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Garbage-Collection Safety for Region-Based Type-Polymorphic Programs

PLDI 7(PLDI)2023
Martin Elsman

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原文摘要(Abstract)

Region inference offers a mechanism to reduce (and sometimes entirely remove) the need for reference-tracing garbage collection by inferring where to insert allocation and deallocation instructions in a program at compile time. When the mechanism is combined with techniques for reference-tracing garbage collection, which is helpful in general to support programs with very dynamic memory behaviours, it turns out that region-inference is complementary to adding generations to a reference-tracing collector. However, region-inference and the associated region-representation analyses that make such a memory management strategy perform well in practice are complex, both from a theoretical point-of-view and from an implementation point-of-view.In this paper, we demonstrate a soundness problem with existing theoretical developments, which have to do with ensuring that, even for higher-order polymorphic programs, no dangling-pointers appear during a reference-tracing collection. This problem has materialised as a practical soundness problem in a real implementation based on region inference. As a solution, we present a modified, yet simple, region type-system that captures garbage-collection effects, even for polymorphic higher-order code, and outline how region inference and region-representation analyses are adapted to the new type system. The new type system allows for associating simpler region type-schemes with functions, compared to original work, makes it possible to combine region-based memory management with partly tag-free reference-tracing (and generational) garbage-collection, and repairs previously derived work that is based on the erroneous published results.

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DOI 原文 ·

BibTeX
@article{paperbot2004,
  title = {Garbage-Collection Safety for Region-Based Type-Polymorphic Programs},
  author = {Martin Elsman},
  journal = {Proceedings of the ACM on Programming Languages},
  volume = {7},
  number = {PLDI},
  year = {2023},
  doi = {10.1145/3591229}
}