Graphics Processing Units for Constraint Satisfaction

Malek Mouhoub, Ahmed Mobaraki

Abstract

A Constraint Satisfaction Problem (CSP) is a powerful formalism to represent constrained problems. A CSP includes a set of variables where each is defined over a set of possible values, and a set of relations restricting the values that the variables can simultaneously take. There are numerous problems that can be represented as CSPs. Solving CSPs is known to be quite challenging in general. The literature poses a great body of work geared towards finding efficient techniques to solve CSPs. These techniques are usually implemented in a system commonly referred to as a constraint solver. While many enhancements have been achieved over earlier ones, solvers still require powerful resources and techniques to solve a given problem in a reasonable running time. In this paper, a new parallel-based approach is proposed for solving CSPs. In particular, we design a new CSP solver that exploits the power of graphics processing units (GPU), which exist in modern day computers, as an affordable parallel computing architecture.

References

  1. Abbasian, R. and Mouhoub, M. (2013). A hierarchical parallel genetic approach for the graph coloring problem. Applied Intelligence, 39(3):510-528.
  2. Abbasian, R. and Mouhoub, M. (2016). A new parallel gabased method for constraint satisfaction problems. International Journal of Computational Intelligence and Applications, 15(03).
  3. Balafoutis, T. and Stergiou, K. (2010). Conflict directed variable selection strategies for constraint satisfaction problems. In Hellenic Conference on Artificial Intelligence, pages 29-38. Springer.
  4. Bessière, C. (1996). A simple way to improve path consistency processing in interval algebra networks. In AAAI'96, pages 375-380, Portland.
  5. Bessière, C., Régin, J., Yap, R. H. C., and Zhang, Y. (2005). An optimal coarse-grained arc consistency algorithm. Artif. Intell., 165(2):165-185.
  6. Cook, S. (2012). CUDA programming: a developer's guide to parallel computing with GPUs. Newnes.
  7. Dechter, R. (2003). Constraint Processing. Morgan Kaufmann.
  8. Haralick, R. and Elliott, G. (1980). Increasing tree search efficiency for Constraint Satisfaction Problems. Artificial Intelligence, 14:263-313.
  9. Lecoutre, C. and Tabary, S. (2008). Abscon 109: a generic csp solver. In 2nd International Constraint Solver Competition, held with CP'06 (CSC'06), pages 55- 63.
  10. Mackworth, A. K. (1977). Consistency in networks of relations. Artificial Intelligence, 8:99-118.
  11. Mouhoub, M. and Jashmi, B. J. (2011). Heuristic techniques for variable and value ordering in csps. In Krasnogor, N. and Lanzi, P. L., editors, GECCO, pages 457-464. ACM.
Download


Paper Citation


in Harvard Style

Mouhoub M. and Mobaraki A. (2017). Graphics Processing Units for Constraint Satisfaction . In Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART, ISBN 978-989-758-220-2, pages 653-657. DOI: 10.5220/0006214806530657


in Bibtex Style

@conference{icaart17,
author={Malek Mouhoub and Ahmed Mobaraki},
title={Graphics Processing Units for Constraint Satisfaction},
booktitle={Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,},
year={2017},
pages={653-657},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006214806530657},
isbn={978-989-758-220-2},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 9th International Conference on Agents and Artificial Intelligence - Volume 2: ICAART,
TI - Graphics Processing Units for Constraint Satisfaction
SN - 978-989-758-220-2
AU - Mouhoub M.
AU - Mobaraki A.
PY - 2017
SP - 653
EP - 657
DO - 10.5220/0006214806530657