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Assessing the limits of program-specific garbage collection performance

Published:02 June 2016Publication History
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Abstract

We consider the ultimate limits of program-specific garbage collector performance for real programs. We first characterize the GC schedule optimization problem using Markov Decision Processes (MDPs). Based on this characterization, we develop a method of determining, for a given program run and heap size, an optimal schedule of collections for a non-generational collector. We further explore the limits of performance of a generational collector, where it is not feasible to search the space of schedules to prove optimality. Still, we show significant improvements with Least Squares Policy Iteration, a reinforcement learning technique for solving MDPs. We demonstrate that there is considerable promise to reduce garbage collection costs by developing program-specific collection policies.

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    • Published in

      cover image ACM SIGPLAN Notices
      ACM SIGPLAN Notices  Volume 51, Issue 6
      PLDI '16
      June 2016
      726 pages
      ISSN:0362-1340
      EISSN:1558-1160
      DOI:10.1145/2980983
      • Editor:
      • Andy Gill
      Issue’s Table of Contents
      • cover image ACM Conferences
        PLDI '16: Proceedings of the 37th ACM SIGPLAN Conference on Programming Language Design and Implementation
        June 2016
        726 pages
        ISBN:9781450342612
        DOI:10.1145/2908080
        • General Chair:
        • Chandra Krintz,
        • Program Chair:
        • Emery Berger

      Copyright © 2016 ACM

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      Association for Computing Machinery

      New York, NY, United States

      Publication History

      • Published: 2 June 2016

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