Dynamic adaptation of sharing granularity in DSM systems

The tradeoff between false sharing elimination and aggregation in Distributed Shared Memory (DSM) systems has a major effect on their performance. Some studies in this area show that fine grain access is advantageous, while others advocate the use of large coherency units. One way to resolve the tradeoff is to dynamically adapt the granularity to the application memory access pattern. In this paper we propose a novel technique for implementing multiple sharing granularities over page based DSMs. We present protocols for efficient switching between small and large sharing units during runtime. We show that applications may benefit from adapting the memory sharing to the memory access pattern, using both coarse grain sharing and fine grain sharing interchangeably in different stages of the computation. Our experiments show a substantial improvement in the performance using adapted granularity level over using a fixed granularity level