SALoBa
Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs
Seongyeon Park (Yonsei University)
Hajin Kim (Yonsei University)
Tanveer Ahmad (TU Delft - Computer Engineering)
Nauman Ahmed (TU Delft - Numerical Analysis)
Zaid Al-Ars (TU Delft - Computer Engineering)
Peter Hofstee (TU Delft - Computer Engineering, IBM)
Youngsok Kim (Yonsei University)
Jinho Lee (Yonsei University)
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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.