Boosting Sequential Consistency Checking Using Saturation

Zennou, Rachid; Atig, Mohamed Faouzi; Biswas, Ranadeep; Bouajjani, Ahmed; Enea, Constantin; Erradi, Mohammed

doi:10.1007/978-3-030-59152-6_20

Cited by 3 publications

(3 citation statements)

References 22 publications

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“…Moreover, since 𝑒 0 → 𝑒 2 in 𝑃, the analysis also inserts 3 and 4 in 𝑃. These are necessary orderings to respect the fact that 𝑒 2 reads from 𝑒 3 -the process of inferring such orderings is known as saturation, and is used widely in dynamic analyses (e.g., [2,23,31,33,34,40,41]). Crucially, both 𝑒 3 and 𝑒 6 have many successors in 𝑃, thus these edges must be propagated along many events, costing 𝑂 (𝑛) time, even if 𝑃 is represented using Vector Clocks.…”

Section: Motivating Examplementioning

confidence: 99%

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CSSTs: A Dynamic Data Structure for Partial Orders in Concurrent Execution Analysis

Tunç,

Deshmukh,

Cirisci

et al. 2024

Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems,

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Dynamic analyses are a standard approach to analyzing and testing concurrent programs. Such techniques observe program traces 𝜎 and analyze them to infer the presence or absence of bugs. At its core, each analysis maintains a partial order 𝑃 that represents order dependencies between the events of 𝜎 . Naturally, the scalability of the analysis largely depends on maintaining 𝑃 efficiently. The standard data structure for this task has thus far been Vector Clocks. These, however, are slow for analyses that follow a non-streaming style, costing 𝑂 (𝑛) time for inserting (and propagating) each new ordering in 𝑃, where 𝑛 is the size of 𝜎, while they cannot handle the deletion of existing orderings.In this paper we develop Collective Sparse Segment Trees (CSSTs), a simple but elegant data structure for maintaining a partial order 𝑃. CSSTs thrive when the width 𝑘 of 𝑃 is much smaller than the size 𝑛 of its domain, allowing inserting, deleting, and querying for orderings in 𝑃 to run in 𝑂 (log 𝑛) time. For a concurrent trace, 𝑘 normally equals the number of its threads, and is orders of magnitude smaller than its size 𝑛, making CSSTs fitting for this setting. Our experiments confirm that CSSTs are the best data structure currently to handle a range of dynamic analyses from existing literature.CCS Concepts: • Software and its engineering → Software verification and validation; • Theory of computation → Theory and algorithms for application domains; Program analysis.

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Section: Motivating Examplementioning

confidence: 99%

“…For non-streaming updates, partial orders are normally represented as plain graphs [2,6,12,30,33,40] and Vector Clocks [17,23]. The closest data structure to CSSTs has been STs, developed in [31] for data-race detection, and subsequently used in other dynamic analyses [8,35,39].…”

Section: Related Workmentioning

confidence: 99%

CSSTs: A Dynamic Data Structure for Partial Orders in Concurrent Execution Analysis

Tunç,

Deshmukh,

Cirisci

et al. 2024

Proceedings of the 29th ACM International Conference on Architectural Support for Programming Languages and Operating Systems,

Self Cite

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“…Several works have considered the problem of checking strong consistency models such as Linearizability and Sequential consistency (SC) [4,5,11,14,16,17,27,30]. Our recent works [33,31] address the problem of verifying SC and TSO (Total store ordering) gradually by using several variants of causal consistency (and other weak consistency models) including the ones we have considered in this work. However, few have addressed the problem of checking weak consistency models.…”

Section: Related Workmentioning

confidence: 99%

Checking Causal Consistency of Distributed Databases

et al. 2019

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The CAP Theorem shows that (strong) Consistency, Availability, and Partition tolerance are impossible to be ensured together. Causal consistency is one of the weak consistency models that can be implemented to ensure availability and partition tolerance in distributed systems. In this work, we propose a tool to check automatically the conformance of distributed/concurrent systems executions to causal consistency models. Our approach consists in reducing the problem of checking if an execution is causally consistent to solving Datalog queries. The reduction is based on complete characterizations of the executions violating causal consistency in terms of the existence of cycles in suitably defined relations between the operations occurring in these executions. We have implemented the reduction in a testing tool for distributed databases, and carried out several experiments on real case studies, showing the efficiency of the suggested approach.

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