Identification And Optimization Of Sharing Patterns For Scalable Shared-Memory Multiprocessors

Distributed shared-memory architectures typically employ a directory-based protocol to maintain cache coherence. Identifying sharing patterns in parallel programs and applying specialized optimizations can increase cache-coherence protocol efficiency and yield performance improvements. In this thesis, I propose and study both optimizations to sharing patterns and techniques to identify sharing patterns. The main thrust of the thesis is GLOW, a comprehensive optimization for wide sharing---a sharing pattern that is a serious obstacle to scalability to large numbers of processors. I present GLOW in the form of extensions to the SCI ANSI/IEEE standard. GLOW is implemented in special network switches and incorporates characteristics that are not found together in previous proposals: scalable writes and scalable reads, network locality (by exploiting the abundance of widely-shared data to satisfy requests locally), simplicity, transparency to the base protocol, and network topology indepen..