The State of Simd in Rust in 2025
Key topics
The article discusses the current state of SIMD (Single Instruction, Multiple Data) support in Rust, sparking a discussion on the trade-offs between using std::simd, other libraries, and the challenges of runtime autodetection and autovectorization.
Snapshot generated from the HN discussion
Discussion Activity
Very active discussionFirst comment
31m
Peak period
78
0-6h
Avg / period
19.4
Based on 155 loaded comments
Key moments
- 01Story posted
Nov 5, 2025 at 1:45 PM EST
2 months ago
Step 01 - 02First comment
Nov 5, 2025 at 2:16 PM EST
31m after posting
Step 02 - 03Peak activity
78 comments in 0-6h
Hottest window of the conversation
Step 03 - 04Latest activity
Nov 8, 2025 at 6:46 AM EST
2 months ago
Step 04
Generating AI Summary...
Analyzing up to 500 comments to identify key contributors and discussion patterns
Want the full context?
Jump to the original sources
Read the primary article or dive into the live Hacker News thread when you're ready.
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/1lh9x43gtqXFh5bP1LeYevWj0...
I’m sure more people than ever are working on the compiler. What’s going on?
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. 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.
AFAIK that’s not a blocker for Rust - the std library is allowed to use unstable at all times.
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.
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 ).
Seems smart to put the language as a requirement for compiling the linux kernel and a bunch of other core projects then!
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.
Can’t APIs be fixed between editions?
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).
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.
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.
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.
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).
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.
Too many promises have been made.
Rust needs more unsafe opt outs. Ironically simd has this so it does not bother me.
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.
Can't you just make a local copy of the existing package and use that? Did you need to re-implement?
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.
e.g. all core::simd addition ends up invoking the single function [1] which is then directly handled by rustc. But these architecture-agnostic intrinsics are unstable[2] (as they're only there as a building block for core::simd), and you can't manually use "#[rustc_intrinsic]" & co in stable rust either.
[1]: https://github.com/rust-lang/rust/blob/b01cc1cf01ed12adb2595...
[2]: https://github.com/rust-lang/rust/blob/b01cc1cf01ed12adb2595...
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
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?
Since then several Rust project developers did find full time jobs in companies like Amazon, Meta etc. But corporate interest ebbs and flows. For example Huawei employed a couple of engineers to improve the compiler for several years but they lost interest a couple of months ago.
The Rust Foundation is powered by donations, but a lot of its expenses went on funding infrastructure, security, legal expenses. But this problem of funding the maintainers of the project is on their radar. Yesterday they started an initiative to fund raise for the Maintainers Fund, with the money going straight to maintainers who aren’t being paid by their employer to do it full time. (https://rustfoundation.org/media/announcing-the-rust-foundat...)
Pure Go code uses registers to pass function arguments whenever possible. Large structs and/or numerous arguments can still spill onto the stack, though.
FFI (cgo) uses whatever the platform's calling convention requires. These conventions also usually favor registers.
Go has its own assembly language, which is neither of those two things. Go assembly technically supports two ABIs, one called "ABI0" and the other called "ABIInternal" [1]. The former passes all arguments on the stack and is stable while the latter passes some arguments through registers but is unstable and subject to change. Accordingly, people writing Go code outside of the Go toolchain almost always use ABI0, so that the code keeps working even when new versions of Go are released.
[1]: https://go.googlesource.com/go/+/refs/heads/dev.regabi/src/c...
Even if you had access to the register convention (which you kind of do), would it be of any benefit? The registers in question are general-purpose registers, not vector registers.
Another example, note how these implementations, one in unsafe C# and another in safe F# have almost identical performance: https://benchmarksgame-team.pages.debian.net/benchmarksgame/..., https://benchmarksgame-team.pages.debian.net/benchmarksgame/...
Eg https://tirania.org/blog/archive/2008/Nov-03.html
You can find the overview of relevant high-level types here: https://learn.microsoft.com/en-us/dotnet/standard/simd
For low-level types and historical context: https://devblogs.microsoft.com/dotnet/dotnet-8-hardware-intr...
It was after all enabling games to use SIMD with C# (with MonoGame and Unity), even if their interface wasn't the winning one in the end.
