Improving spatial locality of programs via data mining

Karlton Sequeira, Mohammed Zaki, Boleslaw Szymanski, Christopher Carothers

Research output: Contribution to conferencePaper

Abstract

In most computer systems, page fault rate is currently minimized by generic page replacement algorithms which try to model the temporal locality inherent in programs. In this paper, we propose two algorithms, one greedy and the other stochastic, designed for program specific code restructuring as a means of increasing spatial locality within a program. Both algorithms effectively decrease average working set size and hence the page fault rate. Our methods are more effective than traditional approaches due to use of domain information. We illustrate the efficacy of our algorithms on actual data mining algorithms.

Original languageEnglish
Pages649-654
Number of pages6
DOIs
Publication statusPublished - 1 Dec 2003
Event9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03 - Washington, DC, United States
Duration: 24 Aug 200327 Aug 2003

Other

Other9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03
CountryUnited States
CityWashington, DC
Period24/8/0327/8/03

Keywords

  • Code restructuring
  • Page clustering
  • Program locality

ASJC Scopus subject areas

  • Software
  • Information Systems

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  • Cite this

    Sequeira, K., Zaki, M., Szymanski, B., & Carothers, C. (2003). Improving spatial locality of programs via data mining. 649-654. Paper presented at 9th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD '03, Washington, DC, United States. https://doi.org/10.1145/956750.956834