2018
Data-centric Dynamic Partial Order Reduction
CHALUPA, Marek, Krishnendu CHATTERJEE, Andreas PAVLOGIANNIS, Nishant SINHA, Kapil VAIDYA et. al.Základní údaje
Originální název
Data-centric Dynamic Partial Order Reduction
Autoři
CHALUPA, Marek (203 Česká republika, domácí), Krishnendu CHATTERJEE (356 Indie), Andreas PAVLOGIANNIS (300 Řecko), Nishant SINHA (356 Indie) a Kapil VAIDYA (356 Indie)
Vydání
New York, Proceedings of Symposium on Principles of Programming Languages 2018, od s. 1-30, 30 s. 2018
Nakladatel
ACM
Další údaje
Jazyk
angličtina
Typ výsledku
Stať ve sborníku
Obor
10201 Computer sciences, information science, bioinformatics
Stát vydavatele
Spojené státy
Utajení
není předmětem státního či obchodního tajemství
Forma vydání
tištěná verze "print"
Kód RIV
RIV/00216224:14330/18:00100728
Organizační jednotka
Fakulta informatiky
ISSN
UT WoS
000688016900031
Klíčová slova anglicky
Concurrency; Partial-order Reduction; Stateless model-checking
Příznaky
Mezinárodní význam, Recenzováno
Změněno: 20. 9. 2022 11:14, RNDr. Pavel Šmerk, Ph.D.
Anotace
V originále
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.
Návaznosti
| GBP202/12/G061, projekt VaV |
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| MUNI/A/0854/2017, interní kód MU |
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| MUNI/A/1038/2017, interní kód MU |
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