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Verifying Lock-Free Search Structure Templates

Authors Nisarg Patel , Dennis Shasha , Thomas Wies

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ACM Subject Classification
  • Theory of computation → Logic and verification
  • Theory of computation → Separation logic
  • Theory of computation → Shared memory algorithms
Keywords
  • skiplists
  • lock-free
  • separation logic
  • linearizability
  • future-dependent linearization points
  • hindsight reasoning

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Abstract

We present and verify template algorithms for lock-free concurrent search structures that cover a broad range of existing implementations based on lists and skiplists. Our linearizability proofs are fully mechanized in the concurrent separation logic Iris. The proofs are modular and cover the broader design space of the underlying algorithms by parameterizing the verification over aspects such as the low-level representation of nodes and the style of data structure maintenance. As a further technical contribution, we present a mechanization of a recently proposed method for reasoning about future-dependent linearization points using hindsight arguments. The mechanization builds on Iris' support for prophecy reasoning and user-defined ghost resources. We demonstrate that the method can help to reduce the proof effort compared to direct prophecy-based proofs.

Cite As Get BibTex

Nisarg Patel, Dennis Shasha, and Thomas Wies. Verifying Lock-Free Search Structure Templates. In 38th European Conference on Object-Oriented Programming (ECOOP 2024). Leibniz International Proceedings in Informatics (LIPIcs), Volume 313, pp. 30:1-30:28, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2024) https://doi.org/10.4230/LIPIcs.ECOOP.2024.30

Author Details

Nisarg Patel
  • New York University, NY, USA
Dennis Shasha
  • New York University, NY, USA
Thomas Wies
  • New York University, NY, USA

Funding

This work is funded in parts by NYU Wireless and by the United States National Science Foundation under grants CCF-2304758, 1840761, 2304758, and 25-74100-F1202. Further funding came from an Amazon Research Award Fall 2021. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not reflect the views of Amazon.

Acknowledgements

We thank Sebastian Wolff for many insightful discussions and his suggestions to improve the presentation of the paper.

Supplementary Materials

References

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