ParaSCIP: A Parallel Extension of SCIP

  • Yuji Shinano
  • Tobias Achterberg
  • Timo Berthold
  • Stefan Heinz
  • Thorsten Koch
Conference paper

Abstract

Mixed integer programming (MIP)has become one of the most important techniques in Operations Research and Discrete Optimization. SCIP (Solving Constraint Integer Programs) is currently one of the fastest non-commercial MIP solvers. It is based on the branchandboundprocedure in which the problem is recursively split into smaller subproblems, thereby creating a so-called branching tree. We present ParaSCIP, an extension of SCIP, which realizes a parallelization on a distributed memory computing environment. ParaSCIP uses SCIP solvers as independently running processes to solve subproblems (nodes of the branching tree) locally. This makes the parallelization development independent of the SCIP development. Thus, ParaSCIP directly profits from any algorithmic progress in future versions of SCIP. Using a first implementation of ParaSCIP, we were able to solve two previously unsolved instances from MIPLIB2003, a standard test set library for MIP solvers. For these computations, we used up to 2048 cores of the HLRN II supercomputer.

References

  1. 1.
    Gurobi Optimizer. http://www.gurobi.com/
  2. 2.
    HLRN – Norddeutscher Verbund zur Förderung des Hoch- und Höchstleistungsrechnens. http://www.hlrn.de/
  3. 3.
  4. 4.
    Mixed Integer Problem Library (MIPLIB) 2003. http://miplib.zib.de/
  5. 5.
    SCIP: Solving Constraint Integer Programs. http://scip.zib.de/
  6. 6.
    TOP500 Supercomputer Sites. http://www.top500.org/list/2010/11/100
  7. 7.
    Achterberg, T.: Constraint integer programming. Ph.D. thesis, Technische Universität Berlin (2007)Google Scholar
  8. 8.
    Achterberg, T.: SCIP: Solving constraint integer programs. Mathematical Programming Computation 1(1), 1–41 (2009)MathSciNetMATHCrossRefGoogle Scholar
  9. 9.
    Achterberg, T., Koch, T., Martin, A.: MIPLIB 2003. Operations Research Letters 34(4), 1–12 (2006)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Bixby, R., Rothberg, E.: Progress in computational mixed integer programming – A look back from the other side of the tipping point. Annals of Operations Research 149(1), 37–41 (2007)MathSciNetMATHCrossRefGoogle Scholar
  11. 11.
    Bixby, R.E., Boyd, E.A., Indovina, R.R.: MIPLIB: A test set of mixed integer programming problems. SIAM News 25, 16 (1992)Google Scholar
  12. 12.
    Karp, R.M.: Reducibility among combinatorial problems. In: R.E. Miller, J.W. Thatcher (eds.) Complexity of Computer Computations, pp. 85–103. Plenum Press, New York, USA (1972)Google Scholar
  13. 13.
    Laundy, R., Perregaard, M., Tavares, G., Tipi, H., Vazacopoulos, A.: Solving hard mixed-integer programming problems with Xpress-MP: A miplib 2003 case study. INFORMS Journal on Computing 21(2), 304–313 (2009)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Mittelmann, H.: Mixed integer linear programming benchmark (serial codes). http://plato.asu.edu/ftp/milpf.html
  15. 15.
    Padberg, M., Rinaldi, G.: A branch-and-cut algorithm for the resolution of large-scale symmetric traveling salesman problems. SIAM Review 33, 60–100 (1991)MathSciNetMATHCrossRefGoogle Scholar
  16. 16.
    Ralphs, T.K., Ladányi, L., Saltzman, M.J.: Parallel branch, cut and price for large-scale discrete optimization. Mathematical Programming Series B 98(1–3), 253–280 (2003)MATHCrossRefGoogle Scholar
  17. 17.
    Shinano, Y., Achterberg, T., t: Fujie: A dynamic load balancing mechanism for new paralex. In: Proceedings of ICPADS 2008, pp. 455–462 (2008)Google Scholar
  18. 18.
    Xu, Y., Ralphs, T.K., Ladányi, L., Saltzmann, M.J.: Computational experience with a software framework for parallel integer programming. INFORMS Journal on Computing 21(3), 383–397 (2009)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Yuji Shinano
    • 1
  • Tobias Achterberg
    • 2
  • Timo Berthold
    • 1
  • Stefan Heinz
    • 1
  • Thorsten Koch
    • 1
  1. 1.Zuse Institute BerlinBerlinGermany
  2. 2.IBM Deutschland GmbHBad Homburg v.d.H.Germany

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