Predicting the Energy and Power Consumption of Strong and Weak Scaling HPC Applications


  • Hayk Shoukourian Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities, Garching bei München Technische Universität München (TUM), Garching bei München
  • Torsten Wilde Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities, Garching bei München
  • Axel Auweter Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities, Garching bei München
  • Arndt Bode Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities, Garching bei München Technische Universität München (TUM), Garching bei München



Keeping energy costs in budget and operating within available capacities of power distribution and cooling systems is becoming an important requirement for High Performance Computing (HPC) data centers. It is even more important when considering the estimated power requirements for Exascale computing. Power and energy capping are two of emerging techniques aimed towards controlling and efficient budgeting of power and energy consumption within the data center. Implementation of both techniques requires a knowledge of, potentially unknown, power and energy consumption data of the given parallel HPC applications for different numbers of compute servers (nodes).

This paper introduces an Adaptive Energy and Power Consumption Prediction (AEPCP) model capable of predicting the power and energy consumption of parallel HPC applications for different number of compute nodes. The suggested model is application specific and describes the behavior of power and energy with respect to the number of utilized compute nodes, taking as an input the available history power/energy data of an application. It provides a generic solution that can be used for each application but it produces an application specific result. The AEPCP model allows for ahead of time power and energy consumption prediction and adapts with each additional execution of the application improving the associated prediction accuracy. The model does not require any application code instrumentation and does not introduce any application performance degradation. Thus it is a high level application energy and power consumption prediction model. The validity and the applicability of the suggested AEPCP model is shown in this paper through the empirical results achieved using two application-benchmarks on the SuperMUC HPC system (the 10th fastest supercomputer in the world, according to Top500 November 2013 rankings) deployed at Leibniz Supercomputing Centre.


Gene M Amdahl. Validity of the single processor approach to achieving large scale computing capabilities. In Proceedings of the April 18-20, 1967, spring joint computer conference, pages 483–485. ACM, 1967.

T. Arber and et al. EPOCH: Extendable PIC Open Collaboration., 2014.

Axel Auweter and Herbert Huber. Direct Warm Water Cooled Linux Cluster Munich: CoolMUC., 2012.

Arndt Bode. Energy to solution: A new mission for parallel computing. In Felix Wolf, Bernd Mohr, and Dieter Mey, editors, Euro-Par 2013 Parallel Processing, volume 8097 of Lecture Notes in Computer Science, pages 1–2. Springer Berlin Heidelberg, 2013.

Luigi Brochard, Raj Panda, and Sid Vemuganti. Optimizing performance and energy of hpc application on POWER7. Computer Science - Research and Development, 25(3-4):135–140, 2010.

G.L. Tsafack Chetsa, L. Lef`evre, J.M. Pierson, P. Stolf, and G. Da Costa. Exploiting performance counters to predict and improve energy performance of HPC systems. Future Generation Computer Systems, 2013.

Xiaobo Fan, Wolf-Dietrich Weber, and Luiz Andre Barroso. Power provisioning for a warehouse-sized computer. In Proceedings of the 34th Annual International Symposium on Computer Architecture, ISCA ’07, pages 13–23, New York, NY, USA, 2007. ACM.

Anshul Gandhi, Mor Harchol-Balter, Rajarshi Das, and Charles Lefurgy. Optimal power allocation in server farms. SIGMETRICS Perform. Eval. Rev., 37(1):157–168, June 2009.

Sergei Konstantinovich Godunov. A difference method for numerical calculation of discontinuous solutions of the equations of hydrodynamics. Matematicheskii Sbornik, 89(3):271–306, 1959.

Great Internet Mersenne Prime Search., 2014.

John L Gustafson. Reevaluating Amdahl’s law. Communications of the ACM, 31(5):532– 533, 1988.

Georg Hager, Jan Treibig, Johannes Habich, and Gerhard Wellein. Exploring performance and power properties of modern multi-core chips via simple machine models. Concurrency and Computation: Practice and Experience, 2014.

Can Hankendi and Ayse K Coskun. Adaptive power and resource management techniques for multi-threaded workloads. In Proceedings of the 2013 IEEE 27th International Symposium on Parallel and Distributed Processing Workshops and PhD Forum, pages 2302–2305. IEEE Computer Society, 2013.

