, Volume 96, Issue 12, pp 1163–1177 | Cite as

Energy-centric DVFS controlling method for multi-core platforms

  • Shin-gyu Kim
  • Hyeonsang Eom
  • Heon Y. Yeom
  • Sang Lyul Min


Dynamic voltage and frequency scaling (DVFS) is a well-known and effective technique for reducing energy consumption in modern processors. However, accurately predicting the effect of frequency scaling on system performance is a challenging problem in real environments. In this paper, we propose a realistic DVFS performance prediction method, and a practical DVFS control policy (eDVFS) that aims to minimize total energy consumption in multi-core platforms. We also present power consumption estimation models for CPU and DRAM by exploiting a hardware energy monitoring unit. We implemented eDVFS in Linux, and our evaluation results show that eDVFS can save a substantial amount of energy compared with Linux “on-demand” CPU governor in diverse environments.


DVFS performance prediction CPU and DRAM power prediction Energy saving Multi-core processor 

Mathematics Subject Classification




This research was supported by Next-Generation Information Computing Development Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education, Science and Technology (No. 2010-0020731). The ICT at Seoul National University provided research facilities for this study.


  1. 1.
    Carothers NL (2000) Real analysis. Cambridge University Press, CambridgeCrossRefzbMATHGoogle Scholar
  2. 2.
    Choi K, Soma R, Pedram M (2004) Dynamic voltage and frequency scaling based on workload decomposition. In: Proceedings of the 2004 international symposium on Low power electronics and designGoogle Scholar
  3. 3.
    David H, Fallin C, Gorbatov E, Hanebutte UR, Mutlu O (2011) Memory power management via dynamic voltage/frequency scaling. In: Proceedings of the 8th ACM international conference on Autonomic computing, ICAC ’11. ACM, New York, pp 31–40Google Scholar
  4. 4.
    Dhiman G, Kontorinis V, Tullsen D, Rosing T, Saxe E, Chew J (2010) Dynamic workload characterization for power efficient scheduling on cmp systems. In: Proceedings of the 16th ACM/IEEE international symposium on low power electronics and designGoogle Scholar
  5. 5.
    Eyerman S, Eeckhout L (2010) A counter architecture for online DVFS profitability estimation. IEEE Trans Comput 59(11):1576–1583. doi: 10.1109/TC.2010.65 CrossRefMathSciNetGoogle Scholar
  6. 6.
    Google: Google data centers.
  7. 7.
    Greenpeace: how dirty is your data?
  8. 8.
    Intel: Intel Performance Counter Monitor.
  9. 9.
  10. 10.
    Isci C, Martonosi M (2006) Phase characterization for power: evaluating control-flow-based and event-counter-based techniques. In: The twelfth international symposium on high-performance computer architecture, pp 121–132Google Scholar
  11. 11.
    Keramidas G, Spiliopoulos V, Kaxiras S (2010) Interval-based models for run-time dvfs orchestration in superscalar processors. In: Proceedings of CF. New York. doi: 10.1145/1787275.1787338
  12. 12.
    McVoy L, Staelin C (1996) LMbench—tools for performance analysis.
  13. 13.
    Le Sueur E, Heiser G (2010) Dynamic voltage and frequency scaling: the laws of diminishing returns. In: Proceedings of the 2010 international conference on power aware computing and systemsGoogle Scholar
  14. 14.
    Li C, Qouneh A, Li T (2012) iSwitch: coordinating and optimizing renewable energy powered server clusters. In: Proceedings of the 39th ACM/IEEE international symposium on computer architecture. Portland, OR, USAGoogle Scholar
  15. 15.
    Lim H, Kansal A, Liu J (2011) Power budgeting for virtualized data centers. In: Proceedings of the 2011 USENIX conference on USENIX annual technical conference. Berkeley, CA, USA.
  16. 16.
    Ma K, Li X, Chen M, Wang X (2011) Scalable power control for many-core architectures running multi-threaded applications. In: Proceedings of the 38th annual international symposium on Computer architecture. New York. doi: 10.1145/2000064.2000117
  17. 17.
    Micron Technology Inc.: Tn-41-01: calculating memory system power for ddr3.
  18. 18.
    Miftakhutdinov R, Ebrahimi E, Patt YN (2012) Predicting performance impact of dvfs for realistic memory systems. In: Proceedings of MICRO-45. Vancouver, BC, CanadaGoogle Scholar
  19. 19.
    Miller R. The evolution of facebooks data center cooling.
  20. 20.
    Rountree B, Lowenthal DK, Schulz M, de Supinski BR (2011) Practical performance prediction under dynamic voltage frequency scaling. Proceedings of IGCC. Washington, DC, USA, In. doi: 10.1109/IGCC.2011.6008553
  21. 21.
    Schöne R, Hackenberg D, Molka D (2012) Memory performance at reduced cpu clock speeds: an analysis of current x86_64 processors. In: Proceedings of HotPower. Berkeley, CA, USA.
  22. 22.
    Sharma N, Barker S, Irwin D, Shenoy P (2011) Blink: managing server clusters on intermittent power. In: Proceedings of the sixteenth international conference on Architectural support for programming languages and operating systems, ASPLOS XVI, pp. 185–198. ACM, New York, NY, USAGoogle Scholar
  23. 23.
    Sherwood T, Sair S, Calder B (2003) Phase tracking and prediction. In: Proceedings of the 30th annual international symposium on Computer architecture, ISCA ’03, pp 336–349Google Scholar
  24. 24.
    U.S. Department of Energy: Data center energy consumption trends.
  25. 25.
    Weissel A, Bellosa F (2002) Process cruise control: event-driven clock scaling for dynamic power management. In: Proceedings of the 2002 international conference on Compilers, architecture, and synthesis for embedded systemsGoogle Scholar
  26. 26.
    Wu Q, Martonosi M, Clark DW, Reddi VJ, Connors D, Wu Y, Lee J, Brooks D (2005) A dynamic compilation framework for controlling microprocessor energy and performance. In: Proceedings of the 38th annual IEEE/ACM international symposium on microarchitectureGoogle Scholar

Copyright information

© Springer-Verlag Wien 2013

Authors and Affiliations

  • Shin-gyu Kim
    • 1
  • Hyeonsang Eom
    • 1
  • Heon Y. Yeom
    • 1
  • Sang Lyul Min
    • 1
  1. 1.School of Computer Science and Engineering, Seoul National UniversitySeoulKorea

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