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Leveraging FPGAs for Accelerating Short Read Alignment.

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    Field-Programmable Gate Arrays (FPGAs) accelerate genomic data analysis. This study presents a novel FPGA architecture for rapid short read alignment, outperforming existing CPU and GPU methods.

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    Area of Science:

    • Genomics
    • Bioinformatics
    • Computer Engineering

    Background:

    • Genomic data analysis faces challenges due to massive datasets from next-generation sequencing.
    • General-purpose computing systems struggle with the computational demands of genomic analysis.
    • Field-Programmable Gate Arrays (FPGAs) offer potential for specialized acceleration.

    Purpose of the Study:

    • To present a novel runtime reconfigurable architecture for accelerating short read alignment using FPGAs.
    • To develop a flexible and efficient alignment design leveraging FPGA reconfigurability.
    • To demonstrate the performance benefits of FPGA-based acceleration for genomic data analysis.

    Main Methods:

    • Developed a runtime reconfigurable architecture for FPGA-based short read alignment.
    • Implemented optimizations including the n-step FM-index, index oversampling, seed-and-compare, and bi-directional backtracking.
    • Evaluated the design on a Maxeler MPC-X2000 dataflow node with Altera Stratix-V FPGAs.

    Main Results:

    • The FPGA design supports exact and approximate alignment with up to two mismatches.
    • Achieved a 28x speedup compared to Bowtie2 (16 threads, dual CPUs).
    • Achieved a 9x speedup compared to Soap3-dp (NVIDIA Tesla C2070 GPU).

    Conclusions:

    • FPGA-based acceleration is highly effective for short read alignment.
    • The proposed architecture offers significant performance improvements over traditional CPU and GPU solutions.
    • Runtime reconfigurability enables fast yet flexible genomic data analysis on FPGAs.