somewhat new GF2P8AFFINEQB (https://www.felixcloutier.com/x86/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 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 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 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 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.
the__alchemist 11mo ago
Of interest, I've written my own core::simd mimic so I don't have to make all my libs and programs use nightly. It started as me just making my Quaternion and Vec lib (lin-alg) have their own SoA SIMD variants (Vec3x16 etc), but I ended up implementing and publicly exposing f32x16 etc. Will remove those once core::simd is stable. Downside: These are x86 only; no ARM support. I also added packing and unpacking helpers that assist with handling final lane 0 values etc. But there is still some subtly, as the article pointed out, compared to using Rayon or non-SIMD CPU code related to packing and unpacking. E.g. you should try to keep things in their SIMD form throughout the whole pipeline, how you pair them with non-SIMD values (Like you might pair [T; 8] with f32x8 etc) etc.
- ____tom____ 11mo ago
  I'm not a rust programmer. Can't you just make a local copy of the existing package and use that? Did you need to re-implement?
  - the__alchemist 11mo ago
    Good question. Probably, but I don't know how and haven't tried.
  - deleted 11mo ago
    [deleted]
  - dzaima 11mo ago
    The nightly built-in core::simd makes use of a bunch of intrinsics to "implement" the SIMD ops (or, rather, directly delegate the implementation to LLVM which you otherwise cannot do from plain Rust), which are as much if not more volatile than core::simd itself (and also nightly-only).
    - vlovich123 11mo ago
      > or, rather, directly delegate the implementation to LLVM which you otherwise cannot do from plain Rust I thought the intrinsic specifically were available in plain safe rust and the alignment required intrinsics were allowed in unsafe rust. I’m not sure I understand this “direct to llvm dispatch” argument or how that isn’t accessible to stable Rust today.
bencyoung 11mo ago
Odd that c# has a better stable SIMD story than Rust! It has both generic vector types across a range of sizes and a good set of intrinsics across most of the common instruction sets
- kelnos 11mo ago
  Why would that be odd? C# is an older and mature language backed by a corporation, while Rust is younger and has been run by a small group of volunteers for years now.
  - booi 11mo ago
    not just any corporation.. the largest software corporation on the planet
    - Arch-TK 11mo ago
      not just any largest software corporation, one of my two least favourite largest software corporations on the planet.
      - hu3 11mo ago
        not just any least favourite largest software corporation of yours... the one that most contributes to open source from the largest corporations. so one of my favourites because of that they were also one of the first of the large corps to show interest in Rust
  - josefx 11mo ago
    > hile Rust is younger and has been run by a small group of volunteers for years now I thought Rust was getting financial support from Google, Microsoft and Mozilla? Or was the Rust Foundation just a convenient way for Mozilla to fire a large amount of developers and we are actually rapidly approaching the OpenSSL Heartbleed state. Where everyone is happily building on a secure foundation that is maintained by a half dead intern when he isn't busy begging for scraps on the street?
IshKebab 11mo ago
> Fortunately, this problem only exists on x86. Also RISC-V, where you can't even probe for extension support in user space unfortunately.
- raphlinus 11mo ago
  It's not strictly x86 either, the other case you care about is fp16 support on ARM. But it is included in the M1 target, so really only on other ARM.
- dzaima 11mo ago
  Linux of course does have an interface for RISC-V extension probing via hwprobe. And there's a C interface[1] for probing that's OS-agnostic (though it's rather new). [1]: https://github.com/riscv-non-isa/riscv-c-api-doc/blob/main/src/c-api.adoc#function-multi-version https://github.com/riscv-non-isa/riscv-c-api-doc/blob/main/s...
jtrueb 11mo ago
simd was one I thought we needed. Then, i started benchmarking using iter with chunks and a nested if statement to check the chunk size. If it was necessary to do more, it was typically time to drop down to asm rather than worry about another layer in between the code and the machine.
- b33j0r 11mo ago
  This is the most surprising comment to me. It’s that bad? I haven’t benchmarked it myself. Zig has @Vector. This is a builtin, so it gets resolved at comptime. Is the problem with Rust here too much abstraction?
  - oasisaimlessly 11mo ago
    I think you misinterpreted GP; he's saying that with some hints (explicit chunking with a branch on the chunk size), the compiler's auto-vectorization can handle the rest, inferring SIMD instructions in a manner that's 'good enough'.
waffletower 11mo ago
I am torn -- while I love the bitter critique of std::simd's nightly builds (why bother with any public release if it is never stable?), I cringed at the critique of "(c)urrently things are well fleshed out for i32, i64, f32, and f64 types". f64 and i64 go a long way for most numerical applications -- the OP seemed snowflaky to me with that entitled concern.
- zamadatix 11mo ago
  Not supporting other types (particularly smaller ones) can be quite limiting on portable SIMD, especially when it doesn't support AVX512 either, but those are certainly a good core group - just not the whole story. Regardless, I'm not sure how painting the OP as an entitled snowflake helps anything over just asking the question.
taeric 11mo ago
