Parallel programming is a difficult task involving many complex issues such as resource allocation, and process coordination. We propose a solution to this problem based on the use of a repertoire of parallel algorithmic forms, known as skeletons. The use of skeletons enables the meaning of a parallel program to be separated from its behaviour. Central to this methodology is the use of transformations and performance models. Transformations provide portability and implementations choices, whilst performance models guide the choices by providing predictions of execution time. In this paper we describe the methodology and investigate the use and construction of performance models by studying an example.

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. Index Terms---Parallel programming paradigms, performance evaluation, processor farm, tree networks, message passing architecture, network flow, master-slave. ------------------------------ F ------------------------------ 1I NTRODUCTION HE major problems in parallel computation revolve around questions of ease of...