Parallel Beam Search for Combinatorial Optimization (Extended Abstract)

Inspired by the recent success of parallelized exact methods to solve difficult scheduling problems, we present preliminary results of a general parallel beam search framework for combinatorial optimization problems. Beam search is a constructive metaheuristic traversing a search tree layer by layer...

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Published inProceedings of the International Symposium on Combinatorial Search Vol. 15; no. 1; pp. 273 - 275
Main Authors Frohner, Nikolaus, Gmys, Jan, Melab, Nouredine, Raidl, Günther R., Talbi, El-ghazali
Format Journal Article
LanguageEnglish
Published 17.07.2022
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ISSN2832-9171
2832-9163
2832-9163
DOI10.1609/socs.v15i1.21783

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Abstract Inspired by the recent success of parallelized exact methods to solve difficult scheduling problems, we present preliminary results of a general parallel beam search framework for combinatorial optimization problems. Beam search is a constructive metaheuristic traversing a search tree layer by layer while keeping in each layer a bounded number of promising nodes to consider many partial solutions in parallel. We propose a variant which is suitable for intra-node parallelization by multithreading with data parallelism. For sufficiently large problem instances and beam widths our work-in-progress implementation in the JIT-compiled Julia language admits promising speed-ups over 30x on 32 cores with uniform memory access for the Permutation Flow Shop Scheduling (PFSP) problem with flowtime objective.
AbstractList Inspired by the recent success of parallelized exact methods to solve difficult scheduling problems, we present preliminary results of a general parallel beam search framework for combinatorial optimization problems. Beam search is a constructive metaheuristic traversing a search tree layer by layer while keeping in each layer a bounded number of promising nodes to consider many partial solutions in parallel. We propose a variant which is suitable for intra-node parallelization by multithreading with data parallelism. For sufficiently large problem instances and beam widths our work-in-progress implementation in the JIT-compiled Julia language admits promising speed-ups over 30x on 32 cores with uniform memory access for the Permutation Flow Shop Scheduling (PFSP) problem with flowtime objective.
Author Raidl, Günther R.
Melab, Nouredine
Gmys, Jan
Frohner, Nikolaus
Talbi, El-ghazali
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Title Parallel Beam Search for Combinatorial Optimization (Extended Abstract)
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