SoftFLASH: analyzing the performance of clustered distributed virtual shared memory

  • Authors:

  • Andrew Erlichson;Neal Nuckolls;Greg Chesson;John Hennessy

  • Affiliations:

  • Computer Systems Lab, Stanford University, Stanford, CA;Silicon Graphics Inc., 2011 North Shoreline Blvd., Mountain View, CA;Silicon Graphics Inc., 2011 North Shoreline Blvd., Mountain View, CA;Computer Systems Lab, Stanford University, Stanford, CA

  • Venue:
  • Proceedings of the seventh international conference on Architectural support for programming languages and operating systems
  • Year:
  • 1996

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Abstract

One potentially attractive way to build large-scale shared-memory machines is to use small-scale to medium-scale shared-memory machines as clusters that are interconnected with an off-the-shelf network. To create a shared-memory programming environment across the clusters, it is possible to use a virtual shared-memory software layer. Because of the low latency and high bandwidth of the interconnect available within each cluster, there are clear advantages in making the clusters as large as possible. The critical question then becomes whether the latency and bandwidth of the top-level network and the software system are sufficient to support the communication demands generated by the clusters.To explore these questions, we have built an aggressive kernel implementation of a virtual shared-memory system using SGI multiprocessors and 100Mbyte/sec HIPPI interconnects. The system obtains speedups on 32 processors (four nodes, eight processors per node plus additional reserved protocol processors) that range from 6.9 on the communication-intensive FFT program to 21.6 on Ocean (both from the SPLASH 2 suite). In general, clustering is effective in reducing internode miss rates, but as the cluster size increases, increases in the remote latency, mostly due to increased TLB synchronization cost, offset the advantages. For communication-intensive applications, such as FFT, the overhead of sending out network requests, the limited network bandwidth, and the long network latency prevent the achievement of good performance. Overall, this approach still appears promising, but our results indicate that large low latency networks may be needed to make cluster-based virtual shared-memory machines broadly useful as large-scale shared-memory multiprocessors.