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Compact and efficiently verifiable models for concurrent systems

Lookup NU author(s): Dr Andrey Mokhov

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This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License (CC BY-NC 4.0).


Abstract

© 2018 Springer Science+Business Media, LLC, part of Springer Nature. Partial orders are a fundamental mathematical structure capable of representing concurrency and causality on a set of atomic events. In many applications it is essential to consider multiple partial orders, each representing a particular behavioral scenario or an operating mode of a system. With the exploding growth of the complexity of systems that software and hardware engineers design today, it is no longer feasible to represent each partial order of a large system explicitly, therefore compressed representations of sets of partial orders become essential for improving the scalability of design automation tools. In this paper we study two well known mathematical formalisms capable of the compressed representation of sets of partial orders: Labeled Event Structures and Conditional Partial Order Graphs. We discuss their advantages and disadvantages and propose efficient algorithms for transforming a set of partial orders from a given compressed representation in one formalism into an equivalent representation in another formalism without explicitly enumerating every partial order. The proposed algorithms make use of an intermediate mathematical formalism which we call Conditional Labeled Event Structures that combines the advantages of both structures. Finally, we compare these structures on a number of benchmarks coming from concurrent software and hardware domains.


Publication metadata

Author(s): Ponce de León H, Mokhov A

Publication type: Article

Publication status: Published

Journal: Formal Methods in System Design

Year: 2018

Volume: 53

Issue: 3

Pages: 407-431

Print publication date: 01/12/2018

Online publication date: 19/02/2018

Acceptance date: 02/04/2016

Date deposited: 08/10/2017

ISSN (print): 0925-9856

ISSN (electronic): 1572-8102

Publisher: Springer New York LLC

URL: https://doi.org/10.1007/s10703-018-0316-0

DOI: 10.1007/s10703-018-0316-0


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