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Study of application aware techniques for system and runtime power management
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Doddaghatta Shashidhar, Sharat Chandra. Study of application aware techniques for system and runtime power management. Retrieved from https://doi.org/doi:10.7282/T32N512G

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TitleStudy of application aware techniques for system and runtime power management
NameDoddaghatta Shashidhar, Sharat Chandra (author); Parashar, Manish (chair); Pompili, Dario (co-chair); Rodero, Ivan (internal member); Rutgers University; Graduate School - New Brunswick
Date Created2012
Other Date2012-10 (degree)
SubjectElectrical and Computer Engineering, High performance computing--Energy consumption, High performance computing--Management
Extentvi, 69 p. : ill.
DescriptionEnergy efficiency of large-scale data centers is becoming a major concern not only for reasons of energy conservation, failures, and cost reduction, but also because such systems are soon reaching the limits of power available to them. Like High Performance Computing (HPC) systems, large-scale cluster-based data centers can consume power in megawatts, and of all the power consumed by such a system, only a fraction is used for actual computations. In this paper, we study the potential of application-centric aggressive power management of data center's resources for HPC workloads. Specifically, we consider power management mechanisms and controls (currently or soon to be) available at different levels and for different subsystems, and leverage several innovative approaches that have been taken to tackle this problem in the last few years, can be effectively used in an application-aware manner for HPC workloads. To do this, we first profile standard HPC benchmarks with respect to behaviors, resource usage and power impact on individual computing nodes. Based on a 4 power and latency model and the workload profiles, we develop an algorithm that can improve energy efficiency with little or no performance loss. We then evaluate our proposed algorithm through simulations using empirical power characterization and quantification. Finally, we validate the simulation results with actual executions on real hardware. The obtained results show that by using application aware power management, we can reduce the average energy consumption without significant penalty in performance. This motivates us to investigate autonomic approaches for application-aware aggressive power management and cross layer and cross function predictive subsystem level power management for large-scale data centers.
NoteM.S.
NoteIncludes bibliographical references
Noteby Sharat Chandra Doddaghatta Shashidhar
Genretheses, ETD graduate
Persistent URLhttps://doi.org/doi:10.7282/T32N512G
Languageeng
CollectionGraduate School - New Brunswick Electronic Theses and Dissertations
Organization NameRutgers, The State University of New Jersey
RightsThe author owns the copyright to this work.
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