Also RISC-V, where you can't even probe for extension support in user space unfortunately.
[1]: https://github.com/riscv-non-isa/riscv-c-api-doc/blob/main/s...
Zig has @Vector. This is a builtin, so it gets resolved at comptime. Is the problem with Rust here too much abstraction?
How many people are writing somewhat bog standard RUST/C and expect optimal assembly to be created?
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.
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?
By "calling math functions" I mean things like:
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).Is that not the case?
In the end the only bits where I resorted to assembly were the ones where it wouldn't make any sense to write stuff in C. Bootloaders, for instance, when all you have to work with is 512 bytes the space/speed constraints are much more on the space side and that's where I find I still have a (slight) edge. Which I guess means that 'optimal' is context dependent and that the typical 'optimal' defaults to 'speed'.
I've literally had debates with people that thought a CSV file would be smaller than holding the same data in memory. Senior level developers at both startups and established companies. My hunch is they had only ever done this using object oriented modeling of the data. Worse, usually in something like python, where everything is default boxed to hell and back.
> How many people are writing somewhat bog standard RUST/C and expect optimal assembly to be created?
As for:
> I don't necessarily think "low level" has to be "writing assembly." I do think it means, "knows the full layout of the data in memory." Something a surprising number of developers do not know.
Agreed. But then again, there are ton of things a surprising number of developers don't know, this is just another one of those.
Similar stuff:
- computers always work
- the CPU is executing my instructions one after the other in the same order in which they are written on the line(s)
- the CPU is executing my instructions only one at the time
- if I have a switch (or whatever that construct is called in $language) I don't need to check for values I do not expect because that will never happen
- the data I just read in is as I expect it to be
You can probably extend that list forever.
Your CSV example is an interesting one, I can think of cases where both could be true, depending on the kind of character encoding used and the way the language would deal with such a character encoding. For instance in a language where upon reading that file all of the data would be turned into UTF-16 then, indeed, the in memory representation of a plain ASCII CSV file could well be larger than the input file. Conversely, if the file contained newlines and carriage returns then the in-memory representation could omit the CRs and then the in memory representation would be smaller. If you turn the whole thing into a data structure then it could be larger, or smaller, depending on how clever the data structure was and whether or not the representation would efficiently encode the values in the CSV.
> My hunch is they had only ever done this using object oriented modeling of the data.
Yes, that would be my guess as well.
> Worse, usually in something like python, where everything is default boxed to hell and back.
And you often have multiple representations of the same data because not every library uses the same conventions.
I think my more general idea, that I was vaguely grasping towards, is that the style of programming that produces optimal assembly is already a different style than most people use. Certainly a different one than most people talk about using.
And fair on the CSV points. I definitely meant that to be in numeric heavy CSV files. Strings are a whole other beast. I put https://taeric.github.io/challenging-my-filesize-intuition.h... together a few years ago when I had this discussion at work the last time.
(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)
> 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?
https://doc.rust-lang.org/std/intrinsics/index.html
Specifically the *_fast intrinsics.
The reason it’s a thing is from LLVM and I’m not sure you can “language design” your way out of this problem as it seems intrinsic to IEEE 754.
a+b+c != c+b+a
That’s why you need techniques like Kahan summation.
IEEE 754 operations are nonassociative, but they are commutative (at least if you ignore the effect of NaN payloads).
As a side note: all compilers I'm aware of make almost no guarantees on preserving the value of NaN payloads, hence they consider floating-point operations to be fully commutative, and there's no general way to guarantee that they evaluate in exactly the order you specified.
You're supposed to do (a+b) to demonstrate the effect, because floating point subtraction that results in a number near zero is sensitive to rounding (worst case, a non-zero number gets you a zero number), which can introduce a huge error when a and b are very similar numbers.
The confusion between associativity and commutativity is the entire point of this thread!
Edit: Wikipedia actually says associativity is definitionally about changing parens[0]. Mostly amounts to the same thing for standard arithmetic operators, but it’s an interesting distinction.
[0]: https://en.wikipedia.org/wiki/Associative_property
Rounding and eventual underflow in IEEE means an expression X•Y for any algebraic operation • produces, if finite, a result (X•Y)·( 1 + ß ) + µ where |µ| cannot exceed half the smallest gap between numbers in the destination’s format, and |ß| < 2^-N , and ß·µ = 0 . ( µ ≠ 0 only when Underflow occurs.)
