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

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Authors

Park, Seongyeon; Kim, Hajin; Ahmad, Tanveer; Ahmed, Nauman; Al-Ars, Zaid; Hofstee, H. Peter; Kim, Youngsok; Lee, Jinho

Issue Date
2022
Publisher
IEEE
Citation
2022 IEEE 36TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS 2022), pp.728-738
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 workload balancing. The experimental results reveal that SALoBa significantly improves the seed extension kernel compared to state-of-the-art GPU-based methods.
ISSN
1530-2075
URI
https://hdl.handle.net/10371/195393
DOI
https://doi.org/10.1109/IPDPS53621.2022.00076
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  • Department of Electrical and Computer Engineering
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