Reference Counting of Cyclic Graphs for Functional Programs

Axford, Tom

doi:10.1093/comjnl/33.5.466

Cited by 6 publications

(3 citation statements)

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“…Because of its popularity in the mobile environment, where memory overhead is a primary concern, Apple's implementation of Swift uses non-deferred RC and uses the optimizations described in Section 2.2.2. Swift uses weak references to avoid cyclic references, an approach popular in previous literature [11,16].…”

Section: Swift Programming Languagementioning

confidence: 99%

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Biased reference counting

Choi

Shull

Torrellas

2018

Proceedings of the 27th International Conference on Parallel Architectures and Compilation Techniques

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Reference counting (RC) is one of the two fundamental approaches to garbage collection. It has the desirable characteristics of low memory overhead and short pause times, which are key in today's interactive mobile platforms. However, RC has a higher execution time overhead than its counterpart, tracing garbage collection. The reason is that RC implementations maintain per-object counters, which must be continually updated. In particular, the execution time overhead is high in environments where low memory overhead is critical and, therefore, non-deferred RC is used. This is because the counter updates need to be performed atomically. To address this problem, this paper proposes a novel algorithm called Biased Reference Counting (BRC), which significantly improves the performance of non-deferred RC. BRC is based on the observation that most objects are only accessed by a single thread, which allows most RC operations to be performed non-atomically. BRC leverages this by biasing each object towards a specific thread, and keeping two counters for each object-one updated by the owner thread and another updated by the other threads. This allows the owner thread to perform RC operations non-atomically, while the other threads update the second counter atomically. We implement BRC in the Swift programming language runtime, and evaluate it with client and server programs. We find that BRC makes each RC operation more than twice faster in the common case. As a result, BRC reduces the average execution time of client programs by 22.5%, and boosts the average throughput of server programs by 7.3%. CCS CONCEPTS • Software and its engineering → Garbage collection; Software performance; Runtime environments;

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Section: Swift Programming Languagementioning

confidence: 99%

“…Another category of works attempt to efficiently detect and remove cyclic references [13,24,27,31,33]. Swift solves this problem through weak references, an approach popular in previous literature [11,16]. We believe that BRC can also be integrated into RC implementations with cyclic reference detection and removal algorithms.…”

Section: Related Workmentioning

confidence: 99%

Biased reference counting

Choi

Shull

Torrellas

2018

Proceedings of the 27th International Conference on Parallel Architectures and Compilation Techniques

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“…Space profiling information is recorded separately for each c «-o combination and requires only small changes to be made to the cell allocation and garbage collection code so that the relevant registers are updated each time a cell is allocated or deallocated. To monitor continuously the number of heap cells that are currently active we assume referencecount garbage collection (Glaser et al, 1988;Hughes, 1987;Axford, 1990;Hudak, 1986;Lermen and Maurer, 1986;Shute, 1988). If a different style of garbage collection were used, such as two-space copying (Baker, 1978;Rudalics, 1986) or mark/sweep (Cohen, 1981;Hughes, 1985) then the number of active heap cells could only be approximated by an upper bound.…”

Section: Space Profilingmentioning

confidence: 99%

Lexical profiling: theory and practice

1995

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This paper addresses the issue of analysing the run-time behaviour of lazy, higher-order functional programs. We examine the difference between the way that functional programmers and functional language implementors view program behaviour. Existing profiling techniques are discussed and a new technique is proposed which produces results that are straightforward for programmers to assimilate. The new technique, which we call lexical profiling, collects information about the run-time behaviour of functional programs, and reports the results with respect to the original source code rather than simply listing the actions performed at run-time. Lexical profiling complements implementation-specific profiling and is important because it provides a view of program activity which is largely independent of the underlying evaluation mechanism. Using the lexical profiler, programmers may easily relate results back to the source program. We give a full implementation of the lexical profiling technique for a sequential, interpretive graph reduction engine, and extensions for compiled and parallel graph reduction are discussed.

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Gcode

Joy

Axford

1991

SIGPLAN Not.

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The data structures used in the authors' functional language graph reduction implementations are described, together with a standard notation for representing the graphs in a textual format. The data structures employed are tailored to the intermediate functional language FLIC. The textual format is designed to be transmittable easily across networks. This document details the changes in the notation that have evolved since the previous specification [1] was published.

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Reference Counting of Cyclic Graphs for Functional Programs

Cited by 6 publications

References 0 publications

Biased reference counting

Biased reference counting

Lexical profiling: theory and practice

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