gcn.MOPS: accelerating cn.MOPS with GPU

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dc.contributor.authorAlkhamis, Mohammad
dc.contributor.supervisorBaniasadi, Amirali
dc.date.accessioned2017-06-16T15:22:51Z
dc.date.available2017-06-16T15:22:51Z
dc.date.copyright2017en_US
dc.date.issued2017-06-16
dc.degree.departmentDepartment of Electrical and Computer Engineering
dc.degree.levelMaster of Applied Science M.A.Sc.en_US
dc.description.abstractcn.MOPS is a model-based algorithm used to quantitatively detect copy-number variations in next-generation, DNA-sequencing data. The algorithm is implemented as an R package and can speed up processing with multi-CPU parallelism. However, the maximum achievable speedup is limited by the overhead of multi-CPU parallelism, which increases with the number of CPU cores used. In this thesis, an alternative mechanism of process acceleration is proposed. Using one CPU core and a GPU device, the proposed solution, gcn.MOPS, achieved a speedup factor of 159× and decreased memory usage by more than half. This speedup was substantially higher than the maximum achievable speedup in cn.MOPS, which was ∼20×.en_US
dc.description.proquestcode0984en_US
dc.description.proquestcode0544en_US
dc.description.proquestcode0715en_US
dc.description.proquestemailalkhamis@uvic.caen_US
dc.description.scholarlevelGraduateen_US
dc.identifier.urihttp://hdl.handle.net/1828/8286
dc.languageEnglisheng
dc.language.isoenen_US
dc.rightsAvailable to the World Wide Weben_US
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/2.5/ca/*
dc.subjectGPUen_US
dc.subjectGPGPUen_US
dc.subjectcn.MOPSen_US
dc.subjectgcn.MOPSen_US
dc.subjectCUDAen_US
dc.subjectC++en_US
dc.subjectparallel computingen_US
dc.subjectCNVen_US
dc.titlegcn.MOPS: accelerating cn.MOPS with GPUen_US
dc.typeThesisen_US

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