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Study of application aware techniques for system and runtime power management
Descriptive
TitleInfo
Title
Study of application aware techniques for system and runtime power management
Name
(type = personal)
NamePart
(type = family)
Doddaghatta Shashidhar
NamePart
(type = given)
Sharat Chandra
NamePart
(type = date)
1986-
DisplayForm
Sharat Chandra Doddaghatta Shashidhar
Role
RoleTerm
(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
Parashar
NamePart
(type = given)
Manish
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Manish Parashar
Affiliation
Advisory Committee
Role
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chair
Name
(type = personal)
NamePart
(type = family)
Pompili
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(type = given)
Dario
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Dario Pompili
Affiliation
Advisory Committee
Role
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(authority = RULIB)
co-chair
Name
(type = personal)
NamePart
(type = family)
Rodero
NamePart
(type = given)
Ivan
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Ivan Rodero
Affiliation
Advisory Committee
Role
RoleTerm
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internal member
Name
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NamePart
Rutgers University
Role
RoleTerm
(authority = RULIB)
degree grantor
Name
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NamePart
Graduate School - New Brunswick
Role
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(authority = RULIB)
school
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Text
Genre
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theses
OriginInfo
DateCreated
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2012
DateOther
(qualifier = exact);
(type = degree)
2012-10
CopyrightDate
(qualifier = exact)
2012
Place
PlaceTerm
(type = code)
xx
Language
LanguageTerm
(authority = ISO639-2b);
(type = code)
eng
Abstract
(type = abstract)
Energy 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.
Subject
(authority = RUETD)
Topic
Electrical and Computer Engineering
RelatedItem
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
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ETD
Identifier
ETD_4345
PhysicalDescription
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electronic resource
InternetMediaType
application/pdf
InternetMediaType
text/xml
Extent
vi, 69 p. : ill.
Note
(type = degree)
M.S.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = statement of responsibility)
by Sharat Chandra Doddaghatta Shashidhar
Subject
(authority = ETD-LCSH)
Topic
High performance computing--Energy consumption
Subject
(authority = ETD-LCSH)
Topic
High performance computing--Management
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000066725
RelatedItem
(type = host)
TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
Identifier
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rucore19991600001
Location
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(displayLabel = Rutgers, The State University of New Jersey)
NjNbRU
Identifier
(type = doi)
doi:10.7282/T32N512G
Genre
(authority = ExL-Esploro)
ETD graduate
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Rights
RightsDeclaration
(ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder
(type = personal)
Name
FamilyName
Doddaghatta Shashidhar
GivenName
Sharat Chandra
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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(qualifier = exact);
(point = start)
2012-10-01 12:34:40
AssociatedEntity
Name
Sharat Chandra Doddaghatta Shashidhar
Role
Copyright holder
Affiliation
Rutgers University. Graduate School - New Brunswick
AssociatedObject
Type
License
Name
Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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Technical
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1632768
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1638400
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