CSSTs: A Dynamic Data Structure for Partial Orders in Concurrent Execution Analysis
<|reference_start|>CSSTs: A Dynamic Data Structure for Partial Orders in Concurrent Execution Analysis: Dynamic analyses are a standard approach to analyzing and testing concurrent programs. Such techniques observe program traces 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 events of the analyzed trace $\sigma$. Naturally, the scalability of the analysis largely depends on how efficiently it maintains $P$. 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 $O(n)$ for inserting (and propagating) each new ordering in $P$, where $n$ is the size of $\sigma$, 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 generically 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 $O(logn)$ time. For a concurrent trace, $k$ is bounded by the number of its threads, and is normally orders of magnitude smaller than its size $n$, making CSSTs fitting for this setting. Our experimental results confirm that CSSTs are the best data structure currently to handle a range of dynamic analyses from existing literature.<|reference_end|>
@article{tunç2024cssts:,
    title={CSSTs: A Dynamic Data Structure for Partial Orders in Concurrent
  Execution Analysis},
    author={H"unkar Can Tunc{c}, Ameya Prashant Deshmukh, Berk c{C}irisci,
  Constantin Enea, Andreas Pavlogiannis},
    journal={arXiv preprint arXiv:2403.17818},
    year={2024},
    archivePrefix={arXiv},
    eprint={2403.17818},
    primaryClass={cs.PL}
}