A Formal Approach to Detect Functionally Irrelevant Barriers in MPI Programs

  • Subodh Sharma
  • Sarvani Vakkalanka
  • Ganesh Gopalakrishnan
  • Robert M. Kirby
  • Rajeev Thakur
  • William Gropp
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5205)


We examine the unsolved problem of automatically and efficiently detecting functionally irrelevant barriers in MPI programs. A functionally irrelevant barrier is a set of MPI_Barrier calls, one per MPI process, such that their removal does not alter the overall MPI communication structure of the program. Static analysis methods are incapable of solving this problem, as MPI programs can compute many quantities at runtime, including send targets, receive sources, tags, and communicators, and also can have data-dependent control flows. We offer an algorithm called Fib to solve this problem based on dynamic (runtime) analysis. Fib applies to MPI programs that employ 24 widely used two-sided MPI operations. We show that it is sufficient to detect barrier calls whose removal causes a wildcard receive statement placed before or after a barrier to now begin matching a send statement with which it did not match before. Fib determines whether a barrier becomes relevant in any interleaving of the MPI processes of a given MPI program. Since the number of interleavings can grow exponentially with the number of processes, Fib employs a sound method to drastically reduce this number, by computing only the relevant interleavings. We show that many MPI programs do not have data dependent control flows, thus making the results of Fib applicable to all the input data the program can accept.


Model Check Formal Approach Partial Order Reduction Vector Clock Barrier Call 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
  2. 2.
    Avrunin, G.S., Siegel, S.F., Siegel, A.R.: Finite-state Verification for High Performance Computing. In: Proc. Second Intl. Wkshp. on Soft. Eng. for High Perf. Computing Syst. Apps., pp. 68–72 (2005)Google Scholar
  3. 3.
    Rabenseifner, R.: Automatic MPI Counter Profiling. In: Proceedings of the 42nd Cray User Group Conference, CUG SUMMIT 2000, Noorwijk, The Netherlands, May 22-26 (2000)Google Scholar
  4. 4.
    Vakkalanka, S., DeLisi, M., Gopalakrishnan, G., Kirby, R.M., Thakur, R., Gropp, W.: Implementing Efficient Dynamic Formal Verification Methods for MPI Programs. In: Proceeding - EuroPVM/MPI 2008 (2008)Google Scholar
  5. 5.
    Vakkalanka, S., Gopalakrishnan, G., Kirby, R.M.: Dynamic verification of mpi programs with reductions in presence of split operations and relaxed orderings. In: Computer Aided Verification (2008) (accepted)Google Scholar
  6. 6.
    Siegel, S.F., Avrunin, G.S.: Modeling Wildcard-free MPI Programs for Verification. In: PPoPP, pp. 95–106 (2005)Google Scholar
  7. 7.
    Mattern, F.: Virtual Time and Global States of Distributed Systems. In: Parallel and Distributed Algorithms: Proc. Intl. Wkshp. Par. and Dist. Algo. (1989)Google Scholar
  8. 8.
    Netzer, R.H.B., Miller, B.P.: Optimal Tracing and Replay for Debugging Message Passing Parallel Programs. Supercomputing, 502–511 (1992)Google Scholar
  9. 9.
    Pervez, S., et al.: Practical model checking method for verifying correctness of MPI programs. In: EuroPVM/MPI, pp. 344–353 (2007)Google Scholar
  10. 10.
    Vetter, J.S., de Supinski, B.R.: Dynamic Software Testing of MPI Applications with Umpire. In: Proc. of SC 2000, pp. 70–79 (2000)Google Scholar
  11. 11.
    Clarke, E.M., Grumberg, O., Peled, D.A.: Model Checking. MIT Press, Cambridge (2000)Google Scholar
  12. 12.
    Flanagan, C., Godefroid, P.: Dynamic partial-order reduction for model checking software. In: POPL, pp. 110–121. ACM, New York (2005)Google Scholar
  13. 13.
    Vakkalanka, S., Sharma, S.V., Gopalakrishnan, G., Kirby, R.M.: ISP: A tool for model checking MPI programs. In: PPoPP 2008, pp. 285–286 (2008)Google Scholar
  14. 14.
    Necula, G.C.: CIL: Intermediate Language and Tools for Analysis and Transformation of C Programs. In: Horspool, R.N. (ed.) CC 2002. LNCS, vol. 2304, pp. 213–228. Springer, Heidelberg (2002)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2008

Authors and Affiliations

  • Subodh Sharma
    • 1
  • Sarvani Vakkalanka
    • 1
  • Ganesh Gopalakrishnan
    • 1
  • Robert M. Kirby
    • 1
  • Rajeev Thakur
    • 2
  • William Gropp
    • 3
  1. 1.School of ComputingUniv. of UtahSalt Lake CityUSA
  2. 2.Math. and Comp. Sci. Div., Argonne Nat. Lab.ArgonneUSA
  3. 3.Dept. of Computer Sci.Univ. of IllinoisUrbana, IllinoisUSA

Personalised recommendations