I'm curious on the uptake of SIMD and other assembly level usage through high level code? I'd assume most is done either by people writing very low level code that directly manages the data, or by using very high level libraries that are prescriptive on what data they work with? How many people are writing somewhat bog standard RUST/C and expect optimal assembly to be created?
- zamadatix 11mo ago
  It's really only comparable to assembly level usage in the SIMD intrinsics style cases. Portable SIMD, like std::simd, is no more assembly level usage than calling math functions from the standard library. Usually one only bothers with the intrinsic level stuff for the use cases you're saying. E.g. video encoders/decoders needing hyper-optimized, per architecture loops for the heavy lifting where relying on the high level SIMD abstractions can leave cycles on the table over directly targeting specific architectures. If you're just processing a lot of data in bulk with no real time requirements, high level portable SIMD is usually more than good enough.
  - taeric 11mo ago
    My understanding was that the difficulty with the intrinsics was more in how restrictive they are in what data they take in. That is, if you are trying to be very controlling of the SIMD instructions getting used, you have backed yourself into caring about the data that the CPU directly understands. To that end, even "calling math functions" is something that a surprising number of developers don't do. Certainly not with the standard high level data types that people often try to write their software into. No?
    - zamadatix 11mo ago
      More than that: many of the intrinsics can be unsafe in standard Rust. This situation got much better this year but it's still not perfect. Portable SIMD has always been safe, because they are just normal high level interfaces. The other half is intrinsics are specific to the arch. Not only do you need to make sure the CPUs support the type of operation you want to do, but you need to redo all of the work to e.g. compile to ARM for newer MacBooks (even if they support similar operations). This is also not a problem using portable SIMD, the compiler will figure out how to map the lanes to each target architecture. The compiler will even take portable SIMD and compile it for a scalar target for you, so you don't have to maintain a SIMD vs non-SIMD path. By "calling math functions" I mean things like: let x = 5.0f64; let result = x.sqrt() Where most CPUs have a sqrt instruction but the program will automatically compile with a (good) software substitution for targets that don't. It's very similar with portable SIMD - the high level call gets mapped to whatever the target best supports automatically. Neither SIMD nor these kind of math functions work automatically with custom high level data types. The only way to play for those is to write the object to have custom methods which break it down to the basic types so the compiler knows what you want the complex type's behavior to be. If you can't code that then there isn't much you can do with the object, regardless of SIMD. With intrinsics you need to go a step further beyond all that and directly tell the compiler what specific CPU instructions should be used for each step (and make sure that is done safely, for the remaining unsafe operations).
justahuman74 11mo ago
Somewhat related, does rust handle the riscv vector extension in a similar way to simd?
- dzaima 11mo ago
  Scalable vectors as in RVV & SVE aren't available in rust corrently; see https://github.com/rust-lang/rust/issues/145052 https://github.com/rust-lang/rust/issues/145052 (that said autovectorization should work, and fixed-width SIMD should map to RVV as best as possible, though of course missing out on perf if ran on wider-than-minimum hardware not on a native build)
andyferris 11mo ago
Regarding autovectorization: > The other drawback of this method is that the optimizer won’t even touch anything involving floats (f32 and f64 types). It’s not permitted to change any observable outputs of the program, and reordering float operations may alter the result due to precision loss. (There is a way to tell the compiler not to worry about precision loss, but it’s currently nightly-only). Ah - this makes a lot of sense. I've had zero trouble getting excellent performance out of Julia using autovectorization (from LLVM) so I was wondering why this was such a "thing" in Rust. I wonder if that nightly feature is a per-crate setting or what?
- Arch-TK 11mo ago
  It's not something you seem to be able to just enable globally. From what I gather this is what is being referenced: https://doc.rust-lang.org/std/intrinsics/index.html https://doc.rust-lang.org/std/intrinsics/index.html Specifically the *_fast intrinsics.
  - ladyanita22 11mo ago
    Is this equivalent to --ffast-math?
    - Arch-TK 11mo ago
      From what I know of -ffast-math and can read from the docs for *_fast. I am not convinced that the *_fast intrinsics do _everything_ -ffast-math allows. They seem focused around algebraic equivalence (a/b is equivalent to a*(1/b) ) and assumptions of finite math. There's a few other things that -ffast-math allows like ignoring certain errors, ignoring the existence of signed zero, ignoring signalling NaN handling, ignoring SIGFPE handling, etc...
      - Sharlin 11mo ago
        Yes, because many of the traditional "fast math" assumptions are definitely not something that should be hidden behind an attractive option like that. In particular assuming the nonexistence of NaNs is essentially never anything but a ticket to the UB land.
CashWasabi 11mo ago
I really dislike those articles that are language focused. Why not try to share them in a way that is language agnostic?
- capyba 11mo ago
  This article is specifically about the implementation of SIMD in Rust, not other languages.
brundolf 11mo ago
std::simd is so nice and easy to use, even as someone who's never done SIMD before. I wonder why it's stuck as nightly-only
kouteiheika 11mo ago
