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

  • 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.

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Metadaten
Author:John Gunnels, Jon Lee, Susan Margulies
DOI:https://doi.org/10.1007/s12532-010-0014-4
ISSN:1867-2949
Parent Title (English):Mathematical Programming Computation
Publisher:Springer Science and Business Media LLC
Document Type:Article
Language:English
Year of Completion:2010
Tag:Software; Theoretical Computer Science
Volume:2
Issue:2
Page Number:22
First Page:103
Last Page:124
Mathematical Programming Computation :MPC 2010 - Issue 2
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