Print Email Facebook Twitter Efficient GPU Acceleration for Computing Maximal Exact Matches in Long DNA Reads Title Efficient GPU Acceleration for Computing Maximal Exact Matches in Long DNA Reads Author Ahmed, N. (TU Delft Quantum & Computer Engineering) Bertels, K.L.M. (TU Delft QCD/Almudever Lab; TU Delft (OLD)Quantum Computer Architectures) Al-Ars, Z. (TU Delft Computer Engineering) Department Quantum & Computer Engineering Date 2020 Abstract The seeding heuristic is widely used in many DNA analysis applications to speed up the analysis time. In many applications, seeding takes a substantial amount of the total execution time. In this paper, we present an efficient GPU implementation for computing maximal exact matching (MEM) seeds in long DNA reads. We applied various optimizations to reduce the number of GPU global memory accesses and to avoid redundant computation. Our implementation also extracts maximum parallelism from the MEM computation tasks. We tested our implementation using data from the state-of-the-art third generation Pacbio DNA sequencers, which produces DNA reads that are tens of kilobases long. Our implementation is up to 9x faster for computing MEM seeds as compared to the fastest CPU implementation running on a server-grade machine with 24 threads. Computing suffix array intervals (first part of MEM computation) is up to 3x faster whereas calculating the location of the match (second part) is up to 9x faster. The implementation is publicly available at https://github.com/nahmedraja/GPUseed. Subject DNA analysisGPUmaximal exact matchesseeding To reference this document use: http://resolver.tudelft.nl/uuid:712845ad-54ca-45bf-9c5f-dd75ae51ff17 DOI https://doi.org/10.1145/3386052.3386066 Publisher Association for Computing Machinery (ACM), New York ISBN 978-1-4503-7676-1 Source ICBBB 2020: Proceedings of 2020 10th International Conference on Bioscience, Biochemistry and Bioinformatics Event 10th International Conference on Bioscience, Biochemistry and Bioinformatics, ICBBB 2020, 2020-01-19 → 2020-01-22, Kyoto, Japan Series PervasiveHealth: Pervasive Computing Technologies for Healthcare, 2153-1633 Bibliographical note Accepted author manuscript Part of collection Institutional Repository Document type conference paper Rights © 2020 N. Ahmed, K.L.M. Bertels, Z. Al-Ars Files PDF icbbb2020.pdf 894.86 KB Close viewer /islandora/object/uuid:712845ad-54ca-45bf-9c5f-dd75ae51ff17/datastream/OBJ/view