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dc.contributor.author
Cesarini, Daniele
dc.contributor.author
Bartolini, Andrea
dc.contributor.author
Benini, Luca
dc.contributor.editor
Bassini, Sanzio
dc.contributor.editor
Danelutto, Marco
dc.contributor.editor
Dazzi, Patrizio
dc.contributor.editor
Joubert, Gerhard R.
dc.contributor.editor
Peters, Frans
dc.date.accessioned
2019-01-07T08:38:22Z
dc.date.available
2019-01-06T08:21:13Z
dc.date.available
2019-01-07T08:38:22Z
dc.date.issued
2018
dc.identifier.isbn
978-1-61499-842-6
en_US
dc.identifier.isbn
978-1-61499-843-3
en_US
dc.identifier.issn
0927-5452
dc.identifier.issn
1879-808X
dc.identifier.other
10.3233/978-1-61499-843-3-277
en_US
dc.identifier.uri
http://hdl.handle.net/20.500.11850/313829
dc.identifier.doi
10.3929/ethz-b-000313829
dc.description.abstract
With the advent of a new generation of supercomputers characterized by tightly-coupled integration of a large-number of powerful processing cores in the same die, energy and temperature walls are looming threats to the growth in computational power. Scientific computing is characterized by a single application running in parallel on multiple nodes and cores until termination. The message-passing programming model is a widely adopted paradigm for explicitly handling data-sharing between processes of the same application. As an effect of the MPI communication patterns among different processes, the application is characterized by phases which can be exploited by OS power manager. In addition, the large number of cores integrated in the same silicon die introduces large thermal capacitance as well as on-die thermal heterogeneity. Jointly exploiting local workload unbalance and computational node heterogeneity can open interesting opportunities for advanced thermal and energy management. In this paper, we present an exploratory work to assess these opportunities and their limiting factors. We analyze application workload and we identify opportunities to reduce energy consumption and their impact on performance. We test our methodology on a widely-used quantum-chemistry application demonstrating potential benefits of combining the application flow with power and thermal management strategies.
en_US
dc.format
application/pdf
en_US
dc.language.iso
en
en_US
dc.publisher
IOS Press
en_US
dc.rights.uri
http://rightsstatements.org/page/InC-NC/1.0/
dc.subject
HPC
en_US
dc.subject
thermal model
en_US
dc.subject
power model
en_US
dc.subject
energy
en_US
dc.subject
MPI
en_US
dc.subject
runtime
en_US
dc.subject
scientific workload
en_US
dc.title
Energy Saving and Thermal Management Opportunities in a Workload-Aware MPI Runtime for a Scientific HPC Computing Node
en_US
dc.type
Book Chapter
dc.rights.license
In Copyright - Non-Commercial Use Permitted
dc.date.published
2017-09
ethz.book.title
Parallel Computing is Everywhere
en_US
ethz.journal.title
Advances in Parallel Computing
ethz.journal.volume
32
en_US
ethz.journal.abbreviated
Adv. parallel comput.
ethz.pages.start
277
en_US
ethz.pages.end
286
en_US
ethz.size
11 p.
en_US
ethz.version.deposit
acceptedVersion
en_US
ethz.publication.place
Amsterdam; Clifton, VA
en_US
ethz.publication.status
published
en_US
ethz.leitzahl
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::03996 - Benini, Luca / Benini, Luca
en_US
ethz.leitzahl.certified
ETH Zürich::00002 - ETH Zürich::00012 - Lehre und Forschung::00007 - Departemente::02140 - Dep. Inf.technologie und Elektrotechnik / Dep. of Inform.Technol. Electrical Eng.::02636 - Institut für Integrierte Systeme / Integrated Systems Laboratory::03996 - Benini, Luca / Benini, Luca
en_US
ethz.date.deposited
2019-01-06T08:21:37Z
ethz.source
FORM
ethz.eth
yes
en_US
ethz.availability
Open access
en_US
ethz.rosetta.installDate
2019-01-07T08:38:42Z
ethz.rosetta.lastUpdated
2020-02-15T16:30:30Z
ethz.rosetta.versionExported
true
ethz.COinS
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