Analyzing execution traces: critical-path analysis and distance analysis

  • Martijn Hendriks
  • Jacques Verriet
  • Twan Basten
  • Bart Theelen
  • Marco Brassé
  • Lou Somers
Regular Paper

Abstract

System designers make trade-offs between metrics of interest such as execution time, functional quality and cost to create a properly balanced system. Execution traces, which are sequences of timestamped start and end events of system tasks, are a general and powerful means to understand the system behavior that gives rise to these trade-offs. Such traces can be produced by, e.g., executable models or prototype systems. Their interpretation, however, often is non-trivial. We present two automated analysis techniques that work on execution traces to help the system designer with interpretation. First, critical-path analysis can be used to answer the typical “what is the bottleneck” question, and we extend earlier work of [16] with a technique that uses application information to refine the analysis. Second, we define a pseudo-metric on execution traces, which is useful for calibration and validation purposes, and which can be used to visualize the differences between traces. Both techniques are based on a common graph representation of execution traces. We have implemented our techniques in the Trace visualization tool [12], and have applied them in a case study from the digital printing domain.

Keywords

Model-based design Execution trace Critical path Metric Visualization 

Supplementary material

10009_2016_436_MOESM1_ESM.pdf (228 kb)
Supplementary material 1 (pdf 227 KB)

