Utilizing Home Node Prediction to Improve the Performance of Software Distributed Shared Memory

Abstract Many researchers use a home - based lazy release con - sistent protocol (HLRC) to provide a simple, effective, and scalable way to build software distributed shared memory (DSM) systems However, the performance of HLRC is no - toriously sensitive to the initial page distribution among home nodes This paper proposes an adaptive HLRC pro - tocol 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 hav - ing 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 sys - tems Performance evaluations using six well - known DSM benchmarks show that our adaptive protocol outperforms conventional HLRC by up to 60%