Unfortunately SIMD in Rust tends to be pretty painful if you want to gracefully do runtime autodetection of a given SIMD extension (instead of it being a hard requirement for your program to even run). The major problem is that Rust essentially requires you to annotate every (!) function in your whole call stack with e.g. `#[target_feature(enable = "avx2")]` to make sure that the SIMD intrinsics will actually get inlined (if they're not inlined then the performance is horrible, which makes using SIMD completely pointless). This makes it very hard to build any reasonable abstractions because you need to hardcode this all over your code. You can't have e.g. a `DataStructure<S>` where S is the SIMD ISA, so that you could do `DataStructure<AVX2>` or `DataStructure<SSE>` to get a nicely specialized version of it for a given instruction set. You need to copy-paste the whole thing with changed `target_feature` attributes (or use a procedural macro which does the copy-pasting) and have two entirely separate `DataStructureAVX2` and `DataStructureSSE` types.
- burntsushi 11mo ago
  It's not quite that dire. The `memchr` crate uses abstraction to limit the code duplication: https://github.com/BurntSushi/memchr/blob/1230fc5c638a4d922f4e00a87adf8929007f2417/src/vector.rs https://github.com/BurntSushi/memchr/blob/1230fc5c638a4d922f... That is, the `memchr` crate has a `Vector` trait that is generic over the vector type. Which is essentially your `DataStructure<S>` where `S` is the ISA. (Using the vector type isn't load bearing. I could do the ISA. But in `memchr`'s case, the vector type implies the ISA.) It relies on `#[inline(always)]` to work. But you can write the algorithm generically once: https://github.com/BurntSushi/memchr/blob/1230fc5c638a4d922f4e00a87adf8929007f2417/src/arch/generic/memchr.rs https://github.com/BurntSushi/memchr/blob/1230fc5c638a4d922f... And the entry point into a specific instantiation of the generic algorithm is where you apply `#[target_feature(enable = "foo")]`: https://github.com/BurntSushi/memchr/blob/1230fc5c638a4d922f4e00a87adf8929007f2417/src/arch/x86_64/avx2/memchr.rs#L384-L402 https://github.com/BurntSushi/memchr/blob/1230fc5c638a4d922f...
  - kouteiheika 11mo ago
    > It relies on `#[inline(always)]` to work. This is a workaround, but it's still very painful, and there are still many problems with it: 1) you need to design the whole thing with this in mind; you can't just take an existing something which takes some T and get that to use SIMD, 2) `#[inline(always)]` can't be used with `#[target_feature]` at the same time, 3) you need to have `#[inline(always)]` on your whole call stack (I often don't want to inline everything, but I still want to propagate that we're in an SIMD-safe context, which you normally do with `#[target_feature]`, but that's incompatible with being able to abstract over things), 4) you have to sprinkle `unsafe` everywhere (which is especially annoying as recently we gained the ability to finally be able to call a lot of the intrinsics without any `unsafe`), 5) you have to manually make sure you're not calling something you should not be calling (`#[target_feature]` is great here because it prevents you from accidentally calling e.g. AVX2 intrinsics if you're not on the AVX2 codepath), 6) any mistake (e.g. missing an annotation somewhere) you make will just have the compiler silently not inline the intrinsics, completely killing your performance, and you're completely on your own debugging it. I work with SIMD a lot in Rust, and unfortunately currently it's a miserable experience. It's not that bad when you're writing e.g. an algorithm which fits in a single function, but once you want to write something bigger with more sophisticated abstractions and/or you want to integrate SIMD into existing code then it becomes a world of pain.
silentvoice 11mo ago
oh boy I've got opinions here. Basically I just don't want to hear about "the state of SIMD in Rust" unless it is about dramatic improvement in autovectorization in the rust compiler. 80%-90% or so of real life vectorization can be achieved in C or C++ just by writing code in a way that it can be autovectorized. Intrinsics get you the rest of the way on harder code. Autovectorization is essentially a solved problem for the vast majority of floating point code. Not so with rust, because of a dogmatic approach to floating point arithmetic that assumes bitwise reproducibility is the "right" answer for everyone (actually, it's the right answer to almost nobody) to the point of not even allowing a user to even flag on these optimizations. and once you get to the point of writing intrinsics you have to handwrite code for every new architecture when autovectorizers could have gotten you 80%-90% of the way there with a single source and often this is just enough. the contention with the above is that if a user needs SIMD they can just use some SIMD API and make their intention more clear. this is essentially an argument that we should handwrite intrinsics. well guess what. I'm a programmer and I use compilers because they _do this for me_ and indeed are able to do so very easily in C or C++ when I instruct it that I'm ok with with reordering operations and other "accuracy impacting" optimizations. The huge joke on us is that these optimizations generally have the effect of _improving_ accuracy because it will reduce the number of rounding steps either by simply reducing the number of operations or by using fused multiply adds which round only once.
- CryZe 11mo ago
  Keep an eye out for the algebraic operations on floats currently in nightly then: https://doc.rust-lang.org/nightly/std/primitive.f32.html#algebraic-operators https://doc.rust-lang.org/nightly/std/primitive.f32.html#alg...
  - the__alchemist 11mo ago
    I stumbled on these recently; you can do these in CUDA kernels. I have some "todo: mul_add here" in my rust code!
  - TinkersW 11mo ago
    So you have to write fugly code just to get something that should be a compiler switch?

