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A parameterisable and scalable Smith-Waterman algorithm implementation on CUDA-compatible GPUs
http://hdl.handle.net/10069/22671
http://hdl.handle.net/10069/2267184319941-ff9f-4f76-a700-cf53eddc382a
名前 / ファイル | ライセンス | アクション |
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SASP2009_94.pdf (687.5 kB)
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Item type | 会議発表論文 / Conference Paper(1) | |||||
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公開日 | 2010-01-04 | |||||
タイトル | ||||||
タイトル | A parameterisable and scalable Smith-Waterman algorithm implementation on CUDA-compatible GPUs | |||||
言語 | ||||||
言語 | eng | |||||
資源タイプ | ||||||
資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||
資源タイプ | conference paper | |||||
著者 |
Ling, Cheng
× Ling, Cheng× Benkrid, Khaled× Hamada, Tsuyoshi |
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抄録 | ||||||
内容記述タイプ | Abstract | |||||
内容記述 | This paper describes a multi-threaded parallel design and implementation of the Smith-Waterman (SM) algorithm on compute unified device architecture (CUDA)-compatible graphic processing units (GPUs). A novel technique has been put forward to solve the restriction on the length of the query sequence in previous GPU implementations of the Smith-Waterman algorithm. The main reasons behind this limitation in previous GPU implementations were the finite size of local memory and number of threads per block. Our solution to this problem uses a divide and conquer approach to compute the alignment matrix involved in each pairwise sequence alignment, as it divides the entire matrix computation into multiple sub-matrices and allocates the available amount of threads and memory resources to each submatrix iteratively. Intermediate data is stored in shared and global memory on the fly depending on the length of sequences in hand. The proposed technique resulted in up to 4.2 GCUPS (Giga Cell Updates per Second) performance when tested against the SWISS-PROT protein database, which is up to 15 times faster than a equivalent optimised CPU-only implementation running on a Pentium4 3.4GHz desktop computer. Moreover, our implementation can cope with any query or subject sequence size, unlike previously reported GPU implementations of the Smith-Waterman algorithm which makes it fully deployable in real world bioinformatics applications. | |||||
内容記述 | ||||||
内容記述タイプ | Other | |||||
内容記述 | 2009 IEEE 7th Symposium on Application Specific Processors (SASP) : San Francisco, CA, USA, 2009.07.27-2009.07.28 | |||||
書誌情報 |
2009 IEEE 7th Symposium on Application Specific Processors p. 94-100, 発行日 2009-07 |
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ISBN | ||||||
識別子タイプ | ISBN | |||||
関連識別子 | 978-1-4244-4939-2 | |||||
DOI | ||||||
関連タイプ | isIdenticalTo | |||||
識別子タイプ | DOI | |||||
関連識別子 | 10.1109/SASP.2009.5226343 | |||||
権利 | ||||||
権利情報 | c2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. | |||||
著者版フラグ | ||||||
出版タイプ | VoR | |||||
出版タイプResource | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |||||
出版者 | ||||||
出版者 | IEEE | |||||
引用 | ||||||
内容記述タイプ | Other | |||||
内容記述 | 2009 IEEE 7th Symposium on Application Specific Processors, pp.94-100; 2009 |