John L Hennessy and David A Patterson. Computer architecture: a quantitative approach. Elsevier, 2012.

Chung-hsing Hsu and Wu-chun Feng. A power-aware run-time system for high-performance computing. In Proceedings of the 2005 ACM/IEEE Conference on Supercomputing, SC ’05, pages 1–, Washington, DC, USA, 2005. IEEE Computer Society.

Engin Ipek, Bronis R De Supinski, Martin Schulz, and Sally A McKee. An approach to performance prediction for parallel applications. In Euro-Par 2005 Parallel Processing, pages 196–205. Springer, 2005.

Subramanian Kannan, Mark Roberts, Peter Mayes, Dave Brelsford, and Joseph F Skovira. Workload Management with LoadLeveler., 2001.

Jonathan G Koomey. Estimating total power consumption by servers in the US and the world, 2007.

Jonathan G Koomey. Worldwide electricity used in data centers. Environmental Research Letters, 3(3), 2008.

Pierre-Fran¸cois Lavall´ee, Guillaume Colin de Verdi`ere, Philippe Wautelet, Dimitri Lecas, and Jean-Michel Dupays. Porting and optimizing hydro to new platforms and programming paradigms-lessons learnt., December 2012.

Leibniz Supercomputing Centre (LRZ) of the Bavarian Academy of Sciences and Humanities., 2014.

Timo Minartz, JulianM. Kunkel, and Thomas Ludwig. Simulation of power consumption of energy efficient cluster hardware. Computer Science - Research and Development, 25(3- 4):165–175, 2010.

Kevin P Murphy. Machine learning: a probabilistic perspective. The MIT Press, Cambridge, MA, 2012.

Catherine Mills Olschanowsky, Tajana Rosing, Allan Snavely, Laura Carrington, Mustafa M Tikir, and Michael Laurenzano. Fine-grained energy consumption characterization and modeling. In High Performance Computing Modernization Program Users Group Conference (HPCMP-UGC), 2010 DoD, pages 487–497. IEEE, 2010.

Partnership For Advance Computing In Europe., 2014.

Philip L Roe. Approximate Riemann solvers, parameter vectors, and difference schemes. Journal of computational physics, 43(2):357–372, 1981.

Barry Rountree, Dong H Ahn, Bronis R de Supinski, David K Lowenthal, and Martin Schulz. Beyond DVFS: A First Look at Performance Under a Hardware-Enforced Power Bound. In Parallel and Distributed Processing Symposium Workshops & PhD Forum (IPDPSW), 2012 IEEE 26th International, pages 947–953. IEEE, 2012.

Hartmut Ruhl. Classical Particle Simulations with the PSC code. https: // vorlesung/Lecturescript.pdf.

Yuan Shi. Reevaluating Amdahl’s law and Gustafson’s law. Computer Sciences Department, Temple University (MS: 38-24), 1996.

Hayk Shoukourian, Torsten Wilde, Axel Auweter, and Arndt Bode. Monitoring power data: A first step towards a unified energy efficiency evaluation toolset for HPC data centers., 2013.

Hayk Shoukourian, Torsten Wilde, Axel Auweter, Arndt Bode, and Petra Piochacz. Towards a unified energy efficiency evaluation toolset: an approach and its implementation at Leibniz Supercomputing Centre (LRZ)., 2013.

Shuaiwen Leon Song, Kevin Barker, and Darren Kerbyson. Unified performance and power modeling of scientific workloads. In Proceedings of the 1st International Workshop on Energy Efficient Supercomputing, E2SC ’13, pages 4:1–4:8, New York, NY, USA, 2013. ACM.

MEGWARE Computer Vertrieb und Service GmbH. default.aspx, 2014.

SorTech AG., 2014.

Romain Teyssier. The RAMSES Code., 2013.

Top500., 2013.

Dong Hyuk Woo and Hsien-Hsin S Lee. Extending Amdahl’s Law for Energy-Efficient Computing in the Many-Core Era. IEEE computer, 41(12):24–31, 2008.




How to Cite

Shoukourian, H., Wilde, T., Auweter, A., & Bode, A. (2014). Predicting the Energy and Power Consumption of Strong and Weak Scaling HPC Applications. Supercomputing Frontiers and Innovations, 1(2), 20–41.