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dc.contributor.authorBarnes, Bradley James
dc.date.accessioned2014-03-04T20:01:43Z
dc.date.available2014-03-04T20:01:43Z
dc.date.issued2011-08
dc.identifier.otherbarnes_bradley_j_201108_phd
dc.identifier.urihttp://purl.galileo.usg.edu/uga_etd/barnes_bradley_j_201108_phd
dc.identifier.urihttp://hdl.handle.net/10724/27382
dc.description.abstractScalability prediction is one of the key problems facing high performance computing today. Methods to predict scalability accurately are necessary in order to improve throughput and overall efficiency on large-scale machines. This dissertation presents our novel, regression-based system for accurately predicting the scalability of scientific applications on large-scale machines. Our regression-based system provides accurate runtime predictions on large processor counts for multiple scientific applications when run using strong scaling. Our system is also able to provide input parameters leading to accurate time-constrained scaling on larger processor counts. We also discuss the impact of noise on scalability prediction. This work takes large steps towards a general scalability prediction system that could be deployed on supercomputing systems in the near future.
dc.languageeng
dc.publisheruga
dc.rightspublic
dc.subjectModeling
dc.subjectMPI
dc.subjectPrediction
dc.subjectRegression
dc.subjectScalability
dc.subjectNoise
dc.subjectTheses (academic)
dc.titleA regression-based system for accurate scalability prediction on large-scale machines
dc.typeDissertation
dc.description.degreePhD
dc.description.departmentComputer Science
dc.description.majorComputer Science
dc.description.advisorDavid Lowenthal
dc.description.committeeDavid Lowenthal
dc.description.committeeJaxk Reeves
dc.description.committeeLakshmish Ramaswamy
dc.description.committeeKang Li
dc.description.committeeBronis de Supinski


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