StringZilla VS highway

The 3.5x energy-efficiency gap between serial and SIMD code becomes even larger when

A. you do byte-level processing instead of float words;

B. you use embedded, IoT, and other low-energy devices.

A few years ago I've compared Nvidia Jetson Xavier (long before the Orin release), Intel-based MacBook Pro with Core i9, and AVX-512 capable CPUs on substring search benchmarks.

On Xavier one can quite easily disable/enable cores and reconfigure power usage. At peak I got to 4.2 GB/J which was an 8.3x improvement in inefficiency over LibC in substring search operations. The comparison table is still available in the older README: https://github.com/ashvardanian/StringZilla/tree/v2.0.2?tab=...

This is extremely timely! I was working on SIMD variants for collision-resistant rolling-hash variants in the last few weeks for the v3 release of the StringZilla library [1].

I have tried several 4-way and 8-way parallel variants using AVX-512 DQ instructions for 64-bit integer multiplications [2] as well as using integer FMA instructions on Arm NEON with 32-bit multiplications [3]. The latter needs a better mixing approach to be collision-resistant.

So far I couldn't exceed 1 GB/s/core [4], so more research is needed. If you have any ideas - I am all ears!

[1]: https://github.com/ashvardanian/StringZilla/blob/bc1869a8529...

[2]: https://github.com/ashvardanian/StringZilla/blob/bc1869a8529...

[3]: https://github.com/ashvardanian/StringZilla/blob/bc1869a8529...

[4]: https://github.com/ashvardanian/StringZilla/tree/main-dev?ta...

Jokes aside, lookup tables are a common technique to avoid costly operations. I was recently implementing one to avoid integer division. In my case I knew that the nominator and denominator were 8 bit unsigned integers, so I've replaced the division with 2 table lookups and 6 shifts and arithmetic operations [1]. The well known `libdivide` [2] does that for arbitrary 16, 32, and 64 bit integers, and it has precomputed magic numbers and lookup tables for all 16-bit integers in the same repo.

[1]: https://github.com/ashvardanian/StringZilla/blob/9f6ca3c6d3c...

That matches my experience, and goes beyond GCC and Clang. Between 2018 and 2020 I was giving a lot of lectures on this topic and we did a bunch of case studies with Intel on their older ICC and what later became the OneAPI.

Short story, unless you are doing trivial data-parallel operations, like in SimSIMD, compilers are practically useless. As a proof, I wrote what is now the StringZilla library (https://github.com/ashvardanian/stringzilla) and we've spent weeks with an Intel team, tuning the compiler, no result. So if you are processing a lot of strings, or variable-length coded data, like compression/decompression, hand-written SIMD kernels are pretty much unbeatable.

Blog post

Copying my feedback from reddit[1], where I discussed it in the context of the `memchr` crate.[2]

I took a quick look at your library implementation and have some notes:

* It doesn't appear to query CPUID, so I imagine the only way it uses AVX2 on x86-64 is if the user compiles with that feature enabled explicitly. (Or uses something like [`x86-64-v3`](https://en.wikipedia.org/wiki/X86-64#Microarchitecture_level...).) The `memchr` crate doesn't need that. It will use AVX2 even if the program isn't compiled with AVX2 enabled so long as the current CPU supports it.

* Your substring routines have multiplicative worst case (that is, `O(m * n)`) running time. The `memchr` crate only uses SIMD for substring search for smallish needles. Otherwise it flips over to Two-Way with a SIMD prefilter. You'll be fine for short needles, but things could go very very badly for longer needles.

