This article introduces a novel system for deriving upper bounds on the heap-space requirements of functional programs with garbage collection. The space cost model is based on a perfect garbage collector that immediately deallocates memory cells when they become unreachable. Heap-space bounds are derived using type-based automatic amortized resource analysis (AARA), a template-based technique that efficiently reduces bound inference to linear programming. The first technical contribution of the work is a new operational cost semantics that models a perfect garbage collector. The second technical contribution is an extension of AARA to take into account automatic deallocation. A key observation is that deallocation of a perfect collector can be modeled with destructive pattern matching if data structures are used in a linear way. However, the analysis uses destructive pattern matching to accurately model deallocation even if data is shared. The soundness of the extended AARA with respect to the new cost semantics is proven in two parts via an intermediate linear cost semantics. The analysis and the cost semantics have been implemented as an extension to Resource Aware ML (RaML). An experimental evaluation shows that the system is able to derive tight symbolic heap-space bounds for common algorithms. Often the bounds are asymptotic improvements over bounds that RaML derives without taking into account garbage collection.
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Garbology: a study of how Java objects die
In this paper we present a study of how Java programs dispose of objects. Unlike prior work on object demographics and lifetime patterns, our goal is to precisely characterize the actions that cause objects to become unreachable. We use a recently-developed tracing tool, called Elephant Tracks, which can localize object deaths within a specific method and tell us the proximal cause. Our analysis centers around garbage clusters: groups of connected objects that become unreachable at precisely the same time due to a single program action. We classify these clusters using traditional features, such as size, allocation site, and lifetime, and using new ones, such as death site and cause of death. We then use this knowledge about garbage clusters in a new GC algorithm: the Cluster Aware Garbage Collection (CAGC) algorithm.
We present results for a set of standard benchmarks including SPECJVM98, SPECjbb, and DaCapo. We identify several patterns that could inform the design of new collectors or tuning of existing systems. Most garbage clusters are small, suggesting that these programs almost always dispose of large structures in a piecemeal fashion. In addition, most clusters die in one of only a dozen or so places in the program. Furthermore, these death sites are much more stable and predictable than object lifetimes. Finally we show that the CAGC algorithm can in certain cases improve GC performance.
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- Award ID(s):
- 1717373
- NSF-PAR ID:
- 10073830
- Date Published:
- Journal Name:
- Proceedings of the 2017 {ACM} {SIGPLAN} International Symposium on New Ideas, New Paradigms, and Reflections on Programming and Software, Onward! 2017
- Page Range / eLocation ID:
- 168 to 179
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1145/3133850.3133854
