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dc.contributor.advisorWatkins, David S.
dc.creatorAurentz, Jared Lee
dc.date.accessioned2014-11-12T21:49:37Z
dc.date.available2014-11-12T21:49:37Z
dc.date.issued2014
dc.identifier.urihttp://hdl.handle.net/2376/5177
dc.descriptionThesis (Ph.D.), Department of Mathematics, Washington State Universityen_US
dc.description.abstractA new class of methods for accelerating linear system solving and eigenvalue computations for positive definite matrices using GPUs is presented. This method makes use of techniques from polynomial approximation theory to construct new types of polynomial spectral transformations that are easy to parallelize and when combined with GPUs can give a factor of 100 reduction in run times for certain matrices. These methods also require significantly less memory than traditional methods, making it possible to solve large problems on an average workstation.en_US
dc.description.sponsorshipDepartment of Mathematics, Washington State Universityen_US
dc.languageEnglish
dc.rightsIn copyright
dc.rightsPublicly accessible
dc.rightsopenAccess
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/
dc.rights.urihttp://www.ndltd.org/standards/metadata
dc.rights.urihttp://purl.org/eprint/accessRights/OpenAccess
dc.subjectMathematics
dc.subjectComputer science
dc.subjecteigenvalues
dc.subjecteigenvectors
dc.subjectGPU
dc.subjectlinear systems
dc.subjectnumerical linear algebra
dc.titleGPU Accelerated Polynomial Spectral Transformation Methods
dc.typeElectronic Thesis or Dissertation


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