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Sequence alignment forms an important backbone in many sequencingapplications. A commonly used strategy for sequence alignment is an approximatestring matching with a two-dimensional dynamic programming approach. Althoughsome prior work has been conducted on GPU acceleration of a sequence alignment,we identify several shortcomings that limit exploiting the full computationalcapability of modern GPUs. This paper presents SaLoBa, a GPU-acceleratedsequence alignment library focused on seed extension. Based on the analysis ofprevious work with real-world sequencing data, we propose techniques to exploitthe data locality and improve workload balancing. The experimental resultsreveal that SaLoBa significantly improves the seed extension kernel compared tostate-of-the-art GPU-based methods.

SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs