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dc.contributor.authorZidan, Mohammed A.*
dc.contributor.authorBonny, Talal*
dc.contributor.authorSalama, Khaled N.*
dc.date.accessioned2012-07-28T10:21:58Z
dc.date.available2012-07-28T10:21:58Z
dc.date.issued2012-07-28en
dc.identifier.citationZidan MA, Bonny T, Salama KN (2011) High performance technique for database applicationsusing a hybrid GPU/CPU platform. Proceedings of the 21st edition of the great lakes symposium on Great lakes symposium on VLSI - GLSVLSI 11:85-90. doi:10.1145/1973009.1973027.en
dc.identifier.doi10.1145/1973009.1973027en
dc.identifier.urihttp://hdl.handle.net/10754/236114en
dc.descriptionMany database applications, such as sequence comparing, sequence searching, and sequence matching, etc, process large database sequences. we introduce a novel and efficient technique to improve the performance of database applications by using a Hybrid GPU/CPU platform. In particular, our technique solves the problem of the low efficiency resulting from running short-length sequences in a database on a GPU. To verify our technique, we applied it to the widely used Smith-Waterman algorithm. The experimental results show that our Hybrid GPU/CPU technique improves the average performance by a factor of 2.2, and improves the peak performance by a factor of 2.8 when compared to earlier implementations.en
dc.description.abstractMany database applications, such as sequence comparing, sequence searching, and sequence matching, etc, process large database sequences. we introduce a novel and efficient technique to improve the performance of database applica- tions by using a Hybrid GPU/CPU platform. In particular, our technique solves the problem of the low efficiency result- ing from running short-length sequences in a database on a GPU. To verify our technique, we applied it to the widely used Smith-Waterman algorithm. The experimental results show that our Hybrid GPU/CPU technique improves the average performance by a factor of 2.2, and improves the peak performance by a factor of 2.8 when compared to earlier implementations. Copyright © 2011 by ASME.en
dc.language.isoenen
dc.publisherAssociation for Computing Machinery (ACM)en
dc.relation.urlhttp://portal.acm.org/citation.cfm?doid=1973009.1973027en
dc.titleHigh performance technique for database applicationsusing a hybrid GPU/CPU platformen
dc.typeConference Paperen
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division*
dc.contributor.departmentSensors Lab*
dc.identifier.journalProceedings of the 21st edition of the great lakes symposium on Great lakes symposium on VLSI - GLSVLSI '11en
dc.conference.date2 May 2011 through 4 May 2011en
dc.conference.name21st Great Lakes Symposium on VLSI, GLSVLSI 2011en
dc.conference.locationLausanneen
kaust.authorZidan, Mohammed A.*
kaust.authorBonny, Mohamed Talal*
kaust.authorSalama, Khaled N.*
refterms.dateFOA2018-06-13T20:12:41Z


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