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dc.contributor.authorCurtis-Maury, Matthewen_US
dc.description.abstractThe scalability of parallel applications executing on multithreaded and multicore multiprocessors is often quite limited due to large degrees of contention over shared resources on these systems. In fact, negative scalability frequently occurs such that a non-negligable performance loss is observed through the use of more processors and cores. In this dissertation, we present a prediction model for identifying efficient operating points of concurrency in multithreaded scientific applications in terms of both performance as a primary objective and power secondarily. We also present a runtime system that uses live analysis of hardware event rates through the prediction model to optimize applications dynamically. We discuss a dynamic, phase-aware performance prediction model (DPAPP), which combines statistical learning techniques, including multivariate linear regression and artificial neural networks, with runtime analysis of data collected from hardware event counters to locate optimal operating points of concurrency. We find that the scalability model achieves accuracy approaching 95%, sufficiently accurate to identify improved concurrency levels and thread placements from within real parallel scientific applications.

Using DPAPP, we develop a prediction-driven runtime optimization scheme, called ACTOR, which throttles concurrency so that power consumption can be reduced and performance can be set at the knee of the scalability curve of each parallel execution phase in an application. ACTOR successfully identifies and exploits program phases where limited scalability results in a performance loss through the use of more processing elements, providing simultaneous reductions in execution time by 5%-18% and power consumption by 0%-11% across a variety of parallel applications and architectures. Further, we extend DPAPP and ACTOR to include support for runtime adaptation of DVFS, allowing for the synergistic exploitation of concurrency throttling and DVFS from within a single, autonomically-acting library, providing improved energy-efficiency compared to either approach in isolation.

dc.publisherVirginia Techen_US
dc.rightsI hereby certify that, if appropriate, I have obtained and attached hereto a written permission statement from the owner(s) of each third party copyrighted matter to be included in my thesis, dissertation, or project report, allowing distribution as specified below. I certify that the version I submitted is the same as that approved by my advisory committee. I hereby grant to Virginia Tech or its agents the non-exclusive license to archive and make accessible, under the conditions specified below, my thesis, dissertation, or project report in whole or in part in all forms of media, now or hereafter known. I retain all other ownership rights to the copyright of the thesis, dissertation or project report. I also retain the right to use in future works (such as articles or books) all or part of this thesis, dissertation, or project report.en_US
dc.subjectpower-aware computingen_US
dc.subjecthigh-performance computingen_US
dc.subjectperformance predictionen_US
dc.subjectmulticore processorsen_US
dc.subjectruntime adaptationen_US
dc.subjectconcurrency throttlingen_US
dc.titleImproving the Efficiency of Parallel Applications on Multithreaded and Multicore Systemsen_US
dc.contributor.departmentComputer Scienceen_US
dc.description.degreePh. D.en_US D.en_US Polytechnic Institute and State Universityen_US Scienceen_US
dc.contributor.committeechairNikolopoulos, Dimitrios S.en_US
dc.contributor.committeememberFeng, Wu-Chunen_US
dc.contributor.committeememberCameron, Kirk W.en_US
dc.contributor.committeememberde Supinski, Bronis R.en_US
dc.contributor.committeememberRibbens, Calvin J.en_US

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