Runtime specialization with optimistic heap analysis

  • Authors:

  • Ajeet Shankar;S. Subramanya Sastry;Rastislav Bodík;James E. Smith

  • Affiliations:

  • University of California, Berkeley;University of Wisconsin, Madison;University of California, Berkeley;University of Wisconsin, Madison

  • Venue:
  • OOPSLA '05 Proceedings of the 20th annual ACM SIGPLAN conference on Object-oriented programming, systems, languages, and applications
  • Year:
  • 2005

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Abstract

We describe a highly practical program specializer for Java programs. The specializer is powerful, because it specializes optimistically, using (potentially transient) constants in the heap; it is precise, because it specializes using data structures that are only partially invariant; it is deployable, because it is hidden in a JIT compiler and does not require any user annotations or offline preprocessing; it is simple, because it uses existing JIT compiler ingredients; and it is fast, because it specializes programs in under 1s.These properties are the result of (1) a new algorithm for selecting specializable code fragments, based on a notion of influence; (2) a precise store profile for identifying constant heap locations; and (3) an efficient invalidation mechanism for monitoring optimistic assumptions about heap constants. Our implementation of the specializer in the Jikes RVM has low overhead, selects specialization points that would be chosen manually, and produces speedups ranging from a factor of 1.2 to 6.4, comparable with annotation-guided specializers.