This paper considers the problem of processor partitioning and task mapping for large scale mesh networks. A simple adaptive partitioning and dynamic allocation strategy is proposed to provide constant closetoTe.qu.al resource allocation and, at the same time, to minimize communication distance within each partition. Several natural performance metrics are introduced to gauge fairness achieved in partitioning results in terms of resource allocation and expected communication cost. Compared with known techniques, results delivered by the proposed technique show mostly better performance readings and a much steadier performance spectrum when number of partitions changes. In addition, the technique is applicable to all sizes of mesh.
In this paper, the problem of parallel loop scheduling for heterogeneous networks of workstations is discussed. A practical network of workstations is heterogeneous where computing power varies in different composing workstations. A simple loop scheduling technique is insufficient in exploiting maximum computing power of such a system. We propose a fundamental idea for a performance prediction tool to gauge the relative computing power among composing workstations so that parallel performance of a program run on a given network can be predicted. In view of the performance prediction results, a loop scheduling approach is then incorporated into the system to achieve close-to-optimal parallel performance. Examples running benchmark programs demonstrate a significant gain from the proposed approach over traditional scheduling approaches.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.