Abstract
Recent years have witnessed growing interest in parallelising constraint solving based on tree search (see [1] for a brief overview). One approach is search-space splitting in which different parts of the tree are explored in parallel (e.g. [2]). Another approach is the use of algorithm portfolios. This technique exploits the significant variety in performance observed between different algorithms and combines them in a portfolio [3]. In constraint solving, an algorithm can be a solver or a tuning of a solver. Portfolios have often been run in an interleaving fashion (e.g. [4]). Their use in a parallel context is more recent ([5], [1]).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bordeaux, L., Hamadi, Y., Samulowitz, H.: Experiments with massively parallel constraint solving. In: Proceedings of the 21st International Joint Conference on Artificial Intelligence (IJCAI 2009), pp. 443–448 (2009)
Jaffar, J., Santosa, A.E., Yap, R.H.C., Zhu, K.Q.: Scalable distributed depth-first search with greedy work stealing. In: Proceedings of the 16th IEEE International Conference on Tools with Artificial Intelligence (ICTAI 2004), pp. 98–103 (2004)
Gomes, C., Selman, B.: Algorithm portfolios. Artificial Intelligence 1-2, 43–62 (2001)
O’Mahony, E., Hebrard, E., Holland, A., Nugent, C., O’Sullivan, B.: Using case-based reasoning in an algorithm portfolio for constraint solving. In: Proceedings of the 19th Irish Conference on Artificial Intelligence, AICS 2008 (2008)
Hamadi, Y., Jabbour, S., Sais, L.: Manysat: Solver description. In: SAT-Race 2008 (2008)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Kiziltan, Z., Mauro, J. (2010). Service-Oriented Volunteer Computing for Massively Parallel Constraint Solving Using Portfolios. In: Lodi, A., Milano, M., Toth, P. (eds) Integration of AI and OR Techniques in Constraint Programming for Combinatorial Optimization Problems. CPAIOR 2010. Lecture Notes in Computer Science, vol 6140. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13520-0_27
Download citation
DOI: https://doi.org/10.1007/978-3-642-13520-0_27
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13519-4
Online ISBN: 978-3-642-13520-0
eBook Packages: Computer ScienceComputer Science (R0)