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Exploration of Load Balancing Thresholds to Save Energy on Iterative Applications

  • Edson L. Padoin
  • Laércio L. Pilla
  • Márcio Castro
  • Philippe O. A. Navaux
  • Jean-François Méhaut
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 697)

Abstract

The power consumption of High Performance Computing systems is an increasing concern as large-scale systems grow in size and, consequently, consume more energy. In response to this challenge, we proposed two variants of a new energy-aware load balancer that aim at reducing the energy consumption of parallel platforms running imbalanced scientific applications without degrading their performance. Our research combines Dynamic Load Balancing with Dynamic Voltage and Frequency Scaling techniques in order to reduce the clock frequency of underloaded computing cores which experience some residual imbalance even after tasks are remapped. This work presents a trade-off evaluation between runtime, power demand and total energy consumption when applying these two energy-aware load balancer variants on real-world applications. In this way, we can define which is the best threshold value for each application under the total energy consumption, total execution time or the average power demand focus.

Keywords

Load Balancer Total Energy Consumption Clock Frequency Power Demand Total Execution Time 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by CNPq, CAPES, FAPERGS and FINEP. This research has received funding from the European Community’s Seventh Framework Programme (FP7-PEOPLE) under grant agreement number 295217, funding from the EU H2020 Programme and from MCTI/RNP-Brazil under the HPC4E Project, grant agreement number 689772 and STIC-AmSud/CAPES scientific-technological cooperation program under EnergySFE research project grant 99999.007556/2015-02.

