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    This study presents an event-driven approach for genotype imputation, significantly reducing DNA data processing time. The new method achieves massive speedups on parallel hardware, making genetic analysis more efficient.

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    Area of Science:

    • Genetics
    • Computer Science
    • Bioinformatics

    Background:

    • Genotype imputation is crucial for inferring missing genetic markers in DNA.
    • The Li and Stephens model is computationally intensive for modern x86 architectures.

    Purpose of the Study:

    • To develop an event-driven solution for genotype imputation.
    • To evaluate the performance of the Li and Stephens model in an event-driven domain.
    • To assess the scalability of the proposed algorithm on parallel hardware.

    Main Methods:

    • Implementation of the Li and Stephens model using graph-based Hidden Markov Modeling.
    • Customized forward/backward dynamic programming algorithm.
    • Event-driven paradigm mapping to thousands of concurrent cores on a RISC-V NoC cluster (POETS).
    • Optimization using linear interpolation.

    Main Results:

    • A multi-core run achieved a 270X reduction in wall-clock processing time compared to a single-threaded x86 solution.
    • Optimized algorithm demonstrated a reduction of approximately 5 orders of magnitude in wall-clock time.
    • Demonstrated scalability of the event-driven algorithm with increasing hardware resources.

    Conclusions:

    • The event-driven paradigm is a viable and highly efficient approach for genotype imputation.
    • Significant performance gains are achievable for genetic data analysis using parallel processing.
    • Further investigation into event-driven solutions for complex genetic algorithms is warranted.