237 points by ashvardanian 11mo ago

> TL;DR: use std::simd if you don’t mind nightly, wide if you don’t need multiversioning, and otherwise pulp or macerator. This matches the conclusion we reached for Chromium. We were okay with nightly, so we're using `std::simd` but trying to avoid the least stable APIs. More details: https://docs.google.com/document/d/1lh9x43gtqXFh5bP1LeYevWj0EcIRgIu0XGahHY08aeY/edit?tab=t.0#heading=h.7nki9mck5t64 https://docs.google.com/document/d/1lh9x43gtqXFh5bP1LeYevWj0...

vlovich123 11mo ago
Do you compile the whole project with nightly or just specific components?

josephg 11mo ago

Why isn’t std::simd in stabile yet? Why do so many great features seem stuck in the same nightly-forever limbo land - like generators? I’m sure more people than ever are working on the compiler. What’s going on?

the__alchemist 11mo ago
Would love this. I've heard it's not planned to be in the near future. Maybe "perfect is the enemy of good enough"?
- CooCooCaCha 11mo ago
  Rust doesn’t have a BDFL so there’s nobody with the power to push things through when they’re good enough. 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.
  - ChadNauseam 11mo ago
    And somehow it has ended up feeling more pleasant and consistent than most languages with a BDFL, even though it was designed by committee. I don't really understand how that happened, but I appreciate the cautious and conservative approach they've taken
singron 11mo ago
There is a GitHub issue that details what's blocking stabilization for a each feature. I've read a few recently and noticed some patterns: 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.
- vlovich123 11mo ago
  > Dependencies on other unstable features AFAIK that’s not a blocker for Rust - the std library is allowed to use unstable at all times.
  - estebank 11mo ago
    I think they meant on unstable features which might yet change their semantics. A stable API relying on unstable implementation is common in Rust (? operator, for example), but that is entirely dependent on having a good idea of what the eventual stable version is going to look like, in such a way that the already stable feature won't break in any way.
- throwup238 11mo ago
  > 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. 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.
ChadNauseam 11mo ago
There really aren't that many people working on the compiler. It's mostly volunteers. 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 https://github.com/sponsors/BoxyUwU ).
- LtWorf 11mo ago
  > There really aren't that many people working on the compiler. It's mostly volunteers. Seems smart to put the language as a requirement for compiling the linux kernel and a bunch of other core projects then!
  - ChadNauseam 11mo ago
    I think it seems just right. Languages these days are either controlled by volunteers or megacorps. Because linux is about freedom and is not aligned with megacorps, I think they'd prefer a volunteer-driven language like Rust or C++ rather than the corporate ones.
    - FuckButtons 11mo ago
      I’m not sure you can argue that Rust and C++ have anything like a similar story around being volunteer oriented, given the number of places that have C++ compiler groups that contribute papers / implementations.
Avi-D-coder 11mo ago
Usually when I go and read the github and zulip threads the reason for paused work comes down to the fact that no one has come up with a design that maintains every existing promise the compiler has made. The most common ones I see are the feature conflicts with safety, semver/encapsulation, interacts weirdly with object safety, causes post post-monomorphization errors, breaks perfect type class coherence (see haskells unsound specialization). Too many promises have been made. Rust needs more unsafe opt outs. Ironically simd has this so it does not bother me.
IshKebab 11mo ago
I would love generators too but I think the more features they add the more interactions with existing features they have to deal with, so it's not surprising that its slowing down.
- estebank 11mo ago
  Generators in particular has been blocked on the AsyncIterator trait. There are also open questions around consuming those (`for await i in stream`, or just keep to `while let Some(i) in stream.next().await`? What about parallel iteration? What about pinning obligations? Do that as part of desugaring or making it explicit?). It is a shame because it is almost orthogonal, but any given decision might not be compatible with different approaches for generators. The good news is that some people are working on it again.
