Abstract
In this paper, we propose a parallel implementation of the branch-and- bound optimization technique on distributed-memory systems using Message Passing Interface (MPI). We employ parallel branch-and-bound to accelerate a real-world example application: optimal selection of production equipment for multi-product batch plants. We describe the master-worker organization of our parallel algorithm: a single master process dispatches a subset of computations to multiple worker processes and gathers computed results from them. For exchanging messages between master and worker we use MPI's point-to-point communication functions. We report experimental results about the speedup and efficiency of our parallel implementation. We observe that the master process may become a bottleneck for the overall application performance if it controls too many workers processes, because of the increasing communication overhead.
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