Prediction Models for Multi-dimensional Power-Performance Optimization on Many Cores

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dc.contributor.authorShah, Ankur Savailalen
dc.contributor.committeechairNikolopoulos, Dimitrios S.en
dc.contributor.committeememberCameron, Kirk W.en
dc.contributor.committeememberFeng, Wu-chunen
dc.contributor.departmentComputer Scienceen
dc.date.accessioned2014-03-14T20:34:02Zen
dc.date.adate2008-05-28en
dc.date.available2014-03-14T20:34:02Zen
dc.date.issued2008-04-18en
dc.date.rdate2008-05-28en
dc.date.sdate2008-04-22en
dc.description.abstractPower has become a primary concern for HPC systems. Dynamic voltage and frequency scaling (DVFS) and dynamic concurrency throttling (DCT) are two software tools (or knobs) for reducing the dynamic power consumption of HPC systems. To date, few works have considered the synergistic integration of DVFS and DCT in performance-constrained systems, and, to the best of our knowledge, no prior research has developed application-aware simultaneous DVFS and DCT controllers in real systems and parallel programming frameworks. We present a multi-dimensional, online performance prediction framework, which we deploy to address the problem of simultaneous runtime optimization of DVFS, DCT, and thread placement on multi-core systems. We present results from an implementation of the prediction framework in a runtime system linked to the Intel OpenMP runtime environment and running on a real dual-processor quad-core system as well as a dual-processor dual-core system. We show that the prediction framework derives near-optimal settings of the three power-aware program adaptation knobs that we consider. Our overall runtime optimization framework achieves significant reductions in energy (12.27% mean) and ED² (29.6% mean), through simultaneous power savings (3.9% mean) and performance improvements (10.3% mean). Our prediction and adaptation framework outperforms earlier solutions that adapt only DVFS or DCT, as well as one that sequentially applies DCT then DVFS. Further, our results indicate that prediction-based schemes for runtime adaptation compare favorably and typically improve upon heuristic search-based approaches in both performance and energy savings.en
dc.description.degreeMaster of Scienceen
dc.identifier.otheretd-04222008-133454en
dc.identifier.sourceurlhttp://scholar.lib.vt.edu/theses/available/etd-04222008-133454/en
dc.identifier.urihttp://hdl.handle.net/10919/31826en
dc.publisherVirginia Techen
dc.relation.haspartAnkur_Thesis.pdfen
dc.rightsIn Copyrighten
dc.rights.urihttp://rightsstatements.org/vocab/InC/1.0/en
dc.subjectconcurrency throttlingen
dc.subjectpower-aware computingen
dc.subjectruntime adaptationen
dc.subjectperformance predictionen
dc.subjecthigh-performance computingen
dc.subjectMulticore processorsen
dc.titlePrediction Models for Multi-dimensional Power-Performance Optimization on Many Coresen
dc.typeThesisen
thesis.degree.disciplineComputer Scienceen
thesis.degree.grantorVirginia Polytechnic Institute and State Universityen
thesis.degree.levelmastersen
thesis.degree.nameMaster of Scienceen

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