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

Seongyeon Park, Hajin Kim, Tanveer Ahmad, Nauman Ahmed, Zaid Al-Ars, H. Peter Hofstee, Youngsok Kim, Jinho Lee
Yonsei University
arXiv:2301.09310 [cs.DB], (23 Jan 2023)




   author={Park, Seongyeon and Kim, Hajin and Ahmad, Tanveer and Ahmed, Nauman and Al-Ars, Zaid and Hofstee, H. Peter and Kim, Youngsok and Lee, Jinho},

   keywords={Databases (cs.DB), Distributed, Parallel, and Cluster Computing (cs.DC), FOS: Computer and information sciences, FOS: Computer and information sciences},

   title={SaLoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs},



   copyright={Creative Commons Attribution 4.0 International}


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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.
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