An Empirical Study of Selective Optimization

  • Authors:

  • Matthew Arnold;Michael Hind;Barbara G. Ryder

  • Affiliations:

  • -;-;-

  • Venue:
  • LCPC '00 Proceedings of the 13th International Workshop on Languages and Compilers for Parallel Computing-Revised Papers
  • Year:
  • 2000

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Abstract

This paper describes an empirical study of selective optimization using the Jalapeño Java virtual machine. The goal of the study is to provide insight into the design and implementation of an adaptive system by investigating the performance potential of selective optimization and identifying the classes of applications for which this performance can be expected. Two types of offline profiling information are used to guide selective optimization, and several strategies for selecting the methods to optimize are compared. The results show that selective optimization can offer substantial improvement over an optimize-all-methods strategy for short-running applications, and for longer-running applications there is a significant range of methods that can be selectively optimized to achieve close to optimal performance. The results also show that a coarse-grained sampling system can provide enough accuracy to successfully guide selective optimization.