Predicting locality phases for dynamic memory optimization

Authors:
Xipeng Shen;Yutao Zhong;Chen Ding
Affiliations:
Department of Computer Science, The College of William and Mary, Williamsburg, VA, USA;Department of Computer Science, George Mason University, Fairfax, VA, USA;Department of Computer Science, University of Rochester, P.O. Box 270226, Rochester, NY 14627, USA
Venue:
Journal of Parallel and Distributed Computing
Year:
2007

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Abstract

Dynamic data, cache, and memory adaptation can significantly improve program performance when they are applied on long continuous phases of execution that have dynamic but predictable locality. To support phase-based adaptation, this paper defines the concept of locality phases and describes a four-component analysis technique. Locality-based phase detection uses locality analysis and signal processing techniques to identify phases from the data access trace of a program; frequency-based phase marking inserts code markers that mark phases in all executions of the program; phase hierarchy construction identifies the structure of multiple phases; and phase-sequence prediction predicts the phase sequence from program input parameters. The paper shows the accuracy and the granularity of phase and phase-sequence prediction as well as its uses in dynamic data packing, memory remapping, and cache resizing.