Operating system support for improving data locality on CC-NUMA compute servers

  • Authors:

  • Ben Verghese;Scott Devine;Anoop Gupta;Mendel Rosenblum

  • Affiliations:

  • Computer Systems Laboratory, Stanford University, CA;Computer Systems Laboratory, Stanford University, CA;Computer Systems Laboratory, Stanford University, CA;Computer Systems Laboratory, Stanford University, CA

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

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Abstract

The dominant architecture for the next generation of shared-memory multiprocessors is CC-NUMA (cache-coherent non-uniform memory architecture). These machines are attractive as compute servers because they provide transparent access to local and remote memory. However, the access latency to remote memory is 3 to 5 times the latency to local memory. CC-NOW machines provide the benefits of cache coherence to networks of workstations, at the cost of even higher remote access latency. Given the large remote access latencies of these architectures, data locality is potentially the most important performance issue. Using realistic workloads, we study the performance improvements provided by OS supported dynamic page migration and replication. Analyzing our kernel-based implementation, we provide a detailed breakdown of the costs. We show that sampling of cache misses can be used to reduce cost without compromising performance, and that TLB misses may not be a consistent approximation for cache misses. Finally, our experiments show that dynamic page migration and replication can substantially increase application performance, as much as 30%, and reduce contention for resources in the NUMA memory system.