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Related Experiment Video

Updated: Feb 28, 2026

Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
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Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

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k ache-hash: A dynamic, concurrent, and cache-efficient hash table for streaming k -mer operations.

Jamshed Khan, Rob Patro, Prashant Pandey

    Biorxiv : the Preprint Server for Biology
    |February 27, 2026
    PubMed
    Summary

    We developed k-ache-hash, a novel hash table for computational genomics that leverages k-mer locality for faster streaming data operations. This new structure significantly improves insertion and query throughput while reducing cache misses compared to existing methods.

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    Last Updated: Feb 28, 2026

    Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations
    08:03

    Heuristic Mining of Hierarchical Genotypes and Accessory Genome Loci in Bacterial Populations

    Published on: December 7, 2021

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    Area of Science:

    • Computational genomics
    • Data structures
    • Bioinformatics

    Background:

    • Hash tables are crucial in computational genomics for processing k-mers, which have a streaming property.
    • Existing dynamic hash tables neglect k-mer locality, while static structures lack dynamic update capabilities.

    Purpose of the Study:

    • Introduce k-ache-hash, the first dynamic, concurrent, and resizable hash table optimized for k-mer locality.
    • Improve performance of genomic data processing by exploiting the streaming nature of k-mers.

    Main Methods:

    • Developed k-ache-hash using a multi-level design (Iceberg hashing) with minimizer-based hashing.
    • Ensured consecutive k-mers map to the same buckets to maintain cache residency.

    Main Results:

    • k-ache-hash demonstrated 1.58-2.62x higher insertion throughput and up to 6.1x higher query throughput on the human genome compared to IcebergHT.
    • Achieved 7.39x fewer cache misses and near-linear scaling to 16 threads.
    • Theoretical analysis shows O(1/r) amortized cache misses for streaming k-mer operations.

    Conclusions:

    • k-ache-hash offers significant performance improvements for dynamic, concurrent, and local k-mer processing in genomics.
    • The hash table supports dynamic resizing without compromising locality.
    • The minimizer-based approach effectively exploits k-mer streaming properties for enhanced cache performance.