Patterns of memory inefficiency

  • Authors:

  • Adriana E. Chis;Nick Mitchell;Edith Schonberg;Gary Sevitsky;Patrick O'Sullivan;Trevor Parsons;John Murphy

  • Affiliations:

  • University College Dublin, Dublin, Ireland;IBM T.J. Watson Research Center, Hawthorne, NY;IBM T.J. Watson Research Center, Hawthorne, NY;IBM T.J. Watson Research Center, Hawthorne, NY;IBM Software Group, Dublin, Ireland;University College Dublin, Dublin, Ireland;University College Dublin, Dublin, Ireland

  • Venue:
  • Proceedings of the 25th European conference on Object-oriented programming
  • Year:
  • 2011

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Abstract

Large applications often suffer from excessive memory consumption. The nature of these heaps, their scale and complex interconnections, makes it difficult to find the low hanging fruit. Techniques relying on dominance or allocation tracking fail to account for sharing, and overwhelm users with small details. More fundamentally, a programmer still needs to know whether high levels of consumption are too high. We present a solution that discovers a small set of high-impact memory problems, by detecting patterns within a heap. Patterns are expressed over a novel ContainerOrContained relation, which overcomes challenges of reuse, delegation, sharing; it induces equivalence classes of objects, based on how they participate in a hierarchy of data structures. We present results on 34 applications, and case studies for nine of these. We demonstrate that eleven patterns cover most memory problems, and that users need inspect only a small number of pattern occurrences to reap large benefits.