Application and architectural bottlenecks in large scale distributed shared memory machines

  • Authors:

  • Chris Holt;Jaswinder Pal Singh;John Hennessy

  • Affiliations:

  • Computer Systems Laboratory, Stanford University, Stanford, CA;Department of Computer Science, Princeton University, Princeton, NJ;Computer Systems Laboratory, Stanford University, Stanford, CA

  • Venue:
  • ISCA '96 Proceedings of the 23rd annual international symposium on Computer architecture
  • Year:
  • 1996

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Abstract

Many of the programming challenges encountered in small to moderate-scale hardware cache-coherent shared memory machines have been extensively studied. While work remains to be done, the basic techniques needed to efficiently program such machines have been well explored. Recently, a number of researchers have presented architectural techniques for scaling a cache coherent shared address space to much larger processor counts. In this paper, we examine the extent to which applications can achieve reasonable performance on such large-scale, cache-coherent, distributed shared address space machines, by determining the problems sizes needed to achieve a reasonable level of efficiency. We also look at how much programming effort and optimization is needed to achieve high efficiency, beyond that needed at small processor counts. For each application, we discuss the main architectural bottlenecks that prevent smaller problem sizes or less optimized programs from achieving good efficiency. Our results show that while there are some applications that either do not scale or must be heavily optimized to do so, for most of the applications we studied it is not necessary to heavily modify the code or restructure algorithms to scale well upto several hundred processors, once the basic techniques for load balancing and data locality are used that are needed for small-scale systems as well. Programs written with some care perform well without substantially compromising the ease of programming advantage of a shared address space, and the problem sizes required to achieve good performance are surprisingly small. It is important to be careful about how data structures and layouts interact with system granularities, but these optimizations are usually needed for moderate-scale machines as well.