Constraints

, Volume 21, Issue 1, pp 77–94 | Cite as

Visual search tree profiling

  • Maxim Shishmarev
  • Christopher Mears
  • Guido Tack
  • Maria Garcia de la Banda
Article

Abstract

Understanding how the search space is explored for a given constraint problem – and how it changes for different models, solvers or search strategies – is crucial for efficient solving. Yet programmers often have to rely on the crude aggregate measures of the search that are provided by solvers, or on visualisation tools that can show the search tree, but do not offer sophisticated ways to navigate and analyse it, particularly for large trees. We present an architecture for profiling a constraint programming search that is based on a lightweight instrumentation of the solver. The architecture combines a visualisation of the search tree with various tools for convenient navigation and analysis of the search. These include identifying repeated subtrees, high-level abstraction and navigation of the tree, and the comparison of two search trees. The resulting system is akin to a traditional program profiler, which helps the user to focus on the parts of the execution where an improvement to their program would have the greatest effect.

Keywords

Constraint programming Search tree Profiling Comparison Visualisation 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bauer, A., Botea, V., Brown, M., Gray, M., Harabor, D., & Slaney, J. (2010). An integrated modelling, debugging, and visualisation environment for G12. In CP 2010. LNCS, (Vol. 6308 pp. 522–536): Springer.Google Scholar
  2. 2.
    Burch, M., Raschke, M., & Weiskopf, D. (2010). Indented pixel tree plots. In Advances in Visual Computing (pp. 338–349): Springer.Google Scholar
  3. 3.
    Carro, M., & Hermenegildo, M. Tools for constraint visualisation: The VIFID/TRIFID tool. In: Deransart et al. [6], pp. 253–272.Google Scholar
  4. 4.
    Choi, C.W., Lee, J.H.M., & Stuckey, P.J. (2007). Removing propagation redundant constraints in redundant modeling. ACM Transactions on Computational Logic, 8(4). doi:10.1145/1276920.1276925.
  5. 5.
    Chu, G.G. (2011). Improving combinatorial optimization. Ph.D. thesis: University of Melbourne.Google Scholar
  6. 6.
    Deransart, P., Hermenegildo, M.V., & Maluszynski, J. (Eds.) (2000). Analysis and visualization tools for constraint programming, constraint debugging (DiSCiPl project), LNCS, Vol. 1870: Springer.Google Scholar
  7. 7.
    Deransart, P. (2004). Main results of the OADymPPaC project. In B. Demoen, & V. Lifschitz (Eds.), ICLP, LNCS, (Vol. 3132 pp. 456–457): Springer.Google Scholar
  8. 8.
    Dooms, G., Van Hentenryck, P., & Michel, L. (2007). Model-driven visualizations of constraint-based local search. In CP 2007, LNCS, (Vol. 4741 pp. 271–285): Springer.Google Scholar
  9. 9.
    Feydy, T., & Stuckey, P.J. (2009). Lazy clause generation reengineered. In I.P. Gent (Ed.), CP, Lecture Notes in Computer Science, (Vol. 5732 pp. 352–366): Springer.Google Scholar
  10. 10.
    Google (2015). Protocol buffers. https://developers.google.com/protocol-buffers/.
  11. 11.
    Goualard, F., & Benhamou, F. Debugging constraint programs by store inspection. In: Deransart et al. [6], pp. 273–297.Google Scholar
  12. 12.
    iMatix (2015). ZeroMQ. http://zeromq.org.
  13. 13.
    Jussien, N., Rochart, G., & Lorca, X. (2008). Choco: an open source Java constraint programming library. In CPAIOR’08 Workshop on Open-Source Software for Integer and Contraint Programming (OSSICP’08) (pp. 1–10).Google Scholar
  14. 14.
    Meier, M. (1995). Debugging constraint programs. In CP’95, LNCS, (Vol. 976 pp. 204–221): Springer.Google Scholar
  15. 15.
    MinisatID team (2015). MinisatID solver. https://dtai.cs.kuleuven.be/software/minisatid.
  16. 16.
    Müller, T. (1999). Practical investigation of constraints with graph views. In K. Sagonas, & P. Tarau (Eds.) Proceedings of the International Workshop on Implementation of Declarative Languages (IDL’99).Google Scholar
  17. 17.
    Newsham, Z., Lindsay, W., Liang, J.H., Czarnecki, K., Fischmeister, S., & Ganesh, V. (2014). SATGraf: Visualizing community structure in boolean SAT instances. https://ece.uwaterloo.ca/vganesh/EvoGraph/Home.html.
  18. 18.
    Schulte, C. (1997). Oz explorer: a visual constraint programming tool. In L. Naish (Ed.), ICLP (pp. 286–300): MIT Press.Google Scholar
  19. 19.
    Schulte, C., Lagerkvist, M.Z., & Tack, G. (2006). Gecode: Generic constraint development environment. http://www.gecode.org.
  20. 20.
    Schulte, C., Tack, G., & Lagerkvist, M.Z. (2015). Modeling and programming with Gecode. http://www.gecode.org/doc-latest/MPG.pdf.
  21. 21.
    Simonis, H., & Aggoun, A. Search-tree visualisation. In: Deransart et al. [6], pp. 191–208.Google Scholar
  22. 22.
    Simonis, H., Davern, P., Feldman, J., Mehta, D., Quesada, L., & Carlsson, M. (2010). A generic visualization platform for CP. In CP 2010, LNCS, (Vol. 6308 pp. 460–474): Springer.Google Scholar
  23. 23.
    Sinz, C. (2004). Visualizing the internal structure of SAT instances (preliminary report). In SAT.Google Scholar
  24. 24.
    Sinz, C., & Dieringer, E.M. (2005). DPvis – a tool to visualize the structure of SAT instances. In F. Bacchus, & T. Walsh (Eds.), Theory and applications of satisfiability testing, lecture notes in computer science, (Vol. 3569 pp. 257–268). Berlin Heidelberg: Springer. doi:10.1007/11499107_19.Google Scholar
  25. 25.
    Smith, B.M., Stergiou, K., & Walsh, T. (1999). Modelling the Golomb ruler problem. Tech. rep., University of Leeds School of Computer Studies.Google Scholar
  26. 26.
    Van Cauwelaert, S., Lombardi, M., & Schaus, P. (2015). Understanding the potential of propagators. In Integration of AI and OR Techniques in Constraint Programming (pp. 427–436): Springer.Google Scholar
  27. 27.
    Wallace, M.G., Novello, S., & Schimpf, J. (1997). ECLiPSe : a platform for constraint logic programming. ICL Systems Journal, 12(1).Google Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Maxim Shishmarev
    • 1
  • Christopher Mears
    • 1
  • Guido Tack
    • 1
    • 2
  • Maria Garcia de la Banda
    • 1
    • 2
  1. 1.Faculty of ITMonash UniversityMelbourneAustralia
  2. 2.National ICT Australia (NICTA) VictoriaMelbourneAustralia

Personalised recommendations