Power-aware dynamic cache partitioning for CMPs

Isao Kotera, Kenta Abe, Ryusuke Egawa, Hiroyuki Takizawa, Hiroaki Kobayashi

Research output: Chapter in Book/Report/Conference proceedingChapter

19 Citations (Scopus)

Abstract

Cache partitioning and power-gating schemes are major research topics to achieve a high-performance and low-power shared cache for next generation chip multiprocessors(CMPs). We propose a poweraware cache partitioning mechanism, which is a scheme to realize both low power and high performance using power-gating and cache partitioning at the same time. The proposed cache mechanism is composed of a way-allocation function and power control function; each function works based on the cache locality assessment. The performance evaluation results show that the proposed cache mechanism with a performanceoriented parameter setting can reduce energy consumption by 20% while keeping the performance, and the mechanism with an energy-oriented parameter setting can reduce 54% energy consumption with a performance degradation of 13%. The hardware implementation results indicate that the delay and area overheads to control the proposed mechanism are negligible, and therefore hardly affect both the entire chip design and performance.

Original languageEnglish
Title of host publicationTransactions on High-Performance Embedded Architectures and Compilers III
EditorsPer Stenstrom
Pages135-153
Number of pages19
DOIs
Publication statusPublished - 2011 Apr 28

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume6590
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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    Kotera, I., Abe, K., Egawa, R., Takizawa, H., & Kobayashi, H. (2011). Power-aware dynamic cache partitioning for CMPs. In P. Stenstrom (Ed.), Transactions on High-Performance Embedded Architectures and Compilers III (pp. 135-153). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6590). https://doi.org/10.1007/978-3-642-19448-1_8