JoshTriplett 11mo ago
> Why isn’t std::simd in stable yet? 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.
- vlovich123 11mo ago
  > we can't fix any API issues. Can’t APIs be fixed between editions?
  - JoshTriplett 11mo ago
    Partially (with upcoming support for renaming things across editions), but it's a pain if the types change (because then they're no longer common vocabulary), and all the old APIs still have to exist.
- colonial 11mo ago
  > 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.
  - JoshTriplett 11mo ago
    Exactly. Many parts of SIMD are entirely stable, for x86, ARM, WebAssembly... 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.
duped 11mo ago
std::arch::* intrinsics for SIMD are stable and you can use them today. The situation is only slightly worse than C/C++ because the rust compilers cares a lot about undefined behavior, so there's some safe-but-technically-unsafe/annoying cfg stuff to make sure the intrinsics are actually emitted as you intend. 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.
capyba 11mo ago
Given the “blazingly fast” branding, I too would have thought this would be in stable Rust by now. 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.
- steveklabnik 11mo ago
  Don’t forget that autovectorization does a lot too. This is only for when you want to ensure you get exactly what you want, for many applications, they just kinda get it for free sometimes.
- jandrewrogers 11mo ago
  Getting maximum performance out of SIMD requires rolling your own code with intrinsics. It is something a compiler can't do for you at a pretty fundamental level. Most interesting performance optimizations from vector ISAs can't be done by the compiler.
  - capyba 11mo ago
    Interesting, how so? I’ve had really good success with the autovectorization in gcc and the intel c compiler. Often it’s faster than my own instrinsics, though not always. One notable example though is that it seems to struggle with reduction - when I’m updating large arrays ie `A[i] += a` the compiler struggles to use simd for this and I need to do it myself.
    - burntsushi 11mo ago
      There's no optimal portable `movemask` operation. Because aarch64 NEON doesn't have it.
  - exDM69 11mo ago
    > Getting maximum performance out of SIMD requires rolling your own code with intrinsics 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.
stevefan1999 11mo ago
As someone who used std::simd in an attempt for submitting to an academic conference CFP*, I have look deep into how std::simd and I would conclude that there are a couple of reasons it isn't stable yet (this is rather long and maybe need 10 minutes to read): 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 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 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_reference.md 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.pdf 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 https://arxiv.org/pdf/1102.4772) e. Use the somewhat new GF2P8AFFINEQB (https://www.felixcloutier.com/x86/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 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 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 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 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.

the__alchemist 11mo ago

Of interest, I've written my own core::simd mimic so I don't have to make all my libs and programs use nightly. It started as me just making my Quaternion and Vec lib (lin-alg) have their own SoA SIMD variants (Vec3x16 etc), but I ended up implementing and publicly exposing f32x16 etc. Will remove those once core::simd is stable. Downside: These are x86 only; no ARM support. I also added packing and unpacking helpers that assist with handling final lane 0 values etc. But there is still some subtly, as the article pointed out, compared to using Rayon or non-SIMD CPU code related to packing and unpacking. E.g. you should try to keep things in their SIMD form throughout the whole pipeline, how you pair them with non-SIMD values (Like you might pair [T; 8] with f32x8 etc) etc.

