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@inproceedings{1392167,
author = {Chalupa, Marek and Chatterjee, Krishnendu and Pavlogiannis, Andreas and Sinha, Nishant and Vaidya, Kapil},
address = {New York},
booktitle = {Proceedings of Symposium on Principles of Programming Languages 2018},
doi = {http://dx.doi.org/10.1145/3158119},
keywords = {Concurrency; Partial-order Reduction; Stateless model-checking},
howpublished = {tištěná verze "print"},
language = {eng},
location = {New York},
pages = {1-30},
publisher = {ACM},
title = {Data-centric Dynamic Partial Order Reduction},
year = {2018}
}
TY - JOUR
ID - 1392167
AU - Chalupa, Marek - Chatterjee, Krishnendu - Pavlogiannis, Andreas - Sinha, Nishant - Vaidya, Kapil
PY - 2018
TI - Data-centric Dynamic Partial Order Reduction
PB - ACM
CY - New York
KW - Concurrency
KW - Partial-order Reduction
KW - Stateless model-checking
N2 - We present a new dynamic partial-order reduction method for stateless model checking of concurrent programs. A common approach for exploring program behaviors relies on enumerating the traces of the program, without storing the visited states (aka stateless exploration). As the number of distinct traces grows exponentially, dynamic partial-order reduction (DPOR) techniques have been successfully used to partition the space of traces into equivalence classes (Mazurkiewicz partitioning), with the goal of exploring only few representative traces from each class. We introduce a new equivalence on traces under sequential consistency semantics, which we call the observation equivalence. Two traces are observationally equivalent if every read event observes the same write event in both traces. While the traditional Mazurkiewicz equivalence is control-centric, our new definition is data-centric. We show that our observation equivalence is coarser than the Mazurkiewicz equivalence, and in many cases even exponentially coarser. We devise a DPOR exploration of the trace space, called data-centric DPOR, based on the observation equivalence. For acyclic architectures, our algorithm is guaranteed to explore exactly one representative trace from each observation class, while spending polynomial time per class. Hence, our algorithm is optimal wrt the observation equivalence, and in several cases explores exponentially fewer traces than any enumerative method based on the Mazurkiewicz equivalence. For cyclic architectures, we consider an equivalence between traces which is finer than the observation equivalence; but coarser than the Mazurkiewicz equivalence, and in some cases is exponentially coarser. Our data-centric DPOR algorithm remains optimal under this trace equivalence. Finally, we perform a basic experimental comparison between the existing Mazurkiewicz-based DPOR and our data-centric DPOR on a set of academic benchmarks. Our results show a significant reduction in both running time and the number of explored equivalence classes.
ER -
CHALUPA, Marek, Krishnendu CHATTERJEE, Andreas PAVLOGIANNIS, Nishant SINHA and Kapil VAIDYA. Data-centric Dynamic Partial Order Reduction. In \textit{Proceedings of Symposium on Principles of Programming Languages 2018}. New York: ACM. p.~1-30. ISSN~2475-1421. doi:10.1145/3158119. 2018.
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