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Adaptive memory power management techniques for HPC workloads
Descriptive
TitleInfo
Title
Adaptive memory power management techniques for HPC workloads
Name
(type = personal)
NamePart
(type = family)
Elangovan
NamePart
(type = given)
Karthik
NamePart
(type = date)
1988-
DisplayForm
Karthik Elangovan
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
RoleTerm
(authority = RULIB)
chair
Name
(type = personal)
NamePart
(type = family)
Zhang
NamePart
(type = given)
Yanyong
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Yanyong Zhang
Affiliation
Advisory Committee
Role
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(authority = RULIB)
internal member
Name
(type = personal)
NamePart
(type = family)
Rodero
NamePart
(type = given)
Ivan
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Ivan Rodero
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Advisory Committee
Role
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(authority = RULIB)
outside member
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NamePart
Rutgers University
Role
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(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
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theses
OriginInfo
DateCreated
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2012
DateOther
(qualifier = exact);
(type = degree)
2012-01
Place
PlaceTerm
(type = code)
xx
Language
LanguageTerm
(authority = ISO639-2b);
(type = code)
eng
Abstract
(type = abstract)
The memory subsystem is responsible for a large fraction of the energy consumed by compute nodes in High Performance Computing (HPC) systems. The rapid increase in the number of cores has been accompanied by a proportional increase in the DRAM capacity and bandwidth. Thus, the memory system consumes a significant amount of the power budget available to a compute node. There is a broad research effort focused on power management techniques using DRAM low-power modes. However, memory power management still presents many challenges towards Exascale. In this thesis, the potential of Dynamic Voltage and Frequency memory Scaling (DVFS) is studied considering the ability to select different frequencies for different memory channels. The approach adopted is based on tuning voltage and frequency dynamically to maximize the energy savings while maintaining performance degradation within tolerable limits. It was observed that HPC workloads rarely require maximum bandwidth, and the bandwidth demand placed by applications is spread over different channels. Also, HPC applications do not use all the bandwidth in a sustained manner, and they have phases where this bandwidth demand is not at its peak. Hence applications can tolerate reduction in bandwidth to save energy. Channel access patterns of applications are studied to determine the potential additional energy savings by controlling channels independently. Evaluation of proposed DVFS algorithm is conducted through a novel hybrid evaluation methodology that includes simulation and executions on real hardware. Results show the large potential of adaptive memory power management techniques based on DVFS for HPC workloads.
Subject
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Topic
Electrical and Computer Engineering
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TitleInfo
Title
Rutgers University Electronic Theses and Dissertations
Identifier
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ETD
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ETD_3744
PhysicalDescription
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electronic resource
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application/pdf
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text/xml
Extent
vii, 50 p. : ill.
Note
(type = degree)
M.S.
Note
(type = bibliography)
Includes bibliographical references
Note
(type = statement of responsibility)
by Karthik Elangovan
Subject
(authority = ETD-LCSH)
Topic
High performance computing
Subject
(authority = ETD-LCSH)
Topic
Magnetic memory (Computers)
Identifier
(type = hdl)
http://hdl.rutgers.edu/1782.1/rucore10001600001.ETD.000064079
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TitleInfo
Title
Graduate School - New Brunswick Electronic Theses and Dissertations
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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/T3GF0SJM
Genre
(authority = ExL-Esploro)
ETD graduate
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Rights
RightsDeclaration
(ID = rulibRdec0006)
The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
Elangovan
GivenName
Karthik
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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(qualifier = exact);
(point = start)
2011-12-20 19:33:53
AssociatedEntity
Name
Karthik Elangovan
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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3146752
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