Energy footprint of advanced dense numerical linear algebra using tile algorithms on multicore architectures

Handle URI:
http://hdl.handle.net/10754/575808
Title:
Energy footprint of advanced dense numerical linear algebra using tile algorithms on multicore architectures
Authors:
Dongarra, Jack; Ltaief, Hatem ( 0000-0002-6897-1095 ) ; Luszczek, Piotr R.; Weaver, Vincent M.
Abstract:
We propose to study the impact on the energy footprint of two advanced algorithmic strategies in the context of high performance dense linear algebra libraries: (1) mixed precision algorithms with iterative refinement allow to run at the peak performance of single precision floating-point arithmetic while achieving double precision accuracy and (2) tree reduction technique exposes more parallelism when factorizing tall and skinny matrices for solving over determined systems of linear equations or calculating the singular value decomposition. Integrated within the PLASMA library using tile algorithms, which will eventually supersede the block algorithms from LAPACK, both strategies further excel in performance in the presence of a dynamic task scheduler while targeting multicore architecture. Energy consumption measurements are reported along with parallel performance numbers on a dual-socket quad-core Intel Xeon as well as a quad-socket quad-core Intel Sandy Bridge chip, both providing component-based energy monitoring at all levels of the system, through the Power Pack framework and the Running Average Power Limit model, respectively. © 2012 IEEE.
KAUST Department:
KAUST Supercomputing Laboratory (KSL); Extreme Computing Research Center
Publisher:
Institute of Electrical and Electronics Engineers (IEEE)
Journal:
2012 Second International Conference on Cloud and Green Computing
Conference/Event name:
2nd International Conference on Cloud and Green Computing, CGC 2012, Held Jointly with the 2nd International Conference on Social Computing and Its Applications, SCA 2012
Issue Date:
Nov-2012
DOI:
10.1109/CGC.2012.113
Type:
Conference Paper
ISBN:
9780769548647
Appears in Collections:
Conference Papers; KAUST Supercomputing Laboratory (KSL); Extreme Computing Research Center; Extreme Computing Research Center

Full metadata record

DC FieldValue Language
dc.contributor.authorDongarra, Jacken
dc.contributor.authorLtaief, Hatemen
dc.contributor.authorLuszczek, Piotr R.en
dc.contributor.authorWeaver, Vincent M.en
dc.date.accessioned2015-08-24T09:26:46Zen
dc.date.available2015-08-24T09:26:46Zen
dc.date.issued2012-11en
dc.identifier.isbn9780769548647en
dc.identifier.doi10.1109/CGC.2012.113en
dc.identifier.urihttp://hdl.handle.net/10754/575808en
dc.description.abstractWe propose to study the impact on the energy footprint of two advanced algorithmic strategies in the context of high performance dense linear algebra libraries: (1) mixed precision algorithms with iterative refinement allow to run at the peak performance of single precision floating-point arithmetic while achieving double precision accuracy and (2) tree reduction technique exposes more parallelism when factorizing tall and skinny matrices for solving over determined systems of linear equations or calculating the singular value decomposition. Integrated within the PLASMA library using tile algorithms, which will eventually supersede the block algorithms from LAPACK, both strategies further excel in performance in the presence of a dynamic task scheduler while targeting multicore architecture. Energy consumption measurements are reported along with parallel performance numbers on a dual-socket quad-core Intel Xeon as well as a quad-socket quad-core Intel Sandy Bridge chip, both providing component-based energy monitoring at all levels of the system, through the Power Pack framework and the Running Average Power Limit model, respectively. © 2012 IEEE.en
dc.publisherInstitute of Electrical and Electronics Engineers (IEEE)en
dc.subjectDense Linear Algebraen
dc.subjectDynamic Schedulingen
dc.subjectMixed Precision Algorithmsen
dc.subjectPower Consumptionen
dc.subjectPowerPacken
dc.subjectRAPLen
dc.subjectTile Algorithmsen
dc.subjectTree Reductionen
dc.titleEnergy footprint of advanced dense numerical linear algebra using tile algorithms on multicore architecturesen
dc.typeConference Paperen
dc.contributor.departmentKAUST Supercomputing Laboratory (KSL)en
dc.contributor.departmentExtreme Computing Research Centeren
dc.identifier.journal2012 Second International Conference on Cloud and Green Computingen
dc.conference.date1 November 2012 through 3 November 2012en
dc.conference.name2nd International Conference on Cloud and Green Computing, CGC 2012, Held Jointly with the 2nd International Conference on Social Computing and Its Applications, SCA 2012en
dc.conference.locationXiangtan, Hunanen
dc.contributor.institutionInnovative Computing Laboratory, University of Tennessee, Knoxville, TN 37996, United Statesen
dc.contributor.institutionComputer Science and Mathematics Division, Oak Ridge National Laboratory, Oak Ridge, TN, United Statesen
dc.contributor.institutionSchool of Mathematics, School of Computer Science, University of Manchester, United Kingdomen
kaust.authorLtaief, Hatemen
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