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Section: Software

PMaP: A Parallel Macro Partitioning Framework for Solving Mixed Integer Programs

contact: Andrew Miller

For many applications, it would be interesting to be able to use parallel resources to solve realistic size MIPs that take too long to solve on a single workstation. However, using parallel computing resources to solve MIP is difficult, as parallelizing the standard branch-and-bound framework presents an array of challenges in terms of ramp-up, interprocessor communication, and load balancing. In [133] we propose a new framework (the Parallel Macro Partitioning (PMaP) framework) for MIPs that partitions the feasible domain of an MIP by using concepts derived from recently developed primal heuristics. Initial computational resources suggest they enable PMaP to use many processors effectively to solve difficult problems.


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