LOGAN: High-Performance GPU-Based X-Drop Long-Read Alignment

Journal Article · Mon May 18 00:00:00 EDT 2020 · Proceedings - IEEE International Parallel and Distributed Processing Symposium (IPDPS)

DOI:https://doi.org/10.1109/ipdps47924.2020.00055· OSTI ID:1650093

Zeni, Alberto ^[1]; Guidi, Giulia ^[2]; Ellis, Marquita ^[2]; Ding, Nan ^[3]; Santambrogio, Marco D. ^[1]; Hofmeyr, Steven ^[3]; Buluc, Aydin ^[2]; Oliker, Leonid ^[3]; Yelick, Katherine ^[2]

---

1. Politecnico di Milano (Italy)
2. Univ. of California, Berkeley, CA (United States)
3. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Pairwise sequence alignment is one of the most computationally intensive kernels in genomic data analysis, accounting for more than 90% of the runtime for key bioinformatics applications. This method is particularly expensive for third-generation sequences due to the high computational cost of analyzing sequences of length between 1Kb and 1Mb. Given the quadratic overhead of exact pairwise algorithms for long alignments, the community primarily relies on approximate algorithms that search only for high-quality alignments and stop early when one is not found. In this work, we present the first GPU optimization of the popular X-drop alignment algorithm, that we named LOGAN. Results show that our high-performance multi-GPU implementation achieves up to 181.6 GCUPS and speed-ups up to 6.6× and 30.7× using 1 and 6 NVIDIA Tesla V100, respectively, over the state-of-the-art software running on two IBM Power9 processors using 168 CPU threads, with equivalent accuracy. We also demonstrate a 2.3× LOGAN speed-up versus ksw2, a state-of-art vectorized algorithm for sequence alignment implemented in minimap2, a long-read mapping software. Furthermore, to highlight the impact of our work on a real-world application, we couple LOGAN with a many-to-many long-read alignment software called BELLA, and demonstrate that our implementation improves the overall BELLA runtime by up to 10.6×. Finally, we adapt the Roofline model for LOGAN and demonstrate that our implementation is near optimal on the NVIDIA Tesla V100s.

• Research Organization: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Basic Energy Sciences (BES)
• Grant/Contract Number: AC02-05CH11231; AC05-00OR22725
• OSTI ID: 1650093
• Journal Information: Proceedings - IEEE International Parallel and Distributed Processing Symposium (IPDPS), Vol. 2020; Conference: 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), 18-22 May 2020; ISSN 1530-2075
• Publisher: IEEE
• Country of Publication: United States
• Language: English

Related Subjects

97 MATHEMATICS AND COMPUTING