Score-P: A Unified Performance Measurement System for Petascale Applications

  • Dieter an Mey
  • Scott Biersdorf
  • Christian Bischof
  • Kai Diethelm
  • Dominic Eschweiler
  • Michael Gerndt
  • Andreas Knüpfer
  • Daniel Lorenz
  • Allen Malony
  • Wolfgang E. Nagel
  • Yury Oleynik
  • Christian Rössel
  • Pavel Saviankou
  • Dirk Schmidl
  • Sameer Shende
  • Michael Wagner
  • Bert Wesarg
  • Felix Wolf
Conference paper

Abstract

The rapidly growing number of cores on modern supercomputers imposes scalability demands not only on applications but also on the software tools needed for their development. At the same time, increasing application and system complexity makes the optimization of parallel codes more difficult, creating a need for scalable performance-analysis technology with advanced functionality. However, delivering such an expensive technology can hardly be accomplished by single tool developers and requires higher degrees of collaboration within the HPC community. The unified performance-measurement system Score-P is a joint effort of several academic performance-tool builders, funded under the BMBF program HPC-Software für skalierbare Parallelrechner in the SILC project (Skalierbare Infrastruktur zur automatischen Leistungsanalyse paralleler Codes). It is being developed with the objective of creating a common basis for several complementary optimization tools in the service of enhanced scalability, improved interoperability, and reduced maintenance cost.

