Exploiting inter-sequence correlations for program behavior prediction

Authors:
Bo Wu;Zhijia Zhao;Xipeng Shen;Yunlian Jiang;Yaoqing Gao;Raul Silvera
Affiliations:
The College of William and Mary, Williamsburg, VA, USA;The College of William and Mary, Williamsburg, VA, USA;The College of William and Mary, Williamsburg, VA, USA;Google, Mountain View, CA, USA;IBM Toronto Lab, Toronto, Canada;IBM Toronto Lab, Toronto, Canada
Venue:
Proceedings of the ACM international conference on Object oriented programming systems languages and applications
Year:
2012

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Abstract

Prediction of program dynamic behaviors is fundamental to program optimizations, resource management, and architecture reconfigurations. Most existing predictors are based on locality of program behaviors, subject to some inherent limitations. In this paper, we revisit the design philosophy and systematically explore a second source of clues: statistical correlations between the behavior sequences of different program entities. Concentrated on loops, it examines the correlations' existence, strength, and values in enhancing the design of program behavior predictors. It creates the first taxonomy of program behavior sequence patterns. It develops a new form of predictors, named sequence predictors, to effectively translate the correlations into large-scope, proactive predictions of program behavior sequences. It demonstrates the usefulness of the prediction in dynamic version selection and loop importance estimation, showing 19% average speedup on a number of real-world utility applications. By taking scope and timing of behavior prediction as the first-order design objectives, the new approach overcomes limitations of existing program behavior predictors, opening up many new opportunities for runtime optimizations at various layers of computing.