The key to making program analysis practical for large concurrent programs is to isolate a small set of interleavings to be explored without losing precision of the analysis at hand. The state-of-the-art in restricting the set of interleavings while guaranteeing soundness is partial order reduction (POR). The main idea behind POR is to partition all interleavings of the given program into equivalence classes based on the partial orders they induce on shared objects. Then for each partial order at least one interleaving need be explored. POR classifies two interleavings as non-equivalent if executing them leads to different values of shared variables. However, some of the most common properties about concurrent programs like detection of data races, deadlocks and atomicity as well as assertion violations reduce to control state reachability. We exploit the key observation that even though different interleavings may lead to different values of program variables, they may induce the same control behavior. Hence these interleavings, which induce different partial orders, can in fact be treated as being equivalent. Since in most concurrent programs threads are loosely coupled, i.e., the values of shared variables typically flow into a small number of conditional statements of threads, we show that classifying interleavings based on the control behaviors rather than the partial orders they induce, drastically reduces the number of interleavings that need be explored. In order to exploit this loose coupling we leverage the use of dataflow analysis for concurrent programs, specifically numerical domains. This, in turn, greatly enhances the scalability of concurrent program analysis.


Partial Order Model Check Shared Variable Control Behavior Concurrent Program 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


  1. 1.
    Biere, A., Cimatti, A., Clarke, E., Zhu, Y.: Symbolic Model Checking without BDDs. In: Cleaveland, W.R. (ed.) TACAS 1999. LNCS, vol. 1579, pp. 193–207. Springer, Heidelberg (1999)CrossRefGoogle Scholar
  2. 2.
    Cousot, P., Halbwachs, N.: Automatic discovery of linear restraints among the variables of a program. In: ACM POPL, pp. 84–97 (January 1978)Google Scholar
  3. 3.
    Godefroid, P.: Partial-Order Methods for the Verification of Concurrent Systems. LNCS, vol. 1032. Springer, Heidelberg (1996)CrossRefGoogle Scholar
  4. 4.
    Kahlon, V., Sankaranarayanan, S., Gupta, A.: Semantic Reduction of Thread Interleavings in Concurrent Programs. In: Kowalewski, S., Philippou, A. (eds.) TACAS 2009. LNCS, vol. 5505, pp. 124–138. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  5. 5.
    Kahlon, V., Wang, C., Gupta, A.: Monotonic Partial Order Reduction: An Optimal Symbolic Partial Order Reduction Technique. In: Bouajjani, A., Maler, O. (eds.) CAV 2009. LNCS, vol. 5643, pp. 398–413. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  6. 6.
    Kahlon, V., Yang, Y., Sankaranarayanan, S., Gupta, A.: Fast and Accurate Static Data-Race Detection for Concurrent Programs. In: Damm, W., Hermanns, H. (eds.) CAV 2007. LNCS, vol. 4590, pp. 226–239. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Miné, A.: A New Numerical Abstract Domain Based on Difference-Bound Matrices. In: Danvy, O., Filinski, A. (eds.) PADO-II. LNCS, vol. 2053, pp. 155–172. Springer, Heidelberg (2001)CrossRefGoogle Scholar
  8. 8.
    Peled, D.: Combining partial order reductions with on-the-fly model checking. In: Formal Aspects of Computing, vol. 8, pp. 39–64 (1996)Google Scholar
  9. 9.
    Valmari, A.: A stubborn attack on state explosion. Formal Methods in System Design 1(4) (1992)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Vineet Kahlon
    • 1
  1. 1.NEC LabsPrincetonUSA

Personalised recommendations