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Accelerating Sequence Alignments Based on FM-Index Using the Intel KNL Processor.

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    This study optimizes the FM-index for faster genome matching by reducing memory bandwidth demands. The new FM-index organization significantly boosts performance on high-bandwidth memory systems like Intel Xeon Phi.

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    Area of Science:

    • Bioinformatics
    • Computer Science
    • Computational Biology

    Background:

    • The FM-index is crucial for efficient short-read alignment to large genomes.
    • Existing FM-index algorithms suffer from irregular memory access, leading to cache misses and performance bottlenecks.
    • Memory bandwidth limitations hinder the speed of sequence alignment on modern processors.

    Purpose of the Study:

    • To analyze existing FM-index implementations focusing on memory access patterns.
    • To propose a novel FM-index organization that minimizes memory bandwidth requirements.
    • To enhance the performance of short-read alignment, particularly on high-bandwidth memory architectures.

    Main Methods:

    • Analysis of various FM-index versions with a focus on data access characteristics.
    • Development of a new FM-index data organization.
    • Performance evaluation using the roofline model on Intel Xeon Phi (KNL) and other Intel Xeon architectures.
    • Benchmarking against GPU-based alignment results.

    Main Results:

    • The proposed FM-index organization significantly reduces memory bandwidth demand.
    • Achieved 95% of the peak random access bandwidth limit on Intel Xeon Phi (KNL).
    • Demonstrated higher throughput on KNL compared to reported GPU results, indicating substantial performance gains.

    Conclusions:

    • The optimized FM-index effectively addresses memory bandwidth limitations in genome alignment.
    • The new organization provides a significant performance improvement on processors with high-bandwidth memory.
    • This work offers a more efficient approach for large-scale genomic data analysis.