Cluster Computing

, Volume 22, Issue 3, pp 839–859 | Cite as

Characterization, modeling and scheduling of power consumption of scientific computing applications in multicores

  • Jonathan MurañaEmail author
  • Sergio Nesmachnow
  • Fermín Armenta
  • Andrei Tchernykh


This article presents an empirical evaluation of power consumption for scientific computing applications in multicore systems. Three types of applications are studied, in single and combined executions on Intel and AMD servers, for evaluating the overall power consumption of each application. The main results indicate that power consumption behavior has a strong dependency with the type of application. Additional performance analysis shows that the best load of the server regarding energy efficiency depends on the type of the applications, with efficiency decreasing in heavily loaded situations. These results allow formulating a model to characterize applications according to power consumption, efficiency, and resource sharing, which provide useful information for resource management and scheduling policies. Several scheduling strategies are evaluated using the proposed energy model over realistic scientific computing workloads. Results confirm that strategies that maximize host utilization provide the best energy efficiency and performance results.


Green computing Energy efficiency Multicores Energy model Cloud simulator 


  1. 1.
    Buyya, R., Vecchiola, C., Selvi, S.: Mastering Cloud Computing: Foundations and Applications Programming. Morgan Kaufmann, San Francisco, CA (2013)Google Scholar
  2. 2.
    Dayarathna, M., Wen, Y., Fan, R.: Data center energy consumption modeling: a survey. IEEE Commun. Surv. Tutor. 18(1), 732–794 (2016)CrossRefGoogle Scholar
  3. 3.
    Nesmachnow, S., Perfumo, C., Goiri, I.: Holistic multiobjective planning of datacenters powered by renewable energy. Clust. Comput. 18(4), 1379–1397 (2015)CrossRefGoogle Scholar
  4. 4.
    Anghel, A., Vasilescu, L., Mariani, G., Jongerius, R., Dittmann, G.: An instrumentation approach for hardware-agnostic software characterization. Int. J. Parallel Program. 44(5), 924–948 (2016)CrossRefGoogle Scholar
  5. 5.
    Brandolese, C., Corbetta, S., Fornaciari, W.: Software energy estimation based on statistical characterization of intermediate compilation code. In: International Symposium on Low Power Electronics and Design, pp. 333–338 (2011)Google Scholar
  6. 6.
    Kurowski, K., Oleksiak, A., Piątek, W., Piontek, T., Przybyszewski, A., Węglarz, J.: Dcworms-a tool for simulation of energy efficiency in distributed computing infrastructures. Simul. Model. Pract. Theory 39, 135–151 (2013)CrossRefGoogle Scholar
  7. 7.
    Hernández, S., Fabra, J., Álvarez, P., Ezpeleta, J.: Simulation and realistic workloads to support the meta-scheduling of scientific workflows. In: Simulation and Modeling Methodologies, Technologies and Applications, pp. 155–167. Springer, Cham (2014)Google Scholar
  8. 8.
    Bak, S., Krystek, M., Kurowski, K., Oleksiak, A., Piatek, W., Waglarz, J.: GSSIM-a tool for distributed computing experiments. Sci. Program. 19(4), 231–251 (2011)Google Scholar
  9. 9.
    Malhotra, R., Jain, P.: Study and comparison of various cloud simulators available in the cloud computing. Int. J. Adv. Res. Comput. Sci. Softw. Eng. 3(9), 347–350 (2013)Google Scholar
  10. 10.
    Calheiros, R.N., Ranjan, R., Beloglazov, A., de Rose, C.A., Buyya, R.: Cloudsim: a toolkit for modeling and simulation of cloud computing environments and evaluation of resource provisioning algorithms. Softw.: Pract. Exp. 41(1), 23–50 (2011)Google Scholar
  11. 11.
    Armenta-Cano, F., Tchernykh, A., Cortes-Mendoza, J., Yahyapour, R., Drozdov, A.Y., Bouvry, P., Kliazovich, D., Avetisyan, A., Nesmachnow, S.: Min\_c: heterogeneous concentration policy for energy-aware scheduling of jobs with resource contention. Program. Comput. Softw. 43(3), 204–215 (2017)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Muraña, J., Nesmachnow, S., Iturriaga, S., Tchernykh, A.: Power consumption characterization of synthetic benchmarks in multicores. In: High Performance Computing, pp. 21–37. Springer, Cham (2018)Google Scholar
  13. 13.
    Repko, A.F.: Interdisciplinary research: process and theory. SAGE, Los Angeles (2008)Google Scholar
  14. 14.
    Iturriaga, S., García, S., Nesmachnow, S.: An empirical study of the robustness of energy-aware schedulers for high performance computing systems under uncertainty. In: High Performance Computing, pp. 143–157. Springer, Berlin (2014)Google Scholar
  15. 15.
    Nesmachnow, S., Dorronsoro, B., Pecero, J., Bouvry, P.: Energy-aware scheduling on multicore heterogeneous grid computing systems. J. Grid Comput. 11(4), 653–680 (2013)CrossRefGoogle Scholar
