Computer Science - Research and Development

, Volume 25, Issue 3–4, pp 165–175 | Cite as

Simulation of power consumption of energy efficient cluster hardware

Special Issue Paper

Abstract

In recent years the power consumption of high-performance computing clusters has become a growing problem because the number and size of cluster installations has been rising. The high power consumption of clusters is a consequence of their design goal: High performance. With low utilization, cluster hardware consumes nearly as much energy as when it is fully utilized. Theoretically, in these low utilization phases cluster hardware can be turned off or switched to a lower power consuming state.

We designed a model to estimate power consumption of hardware based on the utilization. Applications are instrumented to create utilization trace files for a simulator realizing this model. Different hardware components can be simulated using multiple estimation strategies. An optimal strategy determines an upper bound of energy savings for existing hardware without affecting the time-to-solution. Additionally, the simulator can estimate the power consumption of efficient hardware which is energy-proportional. This way the minimum power consumption can be determined for a given application. Naturally, this minimal power consumption provides an upper bound for any power saving strategy.

After evaluating the correctness of the simulator several different strategies and energy-proportional hardware are compared.

Keywords

Simulation Energy-to-solution Power consumption HPC 

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References

  1. 1.
    Aebischer B, Huser A (2003) Energy efficiency of computer power supplies. In: EEDAL ’03: proceedings of the 3rd international conference on energy efficiency in domestic appliances and lighting Google Scholar
  2. 2.
    Agarwal Y, Hodges S, Chandra R, Scott J, Bahl P, Gupta R (2009) Somniloquy: augmenting network interfaces to reduce PC energy usage. In: NSDI’09: proceedings of the 6th USENIX symposium on networked systems design and implementation. USENIX Association, Berkeley, pp 365–380 Google Scholar
  3. 3.
    Barroso LA, Hölzle U (2007) The case for energy-proportional computing. Computer 40(12):33–37. doi: 10.1109/MC.2007.443 CrossRefGoogle Scholar
  4. 4.
    Bircher W, John L (2007) Complete system power estimation: a trickle-down approach based on performance events. In: ISPASS ’07: proceedings of the 2007 IEEE international symposium on performance analysis of systems and software. IEEE Computer Society, Los Alamitos, pp 158–168 Google Scholar
  5. 5.
    Contreras G, Martonosi M (2005) Power prediction for Intel xscale processors using performance monitoring unit events. In: ISLPED ’05: proceedings of the 2005 international symposium on low power electronics and design. ACM, New York, pp 221–226. doi: 10.1145/1077603.1077657 Google Scholar
  6. 6.
    Corporation, HP, Corporation, I, Corporation, M, Ltd, PT, Corporation, T (2005) Advanced configuration and power interface specification Google Scholar
  7. 7.
    Etinski M, Corbalan J, Labarta J, Valero M, Veidenbaum A (2009) Power-aware load balancing of large scale MPI applications. In: IPDPS ’09: proceedings of the 2009 IEEE international symposium on parallel and distributed processing. IEEE Computer Society, Washington, pp 1–8. doi: 10.1109/IPDPS.2009.5160973 Google Scholar
  8. 8.
    Feng X, Ge R, Cameron KW (2005) Power and energy profiling of scientific applications on distributed systems. In: IPDPS ’05: proceedings of the 19th IEEE international parallel and distributed processing symposium (IPDPS’05) papers. IEEE Computer Society, Washington, p 34. doi: 10.1109/IPDPS.2005.346 Google Scholar
  9. 9.
    Freeh V, Pan F, Kappiah N, Lowenthal D, Springer R (2005) Exploring the energy-time tradeoff in MPI programs on a power-scalable cluster. In: IPDPS ’05: proceedings of parallel and distributed processing symposium. doi: 10.1109/IPDPS.2005.214
  10. 10.
    Freeh VW, Lowenthal DK (2005) Using multiple energy gears in MPI programs on a power-scalable cluster. In: PPoPP ’05: proceedings of the tenth ACM SIGPLAN symposium on principles and practice of parallel programming. ACM, New York, pp 164–173. doi: 10.1145/1065944.1065967 CrossRefGoogle Scholar
  11. 11.