____tom____ 11mo ago
I'm not a rust programmer. Can't you just make a local copy of the existing package and use that? Did you need to re-implement?
- the__alchemist 11mo ago
  Good question. Probably, but I don't know how and haven't tried.
- deleted 11mo ago
  [deleted]
- dzaima 11mo ago
  The nightly built-in core::simd makes use of a bunch of intrinsics to "implement" the SIMD ops (or, rather, directly delegate the implementation to LLVM which you otherwise cannot do from plain Rust), which are as much if not more volatile than core::simd itself (and also nightly-only).
  - vlovich123 11mo ago
    > or, rather, directly delegate the implementation to LLVM which you otherwise cannot do from plain Rust I thought the intrinsic specifically were available in plain safe rust and the alignment required intrinsics were allowed in unsafe rust. I’m not sure I understand this “direct to llvm dispatch” argument or how that isn’t accessible to stable Rust today.

bencyoung 11mo ago

Odd that c# has a better stable SIMD story than Rust! It has both generic vector types across a range of sizes and a good set of intrinsics across most of the common instruction sets

kelnos 11mo ago
Why would that be odd? C# is an older and mature language backed by a corporation, while Rust is younger and has been run by a small group of volunteers for years now.
- booi 11mo ago
  not just any corporation.. the largest software corporation on the planet
  - Arch-TK 11mo ago
    not just any largest software corporation, one of my two least favourite largest software corporations on the planet.
    - hu3 11mo ago
      not just any least favourite largest software corporation of yours... the one that most contributes to open source from the largest corporations. so one of my favourites because of that they were also one of the first of the large corps to show interest in Rust
- josefx 11mo ago
  > hile Rust is younger and has been run by a small group of volunteers for years now I thought Rust was getting financial support from Google, Microsoft and Mozilla? Or was the Rust Foundation just a convenient way for Mozilla to fire a large amount of developers and we are actually rapidly approaching the OpenSSL Heartbleed state. Where everyone is happily building on a secure foundation that is maintained by a half dead intern when he isn't busy begging for scraps on the street?

IshKebab 11mo ago

> Fortunately, this problem only exists on x86. Also RISC-V, where you can't even probe for extension support in user space unfortunately.

raphlinus 11mo ago
It's not strictly x86 either, the other case you care about is fp16 support on ARM. But it is included in the M1 target, so really only on other ARM.
dzaima 11mo ago
Linux of course does have an interface for RISC-V extension probing via hwprobe. And there's a C interface[1] for probing that's OS-agnostic (though it's rather new). [1]: https://github.com/riscv-non-isa/riscv-c-api-doc/blob/main/src/c-api.adoc#function-multi-version https://github.com/riscv-non-isa/riscv-c-api-doc/blob/main/s...

jtrueb 11mo ago

simd was one I thought we needed. Then, i started benchmarking using iter with chunks and a nested if statement to check the chunk size. If it was necessary to do more, it was typically time to drop down to asm rather than worry about another layer in between the code and the machine.

b33j0r 11mo ago
This is the most surprising comment to me. It’s that bad? I haven’t benchmarked it myself. Zig has @Vector. This is a builtin, so it gets resolved at comptime. Is the problem with Rust here too much abstraction?
- oasisaimlessly 11mo ago
  I think you misinterpreted GP; he's saying that with some hints (explicit chunking with a branch on the chunk size), the compiler's auto-vectorization can handle the rest, inferring SIMD instructions in a manner that's 'good enough'.

waffletower 11mo ago

I am torn -- while I love the bitter critique of std::simd's nightly builds (why bother with any public release if it is never stable?), I cringed at the critique of "(c)urrently things are well fleshed out for i32, i64, f32, and f64 types". f64 and i64 go a long way for most numerical applications -- the OP seemed snowflaky to me with that entitled concern.

zamadatix 11mo ago
Not supporting other types (particularly smaller ones) can be quite limiting on portable SIMD, especially when it doesn't support AVX512 either, but those are certainly a good core group - just not the whole story. Regardless, I'm not sure how painting the OP as an entitled snowflake helps anything over just asking the question.

taeric 11mo ago

I'm curious on the uptake of SIMD and other assembly level usage through high level code? I'd assume most is done either by people writing very low level code that directly manages the data, or by using very high level libraries that are prescriptive on what data they work with? How many people are writing somewhat bog standard RUST/C and expect optimal assembly to be created?

