Efficient high-precision matrix algebra on parallel architectures for nonlinear combinatorial optimization

Handle URI:
http://hdl.handle.net/10754/598106
Title:
Efficient high-precision matrix algebra on parallel architectures for nonlinear combinatorial optimization
Authors:
Gunnels, John; Lee, Jon; Margulies, Susan
Abstract:
We provide a first demonstration of the idea that matrix-based algorithms for nonlinear combinatorial optimization problems can be efficiently implemented. Such algorithms were mainly conceived by theoretical computer scientists for proving efficiency. We are able to demonstrate the practicality of our approach by developing an implementation on a massively parallel architecture, and exploiting scalable and efficient parallel implementations of algorithms for ultra high-precision linear algebra. Additionally, we have delineated and implemented the necessary algorithmic and coding changes required in order to address problems several orders of magnitude larger, dealing with the limits of scalability from memory footprint, computational efficiency, reliability, and interconnect perspectives. © Springer and Mathematical Programming Society 2010.
Citation:
Gunnels J, Lee J, Margulies S (2010) Efficient high-precision matrix algebra on parallel architectures for nonlinear combinatorial optimization. Math Prog Comp 2: 103–124. Available: http://dx.doi.org/10.1007/s12532-010-0014-4.
Publisher:
Springer Nature
Journal:
Mathematical Programming Computation
Issue Date:
Jun-2010
DOI:
10.1007/s12532-010-0014-4
Type:
Article
ISSN:
1867-2949; 1867-2957
Sponsors:
We gratefully acknowledge the use of the IBM Shaheen (which at the time of ourexperiments was an 8-rack Blue Gene/P supercomputer housed at the IBM T.J. Watson Research Center).The IBM Shaheen is now owned and operated by the King Abdullah University of Science and Technology(KAUST). We would like to thank Bob Walkup at IBM Research for his help in many aspects of this work,including the use of his performance-counter library for performance evaluation and Fred Mintzer at IBMResearch for arranging for our use of the Blue Gene/P Supercomputer. We are deeply indebted to DavidBailey and his team for the ARPREC software package and documentation. Our work was partially car-ried out, while S. Margulies was a graduate student at U.C. Davis, under an Open Collaborative Researchagreement between IBM and U.C. Davis.
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Full metadata record

DC FieldValue Language
dc.contributor.authorGunnels, Johnen
dc.contributor.authorLee, Jonen
dc.contributor.authorMargulies, Susanen
dc.date.accessioned2016-02-25T13:12:48Zen
dc.date.available2016-02-25T13:12:48Zen
dc.date.issued2010-06en
dc.identifier.citationGunnels J, Lee J, Margulies S (2010) Efficient high-precision matrix algebra on parallel architectures for nonlinear combinatorial optimization. Math Prog Comp 2: 103–124. Available: http://dx.doi.org/10.1007/s12532-010-0014-4.en
dc.identifier.issn1867-2949en
dc.identifier.issn1867-2957en
dc.identifier.doi10.1007/s12532-010-0014-4en
dc.identifier.urihttp://hdl.handle.net/10754/598106en
dc.description.abstractWe provide a first demonstration of the idea that matrix-based algorithms for nonlinear combinatorial optimization problems can be efficiently implemented. Such algorithms were mainly conceived by theoretical computer scientists for proving efficiency. We are able to demonstrate the practicality of our approach by developing an implementation on a massively parallel architecture, and exploiting scalable and efficient parallel implementations of algorithms for ultra high-precision linear algebra. Additionally, we have delineated and implemented the necessary algorithmic and coding changes required in order to address problems several orders of magnitude larger, dealing with the limits of scalability from memory footprint, computational efficiency, reliability, and interconnect perspectives. © Springer and Mathematical Programming Society 2010.en
dc.description.sponsorshipWe gratefully acknowledge the use of the IBM Shaheen (which at the time of ourexperiments was an 8-rack Blue Gene/P supercomputer housed at the IBM T.J. Watson Research Center).The IBM Shaheen is now owned and operated by the King Abdullah University of Science and Technology(KAUST). We would like to thank Bob Walkup at IBM Research for his help in many aspects of this work,including the use of his performance-counter library for performance evaluation and Fred Mintzer at IBMResearch for arranging for our use of the Blue Gene/P Supercomputer. We are deeply indebted to DavidBailey and his team for the ARPREC software package and documentation. Our work was partially car-ried out, while S. Margulies was a graduate student at U.C. Davis, under an Open Collaborative Researchagreement between IBM and U.C. Davis.en
dc.publisherSpringer Natureen
dc.subjectHigh-performance computingen
dc.subjectHigh-precision linear algebraen
dc.subjectMatroid optimizationen
dc.subjectNonlinear combinatorial optimizationen
dc.titleEfficient high-precision matrix algebra on parallel architectures for nonlinear combinatorial optimizationen
dc.typeArticleen
dc.identifier.journalMathematical Programming Computationen
dc.contributor.institutionIBM Thomas J. Watson Research Center, Yorktown Heights, United Statesen
dc.contributor.institutionRice University, Houston, United Statesen
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