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Proceedings of the ACM on Programming Languages
Article . 2024 . Peer-reviewed
License: CC BY
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https://dx.doi.org/10.48550/ar...
Article . 2023
License: CC BY
Data sources: Datacite
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Article . 2023
Data sources: DBLP
DBLP
Article . 2024
Data sources: DBLP
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Inductive Diagrams for Causal Reasoning

Authors: Jonathan Castello; Patrick Redmond; Lindsey Kuper;

Inductive Diagrams for Causal Reasoning

Abstract

The Lamport diagram is a pervasive and intuitive tool for informal reasoning about “happens-before” relationships in a concurrent system. However, traditional axiomatic formalizations of Lamport diagrams can be painful to work with in a mechanized setting like Agda. We propose an alternative, inductive formalization — the causal separation diagram (CSD) — that takes inspiration from string diagrams and concurrent separation logic, but enjoys a graphical syntax similar to Lamport diagrams. Critically, CSDs are based on the idea that causal relationships between events are witnessed by the paths that information follows between them. To that end, we model “happens-before” as a dependent type of paths between events. The inductive formulation of CSDs enables their interpretation into a variety of semantic domains. We demonstrate the interpretability of CSDs with a case study on properties of logical clocks , widely-used mechanisms for reifying causal relationships as data. We carry out this study by implementing a series of interpreters for CSDs, culminating in a generic proof of Lamport’s clock condition that is parametric in a choice of clock. We instantiate this proof on Lamport’s scalar clock, on Mattern’s vector clock, and on the matrix clocks of Raynal et al. and of Wuu and Bernstein, yielding verified implementations of each. The CSD formalism and our case study are mechanized in the Agda proof assistant.

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Keywords

FOS: Computer and information sciences, Computer Science - Programming Languages, Programming Languages (cs.PL)

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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!
0
Average
Average
Average
Green
Published in a Diamond OA journal