Understanding Application Performance on Shared Virtual Memory Systems

Many researchers have proposed interesting protocols for shared virtual memory (SVM) systems, and demonstrated performance improvements on parallel programs. However, there is still no clear understanding of the performance potential of SVM systems for different classes of applications. This paper begins to fill this gap, by studying the performance of a range of applications in detail and understanding it in light of application characteristics. We first develop a brief classification of the inherent data sharing patterns in the applications, and how they interact with system granularities to yield the communication patterns relevant to SVM systems. We then use detailed simulation to compare the performance of two SVM approaches--- Lazy Released Consistency (LRC) and Automatic Update Release Consistency (AURC)---with each other and with an all-hardware CC-NUMA approach. We examine how performance is affected by problem size, machine size, key system parameters, and the use of less opt..