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Parallel Hybrid Best-First Search

Authors Abdelkader Beldjilali, Pierre Montalbano , David Allouche, George Katsirelos , Simon de Givry

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Subject Classification

ACM Subject Classification
  • Computing methodologies → Parallel algorithms
Keywords
  • Combinatorial Optimization
  • Parallel Branch-and-Bound
  • CFN

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Abstract

While processor frequency has stagnated over the past two decades, the number of available cores in servers or clusters is still growing, offering the opportunity for significant speed-up in combinatorial optimization. Parallelization of exact methods remains a difficult challenge. We revisit the concept of parallel Branch-and-Bound in the framework of Cost Function Networks. We show how to adapt the anytime Hybrid Best-First Search algorithm in a Master-Worker protocol. The resulting parallel algorithm achieves good load-balancing without introducing new parameters to be tuned as is the case, for example, in Embarrassingly Parallel Search (EPS). It has also a small overhead due to its light communication messages. We performed an experimental evaluation on several benchmarks, comparing our parallel algorithm to its sequential version. We observed linear speed-up in some cases. Our approach compared favourably to the EPS approach and also to a state-of-the-art parallel exact integer programming solver.

Cite As Get BibTex

Abdelkader Beldjilali, Pierre Montalbano, David Allouche, George Katsirelos, and Simon de Givry. Parallel Hybrid Best-First Search. In 28th International Conference on Principles and Practice of Constraint Programming (CP 2022). Leibniz International Proceedings in Informatics (LIPIcs), Volume 235, pp. 7:1-7:10, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2022) https://doi.org/10.4230/LIPIcs.CP.2022.7

Author Details

Abdelkader Beldjilali
  • Université Fédérale de Toulouse, INRAE, UR 875, 31326 Toulouse, France
Pierre Montalbano
  • Université Fédérale de Toulouse, ANITI, INRAE, UR 875, 31326 Toulouse, France
David Allouche
  • Université Fédérale de Toulouse, ANITI, INRAE, UR 875, 31326 Toulouse, France
George Katsirelos
  • Université Fédérale de Toulouse, ANITI, INRAE, MIA Paris, AgroParisTech, 75231 Paris, France
Simon de Givry
  • Université Fédérale de Toulouse, ANITI, INRAE, UR 875, 31326 Toulouse, France

Funding

This work has been partially funded by the French ”Agence Nationale de la Recherche”, through grant ANR-19-P3IA-0004. It was performed using HPC resources from CALMIP (Grant 2022-P21010).

Supplementary Materials

References

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