AGAThA: Fast and Efficient GPU Acceleration of Guided Sequence Alignment for Long Read Mapping
ACM SIGPLAN Symposium on Principles & Practice of Parallel Programming(2024)
摘要
With the advance in genome sequencing technology, the lengths of
deoxyribonucleic acid (DNA) sequencing results are rapidly increasing at lower
prices than ever. However, the longer lengths come at the cost of a heavy
computational burden on aligning them. For example, aligning sequences to a
human reference genome can take tens or even hundreds of hours. The current de
facto standard approach for alignment is based on the guided dynamic
programming method. Although this takes a long time and could potentially
benefit from high-throughput graphic processing units (GPUs), the existing
GPU-accelerated approaches often compromise the algorithm's structure, due to
the GPU-unfriendly nature of the computational pattern. Unfortunately, such
compromise in the algorithm is not tolerable in the field, because sequence
alignment is a part of complicated bioinformatics analysis pipelines. In such
circumstances, we propose AGAThA, an exact and efficient GPU-based acceleration
of guided sequence alignment. We diagnose and address the problems of the
algorithm being unfriendly to GPUs, which comprises strided/redundant memory
accesses and workload imbalances that are difficult to predict. According to
the experiments on modern GPUs, AGAThA achieves 18.8× speedup against
the CPU-based baseline, 9.6× against the best GPU-based baseline, and
3.6× against GPU-based algorithms with different heuristics.
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