Parallel probabilistic computations on a cluster of workstations

Abstract

Probabilistic algorithms are computationally intensive approximate methods for solving intractable problems. Probabilistic algorithms are excellent candidates for cluster computations because they require little communication and synchronization. It is possible to specify a common parallel control structure as a generic algorithm for probabilistic cluster computations. Such a generic parallel algorithm can be glued together with domain-specific sequential algorithms in order to derive approximate parallel solutions for different intractable problems. In this paper we propose a generic algorithm for probabilistic computations on a cluster of workstations. We use this generic algorithm to derive specific parallel algorithms for two discrete optimization problems: the knapsack problem and the traveling salesperson problem. We implement the algorithms on clusters of Sun Ultra SPARC-1 workstations using PVM, the parallel virtual machine software package. Finally, we measure the parallel efficiency of the cluster implementation.