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Recent Advances in Periscope for Performance Analysis and Tuning

  • Yury OleynikEmail author
  • Robert Mijaković
  • Isaías A. Comprés Ureña
  • Michael Firbach
  • Michael Gerndt
Conference paper

Abstract

State of the art High Performance Computing (HPC) systems pose considerable programming challenges to application developers when tuning their applications. Periscope toolkit is one of a number of performance engineering instruments supporting application programmers in meeting those challenges. Due to the variety of architectures, programming models, runtime environments, and compilers on those systems, programmers need to apply multiple tools to understand and improve program performance. In this paper, we present the latest developments in Periscope aiming at (1) improving its interoperability and integration with other tools, (2) integrating automatic tuning support with performance analysis and (3) further extending performance analysis capabilities. The add-on for Periscope, called PAThWay, allows for the integration of multiple tools into performance tuning workflows. Further, Periscope is currently being extended with the ability to automatically tune parallel applications with respect to execution performance and energy consumption. And finally, new analysis capabilities were added to Periscope for the automatic evaluation of the temporal performance behavior of long-running applications.

Keywords

High Performance Computing Parallel Application Application Developer Tuning Process Dynamic Profile 
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.

Notes

Acknowledgements

The authors thank the European Union for supporting AutoTune project under the Seventh Framework Programme, grant no. 288038 and German Federal Ministry of Research and Education (BMBF) for supporting LMAC project under the Grant No. 01IH11006F.

References

  1. 1.
    Allweyer, T.: BPMN 2.0: Introduction to the Standard for Business Process Modeling. BoD–Books on Demand, Norderstedt (2010)Google Scholar
  2. 2.
    Barker, A., Van Hemert, J.: Scientific workflow: a survey and research directions. In: Parallel Processing and Applied Mathematics, pp. 746–753. Springer, Berlin/New York (2008)Google Scholar
  3. 3.
    Casas, M., Badia, R.M., Labarta, J.: Automatic phase detection and structure extraction of MPI applications. Int. J. High Perform. Comput. Appl. 24(3), 335–360 (Aug 2010). http://dx.doi.org/10.1177/1094342009360039
  4. 4.
    Chung, I.H., Hollingsworth, J.: Using information from prior runs to improve automated tuning systems. In: Proceedings of the 2004 ACM/IEEE Conference on Supercomputing, SC ’04, Pittsburgh, pp. 30–. IEEE Computer Society, Washington, DC (2004). http://dx.doi.org/10.1109/SC.2004.65
  5. 5.
    Collins, A., Fensch, C., Leather, H.: MaSiF: machine learning guided auto-tuning of parallel skeletons. In: Yew, P.C., Cho, S., DeRose, L., Lilja, D. (eds.) PACT, Minneapolis, pp. 437–438. ACM (2012). http://dblp.uni-trier.de/db/conf/IEEEpact/pact2012.html#CollinsFL12
  6. 6.
    Fursin, G., Kashnikov, Y., Wahid, A., Chamski, M.Z., Temam, O., Namolaru, M., Yom-tov, E., Mendelson, B., Zaks, A., Courtois, E., Bodin, F., Barnard, P., Ashton, E., Bonilla, E., Thomson, J., Williams, C.: Milepost GCC: machine learning enabled self-tuning compiler (2009)Google Scholar
  7. 7.
    Gonzalez, J., Gimenez, J., Labarta, J.: Automatic evaluation of the computation structure of parallel applications. In: 2009 International Conference on Parallel and Distributed Computing, Applications and Technologies, Higashi Hiroshima, pp. 138–145. IEEE (2009)Google Scholar
  8. 8.
    Haneda, M., Knijnenburg, P., Wijshoff, H.: Automatic selection of compiler options using non-parametric inferential statistics. In: International Conference on Parallel Architectures and Compilation Techniques, Saint Louis, pp. 123–132 (2005)Google Scholar
  9. 9.
    Jordan, D., Evdemon, J., Alves, A., Arkin, A., Askary, S., Barreto, C., Bloch, B., Curbera, F., Ford, M., Goland, Y., et al.: Web Services Business Process Execution Language Version 2.0. OASIS Standard 11 (2007)Google Scholar
  10. 10.
    Jordan, H., Thoman, P., Durillo, J., Pellegrini, S., Gschwandtner, P., Fahringer, T., Moritsch, H.: A multi-objective auto-tuning framework for parallel codes. In: Proceedings of the International Conference on High Performance Computing, Networking, Storage and Analysis, SC ’12, Salt Lake City. IEEE Computer Society Press, Los Alamitos, pp. 10:1–10:12 (2012). http://dl.acm.org/citation.cfm?id=2388996.2389010
  11. 11.
    Knüpfer, A., Rössel, C., Mey, D., Biersdorff, S., Diethelm, K., Eschweiler, D., Geimer, M., Gerndt, M., Lorenz, D., Malony, A., et al.: Score-P: a joint performance measurement run-time infrastructure for periscope, scalasca, TAU, and vampir. In: Tools for High Performance Computing 2011, pp. 79–91. Springer, Berlin/Heidelberg (2012)Google Scholar
  12. 12.
    Leather, H., Bonilla, E.: Automatic feature generation for machine learning based optimizing compilation. In: Code Generation and Optimization (CGO), Seattle, pp. 81–91 (2009)Google Scholar
  13. 13.
    Malony, A.D., Shende, S.S., Morris, A.: Phase-based parallel performance profiling. In: G.R. Joubert, W.E. Nagel, F.J. Peters, O. Plata, P. Tirado, E.Z. (eds.) Proceedings of the International Conference ParCo 2005, Malaga. NIC Series, vol. 33, pp. 203–210. John von Neumann Institute for Computing, Julich, (2006)Google Scholar
  14. 14.
    Nelson, Y., Bansal, B., Hall, M., Nakano, A., Lerman, K.: Model-guided performance tuning of parameter values: a case study with molecular dynamics visualization. In: International Parallel and Distributed Processing Symposium, Miami, pp. 1–8 (2008)Google Scholar
  15. 15.
    Pan, Z., Eigenmann, R.: Fast and effective orchestration of compiler optimizations for automatic performance tuning. In: Proceedings of the International Symposium on Code Generation and Optimization (CGO), New York, pp. 319–332 (2006)Google Scholar
  16. 16.
    Ribler, R., Vetter, J., Simitci, H., Reed, D.: Autopilot: adaptive control of distributed applications. In: Proceedings of the 7th IEEE Symposium on High-Performance Distributed Computing, Chicago, pp. 172–179 (1998)Google Scholar
  17. 17.
    Tiwari, A., Chen, C., Chame, J., Hall, M., Hollingsworth, J.: A scalable auto-tuning framework for compiler optimization. In: International Parallel and Distributed Processing Symposium, Rome, pp. 1–12 (2009)Google Scholar
  18. 18.
    Triantafyllis, S., Vachharajani, M., Vachharajani, N., August, D.: Compiler optimization-space exploration. In: Proceedings of the international symposium on Code generation and optimization, San Francisco, pp. 204–215. IEEE Computer Society (2003)Google Scholar
  19. 19.
    Witkin, A.: Scale-space filtering: a new approach to multi-scale description. In: IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP’84, San Diego, vol. 9, pp. 150–153. IEEE (1984)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Yury Oleynik
    • 1
    Email author
  • Robert Mijaković
    • 1
  • Isaías A. Comprés Ureña
    • 1
  • Michael Firbach
    • 1
  • Michael Gerndt
    • 1
  1. 1.Institute of InformaticsTechnical University of Munich (TUM)GarchingGermany

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