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dc.contributor.authorBonny, Mohamed Talal
dc.contributor.authorSalama, Khaled N.
dc.contributor.authorZidan, Mohammed A.
dc.date.accessioned2012-07-28T10:20:56Z
dc.date.available2012-07-28T10:20:56Z
dc.date.issued2011-02-18
dc.identifier.citationBonny T, Zidan MA, Salama KN (2010) An Adaptive Hybrid Multiprocessor technique for bioinformatics sequence alignment. 2010 5th Cairo International Biomedical Engineering Conference. doi:10.1109/CIBEC.2010.5716098.
dc.identifier.doi10.1109/CIBEC.2010.5716098
dc.identifier.urihttp://hdl.handle.net/10754/236113
dc.descriptionSequence alignment algorithms such as the Smith-Waterman algorithm are among the most important applications in the development of bioinformatics. Sequence alignment algorithms must process large amounts of data which may take a long time. Here, we introduce our Adaptive Hybrid Multiprocessor technique to accelerate the implementation of the Smith-Waterman algorithm. Our technique utilizes both the graphics processing unit (GPU) and the central processing unit (CPU). It adapts to the implementation according to the number of CPUs given as input by efficiently distributing the workload between the processing units. Using existing resources (GPU and CPU) in an efficient way is a novel approach. The peak performance achieved for the platforms GPU + CPU, GPU + 2CPUs, and GPU + 3CPUs is 10.4 GCUPS, 13.7 GCUPS, and 18.6 GCUPS, respectively (with the query length of 511 amino acid).
dc.description.abstractSequence alignment algorithms such as the Smith-Waterman algorithm are among the most important applications in the development of bioinformatics. Sequence alignment algorithms must process large amounts of data which may take a long time. Here, we introduce our Adaptive Hybrid Multiprocessor technique to accelerate the implementation of the Smith-Waterman algorithm. Our technique utilizes both the graphics processing unit (GPU) and the central processing unit (CPU). It adapts to the implementation according to the number of CPUs given as input by efficiently distributing the workload between the processing units. Using existing resources (GPU and CPU) in an efficient way is a novel approach. The peak performance achieved for the platforms GPU + CPU, GPU + 2CPUs, and GPU + 3CPUs is 10.4 GCUPS, 13.7 GCUPS, and 18.6 GCUPS, respectively (with the query length of 511 amino acid). © 2010 IEEE.
dc.language.isoen
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5716098
dc.titleAn Adaptive Hybrid Multiprocessor technique for bioinformatics sequence alignment
dc.typeConference Paper
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.contributor.departmentElectrical Engineering Program
dc.contributor.departmentPhysical Science and Engineering (PSE) Division
dc.contributor.departmentSensors Lab
dc.identifier.journal2010 5th Cairo International Biomedical Engineering Conference
dc.conference.date16 December 2010 through 18 December 2010
dc.conference.name2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010
dc.conference.locationCairo
kaust.personBonny, Mohamed Talal
kaust.personZidan, Mohammed A.
kaust.personSalama, Khaled N.
refterms.dateFOA2018-06-13T20:12:39Z
dc.date.published-online2011-02-18
dc.date.published-print2010-12


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