Document Open Access Logo

Parallel Hybrid Best-First Search

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

PDF
Thumbnail PDF

File

LIPIcs.CP.2022.7.pdf
  • Filesize: 0.75 MB
  • 10 pages

Document Identifiers

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).

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

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.

Subject Classification

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

Metrics

  • Access Statistics
  • Total Accesses (updated on a weekly basis)
    0
    PDF Downloads
    0
    Metadata Views

Supplementary Materials

References

  1. D Allouche, J Davies, S de Givry, G Katsirelos, T Schiex, S Traoré, I André, S Barbe, S Prestwich, and B O'Sullivan. Computational protein design as an optimization problem. Artificial Intelligence, 212:59-79, 2014. Google Scholar
  2. D Allouche, S de Givry, G Katsirelos, T Schiex, and M Zytnicki. Anytime Hybrid Best-First Search with Tree Decomposition for Weighted CSP. In Proc. of CP-15, pages 12-28, Cork, Ireland, 2015. Google Scholar
  3. D. Allouche, S. de Givry, and T. Schiex. Towards parallel non serial dynamic programming for solving hard weighted csp. In Proc. of CP-10, St Andrews, Scotland, 2010. Google Scholar
  4. F Boussemart, F Hemery, C Lecoutre, and L Sais. Boosting systematic search by weighting constraints. In ECAI, volume 16, page 146, 2004. Google Scholar
  5. M. Cooper, S. de Givry, M. Sanchez, T. Schiex, M. Zytnicki, and T. Werner. Soft arc consistency revisited. Artificial Intelligence, 174(7-8):449-478, 2010. Google Scholar
  6. S. de Givry, M. Zytnicki, F. Heras, and J. Larrosa. Existential arc consistency: Getting closer to full arc consistency in weighted csps. In Proc. of IJCAI'05, pages 84-89, Edinburgh, Scotland, 2005. Google Scholar
  7. E Demirovic, G Chu, and P J. Stuckey. Solution-based phase saving for CP: A value-selection heuristic to simulate local search behavior in complete solvers. In Proc. of CP-18, pages 99-108, Lille, France, 2018. Google Scholar
  8. A Favier, S de Givry, A Legarra, and T Schiex. Pairwise decomposition for combinatorial optimization in graphical models. In Proc. of IJCAI-11, Barcelona, Spain, 2011. Google Scholar
  9. I Gent, I Miguel, P Nightingale, C McCreesh, P Prosser, N Moore, and C Unsworth. A review of literature on parallel constraint solving. Theory and Practice of Logic Programming, 18(5-6):725-758, 2018. Google Scholar
  10. C. Gomes, B. Selman, and H. Kautz. Boosting combinatorial search through randomization. In Proc. of AAAI'98, Madison, WI, 1998. Google Scholar
  11. W. D. Harvey and M. L. Ginsberg. Limited discrepancy search. In Proc. of IJCAI'95, Montréal, Canada, 1995. Google Scholar
  12. B Hurley, B O'Sullivan, D Allouche, G Katsirelos, T Schiex, M Zytnicki, and S de Givry. Multi-Language Evaluation of Exact Solvers in Graphical Model Discrete Optimization. Constraints, 21(3):413-434, 2016. Google Scholar
  13. J Kratica, D Tošic, V Filipović, and I Ljubić. Solving the simple plant location problem by genetic alg. RAIRO, 35(1):127-142, 2001. Google Scholar
  14. C. Lecoutre, L Saïs, S. Tabary, and V. Vidal. Reasoning from last conflict(s) in constraint programming. Artificial Intelligence, 173:1592,1614, 2009. Google Scholar
  15. A Malapert, J-C Régin, and M Rezgui. Embarrassingly parallel search in constraint programming. Journal of Artificial Intelligence Research, 57:421-464, 2016. Google Scholar
  16. C McCreesh and P Prosser. The shape of the search tree for the maximum clique problem and the implications for parallel branch and bound. ACM Trans. Parallel Comput., 2(1), 2015. Google Scholar
  17. P. Meseguer, F. Rossi, and T. Schiex. Soft constraints processing. In F. Rossi, P. van Beek, and T. Walsh, editors, Handbook of Constraint Programming, chapter 9. Elsevier, 2006. Google Scholar
  18. L Michel, A See, and P Van Hentenryck. Parallelizing constraint programs transparently. In C Bessière, editor, Principles and Practice of Constraint Programming - CP 2007, pages 514-528, Berlin, Heidelberg, 2007. Springer Berlin Heidelberg. Google Scholar
  19. L Otten and R Dechter. And/or branch-and-bound on a computational grid. JAIR, 59:351-435, 2017. Google Scholar
  20. Abdelkader Ouali, David Allouche, Simon de Givry, Samir Loudni, Yahia Lebbah, Francisco Eckhardt, and Lakhdar Loukil. Iterative Decomposition Guided Variable Neighborhood Search for Graphical Model Energy Minimization. In Proc. of UAI-17, pages 550-559, Sydney, Australia, 2017. Google Scholar
  21. T Ralphs, Y Shinano, T Berthold, and T Koch. Parallel solvers for mixed integer linear optimization. In Handbook of parallel constraint reasoning, pages 283-336. Springer, 2018. Google Scholar
Questions / Remarks / Feedback
X

Feedback for Dagstuhl Publishing


Thanks for your feedback!

Feedback submitted

Could not send message

Please try again later or send an E-mail
© 2023-2024 Schloss Dagstuhl – LZI GmbH About DROPS Imprint Privacy Contact