An Overlap Study for Cluster Computing

Distributed memory systems (DMS) (clusters) are one of the tools being used by researchers to solve a wide spectrum of computational intensive problems in a fraction of the time of a sequential approach. The nature of a DMS does not enforce intense data sharing among computational nodes, this will occur if the problem under analysis happens to be data dependent in nature. The latency associated with dynamic data sharing in a DMS is well known to increase the total execution time. One of the possible techniques that can be used to reduce the negative effects associated with this latency is overlapping. In this paper we show why a characterization of the overlapping capabilities of a cluster is important to justify results.