Transparent adaptation of sharing granularity in multiview-based DSM systems

In this paper we propose a mechanism that provides DSM systems with a flexible sharing granularity. The size of the shared memory units is dynamically determined by the system during runtime. This size can range from that of a single variable up to the size of the entire shared memory space. During runtime the DSM transparently adapts the granularity to the memory access pattern of the application in each phase of its execution. This adaptation, called COMPOSEDVIEW, provides efficient data sharing in software DSM while preserving sequential consistency. Neither complex code analysis nor annotation by the programmer or the compiler are required and no hardware support is necessary to use COMPOSEDVIEW. Our experiments indicate a substantial performance boost (up to 80% speedup improvement) when running a large set of applications using our method, compared to running these benchmark applications with the best fixed granularity.