MGS

Abstract

Parallel workstations, each comprising 10-100 processors, promise cost-effective general-purpose multiprocessing. This paper explores the coupling of such small- to medium-scale shared memory multiprocessors through software over a local area network to synthesize larger shared memory systems. We call these systems Distributed Scalable Shared-memory Multiprocessors (DSSMPs).This paper introduces the design of a shared memory system that uses multiple granularities of sharing, and presents an implementation on the Alewife multiprocessor, called MGS. Multigrain shared memory enables the collaboration of hardware and software shared memory, and is effective at exploiting a form of locality called multigrain locality. The system provides efficient support for fine-grain cache-line sharing, and resorts to coarse-grain page-level sharing only when locality is violated. A framework for characterizing application performance on DSSMPs is also introduced.Using MGS, an in-depth study of several shared memory applications is conducted to understand the behavior of DSSMPs. We find that unmodified shared memory applications can exploit multigrain sharing. Keeping the number of processors fixed, applications execute up to 85% faster when each DSSMP node is a multiprocessor as opposed to a uniprocessor. We also show that tightly-coupled multiprocessors hold a significant performance advantage over DSSMPs on unmodified applications. However, a best-effort implementation of a kernel from one of the applications allows a DSSMP to almost match the performance of a tightly-coupled multiprocessor.