Improving the Mapping of Smith-Waterman Sequence Database Searches onto CUDA-Enabled GPUs.
Sequence alignment lies at heart of the bioinformatics. The Smith-Waterman algorithm is one of the key sequence search algorithms and has gained popularity due to improved implementations and rapidly increasing compute power. Recently, the Smith-Waterman algorithm has been successfully mapped onto t...
| Publicado en: | BioMed Research International Vol. 2015; pp. 1 - 11 |
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| Autores principales: | , , , |
| Formato: | equations & formulas pictorial research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/3/2015
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=109030965&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 109030965 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 23146133 FT2T jtl: BioMed Research International issn: 23146133 maglogo: N pubinfo: dt: 8/3/2015 vid: 2015 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 109030965 109030965 109030965 10.1155/2015/185179 109030965 ppf: 1 ppct: 10 formats: fmt: @attributes: type: P tig: atl: Improving the Mapping of Smith-Waterman Sequence Database Searches onto CUDA-Enabled GPUs. aug: au: Huang, Liang-Tsung Wu, Chao-Chin Lai, Lien-Fu Li, Yun-Ju affil: Department of Medical Informatics, Tzu Chi University, Hualien 970, Taiwan sug: subj: Bioinformatics Databases Algorithms Sequence Analysis Methods Computer Processor Software Data Mining Funding Source Programming Languages Evaluation Research Taiwan Comparative Studies Proteins Computer Graphics Operating Systems Human ab: Sequence alignment lies at heart of the bioinformatics. The Smith-Waterman algorithm is one of the key sequence search algorithms and has gained popularity due to improved implementations and rapidly increasing compute power. Recently, the Smith-Waterman algorithm has been successfully mapped onto the emerging general-purpose graphics processing units (GPUs). In this paper, we focused on how to improve the mapping, especially for short query sequences, by better usage of shared memory. We performed and evaluated the proposed method on two different platforms (Tesla C1060 and Tesla K20) and compared it with two classic methods in CUDASW++. Further, the performance on different numbers of threads and blocks has been analyzed. The results showed that the proposed method significantly improves Smith-Waterman algorithm on CUDA-enabled GPUs in proper allocation of block and thread numbers. pubtype: Academic Journal doctype: equations & formulas pictorial research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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