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.
KeywordsSteiner Tree Linear Programming Relaxation Parallel Computing Environment Mixed Integer Programming Solver Proven Optimality
Supported by the DFG Research Center Matheon Mathematics for key technologies in Berlin. We are thankful to the HRLN II supercompter stuff, a specially Bernd Kallies and Hinnerk Stüben which gave us support at any time we needed it.
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