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Computational Reconstruction of Pancreatic Islets as a Tool for Structural and Functional Analysis
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Parallel Implementation of MAFFT on CUDA-Enabled Graphics Hardware.

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    Accelerating biological sequence alignment using graphic processing units (GPUs) significantly reduces computational time. This study optimizes the MAFFT algorithm on GPUs, achieving substantial speedups for large-scale phylogenetic analyses.

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    Area of Science:

    • Computational Biology
    • Bioinformatics
    • High-Performance Computing

    Background:

    • Multiple sequence alignment (MSA) is crucial for biological research, but traditional methods like MAFFT are computationally intensive on sequential architectures.
    • The increasing size of biological sequence databases necessitates faster alignment algorithms.

    Purpose of the Study:

    • To accelerate the MAFFT algorithm for multiple sequence alignment using graphic processing units (GPUs).
    • To enhance the efficiency of biological data analysis through GPU acceleration.

    Main Methods:

    • Implemented MAFFT on GPUs utilizing the Compute Unified Device Architecture (CUDA).
    • Optimized data organization to overcome memory bandwidth limitations.
    • Developed memory allocation and reuse strategies for efficient GPU memory utilization.
    • Introduced a modified-run-length encoding (MRLE) scheme to decrease memory footprint.
    • Leveraged high-performance shared memory to accelerate input/output operations.

    Main Results:

    • Achieved significant speedups for the MAFFT algorithm on GPUs.
    • Demonstrated up to 11.28x speedup on a Tesla K20m GPU compared to sequential MAFFT 7.015.
    • Successfully addressed memory bandwidth and consumption challenges on GPUs.

    Conclusions:

    • GPUs provide an efficient platform for accelerating the MAFFT algorithm.
    • The implemented optimizations enable faster and more scalable multiple sequence alignment.
    • This approach facilitates large-scale biological data analysis and research.