I just do gcc -O3 and get SIMD without having to learn it

ashton314 9 hours ago | parent | next [-]

In the article, Mitchel mentions how this doesn’t always work. In fact, as someone who’s worked in compiler development, I can say it’s a small miracle when it does work.

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mitchellh 9 hours ago | parent [-]

Case-in-point, the example in my own post doesn't auto-vectorize with LLVM or GCC at highest optimization levels. Basically, compilers will never auto-vectorize loops with an early loop break afaik.

	▲	yunnpp 6 hours ago \| parent [-]
		You need to let the compiler know that there are at least 4 or 8 elements to process. This may require padding data and/or having a second loop after the main one that processes the remainder <4 or <8 elements. You start the post with: > There is an opportunity to use SIMD. SIMD turns those into this: > > for (8 byte chunk in bytes) { /* ... */ } If you actually wrote that loop, there is a good chance the compiler (gcc specifically) will auto-vectorize. In any case, the more manual SIMD optimizations I have seen require reworking the data altogether, not just processing N elements at a time. For example, instead of packing two 4-vectors into two registers to do a dot product, pack the XXXXs, YYYYs, etc. into 4 vectors and compute 4 dot products for the price of one. That not only requires having 4 vectors to process, but also thinking how exactly they are packed in registers. I don't know why qurren is downvoted. You really should see if you can get the compiler to auto-vectorize first (possibly padding data structures and loops) before you write anything by hand.

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jandrewrogers 8 hours ago | parent | prev | next [-]

Most scalar-to-SIMD conversion requires changing the design of data structures and algorithms to be effective. Compilers are required to exactly reproduce the specified data structures in a deterministic way for obvious reasons.

Even if compilers were clever enough to transform your data structures and algorithms for SIMD (they're not), the data structures are a contract that can't be unilaterally modified.

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forrestthewoods 9 hours ago | parent | prev | next [-]

auto-vectorization is not nearly as good as you would hope it to be.

The best SIMD optimizations likely require changing your data format from AoS to SoA.

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nylonstrung 9 hours ago | parent | next [-]

The one feature in Jonathan Blow's Jai language I really envy is a a single keyword to switch AoS to SoA and visa-versa at comptime

	▲	mbStavola 9 hours ago \| parent [-]
		Didn't he drop this feature years ago?

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ethin 9 hours ago | parent | prev | next [-]

Either this or you have to do special tricks like pairwise tree reductions and hand-unroll certain portions of loops.

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raegis 9 hours ago | parent | prev | next [-]

What are AoS and SoA?

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Georgelemental 9 hours ago | parent | next [-]

Array of Structs and Struct of Arrays https://en.wikipedia.org/wiki/AoS_and_SoA

	▲	Rendello 8 hours ago \| parent [-]
		A good introduction to SoA (for anyone curious) are the two most famous Data-Oriented Design talks by Mike Acton (game engine dev) [1] and Andrew Kelley (Zig lead dev) [2] respectively. I read a book book about DoD [3] really which confused me at first with all its talk about database table design (in a book about a high-performance C++ game engine?), but when it finally clicked it was amazing. The point is that you want to think hard about your access patterns and what could constitute good "primary keys", then model it accordingly. SoA ends up being useful a lot of the time, because having your data in homogeneous arrays/vectors is great for cache locality and branch elimination. Even without SIMD you can get huge speedups from that, but that's also where your compiler (or you as a programmer) can get incredible SIMD gains. SoA is not a silver bullet as it may not align well with your access patterns, but it can great to add to your toolkit. --- Mike Acton: Data-Oriented Design and C++: https://www.youtube.com/watch?v=rX0ItVEVjHc Andrew Kelley: A Practical Guide to Applying Data Oriented Design: https://www.youtube.com/watch?v=IroPQ150F6c Richard Fabian: Data-Oriented Design: https://www.dataorienteddesign.com/dodbook/

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nylonstrung 9 hours ago | parent | prev [-]

Array of Structs and Struct of Arrays

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formerly_proven 9 hours ago | parent | prev | next [-]

And -march=native or at least -march=x86-64-v3 or similar, alternatively identifying relevant functions and manually invoking FMV and uarch specialization via target_clones. Plus non-integer code can generally not be autovectorized in normal-math mode since FP is non-commutative.

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Joker_vD 9 hours ago | parent | prev [-]

Well, then I just prompt Claude and get SIMD without having to learn it /s

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llm_nerd 8 hours ago | parent | prev [-]

I have no idea why you're being downvoted. HN has a fetish for SIMD, but if you are hand-rolling SIMD and you aren't writing an explicit acceleration library, you're doing it wrong. Like, 100% of the time.

Every modern language has a vectorization optimizing compiler, and through some fairly straightforward techniques this is automagic. And contrary to the various replies, unless you screwed something up compilers are really good at vectorizing on whatever hardware you're targeting, including SVE.

	▲	saagarjha 30 minutes ago \| parent [-]
		Compilers are really good but really good is not actually that useful in cases where you need SIMD