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International Journal of Parallel Programming

, Volume 45, Issue 5, pp 1026–1045 | Cite as

Energy-Aware Modeling of Scaled Heterogeneous Systems

  • Ami Marowka
Article

Abstract

Many-core processors are accelerating the performance of contemporary high-performance systems. Managing power consumption within these systems demands low-power architectures to increase power savings. One of the promising solutions offered today by microprocessor architects is asymmetric microprocessors that integrate different core architectures on a single die. This paper presents analytical models based on scaled power metrics to analyze the impact of various architectural design choices on scaled performance and power savings. The power consumption implications of different processing schemes and various chip configurations were also analyzed. Analysis shows that by choosing the optimal chip configuration, energy efficiency and energy savings can be increased considerably.

Keywords

Energy efficiency Gustafson–Barsis’s law Hybrid architecture Performance per Watt Modeling techniques 

References

  1. 1.
    Moore, G.: Cramming more components onto integrated circuits. Electronics 38(8), 114–117 (1965)Google Scholar
  2. 2.
    Woo, D.H., Lee, H.S.: Extending Amdahl’s law for energy-efficient computing in the many-core era. IEEE Comput. 38(11), 32–38 (2005)CrossRefGoogle Scholar
  3. 3.
    Kumar, R., et al.: Heterogeneous chip multiprocessors. IEEE Comput. 38(11), 32–38 (2005)CrossRefGoogle Scholar
  4. 4.
    Mantor, M.: Entering the golden age of heterogeneous computing. C-DAC PEEP2008. http://ati.amd.com/technology/streamcomputing/IUCAA_Pune_PEEP_2008
  5. 5.
    Kogge, P., et al.: Exascale Computing Study: Technology Challenges in Achieving Exascale Systems. DARPA, Washington (2008)Google Scholar
  6. 6.
    Fuller, S.H., Millett, L.I.: Computing performance: game over or next level? IEEE Comput. 44(1), 31–38 (2011)CrossRefGoogle Scholar
  7. 7.
    Borkar, S.: Thousand Core Chips: A Technology Perspective. In: Proceedings of 44th Design Automation Conference (DAC 07), ACM Press, pp. 746–749 (2007)Google Scholar
  8. 8.
    Marowka, A.: Back to thin-core massively parallel processors. IEEE Comput. 44(12), 49–54 (2011)CrossRefGoogle Scholar
  9. 9.
    Krishnamurthy, R.K., Kaul, H.: Ultra-low voltage technologies for energy-efficient special-purpose hardware accelerators. Intel Technol. J. 13(4), 100–117 (2009)Google Scholar
  10. 10.
    Hillis, D.: The Pattern on the Stone: The Simple Ideas that Make Computers Work. Basic Books, New York (1998)Google Scholar
  11. 11.
    Shi, Y.: Reevaluating Amdahl’s law and Gustafson’s law. http://www.cis.temple.edu/shi/docs/amdahl/amdahl.html (1996)
  12. 12.
    Amdahl, G.M.: Validity of the Single-Processor Approach to Achieving Large-Scale Computing Capabilities. In: Proceedings of American Federation of Information Processing Societies, AFIPS Press, pp. 483–485 (1967)Google Scholar
  13. 13.
    Gustafson, J.L.: Reevaluating Amdahl’s Law. Communications of the ACM, pp. 532–533 (1988)Google Scholar
  14. 14.
    Gustafson, J.L.: The consequences of fixed time performance measurement. Proceedings of the Twenty-Fifth Hawaii International Conference on System Sciences, vol. 2, pp. 113–124 (1992)Google Scholar
  15. 15.
    Marowka, A.: Analytical modeling of energy efficiency in heterogeneous processors. Comput. Electr. Eng. J. 39(8), 2566–2578 (2013)CrossRefGoogle Scholar
  16. 16.
    Marowka, A.: Extending Amdahl’s law for heterogeneous computing. In: Proceeding of the 2012 10th IEEE International Symposium on Parallel and Distributed Processing with Applications (ISPA-2012), pp. 309–316Google Scholar
  17. 17.
    Marowka, A.: Modeling the effects of DFS on power consumption in hybrid chip multiprocessors. In: Proceeding of 1st International Workshop on Energy Efficient SuperComputing (E2SC) Held in Conjunction with SC’13, Denver, Colorado, USA, November, 17–22, 2013, ACM digital libraryGoogle Scholar
  18. 18.
    Hill, M.D., Marty, M.R.: Amdahl’s law in the multicore era. IEEE Comput. 41(7), 33–38 (2008)Google Scholar
  19. 19.
    Sun, X.H., Chen, Y.: Reevaluating Amdahl’s law in the multicore era. J. Parallel Distrib. Comput. 70, 183–188 (2010)CrossRefzbMATHGoogle Scholar
  20. 20.
    Esmaeilzadeh, H., Blem, E., St. Amant, R., Sankaralingam, K., Burger, D.C.: Dark silicon and the end of multicore scaling. In: Proceeding of 38th International Symposium on Computer Architecture (ISCA), pp. 365–376 (2011)Google Scholar
  21. 21.
    Cho, S., Melhem, R.G.: Corollaries to Amdahl’s law for energy. IEEE Comput. Archit. Lett. 7(1), 25–28 (2008)CrossRefGoogle Scholar
  22. 22.
    Cho, S., Melhem, R.G.: On the interplay of parallelization, program performance, and energy consumption. IEEE Trans. Parallel Distrib. Syst. 21(3), 342–353 (2010)CrossRefGoogle Scholar
  23. 23.
    Hong, S., Kim, H.: An integrated GPU power and performance model. In: Proceeding of ISCA10, ACM, pp. 19–23 (2010)Google Scholar
  24. 24.
    Pei, S., Zhang, J., Xiong, N., Kim M.-S., Gaudiot J.-L.: Performance-energy efficiency model of heterogeneous parallel multicore system. In: Green and Sustainable Computing Conference (IGSC), pp. 1–6 (2015)Google Scholar
  25. 25.
    Karanikolaou, E.M., Milovanovic, E.I., Milovanovic, I.Z., Bekakos, M.P.: Performance scalability and energy consumption on distributed and many-core platforms. J. Supercomput. 70(1), 349–364 (2014)CrossRefGoogle Scholar
  26. 26.
    Kim, S.H., Kim, D., Lee, C., Jeong, W.S., Ro, W.W., Gaudiot, J.L.: A performance-energy model to evaluate single thread execution acceleration. Comput. Archit. Lett. 14(99), 1–4 (2014)Google Scholar
  27. 27.
    Lee, V.W. et al.: Debunking the 100X GPU vs. CPU myth: an evaluation of throughput computing on CPU and GPU. In ISCA’10 Proceedings of the 37th Annual International Symposium on Computer Architecture (2010)Google Scholar

Copyright information

© Springer Science+Business Media New York 2016

Authors and Affiliations

  1. 1.Parallel Research LabTel AvivIsrael

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