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Proceedings of the ACM on Programming Languages
Article . 2023 . Peer-reviewed
License: CC BY
Data sources: Crossref
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DBLP
Article . 2023
Data sources: DBLP
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When Concurrency Matters: Behaviour-Oriented Concurrency

Authors: Luke Cheeseman; Matthew J. Parkinson; Sylvan Clebsch; Marios Kogias; Sophia Drossopoulou; David Chisnall; Tobias Wrigstad; +1 Authors

When Concurrency Matters: Behaviour-Oriented Concurrency

Abstract

Expressing parallelism and coordination is central for modern concurrent programming. Many mechanisms exist for expressing both parallelism and coordination. However, the design decisions for these two mechanisms are tightly intertwined. We believe that the interdependence of these two mechanisms should be recognised and achieved through a single, powerful primitive. We are not the first to realise this: the prime example is actor model programming, where parallelism arises through fine-grained decomposition of a program’s state into actors that are able to execute independently in parallel. However, actor model programming has a serious pain point: updating multiple actors as a single atomic operation is a challenging task. We address this pain point by introducing a new concurrency paradigm: Behaviour-Oriented Concurrency (BoC). In BoC, we are revisiting the fundamental concept of a behaviour to provide a more transactional concurrency model. BoC enables asynchronously creating atomic and ordered units of work with exclusive access to a collection of independent resources. In this paper, we describe BoC informally in terms of examples, which demonstrate the advantages of exclusive access to several independent resources, as well as the need for ordering. We define it through a formal model. We demonstrate its practicality by implementing a C++ runtime. We argue its applicability through the Savina benchmark suite: benchmarks in this suite can be more compactly represented using BoC in place of Actors, and we observe comparable, if not better, performance.

Country
Sweden
Keywords

Datavetenskap (datalogi), Computer Sciences

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    popularity
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    Top 10%
    influence
    This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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    impulse
    This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
    Top 10%
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selected citations
These citations are derived from selected sources.
This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Citations provided by BIP!
popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
9
Top 10%
Average
Top 10%
Green
Published in a Diamond OA journal