Online Phase Detection Algorithms

  • Authors:

  • Priya Nagpurkar;Chandra Krintz;Michael Hind;Peter F. Sweeney;V. T. Rajan

  • Affiliations:

  • University of California, Santa Barbara;University of California, Santa Barbara;IBM T.J. Watson Research Center;IBM T.J. Watson Research Center;IBM T.J. Watson Research Center

  • Venue:
  • Proceedings of the International Symposium on Code Generation and Optimization
  • Year:
  • 2006

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Abstract

Today's virtual machines (VMs) dynamically optimize an application as it is executing, often employing optimizations that are specialized for the current execution profile. An online phase detector determines when an executing program is in a stable period of program execution (a phase) or is in transition. A VM using an online phase detector can apply specialized optimizations during a phase or reconsider optimization decisions between phases. Unfortunately, extant approaches to detecting phase behavior rely on either offline profiling, hardware support, or are targeted toward a particular optimization. In this work, we focus on the enabling technology of online phase detection. More specifically, we contribute (a) a novel framework for online phase detection, (b) multiple instantiations of the framework that produce novel online phase detection algorithms, (c) a novel client- and machine-independent baseline methodology for evaluating the accuracy of an online phase detector, (d) a metric to compare online detectors to this baseline, and (e) a detailed empirical evaluation, using Java applications, of the accuracy of the numerous phase detectors.