Faking SIMD to Search and Sort Strings 5x Faster
I want to share a really dumb, but very practical project I have packaged this summer, to perform operations on strings much faster. I was using Python to work with a multi-terabyte newline-delimited file. Reading, splitting, and shuffling it was a nightmare. So, I wrapped a trivial hardware-friendly heuristic I've been using for the last few years into a CPython library. The part I enjoyed the most is implementing SIMD behavior without SIMD instructions... Using 64-bit words to work at 8-bit granularity. Unlike conventional SIMD, the code would remain the same for ~~almost~~ any hardware.…
In plain words
This CPython library accelerates string searching and sorting operations, achieving up to 5x performance improvements for large text processing tasks. Built for developers working with massive text datasets like multi-terabyte newline-delimited files, it uses a portable bit-level heuristic that simulates SIMD behavior without requiring actual SIMD instructions. The approach maintains consistent code across different hardware architectures while leveraging 64-bit words to operate at 8-bit granularity, making it particularly useful for handling CommonCrawl and similar large-scale textual data.
written from the facts on this page · September 2026
From the sources
In the maker’s words, at launch
I want to share a really dumb, but very practical project I have packaged this summer, to perform operations on strings much faster. I was using Python to work with a multi-terabyte newline-delimited file. Reading, splitting, and shuffling it was a nightmare. So, I wrapped a trivial hardware-friendly heuristic I've been using for the last few years into a CPython library. The part I enjoyed the most is implementing SIMD behavior without SIMD instructions... Using 64-bit words to work at 8-bit granularity. Unlike conventional SIMD, the code would remain the same for ~~almost~~ any hardware. Let this library be a reminder of how awesome bit-level hacks are! Feel free to use it when working with CommonCrawl or any other sizeable textual dataset.
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