Up to 10x faster strings for C, C++, Python, Rust, and Swift, leveraging SWAR and SIMD on Arm Neon and x86 AVX2 & AVX-512-capable chips to accelerate search, sort, edit distances, alignment scores, etc 🦖 (by ashvardanian)

StringZilla Alternatives

Similar projects and alternatives to StringZilla

NOTE: The number of mentions on this list indicates mentions on common posts plus user suggested alternatives. Hence, a higher number means a better StringZilla alternative or higher similarity.

StringZilla reviews and mentions

Posts with mentions or reviews of StringZilla. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-27.
  • Measuring energy usage: regular code vs. SIMD code
    1 project | | 19 Feb 2024
    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:

  • Show HN: StringZilla v3 with C++, Rust, and Swift bindings, and AVX-512 and NEON
    1 project | | 7 Feb 2024
  • How fast is rolling Karp-Rabin hashing?
    1 project | | 4 Feb 2024
    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!





  • 4B If Statements
    5 projects | | 27 Dec 2023
    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.


  • Python, C, Assembly – Faster Cosine Similarity
    5 projects | | 18 Dec 2023
    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 ( 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.

  • Stringzilla: 10x Faster SIMD-accelerated String Class
    1 project | /r/programming | 30 Aug 2023
  • Stringzilla: 10x faster SIMD-accelerated Python `str` class
    2 projects | /r/Python | 30 Aug 2023
    Blog post
  • Stringzilla: Fastest string sort, search, split, and shuffle using SIMD
    9 projects | | 29 Aug 2023
    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`]( 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]( 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`]( 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](" 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](

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



  • Show HN: Faking SIMD to Search and Sort Strings 5x Faster
    1 project | | 26 Aug 2023
    I took a look at 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!

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