## 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.*