And yes that is a binary relation only
a•b•c is really (a•b)•c assuming left operator associativity, one of the properties that IEEE doesn't have.
Commutativity says that a*b = b*a, but that's not enough to allow arbitrary reordering. When you write a*b*c depending on whether * is left or right associative that either means a*(b*c) or (a*b)*c. If those are equal we say the operation is associative. You need both to allow arbitrary reordering. If an operation is only commutative you can turn a*(b*c) into a*(c*b) or (b*c)*a but there is no way to put a in the middle.
+ is a binary operation, and a+b+c can’t be interpreted without knowing whether one treats + as left-associative or right-associative. Let’s assume the former: a+b+c really means (a+b)+c.
If + is commutative, you can turn (a+b)+c into (b+a)+c or c+(a+b) or (commuting twice) c+(b+a).
But that last expression is not the same thing as (c+b)+a. Getting there requires associativity, and floating point addition is not associative.
But remember that commutative is on the operations (+,x) which are binary operations, a+b=b+a and ab=ba, you can get accumulated rounding errors on iterated forms of those binary operations.
For loops without such dependencies Rust should autovectorize just fine as with any other element type.
Edit: go to godbolt and load the rust aligned sum and play around with types. If you see addps that is the packed scalar simd instruction. The more you get packed, the higher your score! You'll need to pass some extra arguments they don't list to get avx512 sized registers vs the xmm or ymm ones. And not all the instances it uses support avx512 so sometimes you have to try a couple times.
Both float multiplication and float addition are equally bad for optimizations though - both are non-associative: https://play.rust-lang.org/?version=stable&mode=debug&editio... ; and indeed changing the aligned-sum example to f64, neither .sum() nor .product() get vectorized.
And e.g. here's a plain rust loop autovectorizing both addition and multiplication (though of course not a reduction): https://rust.godbolt.org/z/6hEcj8zfx
1. dangerous assumptions hidden behind a simple, attractive-looking option [1]. It should be called -fwrong-math or -fdangerous-math or something (GCC does have the funnily named switch -funsafe-math-optimizations – what could go wrong with fun, safe math optimizations?!)
2. Translation-unit scoped, which means that dependencies not consented to "fast math" can break your code (as in UB land) or make the optimizations pointless, and your code can break your dependencies' semantics too via inlining. On the other hand, a library author must think very carefully what float opts to enable in order to be compatible with client code.
Deciding how the scoping of non-IEEE float math operations should work is a very nontrivial question. The scope could be a translation unit, a module, a type, a function, a block, or every individual operation, and none of those is without issues, particularly regarding questions like inlining and interprocedural and link-time-optimization, as well as ergonomics. In other ways, it's yet another function coloring problem.
There are currently-unstable "algebraic_add/mul/etc" methods for floats for letting LLVM treat those particular operations as if floats were real numbers [2]. They're the first step towards safe UB-free float optimizations, but of course those names are rather awkward to use in math-heavy code, and a wrapper type overloading the normal operators would be good to have.
---
[1] See, eg. https://simonbyrne.github.io/notes/fastmath/
[2] In terms of associativity and such, not in eg. assuming the nonexistence of NaNs, which would be very unsafe.
This obviously has a lot of caveats, and should only be enabled on a per function or per file basis.
Unfortunately Rust does not currently have options for adjusting per-function compiler optimization parameters. This is possible in some C compilers using function attributes.
Unfortunately it's a set of functions you have to use to perform arithmetic ops if you want the autovectorizer to touch them
I wonder if it could autovec the simd-ordered code.
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.
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/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/1230fc5c638a4d922f...
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.
I use the same technique in the `aho-corasick` crate (which has more sophistication than `memchr`) and it works there too.
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.
Yep. I was pleasantly surprised by the autovectorization quality with recent clang at work a few days ago. If you write code that the compiler can infer to be multiples of 4, 8, etc the compiler goes off and emits pretty decent NEON/AVX code. The rest as you say is handled quite well by intrinsics these days.
Autovectorization was definitely poorer 5-10 years ago on older compiler toolchains.