SelfSplit parallelization for mixed-integer linear programming

•SelfSplit an easy-to-implement way to parallelize a sequential tree-search code.•SelfSplit is deterministic and requires (almost) no communication among workers.•We investigate the performance of SelfSplit when applied to a MILP solver.•Both ad-hoc and general purpose MILP solvers are considered.•C...

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Bibliographic Details
Published in:Computers & operations research Vol. 93; pp. 101 - 112
Main Authors: Fischetti, Matteo, Monaci, Michele, Salvagnin, Domenico
Format: Journal Article
Language:English
Published: New York Elsevier Ltd 01.05.2018
Pergamon Press Inc
Subjects:
Algorithms
Computational analysis
Enumerative algorithms
Integer programming
Linear programming
Mixed-integer programming
Operations research
Parallel computing
Parallel processing
Solvers
Parallel computing
Computational analysis
Mixed-integer programming
Enumerative algorithms
ISSN:0305-0548, 1873-765X, 0305-0548
Online Access:Get full text
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Summary:•SelfSplit an easy-to-implement way to parallelize a sequential tree-search code.•SelfSplit is deterministic and requires (almost) no communication among workers.•We investigate the performance of SelfSplit when applied to a MILP solver.•Both ad-hoc and general purpose MILP solvers are considered.•Computational results show that good speedups can be achieved in the MILP context. SelfSplit is a simple static mechanism to convert a sequential tree-search code into a parallel one. In this paradigm, tree-search is distributed among a set of identical workers, each of which is able to autonomously determine—without any communication with the other workers—the job parts it has to process. SelfSplit already proved quite effective in parallelizing Constraint Programming solvers. In the present paper we investigate the performance of SelfSplit when applied to a Mixed-Integer Linear Programming (MILP) solver. Both ad-hoc and general purpose MILP codes have been considered. Computational results show that SelfSplit, in spite of its simplicity, can achieve good speedups even in the MILP context.
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ISSN:0305-0548
1873-765X
0305-0548
DOI:10.1016/j.cor.2018.01.011