References

  1. 1.
    Chan, A., Ashton, D., Lusk, R., Gropp, W.: Jumpshot-4 Users Guide. Mathematics and Computer Science Division, Argonne National Laboratory (2007). ftp://ftp.mcs.anl.gov/pub/mpi/slog2/js4-usersguide.pdf
  2. 2.
    Frings, W., Wolf, F., Petkov, V.: Scalable Massively Parallel I/O to Task-Local Files. In: Proc. of the ACM/IEEE Conf. on Supercomputing, pp. 1–11 (2009)Google Scholar
  3. 3.
    Fürlinger, K., Moore, S.: OpenMP-centric Performance Analysis of Hybrid Applications. In: Proc. of the 2008 IEEE Int. Conf. on Cluster Computing, pp. 160–166. Tsukuba (2008)Google Scholar
  4. 4.
    Geimer, M., Shende, S.S., Malony, A.D., Wolf, F.: A Generic and Configurable Source-Code Instrumentation Component. In: ICCS 2009: Proc. of the 9th Int. Conf. on Computational Science, pp. 696–705. Springer, Berlin (2009)Google Scholar
  5. 5.
    Geimer, M., Wolf, F., Wylie, B.J., Ábrahám, E., Becker, D., Mohr, B.: The Scalasca Performance Toolset Architecture. Concurrency and Computation: Practice and Experience 22(6), 702–719 (2010)Google Scholar
  6. 6.
    Gerndt, M., Fürlinger, K., Kereku, E.: Periscope: Advanced Techniques for Performance Analysis. In: Parallel Computing: Current & Future Issues of High-End Computing, Proc. of the Int. Conf. ParCo 2005, NIC Series, vol. 33, pp. 15–26. Forschungszentrum Jülich (2006)Google Scholar
  7. 7.
    Knüpfer, A., Brendel, R., Brunst, H., Mix, H., Nagel, W.E.: Introducing the Open Trace Format (OTF). In: Computational Science – ICCS 2006, LNCS, vol. 3992, pp. 526–533. Springer, Berlin (2006)Google Scholar
  8. 8.
    Knüpfer, A., Brunst, H., Doleschal, J., Jurenz, M., Lieber, M., Mickler, H., Müller, M.S., Nagel, W.E.: The Vampir Performance Analysis Tool Set. In: Tools for High Performance Computing, pp. 139–155. Springer, Berlin (2008)Google Scholar
  9. 9.
    Krammer, B., Müller, M.S., Resch, M.M.: Runtime Checking of MPI Applications with MARMOT. In: Proc. of Parallel Computing (ParCo), pp. 893–900. Málaga (2005)Google Scholar
  10. 10.
    Kufrin, R.: PerfSuite: An Accessible, Open Source Performance Analysis Environment for Linux Development and Performance. In: 6th Int. Conf. on Linux Clusters: The HPC Revolution. Chapel Hill, NC (2005)Google Scholar
  11. 11.
    Labarta, J., Girona, S., Pillet, V., Cortes, T., Gregoris, L.: DiP: A Parallel Program Development Environment. In: Proc. of 2nd Int. EuroPar Conf. (EuroPar 96). Lyon (1996)Google Scholar
  12. 12.
    Lorenz, D., Mohr, B., Rössel, C., Schmidl, D., Wolf, F.: How to Reconcile Event-Based Performance Analysis with Tasking in OpenMP. In: Proc. of 6th Int. Workshop of OpenMP (IWOMP), LNCS, vol. 6132, pp. 109–121. Springer, Berlin (2010)Google Scholar
  13. 13.
    Mellor-Crummey, J., Fowler, R., Marin, G., Tallent, N.: HPCView: A tool for top-down analysis of node performance. J. Supercomput. 23(1), 81–104 (2002)MATHCrossRefGoogle Scholar
  14. 14.
    Mohr, B., Malony, A.D., Shende, S., Wolf, F.: Design and Prototype of a Performance Tool Interface for OpenMP. J. Supercomput. 23(1), 105–128 (2002)MATHCrossRefGoogle Scholar
  15. 15.
    OpenMP Architecture Review Board: OpenMP Application Program Interface, Version 3.0. http://www.openmp.org/mp-documents/spec30.pdf, May 2008
  16. 16.
    Schulz, M., Galarowicz, J., Maghrak, D., Hachfeld, W., Montoya, D., Cranford, S.: Open | SpeedShop: An Open Source Infrastructure for Parallel Performance Analysis. Scientific Programming 16(2-3), 105–121 (2008)Google Scholar
  17. 17.
    Score-P project page. http://www.score-p.org (2010)
  18. 18.
    Shende, S., Malony, A., Morris, A.: Improving the Scalability of Performance Evaluation Tools. In: Proc. of the PARA 2010 Conf. (2010)Google Scholar
  19. 19.
    Shende, S.S., Malony, A.D.: The TAU Parallel Performance System. International Journal of High Performance Computing Applications 20(2), 287–311 (2006)CrossRefGoogle Scholar
  20. 20.
    SILC project page. http://www.vi-hps.org/projects/silc (2009)
  21. 21.
    VI-HPS project page. http://www.vi-hps.org (2010)
  22. 22.
    Wolf, F., Mohr, B.: EPILOG Binary Trace-Data Format. Tech. rep., Forschungzentrum Jülich (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Dieter an Mey
    • 1
  • Scott Biersdorf
    • 2
  • Christian Bischof
    • 1
  • Kai Diethelm
    • 3
  • Dominic Eschweiler
    • 4
  • Michael Gerndt
    • 5
  • Andreas Knüpfer
    • 6
  • Daniel Lorenz
    • 4
  • Allen Malony
    • 2
  • Wolfgang E. Nagel
    • 6
  • Yury Oleynik
    • 5
  • Christian Rössel
    • 4
  • Pavel Saviankou
    • 4
  • Dirk Schmidl
    • 1
  • Sameer Shende
    • 2
  • Michael Wagner
    • 6
  • Bert Wesarg
    • 6
  • Felix Wolf
    • 4
    • 7
    • 8
  1. 1.Center for Computing and CommunicationRWTH Aachen UniversityAachenGermany
  2. 2.Performance Research LaboratoryUniversity of OregonEugeneUSA
  3. 3.GNS Gesellschaft für numerische Simulation mbHBraunschweigGermany
  4. 4.Forschungszentrum Jülich GmbH, Jülich Supercomputing CentreJülichGermany
  5. 5.Fakultät für InformatikTechnische Universität MünchenGarchingGermany
  6. 6.Zentrum für Informationsdienste und Hochleistungsrechnen (ZIH)Technische Universität DresdenDresdenGermany
  7. 7.Laboratory for Parallel ProgrammingGerman Research School for Simulation SciencesAachenGermany
  8. 8.Department of Computer ScienceRWTH Aachen UniversityAachenGermany

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