  16. 16.
    Srikantaiah, S., Kansal, A., Zhao, F.: Energy aware consolidation for cloud computing. In: Conference on Power Aware Computing and Systems, pp. 1–5 (2008)Google Scholar
  17. 17.
    Du Bois, K., Schaeps, T., Polfliet, S., Ryckbosch, F., Eeckhout, L.: Sweep: Evaluating computer system energy efficiency using synthetic workloads. In: 6\(^{th}\) International Conference on High Performance and Embedded Architectures and Compilers, pp. 159–166 (2011)Google Scholar
  18. 18.
    Feng, X., Ge, R., Cameron, K.: Power and energy profiling of scientific applications on distributed systems. In: 19\(^{th}\) IEEE International Parallel and Distributed Processing Symposium, pp. 34–44 (2005)Google Scholar
  19. 19.
    Langer, A., Totoni, E., Palekar, U.S., Kalé, L.: Energy-efficient computing for HPC workloads on heterogeneous manycore chips. In: Proceedings of the 6\(^{th}\) International Workshop on Programming Models and Applications for Multicores and Manycores, pp. 11–19 (2015)Google Scholar
  20. 20.
    Barroso, L., Clidaras, J., Hölzle, U.: The datacenter as a computer: an introduction to the design of warehouse-scale machines. Synth. Lect. Comput. Archit. 8(3), 1–154 (2013)CrossRefGoogle Scholar
  21. 21.
    Malladi, K., Nothaft, F., Periyathambi, K., Lee, B., Kozyrakis, C., Horowitz, M.: Towards energy-proportional datacenter memory with mobile dram. In: 39th Annual International Symposium on Computer Architecture, pp. 37–48 (2012)Google Scholar
  22. 22.
    Totoni, E., Jain, N., Kalé, L.: Toward runtime power management of exascale networks by on/off control of links. In: IEEE International Symposium on Parallel Distributed Processing, Workshops and Phd Forum, pp. 915–922 (2013)Google Scholar
  23. 23.
    Kliazovich, D., Bouvry, P., Audzevich, Y., Khan, S.: Greencloud: A packet-level simulator of energy-aware cloud computing data centers. In: IEEE Global Telecommunications Conference, pp. 1–5 (2010)Google Scholar
  24. 24.
    Núñez, A., Vázquez-Poletti, J., Caminero, A., Castañé, G., Carretero, J., Llorente, I.: Icancloud: a flexible and scalable cloud infrastructure simulator. J. Grid Comput. 10(1), 185–209 (2012)CrossRefGoogle Scholar
  25. 25.
    Kopytov, A.: Sysbench repository., online. Accessed 01 June 2017
  26. 26.
    Nesmachnow, S.: Computación científica de alto desempeño en la Facultad de Ingeniería, Universidad de la República. Revista de la Asociación de Ingenieros del Uruguay 61(1), 12–15 (2010). Text in Spanish Google Scholar
  27. 27.
    Leung, J., Kelly, L., Anderson, J.: Handbook of scheduling: algorithms, models, and performance analysis. CRC Press Inc, Boca Raton, FL (2004)Google Scholar
  28. 28.
    Intel Xeon E52643v3 vs AMD Opteron 6172 comparison., online. Accessed 29 March 2018
  29. 29.
    Gao, Y., Guan, H., Qi, Z., Song, T., Huan, F., Liu, L.: Service level agreement based energy-efficient resource management in cloud data centers. Comput. Electr. Eng. 40(5), 1621–1633 (2014)CrossRefGoogle Scholar
  30. 30.
    McKinney, W.: pandas: a foundational python library for data analysis and statistics. Python High Perform. Sci. Comput., 1–9 (2011)Google Scholar
  31. 31.
    Kluyver, T., Ragan-Kelley, B., Pérez, F., Granger, B., Bussonnier, M., Frederic, J., Kelley, K., Hamrick, J., Grout, J., Corlay, S., Ivanov, P., Avila, D., Abdalla, S., Willing, C.: Jupyter notebooks: a publishing format for reproducible computational workflows. In: Positioning and Power in Academic Publishing: Players, Agents and Agendas, pp. 87–90. IOS Press, Göttingen (2016)Google Scholar
  32. 32.
    Begley, C.G.: Six red flags for suspect work. Nature 497(7450), 433–434 (2013)CrossRefGoogle Scholar
  33. 33.
    Theil, H.: Economic forecasts and policy. North-Holland, Amsterdam (1961)Google Scholar
  34. 34.
    Feitelson, D.G., Tsafrir, D., Krakov, D.: Experience with using the parallel workloads archive. J. Parallel Distrib. Comput. 74(10), 2967–2982 (2014)CrossRefGoogle Scholar
  35. 35.
    Tchernykh, A., Lozano, L., Bouvry, P., Pecero, J.E., Schwiegelshohn, U., Nesmachnow, S.: Energy-aware online scheduling: ensuring quality of service for iaas clouds. In: International Conference on High Performance Computing Simulation, pp. 911–918 (2014)Google Scholar
  36. 36.
    Jackson, D., Snell, Q., Clement, M.: Core algorithms of the Maui scheduler. In: Job Scheduling Strategies for Parallel Processing, pp. 87–102. Springer, Berlin (2001)Google Scholar

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© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Universidad de la RepúblicaMontevideoUruguay
  2. 2.CICESE Research CenterEnsenadaMexico

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