Encyclopedia of Parallel Computing

Editors: David Padua

Scalasca

  • Felix Wolf
Reference work entry
DOI: https://doi.org/10.1007/978-0-387-09766-4_61

Synonyms

The predecessor of Scalasca, from which Scalasca evolved, is known by the name of KOJAK.

Definition

Scalasca is an open-source software tool that supports the performance optimization of parallel programs by measuring and analyzing their runtime behavior. The analysis identifies potential performance bottlenecks – in particular those concerning communication and synchronization – and offers guidance in exploring their causes. Scalasca targets mainly scientific and engineering applications based on the programming interfaces MPI and OpenMP, including hybrid applications based on a combination of the two. The tool has been specifically designed for use on large-scale systems including IBM Blue Gene and Cray XT, but is also well suited for small- and medium-scale HPC platforms.

Discussion

Introduction

Driven by growing application requirements and accelerated by current trends in microprocessor design, the number of processor cores on modern supercomputers is expanding from...

This is a preview of subscription content, log in to check access

Bibliography

  1. 1.
    Becker D, Rabenseifner R, Wolf F, Linford J (2009) Scalable timestamp synchronization for event traces of message-passing applications. Parallel Comput 35(12):595–607MathSciNetGoogle Scholar
  2. 2.
    Becker D, Wolf F, Frings W, Geimer M, Wylie BJN, Mohr B (2007) Automatic trace-based performance analysis of metacomputing applications. In: Proceedings of the international parallel and distributed processing symposium (IPDPS), Long Beach, CA, USA. IEEE Computer Society, Washington, DCGoogle Scholar
  3. 3.
    Böhme D, Geimer M, Wolf F, Arnold L (2010) Identifying the root causes of wait states in large-scale parallel applications. In: Proceedings of the 39th international conference on parallel processing (ICPP), San Diego, CA, USA. IEEE Computer Society, Washington, DC, pp 90–100Google Scholar
  4. 4.
    Frings W, Wolf F, Petkov V (2009) Scalable massively parallel I/O to task-local files. In: Proceedings of the ACM/IEEE conference on supercomputing (SC09), Portland, OR, USA, Nov 2009Google Scholar
  5. 5.
    Geimer M, Wolf F, Wylie BJN, Ábrahám E, Becker D, Mohr B (2010) The Scalasca performance toolset architecture. Concurr Comput Pract Exper 22(6):702–719Google Scholar
  6. 6.
    Geimer M, Wolf F, Wylie BJN, Mohr B (2009) A scalable tool architecture for diagnosing wait states in massively-parallel applications. Parallel Comput 35(7):375–388Google Scholar
  7. 7.
    Gibbon P, Frings W, Dominiczak S, Mohr B (2006) Performance analysis and visualization of the n-body tree code PEPC on massively parallel computers. In: Proceedings of the conference on parallel computing (ParCo), Málaga, Spain, Sept 2005 (NIC series), vol 33. John von Neumann-Institut für Computing, Jülich, pp 367–374Google Scholar
  8. 8.
    Hayes JC, Norman ML, Fiedler RA, Bordner JO, Li PS, Clark SE, ud-Doula A, Mac Low M-M (2006) Simulating radiating and magnetized flows in multiple dimensions with ZEUS-MP. Astrophys J Suppl 165(1):188–228Google Scholar
  9. 9.
    Hermanns M-A, Geimer M, Mohr B, Wolf F (2009) Scalable detection of MPI-2 remote memory access inefficiency patterns. In: Proceedings of the 16th European PVM/MPI users’ group meeting (EuroPVM/MPI), Espoo, Finland. Lecture notes in computer science, vol 5759. Springer, Berlin, pp 31–41Google Scholar
  10. 10.
