Parallel and distributed local search in COMET

The availability of commodity multiprocessors and high-speed networks of workstations offer significant opportunities for addressing the increasing computational requirements of optimization applications. To leverage these potential benefits, it is important, however, to make parallel and distribute...

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Published in:Computers & operations research Vol. 36; no. 8; pp. 2357 - 2375
Main Authors: Michel, Laurent, See, Andrew, Van Hentenryck, Pascal
Format: Journal Article
Language:English
Published: New York Pergamon Press Inc 01.08.2009
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ISSN:0305-0548, 0305-0548
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Abstract The availability of commodity multiprocessors and high-speed networks of workstations offer significant opportunities for addressing the increasing computational requirements of optimization applications. To leverage these potential benefits, it is important, however, to make parallel and distributed processing easily accessible to a wide audience of optimization programmers. This paper addresses this challenge by proposing parallel and distributed programming abstractions that keep the distance from sequential local search algorithms as small as possible. The abstractions, including parallel loops, interruptions, thread pools, and shared objects, are compositional and cleanly separate the optimization program and the parallel instructions. They have been evaluated experimentally on a variety of applications, including warehouse location and coloring, for which they provide significant speedups. [PUBLICATION ABSTRACT]
AbstractList The availability of commodity multiprocessors and high-speed networks of workstations offer significant opportunities for addressing the increasing computational requirements of optimization applications. To leverage these potential benefits, it is important, however, to make parallel and distributed processing easily accessible to a wide audience of optimization programmers. This paper addresses this challenge by proposing parallel and distributed programming abstractions that keep the distance from sequential local search algorithms as small as possible. The abstractions, including parallel loops, interruptions, thread pools, and shared objects, are compositional and cleanly separate the optimization program and the parallel instructions. They have been evaluated experimentally on a variety of applications, including warehouse location and coloring, for which they provide significant speedups. [PUBLICATION ABSTRACT]
The availability of commodity multiprocessors and high-speed networks of workstations offer significant opportunities for addressing the increasing computational requirements of optimization applications. To leverage these potential benefits, it is important, however, to make parallel and distributed processing easily accessible to a wide audience of optimization programmers. This paper addresses this challenge by proposing parallel and distributed programming abstractions that keep the distance from sequential local search algorithms as small as possible. The abstractions, including parallel loops, interruptions, thread pools, and shared objects, are compositional and cleanly separate the optimization program and the parallel instructions. They have been evaluated experimentally on a variety of applications, including warehouse location and coloring, for which they provide significant speedups.
Author See, Andrew
Van Hentenryck, Pascal
Michel, Laurent
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SubjectTerms Distributed processing
High speed
Optimization
Optimization algorithms
Parallel processing
Studies
Title Parallel and distributed local search in COMET
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