Online feedback-directed optimization of Java

  • Authors:
  • Matthew Arnold;Michael Hind;Barbara G. Ryder

  • Affiliations:
  • IBM T.J. Watson Research Center, Hawthorne, NY;IBM T.J. Watson Research Center, Hawthorne, NY;Rutgers University, Piscataway, NJ

  • Venue:
  • OOPSLA '02 Proceedings of the 17th ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
  • Year:
  • 2002

Quantified Score

Hi-index 0.00

Visualization

Abstract

This paper describes the implementation of an online feedback-directed optimization system. The system is fully automatic; it requires no prior (offline) profiling run. It uses a previously developed low-overhead instrumentation sampling framework to collect control flow graph edge profiles. This profile information is used to drive several traditional optimizations, as well as a novel algorithm for performing feedback-directed control flow graph node splitting. We empirically evaluate this system and demonstrate improvements in peak performance of up to 17% while keeping overhead low, with no individual execution being degraded by more than 2% because of instrumentation.