Computing and Visualization in Science

, Volume 17, Issue 2, pp 89–97 | Cite as

Utilization of empirically determined energy-optimal CPU-frequencies in a numerical simulation code

  • Björn Dick
  • Andreas Vogel
  • Dmitry Khabi
  • Martin Rupp
  • Uwe Küster
  • Gabriel Wittum
Article

Abstract

In order to enable exascale computing, concepts for substantial energy savings are required. Dynamic voltage and frequency scaling (DVFS) is widely known to provide suitable energy saving potentials. However, the customarily utilized DVFS mechanism of the Linux kernel determines clock frequencies solely based on an idle time analysis. In contrast to this, we use an empirical approach based on preparatory measurements of the energy consumption at all available frequencies. From the resulting data we deduce energy-optimal frequencies, which are used in subsequent production runs. The described methodology can be deployed with routine granularity to account for varying code characteristics. For evaluation purposes, the approach is applied to the UG4 numerical simulation software. First results exhibit an average energy saving potential of approximately 10 % while increasing the runtime by about 19 %.

Keywords

DVFS Energy empirical 

References

  1. 1.
    Abedi, A: Shiny Profiler - A State of the Art C/C++/Lua Profiler, (2007)Google Scholar
  2. 2.
    Arteaga, A., Ruprecht, D., Krause, R.: A stencil-based implementation of Parareal in the C++ domain specific embedded language STELLA. Appl. Math. Comput. 267, 727–741 (2015)Google Scholar
  3. 3.
    Braess, D.: Finite Elements: Theory, Fast Solvers, and Applications in Solid Mechanics. Cambridge University Press, England (2001)Google Scholar
  4. 4.
    Brodowski, D:. Manpage of cpufreq-set, (2005)Google Scholar
  5. 5.
    Intel\(^{\textregistered }\) M Processor. White Paper, March (2004)Google Scholar
  6. 6.
    Emmett, M., Minion, M.L.: Toward an efficient parallel in time method for partial differential equations. Commun. Appl. Math. Comput. Sci. 7(1), 105–132 (2012)MathSciNetCrossRefMATHGoogle Scholar
  7. 7.
    Freeh, V.W., Lowenthal, D.K.: Using Multiple Energy Gears in MPI Programs on a Power-Scalable Cluster. In: Proceedings of the Tenth ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP ’05, pp. 164–173, New York, NY, USA, ACM (2005)Google Scholar
  8. 8.
    Ge, R., Feng, X., Cameron, K.W.: Performance-constrained distributed DVS scheduling for scientific applications on power-aware clusters. In: Proceedings of the 2005 ACM/IEEE Conference on Supercomputing, IEEE Computer Society, SC 2005, 34 pp, Washington, DC, USA, (2005)Google Scholar
  9. 9.
    Grasedyck, L., Kriemann, R., Löbbert, C., Nägel, A., Wittum, G.,Xylouris, K.: Parallel tensor sampling in the hierarchical Tucker format. Comput. Vis. Sci. (2015). doi:10.1007/s00791-015-0247-x
  10. 10.
    Hackbusch, W.: Multi-Grid Methods and Applications, vol. 4. Springer, Berlin (1985)MATHGoogle Scholar
  11. 11.
    Hackbusch, W.: Iterative Solution of Large Sparse Systems of Equations. Springer, New York (1994)CrossRefMATHGoogle Scholar
  12. 12.
    Heppner, I., Lampe, M., Nägel, A., Reiter, S., Rupp, M., Vogel, A., Wittum, G.: Software Framework ug4: Parallel Multigrid on the Hermit Supercomputer. In: Nagel, W.E., Kröner, D.H., Resch, M.M. (eds.) High Performance Computing in Science and Engineering ’12, pp. 105–132. Springer, Berlin (2013)Google Scholar
  13. 13.
    Hotta, Y., Sato, M., Kimura, H., Matsuoka, S., Boku, T., Takahashi, D.: Profile-based Optimization of Power Performance by using Dynamic Voltage Scaling on a PC cluster. In: Proceedings of the 20th International Conference on Parallel and Distributed Processing, IPDPS’06, IEEE Computer Society Washington, DC, USA, (2006)Google Scholar
  14. 14.
    Hsu, C.H., Feng, W.C.: A Power-Aware Run-Time System for High-Performance Computing. In: Proceedings of the 2005 ACM/IEEE Conference on Supercomputing, SC ’05, IEEE Computer Society, Washington, DC, USA, (2005)Google Scholar
  15. 15.
    Khabi, D., Küster, U.: Power Consumption of Kernel Operations. In: Resch, M.M., Bez, W., Focht, E., Kobayashi, H., Kovalenko, Y. (eds.) Sustained Simulation Performance. Springer, Heidelberg (2013)Google Scholar
  16. 16.
    Kreienbuehl, A., Nägel, A., Ruprecht, D., Speck, R., Wittum, G., Krause, R.: Numerical simulation of skin transport using Parareal. Comput. Vis. Sci. (2015). doi:10.1007/s00791-015-0246-y
  17. 17.
    Lions, J.-L., Maday, Y., Turinici, G.: Résolution d’EDP par un schéma en temps « pararéel ». Comptes Rendus de l’Académie des Sciences - Series I - Mathematics 332(7), 661–668 (2001)MathSciNetCrossRefMATHGoogle Scholar
  18. 18.
    Mazouz, A., Laurent, A., Pradelle, B., Jalby, W.: Evaluation of CPU frequency transition latency. Comput. Sci. Res. Dev. 29(3–4), 187–195 (2014)CrossRefGoogle Scholar
  19. 19.
    Nägel, A., Schulz, V., Siebenborn, M, Wittum G.: Scalable shape optimization methods for structured inverse modeling in 3D diffusive processes. Comput. Vis. Sci, (2015). doi:10.1007/s00791-015-0248-9
  20. 20.
    Pallipadi, V., Starikovskiy, A.: The Ondemand Governor: Past, Present and Future. Proc. Linux Symp. 2, 223–238 (2006)Google Scholar
  21. 21.
    Reiter, S., Vogel, A., Heppner, I., Rupp, M., Wittum, G.: A massively parallel geometric multigrid solver on hierarchically distributed grids. Comput. Vis. Sci. 16(4), 151–164 (2013)Google Scholar
  22. 22.
    Treibig, J., Hager, G., Wellein, G.: LIKWID: A Lightweight Performance-Oriented Tool Suite for x86 Multicore Environments. In: Proceedings of the 2010 39th International Conference on Parallel Processing Workshops, ICPPW ’10, IEEE Computer Society, pp. 207–216, Washington, DC, USA, (2010)Google Scholar
  23. 23.
    Vogel, A., Reiter, S., Rupp, M., Nägel, A., Wittum, G.: UG 4: A novel flexible software system for simulating PDE based models on high performance computers. Comput. Vis. Sci. 16(4), 165–179 (2013)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2015

Authors and Affiliations

  • Björn Dick
    • 1
  • Andreas Vogel
    • 2
  • Dmitry Khabi
    • 1
  • Martin Rupp
    • 2
  • Uwe Küster
    • 1
  • Gabriel Wittum
    • 2
  1. 1.High Performance Computing Center Stuttgart (HLRS)StuttgartGermany
  2. 2.Goethe Center for Scientific Computing (G-CSC)Frankfurt am MainGermany

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