References

  1. 1.
    Aupy, G., Benoit, A., Robert, Y.: Energy-aware scheduling under reliability and makespan constraints. In: Proceedings of International Conference on High Performance Computing (HiPC), pp. 1–10. IEEE Computer Society (2012)Google Scholar
  2. 2.
    Dosanjh, S., Barrett, R., Doerfler, D., Hammond, S., Hemmert, K., Heroux, M., Lin, P., Pedretti, K., Rodrigues, A., Trucano, T., et al.: Exascale design space exploration and co-design. Future Gener. Comput. Syst. 30, 46–58 (2014)CrossRefGoogle Scholar
  3. 3.
    Dupros, F., Aochi, H., Ducellier, A., Komatitsch, D., Roman, J.: Exploiting intensive multithreading for the efficient simulation of 3d seismic wave propagation. In: Proceedings of International Conference on Computational Science and Engineering, pp. 253–260. IEEE, July 2008Google Scholar
  4. 4.
    Gerards, M.E., Hurink, J.L., Holzenspies, P.K., Kuper, J., Smit, G.J.: Analytic clock frequency selection for global DVFS. In: Proceedings of Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), pp. 512–519 (2014)Google Scholar
  5. 5.
    Goel, B., McKee, S.A., Gioiosa, R., Singh, K., Bhadauria, M., Cesati, M.: Portable, scalable, per-core power estimation for intelligent resource management. In: Proceedings of International Green Computing Conference (IGCC), pp. 135–146. IEEE Computer Society (2010)Google Scholar
  6. 6.
    Hartog, J., Dede, E., Govindaraju, M.: Mapreduce framework energy adaptation via temperature awareness. Cluster Comput. 17(1), 111–127 (2013). http://dx.doi.org/10.1007/s10586-013-0270-y CrossRefGoogle Scholar
  7. 7.
    Hosseinimotlagh, S., Khunjush, F., Hosseinimotlagh, S.: A cooperative two-tier energy-aware scheduling for real-time tasks in computing clouds. In: Proceedings of Euromicro International Conference on Parallel, Distributed, and Network-Based Processing (PDP), pp. 178–182 (2014)Google Scholar
  8. 8.
    Huang, C., Lawlor, O., Kalé, L.V.: Adaptive MPI. In: Rauchwerger, L. (ed.) LCPC 2003. LNCS, vol. 2958, pp. 306–322. Springer, Heidelberg (2004). doi: 10.1007/978-3-540-24644-2_20 CrossRefGoogle Scholar
  9. 9.
    Isci, C., Buyuktosunoglu, A., Cher, C.Y., Bose, P., Martonosi, M.: An analysis of efficient multi-core global power management policies: Maximizing performance for a given power budget. In: Proceedings of International Symposium on Microarchitecture (MICRO), pp. 347–358. IEEE Computer Society, December 2006Google Scholar
  10. 10.
    Kalé, L.V., Bohm, E., Mendes, C.L., Wilmarth, T., Zheng, G.: Programming Petascale Applications with Charm++ and AMPI, pp. 421–441. Chapman & Hall/CRC Press (2008)Google Scholar
  11. 11.
    Kalé, L.V., Bhandarkar, M., Brunner, R.: Load balancing in parallel molecular dynamics. In: Ferreira, A., Rolim, J., Simon, H., Teng, S.-H. (eds.) IRREGULAR 1998. LNCS, vol. 1457, pp. 251–261. Springer, Heidelberg (1998). doi: 10.1007/BFb0018544 CrossRefGoogle Scholar
  12. 12.
    Karlin, I., Bhatele, A., Chamberlain, B.L., Cohen, J., Devito, Z., Gokhale, M., Haque, R., Hornung, R., Keasler, J., Laney, D., Luke, E., Lloyd, S., McGraw, J., Neely, R., Richards, D., Schulz, M., Still, C.H., Wang, F., Wong, D.: Lulesh programming model and performance ports overview. Technical report LLNL-TR-608824. http://www.osti.gov/scitech/servlets/purl/1059462
  13. 13.
    Karlin, I., Bhatele, A., Keasler, J., Chamberlain, B.L., Cohen, J., DeVito, Z., Haque, R., Laney, D., Luke, E., Wang, F., Richards, D., Schulz, M., Still, C.: Exploring traditional and emerging parallel programming models using a proxy application. In: Proceedings of 27th IEEE International Parallel & Distributed Processing Symposium (IEEE IPDPS 2013), May 2013Google Scholar
  14. 14.
    Kim, S.g., Eom, H., Yeom, H., Min, S.: Energy-centric DVFS controlling method for multi-core platforms. In: Proceedings of High Performance Computing, Networking, Storage and Analysis (SCC), pp. 685–690. IEEE Computer Society, November 2012Google Scholar
  15. 15.
    Leung, J.Y.T.: Handbook of Scheduling: Algorithms, Models, and Performance Analysis. Chapman & Hall/CRC, Boca Raton (2004)zbMATHGoogle Scholar
  16. 16.
    Menon, H., Jain, N., Zheng, G., Kalé, L.: Automated load balancing invocation based on application characteristics. In: Proceedings of IEEE International Conference on Cluster Computing (CLUSTER), pp. 373–381. IEEE Computer Society (2012)Google Scholar
  17. 17.
    Padoin, E., Castro, M., Pilla, L., Navaux, P., Mehaut, J.F.: Saving energy by exploiting residual imbalances on iterative applications. In: Proceedings of 21st International Conference on High Performance Computing (HiPC), pp. 1–10, December 2014Google Scholar
  18. 18.
    Sarood, O., Meneses, E., Kalé, L.V.: A ‘cool’ way of improving the reliability of HPC machines. In: Proceedings of International Conference on High Performance Computing, Networking, Storage and Analysis (SC), pp. 58:1–58:12. ACM (2013)Google Scholar
  19. 19.
    Spiliopoulos, V., Bagdia, A., Hansson, A., Aldworth, P., Kaxiras, S.: Introducing DVFS-management in a full-system simulator. In: Proceedings of International Symposium on Modelling, Analysis & Simulation of Computer and Telecommunication Systems (MASCOTS), pp. 535–545. IEEE Computer Society (2013)Google Scholar
  20. 20.
    Tesser, R.K., Pilla, L.L., Dupros, F., Navaux, P.O.A., Mehaut, J.F., Mendes, C.: Improving the performance of seismic wave simulations with dynamic load balancing. In: Proceedings of Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 196–203. IEEE Computer Society, February 2014Google Scholar
  21. 21.
    Zheng, G., Bhatelé, A., Meneses, E., Kalé, L.V.: Periodic hierarchical load balancing for large supercomputers. Int. J. High Perform. Comput. Appl. 25(4), 371–385 (2011)CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Edson L. Padoin
    • 1
  • Laércio L. Pilla
    • 2
  • Márcio Castro
    • 2
  • Philippe O. A. Navaux
    • 3
  • Jean-François Méhaut
    • 4
  1. 1.Department of Exact Sciences and EngineeringRegional University of Northwest of Rio Grande do Sul (UNIJUI)IjuíBrazil
  2. 2.Department of Informatics and StatisticsFederal University of Santa Catarina (UFSC)FlorianpolisBrazil
  3. 3.Institute of InformaticsFederal University of Rio Grande do Sul (UFRGS)Porto AlegreBrazil
  4. 4.Laboratoire d’Informatique de Grenoble (LIG) Grenoble UniversityGrenobleFrance

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