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- Hacker News
- Love the pendantic mode setting on the websiteby nperez19
- I don't get what's the value of it not being enabled by default what does the toggle get us, really? Maybe I don't understand web design and it makes it harder to read for some, I am dyslexic and never had any issues.by minraws
- I've noticed a lot of articles about SIMD on the HN front page. That's cool, but just wondering, is there some reason this is more in focus lately?by frollogaston
- Maybe things being on the front page reminds others? After seeing something, sometimes you can have ideas relating to it for a while.by LoganDark
- I’m wondering if AI has made SIMD intrinsics much more approachable for many and so there are just more people working on abstractions for their workflow of choice right now. There’s probably a lot of code out there that could benefit from SIMD but the effort to actually use it was too high for the return.
- SIMD is actually underrated still. Programmers should always be thinking about it. It's a free 4x in a lot of casesby vatsachak
- Congrats to the Rust-GPU folks! Nice to see the good work flowing.by efnx
- Really exciting work and great write up, thanks a lot and all the best to your startup!
`core` instead of `std` is great too!
This will become useful in one of my sideproject where I use bitmaps to speed up pathfinding, exited to try it out!
by dev_dan_2 - Rust SIMD in pgrust, on the GPU?
"Pgrust v0.2: Now faster than Postgres" (2026-06) https://news.ycombinator.com/item?id=49111925
by westurner - Very interesting. But GPU programming gets complicated when you start doing 3d computation on very large data, will be interesting to see how tensor abstraction is built on top of this. Another point is that this is using fixed-width SIMD vectors; unless there is a way to compute this statically based on available GPU info, performance will always be left on the table.by melodyogonna
- Author here, AMA.by LegNeato
- Any thoughts about SIMD-related crates?by PoignardAzur
- Hm is the intent to one day replace the CPU?by shay_ker
- All well and good but where can we install it now?by peterbower
- Given the massive demand for GPUs for LLMs, what sorts of work do you expect to economically benefit from utilizing GPUs more?by Eridrus
- If you have to express your computation using an "array programming DSL" with things like scan and gather anyways - why not opt to use torch/tensorflow/jax or anything else that targets MLIR? An example of writing a relu using an embedded array DSL is really not helping your case either - that's exactly the problem that these other solutions mentioned above are successfully solving for the past ~15y (starting with theano etc). Not sure what this brings to the table - doing that AoT instead of at runtime?by bbminner
- This is really cool! It sounds like y'all have a compiler fork that you are using to make this work. I wanna tinker with this, is your compiler available?by lbhdc
- The post is kind of vague on the IR you're targeting. Can you give some examples of what the SIMD-ized IR looks like, and how it maps to the target PTX?by jcranmer
- What is vectorware's business model? Are you planning to sell support/consulting to companies using your stack? Or are you looking to sell licenses to your tool? Or something else?by lbhdc
- Do you have examples of complex algorithms running on the gpu with rust with competative performance? Radix sort might be a good one to start withby nynx
- I love how ever example of portable SIMD isn't portable.
They specifies a constant SIMD width so it's non-portable. Well, not performance portable, but why are we using SIMD again?
by camel-cdr - Why should it be portable? Honest question.
SIMD seems to me, to be very platform specific. Maybe there are times one SIMD unit is not anothers' SIMD unit?
by MomsAVoxell - > They specifies a constant SIMD width so it's non-portable.
This is incorrect, you can use vectors wider than native SIMD width and the compiler will break them down to register size of the target cpu.
In fact it's sometimes better to used wider than native width, in some applications I see 20% better throughput with f32x16 (512 bits) on an AVX2 CPU (256 bits). It is kinda like loop unrolling it.
by exDM69 - Go's implementation is vector size independant https://pkg.go.dev/simd@masterby tyho
- It should really be read/advertised as "portabler SIMD". It beats hoping the compiler autovectorizes everything well forever or writing architecture specific code manually again but is going to compromise on average performance vs platform specific SIMD.by zamadatix
- The capabilities of various SIMD ISAs don't have enough intersection to be portable outside of relatively trivial cases. Many of the somewhat unique capabilities are load-bearing, so you want to use them on architectures that support them. Taken in whole, someone who cares about performance would be using different data structures and algorithms depending on the specific SIMD architecture and that is nearly impossible to abstract in a library. Too many important but complex details are idiosyncratic to the implementation.
Another way of looking at it is that our programming environments are not sufficiently powerful and expressive to create the necessary abstractions to make SIMD truly portable.
- I would love to have an open source Rust SIMD library with the scope and maturity that https://github.com/google/highway brings to C++.by grokcodec
- This is basically the goal of fearless_simd, but of course achieving the same level of maturity will take time.by raphlinus
- My heard hurts - i was stupid enough to think that SIMD was a CPU only thing - I don't understand why it would be ported to GPU - huge kudos to managing to surprise meby 6r17
- Welcome to the lucky 10,000! SIMD is actually a pretty integral part of how GPUs are able to work efficiently, it's part of why there's such a strong focus on branchless programming in the field.by hingler36