Machine Learning Based DVFS for Energy Efficient Execution of Multithreaded Workloads

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dc.contributor.advisorQasem, Apan
dc.contributor.authorHay, Richard G.
dc.contributor.committeeMemberTamir, Dan
dc.contributor.committeeMemberSalamy, Hassan
dc.date.accessioned2014-12-04T19:37:02Z
dc.date.available2014-12-04T19:37:02Z
dc.date.issued2014-11
dc.description.abstractConcerns over high power consumption of large computations and data centers have been growing in recent years. Many software and hardware strategies for reducing power have been proposed as remedies. Dynamic voltage and frequency scaling (DVFS) is one technique that can be effective if given expert knowledge. However, DVFS effectiveness is sensitive to workload characteristics and architectural parameters. Lack of knowledge can hurt DVFS strategies and render it ineffective. This thesis presents a supervised machine learning (ML) strategy for automatically making smart DVFS decisions to improve energy efficiency of multi threaded and multiprogram workloads. The technique uses hardware performance counters to construct feature vectors that capture program behavior and thread interaction in a meaningful way. The resulting models have high accuracy in picking optimal frequencies. Experimental results on contemporary benchmark suite show that application of a ML technique is able to reduce energy consumption by as much as 24% on memory-intensive workloads.
dc.description.departmentComputer Science
dc.formatText
dc.format.extent50 pages
dc.format.medium1 file (.pdf)
dc.identifier.citationHay, R. (2014). Machine learning based DVFS for energy efficient execution of multithreaded workloads (Unpublished thesis). Texas State University, San Marcos, Texas.
dc.identifier.urihttps://hdl.handle.net/10877/5363
dc.language.isoen
dc.subjectDVFS
dc.subjectmachine Learning
dc.subjectpower-aware scheduling
dc.subjectenergy efficiency
dc.titleMachine Learning Based DVFS for Energy Efficient Execution of Multithreaded Workloads
dc.typeThesis
thesis.degree.departmentComputer Science
thesis.degree.disciplineComputer Science
thesis.degree.grantorTexas State University
thesis.degree.levelMasters
thesis.degree.nameMaster of Science

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