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.
In order to effectively program Multicomputers, users must be able to evaluate how well the system performs for a given application. In this paper we present an efficient execution model that can be used for tree structured computations. We provide a general framework for analyzing the performance of this type of computation for any given topology. This framework is used to derive models for two widely used parallel programming strategies: Processor Farms and Divide and Conquer. These models were validated on a large multicomputer and the accuracy of the model is such that they can be used to predict the performance of applications that use these strategies. We discuss how these models can be used to evaluate performance and how they could be used to restructure the application to improve performance.
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