Dynamic Power Management in a Heterogeneous Processor Architecture

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10172)


Emerging mobile platforms integrate heterogeneous, multicore processors to efficiently deal with the heterogeneity of data (in magnitude, type, and quality). The main goal is to achieve a high degree of energy-proportionality which corresponds with the nature and fluctuation of mobile workloads. Most existing power and energy consumption analyses of these architectures rely on simulation or static benchmarks neither of which truly reflects the type of workload the processors handle in reality. By contrast, we generate two types of stochastic workloads and employ four types of dynamic voltage and frequency scaling (DVFS) policies to investigate the energy proportionality and the dynamic power consumption characteristics of a heterogeneous processor architecture when operating in different configurations. The analysis illustrates, both qualitatively and quantitatively, that knowledge of the statistics of the incoming workload is critical to determine the appropriate processor configuration.


Dynamic power management DVFS Multicore processor Heterogeneous processor architecture Workload 


  1. 1.
    Maiti, S., Kapadia, N., Pasricha, S.: Process variation aware dynamic power management in multicore systems with extended range voltage/frequency scaling. In: MWSCAS, pp. 1–4 (2015)Google Scholar
  2. 2.
    Hanumaiah, V., Vrudhula, S.: Energy-efficient operation of multicore processors by DVFS, task migration, and active cooling. IEEE Trans. Comput. 63, 349–360 (2014)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Möbius, C., Dargie, W., Schill, A.: Power consumption estimation models for processors, virtual machines, and servers. IEEE Trans. Parallel Distrib. Syst. 25(6), 1600–1614 (2014)CrossRefGoogle Scholar
  4. 4.
    Dargie, W.: A stochastic model for estimating the power consumption of a processor. IEEE Trans. Comput. 64(5), 1311–1322 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Dargie, W.: Analysis of the power consumption of a multimedia server under different DVFS policies. In: CLOUD, pp. 779–785. IEEE (2012)Google Scholar
  6. 6.
    Sahuquillo, J., Hassan, H., Petit, S., March, J.L., Duato, J.: A dynamic execution time estimation model to save energy in heterogeneous multicores running periodic tasks. Future Gener. Comput. Syst. 56, 211–219 (2016)CrossRefGoogle Scholar
  7. 7.
    Petrucci, V., Loques, O., Mossé, D., Melhem, R., Gazala, N.A., Gobriel, S.: Energy-efficient thread assignment optimization for heterogeneous multicore systems. ACM Trans. Embeded Comput. Syst. 14(1), 15 (2015)CrossRefGoogle Scholar
  8. 8.
    Liu, G., Park, J., Marculescu, D.: Dynamic thread mapping for high-performance, power-efficient heterogeneous many-core systems. In: ICCD, pp. 54–61. IEEE (2013)Google Scholar
  9. 9.
    Pricopi, M., Muthukaruppan, T.S., Venkataramani, V., Mitra, T., Vishin, S.: Power-performance modeling on asymmetric multi-cores. In: CASES, September 2013Google Scholar
  10. 10.
    Singla, G., Kaur, G., Unver, A.K., Ogras, U.Y.: Predictive dynamic thermal and power management for heterogeneous mobile platforms. In: DATE (2015)Google Scholar
  11. 11.
    Prakash, A., Amrouch, H., Shafique, M., Mitra, T., Henkel, J.: Improving mobile gaming performance through cooperative CPU-GPU thermal management. In: DAC (2016)Google Scholar
  12. 12.
    Pallipadi, V., Starikovisky, A.: The ondemand governer. In: Proceedings of the Linux Symposium, vol. 2 (2006)Google Scholar
  13. 13.
    Brihi, A., Dargie, W.: Dynamic voltage and frequency scaling in multimedia servers. In: AINA, pp. 374–380 (2013)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Faculty of Computer ScienceTU DresdenDresdenGermany

Personalised recommendations