Skip to main content

Energy Consumption Studies of WRF Executions with the LIMITLESS Monitor

  • Conference paper
  • First Online:
High Performance Computing (CARLA 2021)

Abstract

We present a study of the performance of the Weather Research and Forecasting [WRF] code under several hardware configurations in an HPC environment. The WRF code is a standard code for weather prediction, used in several fields of science and industry. The metrics used in this case are the execution time of the run and the energy consumption of the simulation obtained with the LIMITLESS monitor, which is the main novelty of this work. With these results, it is possible to quantify the energy savings of WRF run configurations, which include variations in the number of computing nodes and in the number of processes per node. It is found out that a slight increase in the computing time can drive to a noticeable reduction in the energy consumption of the cluster.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 64.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 84.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. New European Web Atlas. https://www.neweuropeanwindatlas.eu. Accessed June 2021

  2. Red Española de Supercomputación. https://www.res.es/. Accessed June 2021

  3. Nagios - The Industry Standard In IT Infrastructure Monitoring (2018). https://www.nagios.org/. Accessed June 2021

  4. Ganglia Monitoring System (2018). http://ganglia.sourceforge.net/. Accessed June 2021

  5. Collectd - The System Statistics Collection Daemon (2018). https://collectd.org/. Accessed June 2021

  6. Ahmed, K., Tasnim, S., Yoshii, K.: Simulation of auction mechanism model for energy-efficient high performance computing. In: Proceedings of the 2020 ACM SIGSIM Conference on Principles of Advanced Discrete Simulation, SIGSIM-PADS 2020, pp. 99–104. Association for Computing Machinery, New York (2020). https://doi.org/10.1145/3384441.3395991

  7. Calore, E., Gabbana, A., Schifano, S.F., Tripiccione, R.: Energy-efficiency tuning of a lattice Boltzmann simulation using MERIC. In: Wyrzykowski, R., Deelman, E., Dongarra, J., Karczewski, K. (eds.) PPAM 2019. LNCS, vol. 12044, pp. 169–180. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-43222-5_15

    Chapter  Google Scholar 

  8. Cascajo, A., Singh, D.E., Carretero, J.: Performance-aware scheduling of parallel applications on non-dedicated clusters. Electronics 8, 982 (2019). https://doi.org/10.3390/electronics8090982

  9. Cascajo, A., Singh, D.E., Carretero, J.: LIMITLESS - LIght-weight MonItoring tool for LargE scale systems. In: Proceedings - 29th Euromicro International Conference on Parallel, Distributed and Network-Based Processing, PDP 2021, pp. 220–227. Institute of Electrical and Electronics Engineers Inc., March 2021. https://doi.org/10.1109/PDP52278.2021.00042

  10. Cerf, S., Bleuse, R., Reis, V., Perarnau, S., Rutten, É.: Sustaining performance while reducing energy consumption: a control theory approach. In: Sousa, L., Roma, N., Tomás, P. (eds.) Euro-Par 2021. LNCS, vol. 12820, pp. 334–349. Springer, Cham (2021). https://doi.org/10.1007/978-3-030-85665-6_21

    Chapter  Google Scholar 

  11. Dlinnova, E., Biryukov, S., Stegailov, V.: Energy consumption of MD calculations on hybrid and CPU-only supercomputers with air and immersion cooling. Adv. Parallel Comput. 36, 574–582 (2020). https://doi.org/10.3233/APC200087

    Article  Google Scholar 

  12. Dörenkämper, M., et al.: The making of the new European wind atlas - part 2: production and evaluation. Geosci. Model Dev. 13(10), 5079–5102 (2020). https://doi.org/10.5194/gmd-13-5079-2020

  13. Dupont, B., Mejri, N., Da Costa, G.: Energy-aware scheduling of malleable HPC applications using a particle swarm optimised greedy algorithm. Sustain. Comput.: Inform. Syst. 28, 100447 (2020). https://doi.org/10.1016/j.suscom.2020.100447

  14. Garrido, J.L., González-Rouco, J.F., Vivanco, M.G., Navarro, J.: Regional surface temperature simulations over the Iberian Peninsula: evaluation and climate projections. Clim. Dyn. 55, 3445–3468 (2020). https://doi.org/10.1007/s00382-020-05456-3

    Article  Google Scholar 

  15. Hahmann, A.N., et al.: The making of the new European wind atlas - part 1: model sensitivity. Geosci. Model Dev. 13(10), 5073–5078 (2020). https://doi.org/10.5194/gmd-13-5053-2020

