Utilizing home node prediction to improve the performance of software distributed shared memory

Abstract

Many researchers use a home-based lazy release consistent protocol (HLRC) to provide a simple, effective, and scalable way to build software distributed shared memory (DSM) systems. However, the performance of HLRC is notoriously sensitive to the initial page distribution among home nodes. This paper proposes an adaptive HLRC protocol in which the home page designation is able to change according to the observed application sharing pattern. Our system differs from HLRC and other adaptive derivatives in the following respects. First, the number of home nodes for each shared page can be varied, as opposed to having only a single home node. Second, we use prediction in a novel way to dynamically change the the location of home nodes according to different memory access patterns. The home node of each shared page is able to propagate, perish, and migrate. An online home predictor determine whether or not the current node should remain a home node or drop from the current set of home nodes for a given page. Finally, all decisions concerning home node group membership are made locally, eliminating the costly global decision-making communication present in many other systems. Performance evaluations using six well-known DSM benchmarks show that our adaptive protocol outperforms conventional HLRC by up to 60%.