Parallel PIPS-SBB: multi-level parallelism for stochastic mixed-integer programs
- Georgia Inst. of Technology, Atlanta, GA (United States)
- Lawrence Livermore National Lab. (LLNL), Livermore, CA (United States)
- Zuse Inst. Berlin (Germany)
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, Vol. 73, Issue 2; ISSN 0926-6003
- Publisher:
- SpringerCopyright Statement
- Country of Publication:
- United States
- Language:
- English
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