zamadatix 11mo ago
It's really only comparable to assembly level usage in the SIMD intrinsics style cases. Portable SIMD, like std::simd, is no more assembly level usage than calling math functions from the standard library. Usually one only bothers with the intrinsic level stuff for the use cases you're saying. E.g. video encoders/decoders needing hyper-optimized, per architecture loops for the heavy lifting where relying on the high level SIMD abstractions can leave cycles on the table over directly targeting specific architectures. If you're just processing a lot of data in bulk with no real time requirements, high level portable SIMD is usually more than good enough.
- taeric 11mo ago
  My understanding was that the difficulty with the intrinsics was more in how restrictive they are in what data they take in. That is, if you are trying to be very controlling of the SIMD instructions getting used, you have backed yourself into caring about the data that the CPU directly understands. To that end, even "calling math functions" is something that a surprising number of developers don't do. Certainly not with the standard high level data types that people often try to write their software into. No?
  - zamadatix 11mo ago
    More than that: many of the intrinsics can be unsafe in standard Rust. This situation got much better this year but it's still not perfect. Portable SIMD has always been safe, because they are just normal high level interfaces. The other half is intrinsics are specific to the arch. Not only do you need to make sure the CPUs support the type of operation you want to do, but you need to redo all of the work to e.g. compile to ARM for newer MacBooks (even if they support similar operations). This is also not a problem using portable SIMD, the compiler will figure out how to map the lanes to each target architecture. The compiler will even take portable SIMD and compile it for a scalar target for you, so you don't have to maintain a SIMD vs non-SIMD path. By "calling math functions" I mean things like: let x = 5.0f64; let result = x.sqrt() Where most CPUs have a sqrt instruction but the program will automatically compile with a (good) software substitution for targets that don't. It's very similar with portable SIMD - the high level call gets mapped to whatever the target best supports automatically. Neither SIMD nor these kind of math functions work automatically with custom high level data types. The only way to play for those is to write the object to have custom methods which break it down to the basic types so the compiler knows what you want the complex type's behavior to be. If you can't code that then there isn't much you can do with the object, regardless of SIMD. With intrinsics you need to go a step further beyond all that and directly tell the compiler what specific CPU instructions should be used for each step (and make sure that is done safely, for the remaining unsafe operations).

justahuman74 11mo ago

Somewhat related, does rust handle the riscv vector extension in a similar way to simd?

dzaima 11mo ago
Scalable vectors as in RVV & SVE aren't available in rust corrently; see https://github.com/rust-lang/rust/issues/145052 https://github.com/rust-lang/rust/issues/145052 (that said autovectorization should work, and fixed-width SIMD should map to RVV as best as possible, though of course missing out on perf if ran on wider-than-minimum hardware not on a native build)

andyferris 11mo ago

Regarding autovectorization: > The other drawback of this method is that the optimizer won’t even touch anything involving floats (f32 and f64 types). It’s not permitted to change any observable outputs of the program, and reordering float operations may alter the result due to precision loss. (There is a way to tell the compiler not to worry about precision loss, but it’s currently nightly-only). Ah - this makes a lot of sense. I've had zero trouble getting excellent performance out of Julia using autovectorization (from LLVM) so I was wondering why this was such a "thing" in Rust. I wonder if that nightly feature is a per-crate setting or what?

Arch-TK 11mo ago
It's not something you seem to be able to just enable globally. From what I gather this is what is being referenced: https://doc.rust-lang.org/std/intrinsics/index.html https://doc.rust-lang.org/std/intrinsics/index.html Specifically the *_fast intrinsics.
- ladyanita22 11mo ago
  Is this equivalent to --ffast-math?
  - Arch-TK 11mo ago
    From what I know of -ffast-math and can read from the docs for *_fast. I am not convinced that the *_fast intrinsics do _everything_ -ffast-math allows. They seem focused around algebraic equivalence (a/b is equivalent to a*(1/b) ) and assumptions of finite math. There's a few other things that -ffast-math allows like ignoring certain errors, ignoring the existence of signed zero, ignoring signalling NaN handling, ignoring SIGFPE handling, etc...
    - Sharlin 11mo ago
      Yes, because many of the traditional "fast math" assumptions are definitely not something that should be hidden behind an attractive option like that. In particular assuming the nonexistence of NaNs is essentially never anything but a ticket to the UB land.

CashWasabi 11mo ago

I really dislike those articles that are language focused. Why not try to share them in a way that is language agnostic?

capyba 11mo ago
This article is specifically about the implementation of SIMD in Rust, not other languages.

brundolf 11mo ago

std::simd is so nice and easy to use, even as someone who's never done SIMD before. I wonder why it's stuck as nightly-only

kouteiheika 11mo ago

Unfortunately SIMD in Rust tends to be pretty painful if you want to gracefully do runtime autodetection of a given SIMD extension (instead of it being a hard requirement for your program to even run). The major problem is that Rust essentially requires you to annotate every (!) function in your whole call stack with e.g. `#[target_feature(enable = "avx2")]` to make sure that the SIMD intrinsics will actually get inlined (if they're not inlined then the performance is horrible, which makes using SIMD completely pointless). This makes it very hard to build any reasonable abstractions because you need to hardcode this all over your code. You can't have e.g. a `DataStructure<S>` where S is the SIMD ISA, so that you could do `DataStructure<AVX2>` or `DataStructure<SSE>` to get a nicely specialized version of it for a given instruction set. You need to copy-paste the whole thing with changed `target_feature` attributes (or use a procedural macro which does the copy-pasting) and have two entirely separate `DataStructureAVX2` and `DataStructureSSE` types.

