research-article

May-Happen-in-Parallel Analysis for Actor-Based Concurrency

Authors:

Antonio Flores-Montoya,

Enrique Martin-MartinAuthors Info & Claims

ACM Transactions on Computational Logic (TOCL), Volume 17, Issue 2

Article No.: 11, Pages 1 - 39

https://doi.org/10.1145/2824255

Published: 22 December 2015 Publication History

Abstract

This article presents a may-happen-in-parallel (MHP) analysis for languages with actor-based concurrency. In this concurrency model, actors are the concurrency units such that, when a method is invoked on an actor a₂ from a task executing on actor a₁, statements of the current task in a₁ may run in parallel with those of the (asynchronous) call on a₂, and with those of transitively invoked methods. The goal of the MHP analysis is to identify pairs of statements in the program that may run in parallel in any execution. Our MHP analysis is formalized as a method-level (local) analysis whose information can be modularly composed to obtain application-level (global) information. The information yielded by the MHP analysis is essential to infer more complex properties of actor-based concurrent programs, for example, data race detection, deadlock freeness, termination, and resource consumption analyses can greatly benefit from the MHP relations to increase their accuracy. We report on MayPar, a prototypical implementation of an MHP static analyzer for a distributed asynchronous language.

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May-Happen-in-Parallel Analysis for Actor-Based Concurrency

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cover image ACM Transactions on Computational Logic

ACM Transactions on Computational Logic Volume 17, Issue 2

March 2016

266 pages

ISSN:1529-3785

EISSN:1557-945X

DOI:10.1145/2851089

Editor:
Orna Kupferman
School of Computer Science and Engineering, Hebrew University, Israel

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Copyright © 2015 ACM.

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Publication History

Published: 22 December 2015

Accepted: 01 September 2015

Revised: 01 July 2015

Received: 01 September 2014

Published in TOCL Volume 17, Issue 2

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https://dl.acm.org/doi/10.1007/s11227-022-04529-w
Abusdal ODin CPun VStolz V(2022)I Can See Clearly Now: Clairvoyant Assertions for Deadlock CheckingThe Logic of Software. A Tasting Menu of Formal Methods10.1007/978-3-031-08166-8_1(1-18)Online publication date: 4-Jul-2022
https://doi.org/10.1007/978-3-031-08166-8_1
Taamneh SAl-Hami MBani-Salameh HAbdallah A(2021)A Robust Distributed Clustering of Large Data Sets on a Grid of Commodity MachinesData10.3390/data60700736:7(73)Online publication date: 7-Jul-2021
https://doi.org/10.3390/data6070073
Kamburjan EDin CHähnle RJohnsen E(2020)Behavioral Contracts for Cooperative SchedulingDeductive Software Verification: Future Perspectives10.1007/978-3-030-64354-6_4(85-121)Online publication date: 4-Dec-2020
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