References

  1. 1.
    van der Aalst, W.M.P., van Dongen, B.F.: Discovering workflow performance models from timed logs. In: 1st International Conference on Engineering and Deployment of Cooperative Information Systems. Springer, New York (2002)Google Scholar
  2. 2.
    van der Aalst, W.M.P., et al.: Workflow mining: a survey of issues and approaches. Data Knowl. Eng. 47, 237–267 (2003)CrossRefGoogle Scholar
  3. 3.
    Aho, A.V., Garey, M.R., Ullman, J.D.: The transitive reduction of a directed graph. SIAM J. Comput. 1, 131–137 (1972)MathSciNetCrossRefMATHGoogle Scholar
  4. 4.
    Arkhangelski, A.V., Fedorchuk, V.V.: The basic concepts and constructions of general topology. General Topology I. volume 17 of Encyclopaedia of Mathematical Sciences, pp. 1–90. Springer, Berlin (1990)Google Scholar
  5. 5.
    Kluwer, F., et al.: Hardware–Software Co-design of Embedded Systems: The POLIS Approach. Kluwer, Amsterdam (1997)Google Scholar
  6. 6.
    Barford, P., Crovella, M.: Critical path analysis of TCP transactions. SIGCOMM Comput. Commun. Rev. 30, 127–138 (2000)CrossRefGoogle Scholar
  7. 7.
    Basten, T. et al.: Model-driven design-space exploration for embedded systems: the octopus toolset. In: 4th International Symposium on Leveraging Applications of Formal Methods, Verification, and Validation (ISoLA 2010), volume 6415 of Lecture Notes in Computer Science, pp. 90–105. Springer, New York (2010)Google Scholar
  8. 8.
    Bjorn-Jorgensen, P., Madsen, J.: Critical path driven cosynthesis for heterogeneous target architectures. In: 5th International Workshop on Hardware/Software Co-Design, IEEE CS (1997)Google Scholar
  9. 9.
    Bohme, D., Wolf, F., de Supinski, B.R., Schulz, M., Geimer, M.: Scalable critical-path based performance analysis. In: 26th International Parallel Distributed Processing Symposium (IPDPS). IEEE (2012)Google Scholar
  10. 10.
    Cormen, T.H., Leiserson, C.E., Rivest, R.L.: Introduction to Algorithms. The MIT Press, Cambridge (1990)MATHGoogle Scholar
  11. 11.
    Eclipse Foundation. Eclipse website. http://www.eclipse.org/. Accessed 9 Sept 2016
  12. 12.
    Embedded Systems Innovation by TNO. TRACE website. http://trace.esi.nl. Accessed 9 Sept 2016
  13. 13.
    Gao, X., Xiao, B., Tao, D., Li, X.: A survey of graph edit distance. Pattern Anal. Appl. 13(1), 113–129 (2010)MathSciNetCrossRefGoogle Scholar
  14. 14.
    Goldratt, E.: Critical Chain. North River Press, Barrington (1997)Google Scholar
  15. 15.
    Hendriks, M., Basten, T., Verriet, J., Brassé, M., Somers, L.: A blueprint for system-level performance modeling of software-intensive embedded systems. Int. J. Softw. Tool Technol.Transf. 18(1), 21–40 (2016)Google Scholar
  16. 16.
    Hendriks, M., Vaandrager, F.W.: Reconstructing critical paths from execution traces. In: International Conference on Embedded and Ubiquitous Computing (EUC 2012). IEEE Computer Society Press (2012)Google Scholar
  17. 17.
    Henzinger, T.A., Majumdar, R., Prabhu, V.S.: Quantifying similarities between timed systems. In: Formal Modeling and Analysis of Timed Systems, Vol. 3829. Lecture Notes in Computer Science. Springer, Berlin (2005)Google Scholar
  18. 18.
    Hollingsworth, J.K.: An online computation of critical path profiling. In: 1st ACM SIGMETRICS Symposium on Parallel and Distributed Tools. ACM (1996)Google Scholar
  19. 19.
    Huang, J., Voeten, J., Geilen, M.: Real-time property preservation in concurrent real-time systems. In: 10th International Conference on Real-Time and Embedded Computing Systems and Applications (2004)Google Scholar
  20. 20.
    Intel. Intel ARK website. http://ark.intel.com/. Accessed 9 Sept 2016
  21. 21.
    Kastor, A., Sirakoulis, K.: The effectiveness of resource levelling tools for resource constraint project scheduling problem. Int. J. Project Manag. 27, 493–500 (2009)CrossRefGoogle Scholar
  22. 22.
    Kelley, J.E., Walker, M.R.: Critical-path planning and scheduling. In: Eastern Joint IRE-AIEE-ACM Computer Conference. ACM (1959)Google Scholar
  23. 23.
    Kengne, C.K., Ibrahim, N., Rousset, M.-C., Tchuente, M.: Distance-based trace diagnosis for multimedia applications: Help me TED! In: 7th International Conference on Semantic Computing (ICSC). IEEE (2013)Google Scholar
  24. 24.
    Levenshtein, V.I.: Binary codes capable of correcting deletions, insertions and reversals. Sov. Phys. Doklady 10, 707–710 (1966)MathSciNetMATHGoogle Scholar
  25. 25.
    Lockyer, K.G.: Introduction to Critical Path Analysis. Pitman Publishing Co., New Jersey (1964)Google Scholar
  26. 26.
    Luo, J., Jha, N.K.: Static and dynamic variable voltage scheduling algorithms for real-time heterogeneous distributed embedded systems. In: ASP-DAC. IEEE CS (2002)Google Scholar
  27. 27.
    Mannila, H., Ronkainen, P.: Similarity of event sequences. In: 4th International Workshop on Temporal Representation and Reasoning. IEEE Computer Society Press (1997)Google Scholar
  28. 28.
    Nakatsu, N., Kambayashi, Y., Yajima, S.: A longest common subsequence algorithm suitable for similar text strings. Acta Inf. 18(2), 171–179 (1982)CrossRefMATHGoogle Scholar
  29. 29.
    Obweger, H., Suntinger, M., Schiefer, J., Raidl, G.: Similarity searching in sequences of complex events. In: 4th International Conference on Research Challenges in Information Science (RCIS). IEEE (2010)Google Scholar
  30. 30.
    Pimentel, A.D., Thompson, M., Polstra, S., Erbas, C.: Calibration of abstract performance models for system-level design space exploration. J. Signal Process. Syst. 50(2), 99–114 (2008)CrossRefGoogle Scholar
  31. 31.
    Pinedo, M.: Scheduling: Theory, Algorithms and Systems, 2nd edn. Prentice Hall, New Jersey (2002)MATHGoogle Scholar
  32. 32.
    Schulz, M.: Extracting critical path graphs from MPI applications. In: International Conference on Cluster Computing. IEEE (2005)Google Scholar
  33. 33.
    Wiest, J.D.: Some properties of schedules for large projects with limited resources. Oper. Res. 12, 395–418 (1964)CrossRefGoogle Scholar
  34. 34.
    Wu, M.Y., Gajski, D.D.: Hypertool: a programming aid for message-passing systems. IEEE Trans. Parall. Distrib. Syst. 1, 330–343 (1990)CrossRefGoogle Scholar
  35. 35.
    Wu, S., Manber, U., Myers, G., Miller, W.: An O(NP) sequence comparison algorithm. Inf. Process. Lett. 35(6), 317–323 (1990)MathSciNetCrossRefMATHGoogle Scholar
  36. 36.
    Yang, C.-Q., Miller, B.P.: Critical path analysis for the execution of parallel and distributed programs. In: 8th International Conference on Distributed Computing Systems. IEEE (1988)Google Scholar
  37. 37.
    Yang, Y., Geilen, M., Basten, T., Stuijk, S., Corporaal, H.: Automated bottleneck-driven design-space exploration of media processing systems. In: Design, Automation Test in Europe Conference Exhibition (DATE), 2010, pp. 1041–1046. IEEE (2010)Google Scholar
  38. 38.
    Zeng, Z., Tung, A.K.H., Wang, J., Feng, J., Zhou, L.: Comparing stars: on approximating graph edit distance. Proc. VLDB Endow. 2(1), 25–36 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Martijn Hendriks
    • 1
  • Jacques Verriet
    • 1
  • Twan Basten
    • 1
    • 2
  • Bart Theelen
    • 1
  • Marco Brassé
    • 3
  • Lou Somers
    • 2
    • 3
  1. 1.Embedded Systems Innovation by TNOEindhovenThe Netherlands
  2. 2.Eindhoven University of TechnologyEindhovenThe Netherlands
  3. 3.Océ Technologies B.V.VenloThe Netherlands

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