An Adaptive Hybrid Multiprocessor technique for bioinformatics sequence alignment

Handle URI:
http://hdl.handle.net/10754/236113
Title:
An Adaptive Hybrid Multiprocessor technique for bioinformatics sequence alignment
Authors:
Bonny, Talal; Salama, Khaled N. ( 0000-0001-7742-1282 ) ; Zidan, Mohammed A. ( 0000-0003-3843-814X )
Abstract:
Sequence 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.
KAUST Department:
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division; Sensors Lab
Citation:
Bonny 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.
Conference/Event name:
2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010
Issue Date:
28-Jul-2012
DOI:
10.1109/CIBEC.2010.5716098
Type:
Conference Paper
Description:
Sequence 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).
Additional Links:
http://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5716098
Appears in Collections:
Conference Papers; Sensors Lab; Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division

Full metadata record

DC FieldValue Language
dc.contributor.authorBonny, Talalen
dc.contributor.authorSalama, Khaled N.en
dc.contributor.authorZidan, Mohammed A.en
dc.date.accessioned2012-07-28T10:20:56Z-
dc.date.available2012-07-28T10:20:56Z-
dc.date.issued2012-07-28en
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.en
dc.identifier.doi10.1109/CIBEC.2010.5716098en
dc.identifier.urihttp://hdl.handle.net/10754/236113en
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).en
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.en
dc.language.isoenen
dc.relation.urlhttp://ieeexplore.ieee.org/lpdocs/epic03/wrapper.htm?arnumber=5716098en
dc.titleAn Adaptive Hybrid Multiprocessor technique for bioinformatics sequence alignmenten
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Divisionen
dc.contributor.departmentSensors Laben
dc.conference.date16 December 2010 through 18 December 2010en
dc.conference.name2010 5th Cairo International Biomedical Engineering Conference, CIBEC 2010en
dc.conference.locationCairoen
kaust.authorBonny, Mohamed Talalen
kaust.authorZidan, Mohammed A.en
kaust.authorSalama, Khaled N.en
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