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.
@InProceedings{beldjilali_et_al:LIPIcs.CP.2022.7, author = {Beldjilali, Abdelkader and Montalbano, Pierre and Allouche, David and Katsirelos, George and de Givry, Simon}, title = {{Parallel Hybrid Best-First Search}}, booktitle = {28th International Conference on Principles and Practice of Constraint Programming (CP 2022)}, pages = {7:1--7:10}, series = {Leibniz International Proceedings in Informatics (LIPIcs)}, ISBN = {978-3-95977-240-2}, ISSN = {1868-8969}, year = {2022}, volume = {235}, editor = {Solnon, Christine}, publisher = {Schloss Dagstuhl -- Leibniz-Zentrum f{\"u}r Informatik}, address = {Dagstuhl, Germany}, URL = {https://drops.dagstuhl.de/entities/document/10.4230/LIPIcs.CP.2022.7}, URN = {urn:nbn:de:0030-drops-166362}, doi = {10.4230/LIPIcs.CP.2022.7}, annote = {Keywords: Combinatorial Optimization, Parallel Branch-and-Bound, CFN} }
Feedback for Dagstuhl Publishing