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Tools for High Performance Computing 2014 pp 123–136Cite as

Combining Instrumentation and Sampling for Trace-Based Application Performance Analysis

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Abstract

Performance analysis is vital for optimizing the execution of high performance computing applications. Today different techniques for gathering, processing, and analyzing application performance data exist. Application level instrumentation for example is a powerful method that provides detailed insight into an application’s behavior. However, it is difficult to predict the instrumentation-induced perturbation as it largely depends on the application and its input data. Thus, sampling is a viable alternative to instrumentation for gathering information about the execution of an application by recording its state at regular intervals. This method provides a statistical overview of the application execution and its overhead is more predictable than with instrumentation. Taking into account the specifics of these techniques, this paper makes the following contributions: (I) A comprehensive overview of existing techniques for application performance analysis. (II) A novel tracing approach that combines instrumentation and sampling to offer the benefits of complete information where needed with reduced perturbation. We provide examples using selected instrumentation and sampling methods to detail the advantage of such mixed information and discuss arising challenges and prospects of this approach.

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References

  1. Adhianto, L., Banerjee, S., Fagan, M., Krentel, M., Marin, G., Mellor-Crummey, J., Tallent, N.R.: HPCTOOLKIT: tools for performance analysis of optimized parallel programs. Concurr. Comput.: Pract. Exp. 22(6), 685–701 (2010)

    Google Scholar 

  2. Binder, W.: Portable and accurate sampling profiling for Java. Softw.: Pract. Exp. 36(6), 615–650 (2006)

    Google Scholar 

  3. Buck, B., Hollingsworth, J.K.: An API for runtime code patching. Int. J. High Perform. Comput. Appl. 14, 317–329 (2000)

    Article  Google Scholar 

  4. de Melo, A.C.: The new linux ‘perf’ tools. In: Slides from Linux Kongress, The German Unix User Group (2010)

    Google Scholar 

  5. Dietrich, R., Ilsche, T., Juckeland, G.: Non-intrusive performance analysis of parallel hardware accelerated applications on hybrid architectures. In: International Conference on Parallel Processing Workshops, San Diego (2010)

    Book  Google Scholar 

  6. Dongarra, J., Malony, A.D., Moore, S., Mucci, P., Shende, S.: Performance instrumentation and measurement for terascale systems. In: Proceedings of the 2003 International Conference on Computational Science, ICCS’03, Melbourne. Springer (2003)

    Google Scholar 

  7. Geimer, M., Wolf, F., Wylie, B.J.N., Ábrahám, E., Becker, D., Mohr, B.: The Scalasca performance toolset architecture. Concurr. Comput.: Pract. Exp. 22(6), 702–719 (2010)

    Google Scholar 

  8. Graham, S.L., Kessler, P.B., McKusick, M.K.: gprof: a call graph execution profiler. In: SIGPLAN Symposium on Compiler Construction, Boston (1982)

    Book  Google Scholar 

  9. Ilsche, T., Schuchart, J., Cope, J., Kimpe, D., Jones, T., Knüpfer, A., Iskra, K., Ross, R., Nagel, W.E., Poole, S.: Optimizing I/O forwarding techniques for extreme-scale event tracing. Cluster Comput. 9, 1–18 (2013)

    Google Scholar 

  10. Jain, R.K.: The Art of Computer Systems Performance Analysis: Techniques for Experimental Design, Measurement, Simulation, and Modeling. Wiley, New York (1991)

    MATH  Google Scholar 

  11. Juckeland, G.: Trace-based performance analysis for hardware accelerators. PhD thesis, TU Dresden (2012)

    Google Scholar 

  12. Knüpfer, A., Rössel, C., an Mey, D., Biersdorff, S., Diethelm, K., Eschweiler, D., Geimer, M., Gerndt, M., Lorenz, D., Malony, A.D., Nagel, W.E., Oleynik, Y., Philippen, P., Saviankou, P., Schmidl, D., Shende, S.S., Tschüter, R., Wagner, M., Wesarg, B., Wolf, F.: Score-P – a joint performance measurement run-time infrastructure for Periscope, Scalasca, TAU, and Vampir. In: Proceedings of 5th Parallel Tools Workshop, 2011, Dresden. Springer (2012)

    Google Scholar 

  13. Laurenzano, M.A., Tikir, M.M., Carrington, L., Snavely, A.: Pebil: efficient static binary instrumentation for linux. In: IEEE International Symposium on Performance Analysis of Systems Software (ISPASS), White Plains (2010)

    Google Scholar 

  14. Lindlan, K.A., Cuny, J., Malony, A.D., Shende, S., Juelich, F., Rivenburgh, R., Rasmussen, C., Mohr, B.: A tool framework for static and dynamic analysis of object-oriented software with templates. In: Proceedings of the International Conference on Supercomputing, Santa Fe. IEEE (2000)

