GPU Power Modeling of HPC Applications for the Simulation of Heterogeneous Clouds

  • Antonios T. Makaratzis
  • Malik M. Khan
  • Konstantinos M. GiannoutakisEmail author
  • Anne C. Elster
  • Dimitrios Tzovaras
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10778)


Hardware accelerators have been widely used in the scientific community, as the gain in the performance of HPC applications is significant. Hardware accelerators have been used in cloud computing as well, though existing cloud simulation frameworks do not support modeling and simulation of such hardware. Models for the estimation of the power consumption of accelerators have been proposed by many researchers, but they require large number of inputs and computations, making them unsuitable for hyper scale simulations. In previous work, a generic model for the estimation of the power consumption of accelerators has been proposed, that can be combined with generic CPU power models suitable for integration in hyper scale simulation environments. This paper extends this work by providing models for the energy consumption of GPUs and CPU-GPU pairs, that are experimentally validated with the use of different GPU hardware models and GPU intensive applications. The relative error between the actual and the estimated energy consumption is low, thus the proposed models provide accurate estimations and can be efficiently integrated into cloud simulation frameworks.


Power consumption modeling Energy consumption modeling Accelerators High Performance Computing Graphics Processing Units Central processing units Heterogeneous clouds 



This work is partially funded by the European Union’s Horizon 2020 Research and Innovation Programme through CloudLightning project under Grant Agreement No. 643946.


  1. 1.
    Server power and performance characteristics (spec) (2008).
  2. 2.
    Barik, R., Farooqui, N., Lewis, B.T., Hu, C., Shpeisman, T.: A black-box approach to energy-aware scheduling on integrated CPU-GPU systems. In: Proceedings of the 2016 International Symposium on Code Generation and Optimization, pp. 70–81. ACM, New York (2016).
  3. 3.
    Beloglazov, A., Buyya, R.: Optimal online deterministic algorithms and adaptive heuristics for energy and performance efficient dynamic consolidation of virtual machines in cloud data centers. Concurr. Comput.: Pract. Exper. 24(13), 1397–1420 (2012).
  4. 4.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., De Rose, C.A.F., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw. Pract. Exper. 41(1), 23–50 (2011).
  5. 5.
    Fontoura Cupertino, L., Da Costa, G., Oleksiak, A., PiąTek, W., Pierson, J.M., Salom, J., Siso, L., Stolf, P., Sun, H., Zilio, T.: Energy-efficient, thermal-aware modeling and simulation of datacenters: the CoolEmAll approach and evaluation results. Ad Hoc Netw. J. 25(B), 535–553 (2015).
  6. 6.
    Giannoutakis, K.M., Makaratzis, A.T., Tzovaras, D., Filelis-Papadopoulos, C.K., Gravvanis, G.A.: On the power consumption modeling for the simulation of heterogeneous HPC clouds. In: Proceedings of the 1st International Workshop on Next Generation of Cloud Architectures, CloudNG 2017, pp. 1:1–1:6. ACM, New York (2017).
  7. 7.
    Hong, S., Kim, H.: An integrated GPU power and performance model. SIGARCH Comput. Archit. News 38(3), 280–289 (2010).
  8. 8.
    Kliazovich, D., Bouvry, P., Khan, S.U.: Greencloud: a packet-level simulator of energy-aware cloud computing data centers. J. Supercomput. 62(3), 1263–1283 (2012).
  9. 9.
    Kurowski, K., Oleksiak, A., Piatek, W., Piontek, T., Przybyszewski, A.W., Weglarz, J.: DCworms - a tool for simulation of energy efficiency in distributed computing infrastructures. Simul. Model. Practice Theory 39, 135–151 (2013)CrossRefGoogle Scholar
  10. 10.
    Makaratzis, A.T., Giannoutakis, K.M., Tzovaras, D.: Energy modeling in cloud simulation frameworks. Future Gener. Comput. Syst. (2017).
  11. 11.
    Nagasaka, H., Maruyama, N., Nukada, A., Endo, T., Matsuoka, S.: Statistical power modeling of GPU kernels using performance counters. In: International Conference on Green Computing, pp. 115–122, August 2010Google Scholar
  12. 12.
    Núñez, A., Vázquez-Poletti, J.L., Caminero, A.C., Castañé, G.G., Carretero, J., Llorente, I.M.: iCanCloud: a flexible and scalable cloud infrastructure simulator. J. Grid Comput. 10(1), 185–209 (2012).
  13. 13.
    Pouilloux, L., Hirofuchi, T., Lebre, A.: SimGrid VM: virtual machine support for a simulation framework of distributed systems. IEEE Trans. Cloud Comput. (2015).
  14. 14.
    Sîrbu, A., Babaoglu, O.: Power consumption modeling and prediction in a hybrid CPU-GPU-MIC supercomputer. In: Dutot, P.-F., Trystram, D. (eds.) Euro-Par 2016. LNCS, vol. 9833, pp. 117–130. Springer, Cham (2016). Google Scholar
  15. 15.
    Song, S., Su, C., Rountree, B., Cameron, K.W.: A simplified and accurate model of power-performance efficiency on emergent GPU architectures. In: 2013 IEEE 27th International Symposium on Parallel and Distributed Processing, pp. 673–686 (2013)Google Scholar
  16. 16.
    Tighe, M., Keller, G., Bauer, M., Lutfiyya, H.: DCSim: a data centre simulation tool for evaluating dynamic virtualized resource management. In: 2012 8th International Conference on Network and Service Management (CNSM) and 2012 Workshop on Systems Virtualization Management (SVM), pp. 385–392, October 2012Google Scholar
  17. 17.
    Xie, Q., Huang, T., Zou, Z., Xia, L., Zhu, Y., Jiang, J.: An accurate power model for GPU processors. In: 2012 7th International Conference on Computing and Convergence Technology (ICCCT), pp. 1141–1146, December 2012Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Antonios T. Makaratzis
    • 1
  • Malik M. Khan
    • 2
  • Konstantinos M. Giannoutakis
    • 1
    Email author
  • Anne C. Elster
    • 2
  • Dimitrios Tzovaras
    • 1
  1. 1.Information Technologies InstituteCentre for Research and Technology HellasThessalonikiGreece
  2. 2.Norwegian University of Science and TechnologyTrondheimNorway

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