ParaSCIP: A Parallel Extension of SCIP. 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 in zbMATH (referenced in 14 articles )

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  1. Gurski, Frank; Rethmann, Jochen: Distributed solving of mixed-integer programs with GLPK and Thrift (2018)
  2. Helm, Werner E.; Justkowiak, Jan-Erik: Extension of Mittelmann’s benchmarks: comparing the solvers of SAS and Gurobi (2018)
  3. Berthold, Timo; Farmer, James; Heinz, Stefan; Perregaard, Michael: Parallelization of the FICO Xpress-Optimizer (2016)
  4. Fischetti, Matteo; Lodi, Andrea; Monaci, Michele; Salvagnin, Domenico; Tramontani, Andrea: Improving branch-and-cut performance by random sampling (2016)
  5. Keiji Kimura, Hayato Waki: Minimization of Akaike’s Information Criterion in Linear Regression Analysis via Mixed Integer Nonlinear Program (2016) arXiv
  6. Kimura, Keiji; Waki, Hayato: Mixed integer nonlinear program for minimization of Akaike’s information criterion (2016)
  7. Shinano, Yuji; Berthold, Timo; Heinz, Stefan: A first implementation of paraxpress: combining internal and external parallelization to solve MIPs on supercomputers (2016)
  8. Eckstein, Jonathan; Hart, William E.; Phillips, Cynthia A.: PEBBL: an object-oriented framework for scalable parallel branch and bound (2015)
  9. Mason, Luke R.; Mak-Hau, Vicky H.; Ernst, Andreas T.: A parallel optimisation approach for the realisation problem in intensity modulated radiotherapy treatment planning (2015)
  10. Gamrath, Gerald: Improving strong branching by domain propagation (2014)
  11. Gamrath, Gerald: Improving strong branching by propagation (2013)
  12. Koch, Thorsten; Ralphs, Ted; Shinano, Yuji: Could we use a million cores to solve an integer program? (2012)
  13. Koch, Thorsten; Achterberg, Tobias; Andersen, Erling; Bastert, Oliver; Berthold, Timo; Bixby, Robert E.; Danna, Emilie; Gamrath, Gerald; Gleixner, Ambros M.; Heinz, Stefan; Lodi, Andrea; Mittelmann, Hans; Ralphs, Ted; Salvagnin, Domenico; Steffy, Daniel E.; Wolter, Kati: MIPLIB 2010. Mixed integer programming library version 5 (2011) ioport
  14. Achterberg, Tobias; Koch, Thorsten; Martin, Alexander: MIPLIB 2003 (2006)