    Google Scholar 

  15. Malony, A.D., Shende, S.S., Morris, A., Joubert, G.R., Nagel, W.E., Peters, F.J., Plata, O., Tirado, P., Zapata, E.: Phase-based parallel performance profiling. In: Proceedings of the PARCO 2005 Conference, jülich, Malaga (2005)

    Google Scholar 

  16. Mohr, B., Malony, A.D., Shende, S., Wolf, F.: Towards a performance tool interface for OpenMP: An approach based on directive rewriting. In: Proceedings to the Third Workshop on OpenMP (EWOMP), Barcelona (2001)

    Google Scholar 

  17. Morris, A., Malony, A.D., Shende, S., Huck, K.A.: Design and implementation of a hybrid parallel performance measurement system. In: ICPP, San Diego, pp. 492–501 (2010)

    Google Scholar 

  18. Müller, M.S., Knüpfer, A., Jurenz, M., Lieber, M., Brunst, H., Mix, H., Nagel, W.E.: Developing scalable applications with Vampir, VampirServer and VampirTrace. In: Parallel Computing: Architectures, Algorithms and Applications, vol. 15. IOS Press, Amsterdam/Washington, DC (2008)

    Google Scholar 

  19. Osier, J.: The GNU gprof manual (2014)

    Google Scholar 

  20. Schöne, R., Tschüter, R., Ilsche, T., Hackenberg, D.: The vampirtrace plugin counter interface: introduction and examples. In: Euro-Par 2010 Parallel Processing Workshops, Ischia. Volume 6586 of Lecture Notes in Computer Science. Springer (2011)

    Google Scholar 

  21. Servat, H., Llort, G., Giménez, J., Huck, K., Labarta, J.: Folding: detailed analysis with coarse sampling. In: Tools for High Performance Computing 2011, Dresden. Springer (2012)

    Google Scholar 

  22. Shende, S.S., Malony, A.D.: The TAU parallel performance system. Int. J. High Perform. Comput. Appl. 20(2), 287–311 (2006)

    Article  Google Scholar 

  23. Szebenyi, Z., Gamblin, T., Schulz, M., de Supinski, B.R., Wolf, F., Wylie, B.J.N.: Reconciling sampling and direct instrumentation for unintrusive call-path profiling of MPI programs. In: Proceedings of the 25th IEEE International Parallel & Distributed Processing Symposium (IPDPS), Anchorage, May 2011

    Google Scholar 

  24. Szebenyi, Z., Wolf, F., Wylie, B.J.N.: Space-efficient time-series call-path profiling of parallel applications. In: Proceedings of the International Conference on Supercomputing, Yorktown Heights, Nov 2009. ACM (2009)

    Google Scholar 

  25. Wylie, B.J.N., Geimer, M., Mohr, B., Böhme, D., Szebenyi, Z., Wolf, F.: Large-scale performance analysis of Sweep3D with the Scalasca toolset. Parallel Process. Lett. 20(4), 397–414 (2010)

    Article  MathSciNet  Google Scholar 

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Acknowledgements

This work has been funded by the Bundesministerium für Bildung und Forschung via the research project CoolSilicon (BMBF 16N10186) and the Deutsche Forschungsgemeinschaft (DFG) via the Collaborative Research Center 912 “Highly Adaptive Energy-Efficient Computing” (HAEC, SFB 921/1 2011). The authors would like to thank Michael Werner for his support.

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Authors and Affiliations

  1. Center for Information Services and High Performance Computing (ZIH), Technische Universität Dresden, 01062, Dresden, Germany

    Thomas Ilsche, Joseph Schuchart, Robert Schöne & Daniel Hackenberg

Authors
  1. Thomas Ilsche
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  2. Joseph Schuchart
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  3. Robert Schöne
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  4. Daniel Hackenberg
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Corresponding author

Correspondence to Thomas Ilsche .

Editor information

Editors and Affiliations

  1. Höchstleistungszentrum, Stuttgart (HLRS), Universität Stuttgart, Stuttgart, Germany

    Christoph Niethammer

  2. Höchstleistungszentrum, Stuttgart (HLRS), Universität Stuttgart, Stuttgart, Germany

    José Gracia

  3. Zentrum für Informationsdienste und, Hochleistungsrechnen (ZIH), Technische Universität Dresden, Dresden, Germany

    Andreas Knüpfer

  4. Höchstleistungszentrum, Stuttgart (HLRS), Universität Stuttgart, Stuttgart, Germany

    Michael M. Resch

  5. Zentrum für Informationsdienste und, Hochleistungsrechnen (ZIH), Technische Universität Dresden, Dresden, Germany

    Wolfgang E. Nagel

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© 2015 Springer International Publishing Switzerland

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Ilsche, T., Schuchart, J., Schöne, R., Hackenberg, D. (2015). Combining Instrumentation and Sampling for Trace-Based Application Performance Analysis. In: Niethammer, C., Gracia, J., Knüpfer, A., Resch, M., Nagel, W. (eds) Tools for High Performance Computing 2014. Springer, Cham. https://doi.org/10.1007/978-3-319-16012-2_6

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