Task spreading and shrinking on multiprocessor systems and networks of workstations

In this paper, we describe how our computational model can be used for the problems of processor allocation and task mapping. The intended application s for this model include the dynamic mapping problems of shrinking or sprea ding an existing mapping when the available pool of processors changes duri ng execution of the problem. The concept of problem edge class and other fe atures of our model are developed to realistically and efficiently support task partitioning and merging for static and dynamic mapping. The model dic tates realistic changes in the computation and communication characteristic s of a problem when the problem partitioning is modified dynamically. This model forms the basis of our algorithms for shrinking and spreading, and yi elds realistic results for a variety of problems mapped onto real systems. An emulation program running on a network of workstations under PVM is used to measure execution times for the mapping solutions found by the algorith ms. The results indicate that the problem edge class is a crucial considera tion for processor allocation and task mapping.