The availability of commodity multiprocessors offers significant opportunities for addressing the increasing computational requirements of optimization applications. To leverage these potential benefits, it is important however to make parallel processing easily accessible to a wide audience of optimization programmers. This paper addresses this challenge by proposing parallel programming abstractions that keep the distance between sequential and parallel local search algorithms as small as possible. The abstractions, that include parallel loops, interruptions, and thread pools, are compositional and cleanly separate the optimization program and the parallel instructions. They have been evaluated experimentally on a variety of applications, including facility location and coloring, for which they provide significant speedups.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aiex, R., Binato, S., Resende, M.: Parallel GRASP with Path-Relinking for Jobshop Scheduling. Parallel Computing 29(4), 393–430 (2003)CrossRefMathSciNetGoogle Scholar
  2. 2.
    Chandra, R., Dagum, L., Kohr, D., Maydan, D., McDonald, J., Menon, R.: Parallel Programming in OpenMP. Morgan Kaufmann, San Francisco (2000) ISBN:1558606718Google Scholar
  3. 3.
    Clocksin, W.F., Alshawi, H.: A Method for Efficiently Executing Horn Clause Programs Using Multiple Processors. New Generation Computing 5, 361–376 (1988)CrossRefGoogle Scholar
  4. 4.
    Dagum, L., Menon, R.: Openmp: An industry-standard api for shared-memory programming. IEEE Computational Science and Engineering 5, 46–55 (1998)CrossRefGoogle Scholar
  5. 5.
    Dell’Amico, M., Trubian, M.: Applying Tabu Search to the Job-Shop Scheduling Problem. Annals of Operations Research 41, 231–252 (1993)MATHCrossRefGoogle Scholar
  6. 6.
    Dorne, R., Hao, J.K.: Meta-heuristics: Advances and Trends in Local Search Paradigms for Optimization. In: Tabu Search for Graph Coloring, T-Colorings and Set T-Colorings, pp. 77–92. Kluwer Academic Publishers, Dordrecht (1998)Google Scholar
  7. 7.
    Michel, L., Van Hentenryck, P.: A Constraint-Based Architecture for Local Search. In: OOPSLA 2002, Seattle, November 2002, pp. 101–110 (2002)Google Scholar
  8. 8.
    Michel, L., Van Hentenryck, P.: A Decomposition-Based Implementation of Search Strategies. ACM Transactions on Computational Logic 5(2) (2004)Google Scholar
  9. 9.
    Perron, L.: Search Procedures and Parallelism in Constraint Programming. In: Jaffar, J. (ed.) CP 1999. LNCS, vol. 1713, pp. 346–361. Springer, Heidelberg (1999)Google Scholar
  10. 10.
    Van Hentenryck, P., Michel, L.: Control Abstractions for Local Search. In: Rossi, F. (ed.) CP 2003. LNCS, vol. 2833, pp. 65–80. Springer, Heidelberg (2003)CrossRefGoogle Scholar
  11. 11.
    Van Hentenryck, P., Michel, L.: Constraint-Based Local Search. The MIT Press, Cambridge (2005)Google Scholar
  12. 12.
    Van Hentenryck, P., Michel, L.: Nondeterministic Control for Hybrid Search. In: Barták, R., Milano, M. (eds.) CPAIOR 2005. LNCS, vol. 3524, pp. 380–395. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  13. 13.
    Van Hentenryck, P., Michel, L., Liu, L.: Constraint-Based Combinators for Local Search. In: Wallace, M. (ed.) CP 2004. LNCS, vol. 3258, pp. 47–61. Springer, Heidelberg (2004)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Laurent Michel
    • 1
  • Pascal Van Hentenryck
    • 2
  1. 1.University of ConnecticutStorrsUSA
  2. 2.Brown UniversityProvidenceUSA

Personalised recommendations