Generational stack collection and profile-driven pretenuring

  • Authors:

  • Perry Cheng;Robert Harper;Peter Lee

  • Affiliations:

  • School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA;School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA;School of Computer Science, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA

  • Venue:
  • PLDI '98 Proceedings of the ACM SIGPLAN 1998 conference on Programming language design and implementation
  • Year:
  • 1998

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Abstract

This paper presents two techniques for improving garbage collection performance: generational stack collection and profile-driven pretenuring. The first is applicable to stack-based implementations of functional languages while the second is useful for any generational collector. We have implemented both techniques in a generational collector used by the TIL compiler (Tarditi, Morrisett, Cheng, Stone, Harper, and Lee 1996), and have observed decreases in garbage collection times of as much as 70% and 30%, respectively.Functional languages encourage the use of recursion which can lead to a long chain of activation records. When a collection occurs, these activation records must be scanned for roots. We show that scanning many activation records can take so long as to become the dominant cost of garbage collection. However, most deep stacks unwind very infrequently, so most of the root information obtained from the stack remains unchanged across successive garbage collections. Generational stack collection greatly reduces the stack scan cost by reusing information from previous scans.Generational techniques have been successful in reducing the cost of garbage collection (Ungar 1984). Various complex heap arrangements and tenuring policies have been proposed to increase the effectiveness of generational techniques by reducing the cost and frequency of scanning and copying. In contrast, we show that by using profile information to make lifetime predictions, pretenuring can avoid copying data altogether. In essence, this technique uses a refinement of the generational hypothesis (most data die young) with a locality principle concerning the age of data: most allocations sites produce data that immediately dies, while a few allocation sites consistently produce data that survives many collections.