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.

A layered visual parallel programming approach based on object-oriented model is presented in the paper together with the methodology how to construct very high level parallel programs for massively parallel processors. After a concise description of the basic principles and the main features of the language, two examples are elaborated as case studies to demonstrate the feasibility and benefits of the proposed methodology and graphical notation. Introduction For programming Massively Parallel Processors (MPPs) a visual programming language, called Monads-DP Visual (Monads-DPV) 1 [11, 10, 14], is developed. Monads-DPV encourages programming parallel parts of the algorithm by graphical symbols applying object-oriented parallel programming technology. In our parallel programming experience, some kind of drawing had to be always made before actual program coding could begin. We hold that for human beings a good picture of the problem is necessary to think about any parallel algorithm...

This paper proposes a code reuse mechanism called module embedding that enables the building of new modules from existing ones through inheritance, overriding of procedures, and overriding of types; the paper also describes an implementation scheme for this mechanism. Module embedding is beneficial when modules and classes are used in combination and need to be extended together, or when modules are more appropriate medium than classes. Keywords: modules, object-oriented programming, inheritance, extensibility

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