    Hermanns M-A, Geimer M, Wolf F, Wylie BJN (2009) Verifying causality between distant performance phenomena in large-scale MPI applications. In Proceedings of the 17th Euromicro international conference on parallel, distributed, and network-based processing (PDP), Weimar, Germany. IEEE Computer Society, Washington, DC, pp 78–84Google Scholar
  11. 11.
    Jülich Supercomputing Centre and German Research School for Simulation Sciences. Scalasca parallel performance analysis toolset documentation (performance properties). http://www.scalasca.org/download/documentation/
  12. 12.
    Meira W Jr, LeBlanc TJ, Poulos A (1996) Waiting time analysis and performance visualization in Carnival. In: Proceedings of the SIGMETRICS symposium on parallel and distributed tools (SPDT’96), Philadelphia, PA, USA. ACMGoogle Scholar
  13. 13.
    Mohr B, Malony A, Shende S, Wolf F (2002) Design and prototype of a performance tool interface for OpenMP. J Supercomput 23(1):105–128MATHGoogle Scholar
  14. 14.
    Song F, Wolf F, Bhatia N, Dongarra J, Moore S (2004) An algebra for cross-experiment performance analysis. In: Proceedings of the international conference on parallel processing (ICPP), Montreal, Canada. IEEE Computer Society, Washington, DC, pp 63–72Google Scholar
  15. 15.
    Szebenyi Z, Gamblin T, Schulz M, de Supinski BR, Wolf F, Wylie BJN (2011) Reconciling sampling and direct instrumentation for unintrusive call-path profiling of MPI programs. In: Proceedings of the international parallel and distributed processing symposium (IPDPS), Anchorage, AK, USA. IEEE Computer Society, Washington, DCGoogle Scholar
  16. 16.
    Szebenyi Z, Wolf F, Wylie BJN (2009) Space-efficient time-series call-path profiling of parallel applications. In: Proceedings of the ACM/IEEE conference on supercomputing (SC09), Portland, OR, USA, Nov 2009Google Scholar
  17. 17.
    Szebenyi Z, Wylie BJN, Wolf F (2008) SCALASCA parallel performance analyses of SPEC MPI2007 applications. In: Proceedings of the 1st SPEC international performance evaluation workshop (SIPEW), Darmstadt, Germany. Lecture notes in computer science, vol 5119. Springer, Berlin, pp 99–123Google Scholar
  18. 18.
    Szebenyi Z, Wylie BJN, Wolf F (2009) Scalasca parallel performance analyses of PEPC. In: Proceedings of the workshop on productivity and performance (PROPER) in conjunction with Euro-Par, Las Palmas de Gran Canaria, Spain, August 2008. Lecture notes in computer science, vol 5415. Springer, Berlin, pp 305–314Google Scholar
  19. 19.
    Wolf F (2003) Automatic Performance Analysis on Parallel Computers with SMP Nodes. PhD thesis, RWTH Aachen, Forschungszentrum Jülich. ISBN 3-00-010003-2Google Scholar
  20. 20.
    Wolf F, Mohr B (2001) Specifying performance properties of parallel applications using compound events. Parallel Distrib Comput Pract 4(3):301–317Google Scholar
  21. 21.
    Wolf F, Mohr B (2003) Automatic performance analysis of hybrid MPI/OpenMP applications. J Syst Archit 49(10–11):421–439Google Scholar
  22. 22.
    Wolf F, Mohr B, Dongarra J, Moore S (2007) Automatic analysis of inefficiency patterns in parallel applications. Concurr Comput Pract Exper 19(11):1481–1496Google Scholar
  23. 23.
    Wylie BJN, Geimer M, Mohr B, Böhme D, Szebenyi Z, Wolf F (2010) Large-scale performance analysis of Sweep3D with the Scalasca toolset. Parallel Process Lett 20(4):397–414MathSciNetGoogle Scholar
  24. 24.
    Wylie BJN, Geimer M, Wolf F (2008) Performance measurement and analysis of large-scale parallel applications on leadership computing systems. Sci Program 16(2–3):167–181Google Scholar

Copyright information

© Springer Science+Business Media, LLC 2011

Authors and Affiliations

  • Felix Wolf
    • 1
  1. 1.German Research School for Simulation Sciences Jlich Supercomputing Centre RWTH Aachen UniversityAachenGermany