The trick, it turns out, is to use an awful lot of clever and complicated math.
  • NoSpotOfGround@lemmy.worldEnglish
    90·
    1 day ago

    The real meat of the story is in the referenced blog post: https://blog.codingconfessions.com/p/how-unix-spell-ran-in-64kb-ram

    TL;DR

    If you’re short on time, here’s the key engineering story:

    • McIlroy’s first innovation was a clever linguistics-based stemming algorithm that reduced the dictionary to just 25,000 words while improving accuracy.

    • For fast lookups, he initially used a Bloom filter—perhaps one of its first production uses. Interestingly, Dennis Ritchie provided the implementation. They tuned it to have such a low false positive rate that they could skip actual dictionary lookups.

    • When the dictionary grew to 30,000 words, the Bloom filter approach became impractical, leading to innovative hash compression techniques.

    • They computed that 27-bit hash codes would keep collision probability acceptably low, but needed compression.

    • McIlroy’s solution was to store differences between sorted hash codes, after discovering these differences followed a geometric distribution.

    • Using Golomb’s code, a compression scheme designed for geometric distributions, he achieved 13.60 bits per word—remarkably close to the theoretical minimum of 13.57 bits.

    • Finally, he partitioned the compressed data to speed up lookups, trading a small memory increase (final size ~14 bits per word) for significantly faster performance.