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dc.contributor.authorGarren, Jeonifer
dc.date.accessioned2014-03-04T18:27:46Z
dc.date.available2014-03-04T18:27:46Z
dc.date.issued2010-05
dc.identifier.othergarren_jeonifer_201005_ms
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/garren_jeonifer_201005_ms
dc.identifier.urihttp://hdl.handle.net/10724/26322
dc.description.abstractThere is a need for an automated method that facilitates time-constrained scaling for applications to encourage the wider use of parallel computation for computationally intense problems. In this thesis, we show for the first time a self-generated focal training method that is able to accurately achieve time-constrained scaling using a focused regression. This is demonstrated with six benchmark applications, but can be extended to any application of interest for which time-constrained scaling is needed.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjecttime-constrained scaling
dc.subjectparallel computation
dc.subjectregression
dc.titleUsing regression based methods for time-constrained scaling of parallel processor computing applications
dc.typeThesis
dc.description.degreeMS
dc.description.departmentStatistics
dc.description.majorStatistics
dc.description.advisorJaxk Reeves
dc.description.committeeJaxk Reeves
dc.description.committeeLynne Seymour
dc.description.committeeWilliam P. McCormick


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