burntsushi 11mo ago
It's not quite that dire. The `memchr` crate uses abstraction to limit the code duplication: https://github.com/BurntSushi/memchr/blob/1230fc5c638a4d922f4e00a87adf8929007f2417/src/vector.rs https://github.com/BurntSushi/memchr/blob/1230fc5c638a4d922f... That is, the `memchr` crate has a `Vector` trait that is generic over the vector type. Which is essentially your `DataStructure<S>` where `S` is the ISA. (Using the vector type isn't load bearing. I could do the ISA. But in `memchr`'s case, the vector type implies the ISA.) It relies on `#[inline(always)]` to work. But you can write the algorithm generically once: https://github.com/BurntSushi/memchr/blob/1230fc5c638a4d922f4e00a87adf8929007f2417/src/arch/generic/memchr.rs https://github.com/BurntSushi/memchr/blob/1230fc5c638a4d922f... And the entry point into a specific instantiation of the generic algorithm is where you apply `#[target_feature(enable = "foo")]`: https://github.com/BurntSushi/memchr/blob/1230fc5c638a4d922f4e00a87adf8929007f2417/src/arch/x86_64/avx2/memchr.rs#L384-L402 https://github.com/BurntSushi/memchr/blob/1230fc5c638a4d922f...
- kouteiheika 11mo ago
  > It relies on `#[inline(always)]` to work. This is a workaround, but it's still very painful, and there are still many problems with it: 1) you need to design the whole thing with this in mind; you can't just take an existing something which takes some T and get that to use SIMD, 2) `#[inline(always)]` can't be used with `#[target_feature]` at the same time, 3) you need to have `#[inline(always)]` on your whole call stack (I often don't want to inline everything, but I still want to propagate that we're in an SIMD-safe context, which you normally do with `#[target_feature]`, but that's incompatible with being able to abstract over things), 4) you have to sprinkle `unsafe` everywhere (which is especially annoying as recently we gained the ability to finally be able to call a lot of the intrinsics without any `unsafe`), 5) you have to manually make sure you're not calling something you should not be calling (`#[target_feature]` is great here because it prevents you from accidentally calling e.g. AVX2 intrinsics if you're not on the AVX2 codepath), 6) any mistake (e.g. missing an annotation somewhere) you make will just have the compiler silently not inline the intrinsics, completely killing your performance, and you're completely on your own debugging it. I work with SIMD a lot in Rust, and unfortunately currently it's a miserable experience. It's not that bad when you're writing e.g. an algorithm which fits in a single function, but once you want to write something bigger with more sophisticated abstractions and/or you want to integrate SIMD into existing code then it becomes a world of pain.

silentvoice 11mo ago

oh boy I've got opinions here. Basically I just don't want to hear about "the state of SIMD in Rust" unless it is about dramatic improvement in autovectorization in the rust compiler. 80%-90% or so of real life vectorization can be achieved in C or C++ just by writing code in a way that it can be autovectorized. Intrinsics get you the rest of the way on harder code. Autovectorization is essentially a solved problem for the vast majority of floating point code. Not so with rust, because of a dogmatic approach to floating point arithmetic that assumes bitwise reproducibility is the "right" answer for everyone (actually, it's the right answer to almost nobody) to the point of not even allowing a user to even flag on these optimizations. and once you get to the point of writing intrinsics you have to handwrite code for every new architecture when autovectorizers could have gotten you 80%-90% of the way there with a single source and often this is just enough. the contention with the above is that if a user needs SIMD they can just use some SIMD API and make their intention more clear. this is essentially an argument that we should handwrite intrinsics. well guess what. I'm a programmer and I use compilers because they _do this for me_ and indeed are able to do so very easily in C or C++ when I instruct it that I'm ok with with reordering operations and other "accuracy impacting" optimizations. The huge joke on us is that these optimizations generally have the effect of _improving_ accuracy because it will reduce the number of rounding steps either by simply reducing the number of operations or by using fused multiply adds which round only once.

CryZe 11mo ago
Keep an eye out for the algebraic operations on floats currently in nightly then: https://doc.rust-lang.org/nightly/std/primitive.f32.html#algebraic-operators https://doc.rust-lang.org/nightly/std/primitive.f32.html#alg...
- the__alchemist 11mo ago
  I stumbled on these recently; you can do these in CUDA kernels. I have some "todo: mul_add here" in my rust code!
- TinkersW 11mo ago
  So you have to write fugly code just to get something that should be a compiler switch?