Abstract
Parallel graph partitioning is a difficult issue, because the best sequential graph partitioning methods known to date are based on iterative local optimization algorithms that do not parallelize nor scale well. On the other hand, evolutionary algorithms are highly parallel and scalable, but converge very slowly as problem size increases. This paper presents methods that can be used to reduce problem space in a dramatic way when using graph partitioning techniques in a multi-level framework, thus enabling the use of evolutionary algorithms as possible candidates, among others, for the realization of efficient scalable parallel graph partitioning tools. Results obtained on the recursive bipartitioning problem with a multi-threaded genetic algorithm are presented, which show that this approach outperforms existing state-of-the-art parallel partitioners.
Chapter PDF
Similar content being viewed by others
Keywords
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
References
Areibi, S., Zeng, Y.: Effective memetic algorithms for VLSI design automation = genetic algorithms + local search + multi-level clustering. Evolutionary Computation 12(3), 327–353 (2004)
Barnard, S.T., Simon, H.D.: A fast multilevel implementation of recursive spectral bisection for partitioning unstructured problems. Concurrency: Practice and Experience 6(2), 101–117 (1994)
Bui, T.N., Moon, B.R.: Genetic algorithm and graph partitioning. IEEE Trans. Comput. 45(7), 841–855 (1996)
Fiduccia, C.M., Mattheyses, R.M.: A linear-time heuristic for improving network partitions. In: Proc. 19th Design Automat. Conf., pp. 175–181. IEEE, Los Alamitos (1982)
George, A., Liu, J.W.-H.: Computer solution of large sparse positive definite systems. Prentice-Hall, Englewood Cliffs (1981)
Hendrickson, B., Leland, R.: A multilevel algorithm for partitioning graphs. In: Proceedings of Supercomputing 1995 (1995)
Horn, J., Nafpliotis, N., Goldberg, D.E.: A niched Pareto genetic algorithm for multiobjective optimization. IEEE World Congress on Computational Intelligence 1, 82–87 (1994)
Karypis, G., Kumar, V.: A fast and high quality multilevel scheme for partitioning irregular graphs. SIAM J. on Scientific Computing 20(1), 359–392 (1998)
METIS: Family of multilevel partitioning algorithms. http://glaros.dtc.umn.edu/gkhome/views/metis
Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. Bell System Technical Journal 49, 291–307 (1970)
Moscato, P.: On evolution, search, optimization, genetic algorithms and martial arts, towards memetic algorithms. Technical Report 826, California Intitute of Technology, Pasadena, CA 91125, USA (1989)
SCOTCH: Static mapping, graph partitioning, and sparse matrix block ordering package. http://www.labri.fr/~pelegrin/scotch/
Schloegel, K., Karypis, G., Kumar, V.: Parallel multilevel algorithms for multi-constraint graph partitioning. In: Proceedings of EuroPar, pp. 296–310 (2000)
Simon, H.D., Teng, S.-H.: How good is recursive bipartition. SIAM J. Sc. Comput. 18(5), 1436–1445 (1995)
Whitley, D., Rana, S., Heckendorn, R.B.: The island model genetic algorithm: On separability, population size and convergence. Journal of Computing and Information Technology 7, 33–47 (1999)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Chevalier, C., Pellegrini, F. (2006). Improvement of the Efficiency of Genetic Algorithms for Scalable Parallel Graph Partitioning in a Multi-level Framework. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds) Euro-Par 2006 Parallel Processing. Euro-Par 2006. Lecture Notes in Computer Science, vol 4128. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11823285_25
Download citation
DOI: https://doi.org/10.1007/11823285_25
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-37783-2
Online ISBN: 978-3-540-37784-9
eBook Packages: Computer ScienceComputer Science (R0)