Scalable Automatic Performance Analysis on IBM BlueGene/P Systems

  • Yury Oleynik
  • Michael Gerndt
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7156)

Abstract

Nowadays scientific endeavor becomes more and more hungry for computational power of the state-of-the-art supercomputers. However the current trend in the performance increase comes along with tremendous increase in power consumption. One of the approaches allowing to overcome the issue is tight coupling of the simplified low-frequency cores into massively parallel system, such as IBM BlueGene/P (BG/P) combining hundreds of thousands cores. In addition to revolutionary system design this scale requires new approaches in application development and performance tuning. In this paper we present a new scalable BG/P tailored design for an automatic performance analysis tool - Periscope. In this work we have elicited and implemented a new design for porting Periscope to BG/P which features optimal system utilization, minimal monitoring intrusion and high scalability.

Keywords

Performance analysis Scalability of Applications & Tools Supercomputers 

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References

  1. 1.
    IBM BlueGene team: Overview of the IBM BlueGene/P project. IBM Jopurnal of Research and Development 52(1/2), 199–220 (2008)Google Scholar
  2. 2.
    Sosa, C., Knudson, B.: IBM System Blue Gene Solution: Blue Gene/P Application Development. International Technical Support Organization, 4th edn. (August 2009)Google Scholar
  3. 3.
    DelSignore, J.: TotalView on Blue Gene/L, “Presented at” Blue Gene/L: Applications, Architecture and Software Workshop, http://www.llnl.gov/asci/platforms/bluegene/papers/26delsignore.pdf
  4. 4.
    Gerndt, M., Fürlinger, K., Kereku, E.: Advanced techniques for performance analysis. NIC, vol. 33, pp. 15–26 (2006)Google Scholar
  5. 5.
    Benedict, S., Brehm, M., Gerndt, M., Guillen, C., Hesse, W., Petkov, V.: Automatic Performance Analysis of Large Scale Simulations. In: Lin, H.-X., Alexander, M., Forsell, M., Knüpfer, A., Prodan, R., Sousa, L., Streit, A. (eds.) Euro-Par 2009. LNCS, vol. 6043, pp. 199–207. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  6. 6.
    Gerndt, M., Strohhäcker, S.: Distribution of Periscope analysis agents on ALTIX 4700. In: Proceedings of the International Conference on the Parallel Computing (ParCo 2007). Advances in Parallel Computing, vol. 15, pp. 113–120. IOS Press (2007)Google Scholar
  7. 7.
    Fahringer, T., Gerndt, M., Riley, G., Träff, J.: Knowledge specification for automatic performance analysis. APART Technical Report (2001), http://www.fz-juelich.de/apart
  8. 8.
    Wylie, B.J.N., Bohme, D., Mohr, B., Szebenyi, Z., Wolf, F.: Performance analysis of Sweep3D on Blue Gene/P with the Scalasca toolset. In: IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum (IPDPSW 2010). IEEE (2010) - 978-1-4244-6533-0. - S. 1 - 8Google Scholar
  9. 9.

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Yury Oleynik
    • 1
  • Michael Gerndt
    • 1
  1. 1.Fakultät für Informatik I10Technische Universität MünchenGarchingGermany

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