Advertisement

A unified energy footprint for simulation software

  • Hartwig AnztEmail author
  • Armen Beglarian
  • Suren Chilingaryan
  • Andrew Ferrone
  • Vincent Heuveline
  • Andreas Kopmann
Special Issue Paper

Abstract

The focus in High-Performance Computing increasingly turns to energy efficiency. Therefore the pure concentration on floating point operations and runtime performance is no longer sufficient. In terms of hardware, this change of paradigm has already taken place: The GREEN500 list as counterpart to the runtime performance oriented TOP500 list has been established. The new metrics take runtime and energy consumption into account. Nevertheless, all these developments consider hardware only—still an inadequate situation to face the challenges of Energy-Efficient Exascale Computing. The necessity of optimizing simulation software with respect to power and energy draft demands for detailed profiling of the power consumption during the calculations and a norm quantifying the respective efficiency. In this paper we propose a unified energy footprint for simulation software that enables a fast comparison between different models, implementations and hardware configurations, respectively. By way of example we provide the footprints for the tomographic reconstruction code PyHST optimized for CPU and GPU operation as well as the operational numerical weather prediction model COSMO. We then discuss the power and energy profiles and investigate the effects of scaling with respect to hardware resources and simulation parameters.

Keywords

Energy-efficient computing Measurement of CPU and GPU power consumption Benchmarking Meteorological simulations COSMO 

References

  1. 1.
    Bekas C, Curioni A (2010) A new energy aware performance metric. Comput Sci Res Dev 25:187–195. doi: 10.1007/s00450-010-0119-z CrossRefGoogle Scholar
  2. 2.
    Castillo M, Dolz M, Fernandez JC, Mayo R, Quintana-Orti ES, Roca V (2011) Evaluation of the energy performance of dense linear algebra kernels on multi-core and many-core processors. In: 2011 IEEE international symposium on parallel and distributed processing workshops and Phd forum (IPDPSW), pp 846–853 CrossRefGoogle Scholar
  3. 3.
    Chilingaryan S, Mirone A, Hammersley A, Ferrero C, Helfen L, Kopmann A, dos Santos Rolo T, Vagovic P (2011) A GPU-based architecture for real-time data assessment at synchrotron experiments. IEEE Trans Nucl Sci 58:1447–1455 CrossRefGoogle Scholar
  4. 4.
    Doms G, Schättler U (2002) A description of the nonhydrostatic regional model LM. Part I: dynamics and numerics. Deutscher Wetterdienst, Offenbach Google Scholar
  5. 5.
    Dongarra J et al. (2011) The international ExaScale software project roadmap. Int J High Perform Comput Appl 25(1):3–60 CrossRefGoogle Scholar
  6. 6.
    Flato GM (1992) Spherical grid cavitating fluid sea ice model: linear drag, Arakawa C-grid version: documentation. Ice-Ocean Dynamics Laboratory report, Thayer School of Engineering, Dartmouth College Google Scholar
  7. 7.
    Gurumurthi S, Sivasubramaniam A, Irwin MJ, Vijaykrishnan N, Kandemir M (2002) Using complete machine simulation for software power estimation: the softwatt approach. In: Proceedings of eighth international symposium on high-performance computer architecture, 2002, pp 141–150 Google Scholar
  8. 8.
    Hammersley A, Mirone A High speed tomography reference manual. Available online: http://www.esrf.eu/computing/scientific/HST/HST-REF/
  9. 9.
    Ou J, Choi SB, Prasanna VK (2005) Energy-efficient hardware/software Co-synthesis for a class of applications on reconfigurable SoCs. Int J Embed Syst 1:91–102 CrossRefGoogle Scholar
  10. 10.
  11. 11.
    Ritter B, Geleyn JF (1992) A comprehensive radiation scheme for numerical weather prediction models with potential applications in climate simulations. Mon Weather Rev 120(2):303–325 CrossRefGoogle Scholar
  12. 12.
    Slingo J, European Centre for Medium Range Weather Forecasts (1985) Development of the operational parameterization scheme Google Scholar
  13. 13.
    Steinke S, Knauer M, Wehmeyer L, Marwedel P (2001) An accurate and fine grain instruction-level energy model supporting software optimizations. In: Proc int workshop power & timing modeling, optimization & simulation (PATMOS) Google Scholar
  14. 14.
    Steppeler J, Doms G, Schättler U, Bitzer HW, Gassmann A, Damrath U, Gregoric G (2003) Meso-gamma scale forecasts using the nonhydrostatic model LM. Meteorol Atmos Phys 82:75–96. doi: 10.1007/s00703-001-0592-9 CrossRefGoogle Scholar
  15. 15.
    Super Micro Computer, Inc (2010) Supermicro X8DTG-QF User’s Manual, revision 1.0a edition Google Scholar
  16. 16.
    Tiwari V, Malik S, Wolfe A (1994) Power analysis of embedded software: a first step towards software power minimization. IEEE Trans Very Large Scale Integr (VLSI) Syst 2(4):437–445 CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2012

Authors and Affiliations

  • Hartwig Anzt
    • 1
    Email author
  • Armen Beglarian
    • 1
  • Suren Chilingaryan
    • 1
  • Andrew Ferrone
    • 1
  • Vincent Heuveline
    • 1
  • Andreas Kopmann
    • 1
  1. 1.Karlsruhe Institute of TechnologyKarlsruheGermany

Personalised recommendations