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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
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)
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)
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 2008
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)
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)
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
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)
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
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 2006
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)
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
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
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 2013
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 2012
Leung, J.Y.T.: Handbook of Scheduling: Algorithms, Models, and Performance Analysis. Chapman & Hall/CRC, Boca Raton (2004)
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)
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 2014
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)
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)
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 2014
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)
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.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Padoin, E.L., Pilla, L.L., Castro, M., Navaux, P.O.A., Méhaut, JF. (2017). Exploration of Load Balancing Thresholds to Save Energy on Iterative Applications. In: Barrios Hernández, C., Gitler, I., Klapp, J. (eds) High Performance Computing. CARLA 2016. Communications in Computer and Information Science, vol 697. Springer, Cham. https://doi.org/10.1007/978-3-319-57972-6_6
Download citation
DOI: https://doi.org/10.1007/978-3-319-57972-6_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-57971-9
Online ISBN: 978-3-319-57972-6
eBook Packages: Computer ScienceComputer Science (R0)