Parallel PIPS-SBB: multi-level parallelism for stochastic mixed-integer programs View Full Text


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Article Info

DATE

2019-02-15

AUTHORS

Lluís-Miquel Munguía, Geoffrey Oxberry, Deepak Rajan, Yuji Shinano

ABSTRACT

PIPS-SBB is a distributed-memory parallel solver with a scalable data distribution paradigm. It is designed to solve mixed integer programs (MIPs) with a dual-block angular structure, which is characteristic of deterministic-equivalent stochastic mixed-integer programs. In this paper, we present two different parallelizations of Branch & Bound (B&B), implementing both as extensions of PIPS-SBB, thus adding an additional layer of parallelism. In the first of the proposed frameworks, PIPS-PSBB, the coordination and load-balancing of the different optimization workers is done in a decentralized fashion. This new framework is designed to ensure all available cores are processing the most promising parts of the B&B tree. The second, ug[PIPS-SBB,MPI], is a parallel implementation using the Ubiquity Generator, a universal framework for parallelizing B&B tree search that has been sucessfully applied to other MIP solvers. We show the effects of leveraging multiple levels of parallelism in potentially improving scaling performance beyond thousands of cores. More... »

PAGES

1-27

References to SciGraph publications

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  • 2012-08. Could we use a million cores to solve an integer program? in MATHEMATICAL METHODS OF OPERATIONS RESEARCH
  • 2018-06. Algorithmic innovations and software for the dual decomposition method applied to stochastic mixed-integer programs in MATHEMATICAL PROGRAMMING COMPUTATION
  • 2013-07. Parallel distributed-memory simplex for large-scale stochastic LP problems in COMPUTATIONAL OPTIMIZATION AND APPLICATIONS
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  • 2015-12. PEBBL: an object-oriented framework for scalable parallel branch and bound in MATHEMATICAL PROGRAMMING COMPUTATION
  • 2012-06. PySP: modeling and solving stochastic programs in Python in MATHEMATICAL PROGRAMMING COMPUTATION
  • 2005. Alps: A Framework for Implementing Parallel Tree Search Algorithms in THE NEXT WAVE IN COMPUTING, OPTIMIZATION, AND DECISION TECHNOLOGIES
  • 2005-07. The Million-Variable “March” for Stochastic Combinatorial Optimization in JOURNAL OF GLOBAL OPTIMIZATION
  • 2015-12. Progress in presolving for mixed integer programming in MATHEMATICAL PROGRAMMING COMPUTATION
  • 2007. ParaLEX: A Parallel Extension for the CPLEX Mixed Integer Optimizer in RECENT ADVANCES IN PARALLEL VIRTUAL MACHINE AND MESSAGE PASSING INTERFACE
  • 2015-02. Automatic Dantzig–Wolfe reformulation of mixed integer programs in MATHEMATICAL PROGRAMMING
  • 2009-11-06. Reformulation and Decomposition of Integer Programs in 50 YEARS OF INTEGER PROGRAMMING 1958-2008
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    http://scigraph.springernature.com/pub.10.1007/s10589-019-00074-0

    DOI

    http://dx.doi.org/10.1007/s10589-019-00074-0

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    https://app.dimensions.ai/details/publication/pub.1112158545


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