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SALoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Park, Seongyeon | - |
dc.contributor.author | Kim, Hajin | - |
dc.contributor.author | Ahmad, Tanveer | - |
dc.contributor.author | Ahmed, Nauman | - |
dc.contributor.author | Al-Ars, Zaid | - |
dc.contributor.author | Hofstee, H. Peter | - |
dc.contributor.author | Kim, Youngsok | - |
dc.contributor.author | Lee, Jinho | - |
dc.date.accessioned | 2023-08-23T05:55:53Z | - |
dc.date.available | 2023-08-23T05:55:53Z | - |
dc.date.created | 2023-08-21 | - |
dc.date.created | 2023-08-21 | - |
dc.date.created | 2023-08-21 | - |
dc.date.created | 2023-08-21 | - |
dc.date.issued | 2022 | - |
dc.identifier.citation | 2022 IEEE 36TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS 2022), pp.728-738 | - |
dc.identifier.issn | 1530-2075 | - |
dc.identifier.uri | https://hdl.handle.net/10371/195393 | - |
dc.description.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. | - |
dc.language | 영어 | - |
dc.publisher | IEEE | - |
dc.title | SALoBa: Maximizing Data Locality and Workload Balance for Fast Sequence Alignment on GPUs | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/IPDPS53621.2022.00076 | - |
dc.citation.journaltitle | 2022 IEEE 36TH INTERNATIONAL PARALLEL AND DISTRIBUTED PROCESSING SYMPOSIUM (IPDPS 2022) | - |
dc.identifier.wosid | 000854096200068 | - |
dc.identifier.scopusid | 2-s2.0-85136332067 | - |
dc.citation.endpage | 738 | - |
dc.citation.startpage | 728 | - |
dc.description.isOpenAccess | Y | - |
dc.contributor.affiliatedAuthor | Lee, Jinho | - |
dc.type.docType | Proceedings Paper | - |
dc.description.journalClass | 1 | - |
dc.subject.keywordPlus | SHORT-READ ALIGNMENT | - |
dc.subject.keywordPlus | SEARCH | - |
dc.subject.keywordAuthor | Genome sequencing | - |
dc.subject.keywordAuthor | Sequence alignment | - |
dc.subject.keywordAuthor | Smith-Waterman | - |
dc.subject.keywordAuthor | GPU acceleration | - |
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- College of Engineering
- Department of Electrical and Computer Engineering
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