* It seems quite likely that your [confirmation step](https://github.com/ashvardanian/Stringzilla/blob/fab854dc4fd...) is going to absolutely kill performance for even semi-frequently occurring candidates. The [`memchr` crate utilizes information from the vector step to limit where and when it calls `memcmp`](https://github.com/BurntSushi/memchr/blob/46620054ff25b16d22...). Your code might do well in cases where matches are very rare. I took a quick peek at your benchmarks and don't see anything that obviously stresses this particular case. For substring search, the `memchr` crate uses a variant of the "[generic SIMD](http://0x80.pl/articles/simd-strfind.html#first-and-last)" algorithm. Basically, it takes two bytes from the needle, looks for positions where those occur and then attempts to check whether that position corresponds to a match. It looks like your technique uses the first 4 bytes. I suspect that might be overkill. (I did try using 3 bytes from the needle and found that it was a bit slower in some cases.) That is, two bytes is usually enough predictive power to lower the false positive rate enough. Of course, one can write pathological inputs that cause either one to do better than the other. (The `memchr` crat benchmark suite has a [collection of pathological inputs](https://github.com/BurntSushi/memchr/blob/46620054ff25b16d22...).)

It would actually be possible to hook Stringzilla up to `memchr`'s benchmark suite if you were interested. :-)

[1]: https://old.reddit.com/r/rust/comments/163ph8r/memchr_26_now...

[2]: https://github.com/BurntSushi/memchr

I took a look at Stringzilla (https://github.com/ashvardanian/stringzilla), and in addition to the impressive benchmarks, the API looks pretty straightforward. It's a new star in my collection!

Thanks for open-sourcing this project!

The bf16 dot instruction replaces 6 instructions: https://github.com/google/highway/blob/master/hwy/ops/x86_12...

[0] for those interested in Highway.

It's also mentioned in [1], which starts off

> Today we're sharing open source code that can sort arrays of numbers about ten times as fast as the C++ std::sort, and outperforms state of the art architecture-specific algorithms, while being portable across all modern CPU architectures. Below we discuss how we achieved this.

[0] https://github.com/google/highway

[1] https://opensource.googleblog.com/2022/06/Vectorized%20and%2..., which has an associated paper at https://arxiv.org/pdf/2205.05982.pdf.

Thanks so much!

Everyone working on this self-selected into contributing, so I think of it less as my team than ... a team?

Specifically want to call out: Jan Wassenberg (author of https://github.com/google/highway) and I started gemma.cpp as a small project just a few months ago + Phil Culliton, Dan Zheng, and Paul Chang + of course the GDM Gemma team.

C++ users can enjoy Highway [1].

[1] https://github.com/google/highway/

At that point it is better to have some kind of DSL that should not be in the main language, because it would target a much lower level than a typical program. The best effort I've seen in this scene was Google's Highway [1] (not to be confused with HighwayHash) and I even once attempted to recreate it in Rust, but it is still distanced from my ideal.

[1] https://github.com/google/highway

Interesting, thanks for sharing :)

At the time we open-sourced Highway, the standardization process had already started and there were some discussions.

I'm curious why stdlib is the only path you see to default? Compare the activity level of https://github.com/VcDevel/std-simd vs https://github.com/google/highway. As to open-source usage, after years of std::experimental, I see <200 search hits [1], vs >400 for Highway [2], even after excluding several library users.

But that aside, I'm not convinced standardization is the best path for a SIMD library. We and external users extend Highway on a weekly basis as new use cases arise. What if we deferred those changes to 3-monthly meetings, or had to wait for one meeting per WD, CD, (FCD), DIS, (FDIS) stage before it's standardized? Standardization seems more useful for rarely-changing things.

1: https://sourcegraph.com/search?q=context:global+std::experim...

2: https://sourcegraph.com/search?q=context:global+HWY_NAMESPAC...

Thanks for the link. We were previously using GFNI for bit reversal and 8-bit shifts, and I just extended that to our 8-bit BroadcastSignBit (https://github.com/google/highway/pull/1784).

You could study Google's Highway library [1].

[1] https://github.com/google/highway

Runtime feature detection need not be rare nor hard, it's a few dozen lines of boilerplate. You can even write your code just once: see https://github.com/google/highway#examples.