Performance modeling and characterization of large last level caches

  • Authors:
  • Parijat Dube;Michael Tsao;Li Zhang;Alan Bivens

  • Affiliations:
  • IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA;IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA;IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA;IBM T. J. Watson Research Center, Yorktown Heights, NY, 10598, USA

  • Venue:
  • ISPASS '12 Proceedings of the 2012 IEEE International Symposium on Performance Analysis of Systems & Software
  • Year:
  • 2012

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Abstract

For each different cache configuration, we identified parametric functions to characterize the probability density of statistics related to cache performance. Observe that, the parameters (a,β) in (1) and (a, b, n) in (2) depend on cache configuration. We next need to study the sensitivity of the parameters in the density function to cache configuration parameters. Once this is established we can express the distribution of CRT, SRT and IHT in terms of cache size and line size alongwith workload specific constants. Having known these distributions, Tcrt, Tsrt and Tiht and hence m from Model-A and Model-B can be written as an explicit function of cache size and line size for a given workload.