Improving spatial locality of programs via data mining

Authors:
Karlton Sequeira;Mohammed Zaki;Boleslaw Szymanski;Christopher Carothers
Affiliations:
Rensselaer Polytechnic Institute, Troy, NY;Rensselaer Polytechnic Institute, Troy, NY;Rensselaer Polytechnic Institute, Troy, NY;Rensselaer Polytechnic Institute, Troy, NY
Venue:
Proceedings of the ninth ACM SIGKDD international conference on Knowledge discovery and data mining
Year:
2003

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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.