Earlier Version

# Data-centric dynamic partial order reduction

Anonymous 1, Anonymous 2, Anonymous 3, Anonymous 4. 2016. Data-centric dynamic partial order reduction, IST Austria, 20p.

Download

*Technical Report*|

*Published*|

*English*

Author

Anonymous, 1;
Anonymous, 2;
Anonymous, 3;
Anonymous, 4

Series Title

IST Austria Technical Report

Abstract

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.
1. 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.
2. 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.

Publishing Year

Date Published

2016-07-15

Page

20

ISSN

IST-REx-ID

### Cite this

Anonymous 1, Anonymous 2, Anonymous 3, Anonymous 4.

*Data-Centric Dynamic Partial Order Reduction*. IST Austria; 2016.Anonymous, 1, Anonymous, 2, Anonymous, 3, & Anonymous, 4. (2016).

*Data-centric dynamic partial order reduction*. IST Austria.Anonymous, 1, 2 Anonymous, 3 Anonymous, and 4 Anonymous.

*Data-Centric Dynamic Partial Order Reduction*. IST Austria, 2016.1 Anonymous, 2 Anonymous, 3 Anonymous, and 4 Anonymous,

*Data-centric dynamic partial order reduction*. IST Austria, 2016.Anonymous, 1, et al.

*Data-Centric Dynamic Partial Order Reduction*. IST Austria, 2016.**All files available under the following license(s):**

**Copyright Statement:**

**This Item is protected by copyright and/or related rights.**[...]

**Main File(s)**

File Name

IST-2016-620-v1+1_main.pdf
538.88 KB

Access Level

Open Access

Date Uploaded

2018-12-12

MD5 Checksum

1d69252d66bcdf782615ddfb911d2957

File Name

**authornames.txt**121 bytes

Access Level

Closed Access

Date Uploaded

2019-05-10

MD5 Checksum

deabb0eb8f237cae4f9542b28b0b6eb2

### Export

Marked PublicationsOpen Data IST Research Explorer

### Sources

arXiv 1610.01188