Shared virtual memory clusters: Bridging the cost-performance gap between SMPs and hardware DSM systems

Angelos Bilas, Dongming Jiang, Jaswinder Pal Singh

Research output: Contribution to journalArticle

1 Scopus citations

Abstract

Although the shared memory abstraction is gaining ground as a programming abstraction for parallel computing, the main platforms that support it, small-scale symmetric multiprocessors (SMPs) and hardware cache-coherent distributed shared memory systems (DSMs), seem to lie inherently at the extremes of the cost-performance spectrum for parallel systems. In this paper we examine if shared virtual memory (SVM) clusters can bridge this gap by examining how application performance scales on a state-of-the-art shared virtual memory cluster. We find that: (i) The level of application restructuring needed is quite high compared to applications that perform well on a DSM system of the same scale and larger problem sizes are needed for good performance. (ii) However, surprisingly, SVM performs quite well for a fairly wide range of applications, achieving at least half the parallel efficiency of a high-end DSM system at the same scale and often much more.

Original languageEnglish (US)
Pages (from-to)1257-1276
Number of pages20
JournalJournal of Parallel and Distributed Computing
Volume63
Issue number12
DOIs
StatePublished - Dec 2003

All Science Journal Classification (ASJC) codes

  • Software
  • Theoretical Computer Science
  • Hardware and Architecture
  • Computer Networks and Communications
  • Artificial Intelligence

Keywords

  • Clusters
  • Parallel applications
  • Scalability
  • Shared virtual memory
  • System area networks

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