    Ge R, Feng X, Cameron KW (2005) Improvement of power-performance efficiency for high-end computing. In: IPDPS ’05: proceedings of the 19th IEEE international parallel and distributed processing symposium. IEEE Computer Society, Washington, p 233. doi: 10.1109/IPDPS.2005.251 Google Scholar
  12. 12.
    Ge R, Feng X, Cameron KW (2005) Performance-constrained distributed DVS scheduling for scientific applications on power-aware clusters. In: SC ’05: proceedings of the 2005 ACM/IEEE conference on supercomputing. IEEE Computer Society, Washington, p 34. doi: 10.1109/SC.2005.57 Google Scholar
  13. 13.
    Hotta Y, Sato M, Kimura H, Matsuoka S, Boku T, Takahashi D (2006) Profile-based optimization of power performance by using dynamic voltage scaling on a PC cluster. In: IPDPS ’06: proceedings of the 20th international parallel and distributed processing symposium. doi: 10.1109/IPDPS.2006.1639597
  14. 14.
    Hsu Ch, Feng Wc (2005) A power-aware run-time system for high-performance computing. In: SC ’05: proceedings of the 2005 ACM/IEEE conference on supercomputing. IEEE Computer Society, Washington, p 1. doi: 10.1109/SC.2005.3 Google Scholar
  15. 15.
    Huang S, Feng W (2009) Energy-efficient cluster computing via accurate workload characterization. In: CCGRID ’09: proceedings of the 2009 9th IEEE/ACM international symposium on cluster computing and the grid. IEEE Computer Society, Washington, pp 68–75. doi: 10.1109/CCGRID.2009.88 Google Scholar
  16. 16.
    Hylick A, Sohan R, Rice A, Jones B (2008) An analysis of hard drive energy consumption. In: MASCOTS 2008: IEEE international symposium on modeling, analysis and simulation of computers and telecommunication systems, pp 1–10. doi: 10.1109/MASCOT.2008.4770567
  17. 17.
    Intel MTP (2006) Intel Core2 duo mobile processor for Intel centrino duo mobile processor technology datasheet Google Scholar
  18. 18.
    Kappiah N, Freeh VW, Lowenthal DK (2005) Just in time dynamic voltage scaling: exploiting inter-node slack to save energy in MPI programs. In: SC ’05: proceedings of the 2005 ACM/IEEE conference on supercomputing. IEEE Computer Society, Washington, p 33. doi: 10.1109/SC.2005.39 Google Scholar
  19. 19.
    Krempel S, Kunkel J, Ludwig T (2009) Design and implementation of a profiling environment for trace based analysis of energy efficiency benchmarks in high performance computing. Master’s thesis, Institute of Computer Science, University of Heidelberg Google Scholar
  20. 20.
    Lim MY, Freeh VW, Lowenthal DK (2006) Adaptive, transparent frequency and voltage scaling of communication phases in MPI programs. In: SC ’06: proceedings of the 2006 ACM/IEEE conference on supercomputing. ACM, New York, p 107. doi: 10.1145/1188455.1188567 CrossRefGoogle Scholar
  21. 21.
    Lu YH, Benini L, De Micheli G (2000) Operating-system directed power reduction. In: ISLPED ’00: proceedings of the 2000 international symposium on low power electronics and design. ACM, New York, pp 37–42. doi: 10.1145/344166.344189 CrossRefGoogle Scholar
  22. 22.
    Minartz T, Kunkel J, Ludwig T (2009) Model and simulation of power consumption and power saving potential of energy efficient cluster hardware. Master’s thesis, Institute of Computer Science, University of Heidelberg Google Scholar
  23. 23.
    Moona PR, Chole S, Harneja S (2007) Memory management using dynamic memory switching. Project report, Department of Computer Science and Engineering, Indian Institute of Technology Kanpur Google Scholar
  24. 24.
    Pinheiro E, Bianchini R, Carrera E, Heath T (2001) Load balancing and unbalancing for power and performance in cluster-based systems. In: COLP ’01: workshop on compilers and operating systems for low power Google Scholar
  25. 25.
    Rountree B, Lowenthal DK, Funk S, Freeh VW, de Supinski BR, Schulz M (2007) Bounding energy consumption in large-scale MPI programs. In: SC ’07: proceedings of the 2007 ACM/IEEE conference on supercomputing. ACM, New York, pp 1–9. doi: 10.1145/1362622.1362688 CrossRefGoogle Scholar
  26. 26.
    Vasudevan V, Franklin J, Andersen D, Phanishayee A, Tan L, Kaminsky M, Moraru I (2009) FAWNdamentally power-efficient clusters. In: HotOS XII: 12th workshop on hot topics in operating systems Google Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  • Timo Minartz
    • 1
  • Julian M. Kunkel
    • 2
  • Thomas Ludwig
    • 1
  1. 1.Department of InformaticsUniversity of HamburgHamburgGermany
  2. 2.Deutsches Klimarechenzentrum GmbHHamburgGermany

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