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dc.contributor.authorCao, Zhenweien_US
dc.contributor.authorEasterling, David R.en_US
dc.contributor.authorWatson, Layne T.en_US
dc.contributor.authorLi, Dongen_US
dc.contributor.authorCameron, Kirk W.en_US
dc.contributor.authorFeng, Wu-chunen_US
dc.date.accessioned2013-06-19T14:37:04Z
dc.date.available2013-06-19T14:37:04Z
dc.date.issued2009
dc.identifierhttp://eprints.cs.vt.edu/archive/00001078/en_US
dc.identifier.urihttp://hdl.handle.net/10919/20039
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_US
dc.format.mimetypeapplication/pdfen_US
dc.publisherDepartment of Computer Science, Virginia Polytechnic Institute & State Universityen_US
dc.subjectSoftware engineeringen_US
dc.titlePower Saving Experiments for Large Scale Global Optimizationen_US
dc.typeTechnical reporten_US
dc.identifier.trnumberTR-09-14en_US
dc.type.dcmitypeTexten_US
dc.identifier.sourceurlhttp://eprints.cs.vt.edu/archive/00001078/01/TR09-14_new.pdf


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