Sequence alignment forms an important backbone in many sequencing applications. A commonly used strategy for sequence alignment is an approximate string matching with a two-dimensional dynamic programming approach. Although some prior work has been conducted on GPU acceleration of a sequence alignment, we identify several shortcomings that limit exploiting the full computational capability of modern GPUs. This paper presents SALoBa, a GPU-accelerated sequence alignment library focused on seed extension. Based on the analysis of previous work with real-world sequencing data, we propose techniques to exploit the data locality and improve workload balancing. The experimental results reveal that SALoBa significantly improves the seed extension kernel compared to state-of-the-art GPU-based methods.

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Sequence alignment forms an important backbone in many sequencing applications. A commonly used strategy for sequence alignment is an approximate string matching with a two-dimensional dynamic programming approach. Although some prior work has been conducted on GPU acceleration of a sequence alignment, we identify several shortcomings that limit exploiting the full computational capability of modern GPUs. This paper presents SALoBa, a GPU-accelerated sequence alignment library focused on seed extension. Based on the analysis of previous work with real-world sequencing data, we propose techniques to exploit the data locality and improve workload balancing. The experimental results reveal that SALoBa significantly improves the seed extension kernel compared to state-of-the-art GPU-based methods.