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Power Saving Experiments for Large Scale Global Optimization

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dc.contributor.authorCao, Zhenweien
dc.contributor.authorEasterling, David R.en
dc.contributor.authorWatson, Layne T.en
dc.contributor.authorLi, Dongen
dc.contributor.authorCameron, Kirk W.en
dc.contributor.authorFeng, Wu-chunen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2013-06-19T14:37:04Zen
dc.date.available2013-06-19T14:37:04Zen
dc.date.issued2009en
dc.description.abstractGreen computing, an emerging field of research that seeks to reduce excess power consumption in high performance computing (HPC), is gaining popularity among researchers. Research in this field often relies on simulation or only uses a small cluster, typically 8 or 16 nodes, because of the lack of hardware support. In contrast, System G at Virginia Tech is a 2592 processor supercomputer equipped with power aware components suitable for large scale green computing research. DIRECT is a deterministic global optimization algorithm, implemented in the mathematical software package VTDIRECT95. This paper explores the potential energy savings for the parallel implementation of DIRECT, called pVTdirect, when used with a large scale computational biology application, parameter estimation for a budding yeast cell cycle model, on System G. Two power aware approaches for pVTdirect are developed and compared against the CPUSPEED power saving system tool. The results show that knowledge of the parallel workload of the underlying application is beneficial for power management.en
dc.format.mimetypeapplication/pdfen
dc.identifierhttp://eprints.cs.vt.edu/archive/00001078/en
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00001078/01/TR09-14_new.pdfen
dc.identifier.trnumberTR-09-14en
dc.identifier.urihttp://hdl.handle.net/10919/20039en
dc.language.isoenen
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectSoftware engineeringen
dc.titlePower Saving Experiments for Large Scale Global Optimizationen
dc.typeTechnical reporten
dc.type.dcmitypeTexten

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