A hardware evaluation of cache partitioning to improve utilization and energy-efficiency while preserving responsiveness

  • Authors:

  • Henry Cook;Miquel Moreto;Sarah Bird;Khanh Dao;David A. Patterson;Krste Asanovic

  • Affiliations:

  • University of California, Berkeley;University of California, Berkeley and Universitat Politecnica de Catalunya, Jordi Girona, Barcelona, Spain;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley;University of California, Berkeley

  • Venue:
  • Proceedings of the 40th Annual International Symposium on Computer Architecture
  • Year:
  • 2013

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Abstract

Computing workloads often contain a mix of interactive, latency-sensitive foreground applications and recurring background computations. To guarantee responsiveness, interactive and batch applications are often run on disjoint sets of resources, but this incurs additional energy, power, and capital costs. In this paper, we evaluate the potential of hardware cache partitioning mechanisms and policies to improve efficiency by allowing background applications to run simultaneously with interactive foreground applications, while avoiding degradation in interactive responsiveness. We evaluate these tradeoffs using commercial x86 multicore hardware that supports cache partitioning, and find that real hardware measurements with full applications provide different observations than past simulation-based evaluations. Co-scheduling applications without LLC partitioning leads to a 10% energy improvement and average throughput improvement of 54% compared to running tasks separately, but can result in foreground performance degradation of up to 34% with an average of 6%. With optimal static LLC partitioning, the average energy improvement increases to 12% and the average throughput improvement to 60%, while the worst case slowdown is reduced noticeably to 7% with an average slowdown of only 2%. We also evaluate a practical low-overhead dynamic algorithm to control partition sizes, and are able to realize the potential performance guarantees of the optimal static approach, while increasing background throughput by an additional 19%.