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A Statistical Model of Skewed Associativity

Pierre Michaud 1
1 CAPS - Compilation, parallel architectures and system
IRISA - Institut de Recherche en Informatique et Systèmes Aléatoires, Inria Rennes – Bretagne Atlantique
Abstract : This paper presents a statistical model of set-associativity, victim caching and skewed-associativity, with an emphasis on skewed-associativity. We show that set-associativity is not efficient when the working-set size is close to the cache size. We refer to this as the unit working-set problem. We show that victim-caching is not a practical solution to the unit working-se- t problem either, although victim caching emulates full associativity for working-sets much larger than the victim buffer itself. On the other hand we show that 2-way skewed associativity emulates full associativity for working-sets up to half the cache size, and that 3-way skewed-associativity is almost equivalent to full associativity, i.e., skewed-associativity is a practical solution to the unit working-set problem.
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Submitted on : Tuesday, May 23, 2006 - 7:31:02 PM
Last modification on : Friday, February 4, 2022 - 3:24:40 AM
Long-term archiving on: : Sunday, April 4, 2010 - 10:48:26 PM


  • HAL Id : inria-00072003, version 1


Pierre Michaud. A Statistical Model of Skewed Associativity. [Research Report] RR-4582, INRIA. 2002. ⟨inria-00072003⟩



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