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SALoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs | IEEE Conference Publication | IEEE Xplore

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


Abstract:

Sequence alignment forms an important backbone in many sequencing applications. A commonly used strategy for sequence alignment is an approximate string matching with a t...Show More

Abstract:

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 work-load balancing. The experimental results reveal that SALoBa significantly improves the seed extension kernel compared to state-of-the-art GPU-based methods.
Date of Conference: 30 May 2022 - 03 June 2022
Date Added to IEEE Xplore: 15 July 2022
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Conference Location: Lyon, France

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