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Title: Parallel PIPS-SBB: multi-level parallelism for stochastic mixed-integer programs

Abstract

PIPS-SBB is a distributed-memory parallel solver with a scalable data distribution paradigm. It is designed to solve MIPs with a dual-block angular structure, which is characteristic of deterministic-equivalent Stochastic Mixed-Integer Programs (SMIPs). 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 (UG), 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.

Authors:
ORCiD logo [1];  [2];  [2];  [3]
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  1. Georgia Inst. of Technology, Atlanta, GA (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  3. Zuse Inst. Berlin (Germany)
Publication Date:
Research Org.:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Org.:
USDOE National Nuclear Security Administration (NNSA)
OSTI Identifier:
1635781
Report Number(s):
LLNL-JRNL-739981
Journal ID: ISSN 0926-6003; 893506
Grant/Contract Number:  
AC52-07NA27344
Resource Type:
Accepted Manuscript
Journal Name:
Computational Optimization and Applications
Additional Journal Information:
Journal Volume: 73; Journal Issue: 2; Journal ID: ISSN 0926-6003
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
97 MATHEMATICS AND COMPUTING

Citation Formats

Munguía, Lluís-Miquel, Oxberry, Geoffrey, Rajan, Deepak, and Shinano, Yuji. Parallel PIPS-SBB: multi-level parallelism for stochastic mixed-integer programs. United States: N. p., 2019. Web. doi:10.1007/s10589-019-00074-0.
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Munguía, Lluís-Miquel, Oxberry, Geoffrey, Rajan, Deepak, & Shinano, Yuji. Parallel PIPS-SBB: multi-level parallelism for stochastic mixed-integer programs. United States. https://doi.org/10.1007/s10589-019-00074-0
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Munguía, Lluís-Miquel, Oxberry, Geoffrey, Rajan, Deepak, and Shinano, Yuji. Fri . "Parallel PIPS-SBB: multi-level parallelism for stochastic mixed-integer programs". United States. https://doi.org/10.1007/s10589-019-00074-0. https://www.osti.gov/servlets/purl/1635781.
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@article{osti_1635781,
title = {Parallel PIPS-SBB: multi-level parallelism for stochastic mixed-integer programs},
author = {Munguía, Lluís-Miquel and Oxberry, Geoffrey and Rajan, Deepak and Shinano, Yuji},
abstractNote = {PIPS-SBB is a distributed-memory parallel solver with a scalable data distribution paradigm. It is designed to solve MIPs with a dual-block angular structure, which is characteristic of deterministic-equivalent Stochastic Mixed-Integer Programs (SMIPs). 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 (UG), 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.},
doi = {10.1007/s10589-019-00074-0},
journal = {Computational Optimization and Applications},
number = 2,
volume = 73,
place = {United States},
year = {Fri Feb 15 00:00:00 EST 2019},
month = {Fri Feb 15 00:00:00 EST 2019}
}
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Journal Article:
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Publisher's Version of Record
https://doi.org/10.1007/s10589-019-00074-0
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Cited by: 4 works
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Figures / Tables:

Algorithm 1 Algorithm 1: Branch and Bound
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    Figures / Tables found in this record:

    Algorithm 1 (p. 6)figure
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