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

Journal Article · · Computational Optimization and Applications
 [1];  [2];  [2];  [3]
  1. Georgia Inst. of Technology, Atlanta, GA (United States)
  2. Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
  3. Zuse Inst. Berlin (Germany)
+ Show Author Affiliations
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.
Research Organization:
Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States)
Sponsoring Organization:
USDOE National Nuclear Security Administration (NNSA)
Grant/Contract Number:
AC52-07NA27344
OSTI ID:
1635781
Report Number(s):
LLNL-JRNL--739981; 893506
Journal Information:
Computational Optimization and Applications, Journal Name: Computational Optimization and Applications Journal Issue: 2 Vol. 73; ISSN 0926-6003
Publisher:
SpringerCopyright Statement
Country of Publication:
United States
Language:
English

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Figures / Tables (19)

Algorithm 1(p. 6)figure Algorithm 1
Fig. 1(p. 7)figure Fig. 1
Fig. 2(p. 10)figure Fig. 2
Fig. 3(p. 11)figure Fig. 3
Algorithm 2(p. 12)figure Algorithm 2
Fig. 4(p. 13)figure Fig. 4
Algorithm 3(p. 14)figure Algorithm 3
Fig. 5(p. 15)figure Fig. 5
Fig. 6(p. 15)figure Fig. 6
Algorithm 4(p. 16)figure Algorithm 4
Algorithm 5(p. 17)figure Algorithm 5
Fig. 7(p. 17)figure Fig. 7
Fig. 8(p. 19)figure Fig. 8
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Fig. 10(p. 21)figure Fig. 10
Fig. 11(p. 23)figure Fig. 11
Fig. 12(p. 24)figure Fig. 12
Table 1(p. 24)table Table 1
Table 2(p. 25)table Table 2

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