Parallel Probabilistic Computations 
on a Cluster of Workstations

Radenski, A., A. Vann, B. Norris. Parallel Probabilistic Computations on a Cluster of Workstations. In E. D’Hollander, C. Joubert, F. Peters, U. Trottenberg, R. Völpel (Eds), Parallel Computing: Fundamentals, Applications and New Directions, Elsevier, 105-112.

Probabilistic algorithms, such as Monte-Carlo trials, genetic algorithms, hill-climbing, and simulated annealing, are approximate methods for intractable problems, i.e. problems for which no efficient exact algorithms are believed to exist. 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-only 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 a cluster of Alpha DEC workstations using PVM, the parallel virtual machine software package. Our performance measurements demonstrate that the processor efficiency of the cluster implementation is over than 80%.

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Last updated: June 2002.