In this paper, we describe the design, implementation, and modeling of a runtime kernel to support the processor farm paradigm on multicomputers. We present a general topology-independent framework for obtaining performance models to predict the performance of the start-up, steady-state, and wind-down phases of a processor farm. An algorithm is described, which for any interconnection network determines a tree-structured subnetwork that optimizes farm performance. The analysis technique is applied to the important case of k-ary tree topologies. The models are compared with the measured performance on a variety of topologies using both constant and varied task sizes.

An efficient execution model for tree structured computations is presented. A general framework for analyzing the performance of this type of computation for any given topology is discussed. The framework is used to derive models for two widely used parallel programming strategies: processor farms and divide and conquer. The models were validated on a large multicomputer, and it was shown that their accuracy is such that they can be used to predict the performance of applications that use the above strategies. The use of these models to evaluate performance and to restructure the application to improve performance is discussed.>