    Article  Google Scholar 

  16. Hong, S.Y., Noh, Y., Dudhia, J.: A new vertical diffusion package with an explicit treatment of entrainment processes. Mon. Weather Rev. 134(9), 2318–2341 (2006). https://doi.org/10.1175/MWR3199.1

    Article  Google Scholar 

  17. Jiménez, P.A., Dudhia, J.: Improving the representation of resolved and unresolved topographic effects on surface wind in the WRF model. J. Appl. Meteor. Climatol. 51, 300–316 (2012). https://doi.org/10.1175/JAMC-D-11-084.1

    Article  Google Scholar 

  18. Kerbyson, D., Barker, K., Davis, K.: Analysis of the weather research and forecasting (WRF) model on large-scale systems. In: Proceedings of Parallel Computing, PARCO 2007. Parallel Computing: Architectures, Algorithms and Applications. Advances in Parallel Computing, Juelich, Germany, vol. 15, pp. 89–98 (2007)

    Google Scholar 

  19. Mantovani, F., et al.: Performance and energy consumption of HPC workloads on a cluster based on arm ThunderX2 CPU. Futur. Gener. Comput. Syst. 112, 800–818 (2020). https://doi.org/10.1016/j.future.2020.06.033

    Article  Google Scholar 

  20. Massie, M.L., Chun, B.N., Culler, D.E.: The ganglia distributed monitoring system: design, implementation, and experience. Parallel Comput. 30(7), 817–840 (2004)

    Article  Google Scholar 

  21. Moríñigo, J.A., García-Muller, P., Rubio-Montero, A.J., Gómez-Iglesias, A., Meyer, N., Mayo-García, R.: Performance drop at executing communication-intensive parallel algorithms. J. Supercomput. 76(9), 6834–6859 (2020). https://doi.org/10.1007/s11227-019-03142-8

    Article  Google Scholar 

  22. Nakanishi, M., Niino, H.: An improved Mellor-Yamada level-3 model with condensation physics: its design and verification. Boundary-Layer Meteorol. 112, 1–31 (2004). https://doi.org/10.1023/B:BOUN.0000020164.04146.98

    Article  Google Scholar 

  23. Patel, T., Wagenhäuser, A., Eibel, C., Hönig, T., Zeiser, T., Tiwari, D.: What does power consumption behavior of HPC jobs reveal?: demystifying, quantifying, and predicting power consumption characteristics. In: 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS), pp. 799–809 (2020). https://doi.org/10.1109/IPDPS47924.2020.00087

  24. Rodríguez-Pascual, M.A., Moríñigo, J.A., Mayo-García, R.: Effect of MPI tasks location on cluster throughput using NAS. Clust. Comput. 22(4), 1187–1198 (2019). https://doi.org/10.1007/s10586-018-02898-7

  25. Rodríguez-Pascual, M., Cao, J., Moríñigo, J.A., Cooperman, G., Mayo-García, R.: Job migration in HPC clusters by means of checkpoint/restart. J. Supercomput. 75(10), 6517–6541 (2019). https://doi.org/10.1007/s11227-019-02857-y

    Article  Google Scholar 

  26. Rodríguez-Pascual, M., Rubio-Montero, A.J., Moríñigo, J.A., Mayo-García, R.: Execution data logs of a supercomputer workload over its extended lifetime. Data Brief 28, 105006 (2020). https://doi.org/10.1016/j.dib.2019.105006

  27. Shainer, G., Lui, P., Liu, T., Wilde, T., Layton, J.: The impact of inter-node latency versus intranode latency on HPC applications. In: Proceedings of the IASTED International Conference on Parallel and Distributed Computing and Systems, pp. 455–460 (2011). https://doi.org/10.2316/p.2011.757-005

  28. Stull, R.B.: An Introduction to Boundary Layer Meteorology. Kluwer Academic Publishers, Dordrecht, Boston, London (1988)

    Google Scholar 

  29. Szustak, L., Wyrzykowski, R., Olas, T., Mele, V.: Correlation of performance optimizations and energy consumption for stencil-based application on Intel Xeon scalable processors. IEEE Trans. Parallel Distrib. Syst. 31(11), 2582–2593 (2020). https://doi.org/10.1109/TPDS.2020.2996314

    Article  Google Scholar 

  30. Tracey, R., Hoang, L., Subelet, F., Elisseev, V.: AI-driven holistic approach to energy efficient HPC. In: Jagode, H., Anzt, H., Juckeland, G., Ltaief, H. (eds.) ISC High Performance 2020. LNCS, vol. 12321, pp. 267–279. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-59851-8_17

    Chapter  Google Scholar 

  31. Vegas-Cañas, C., et al.: An assessment of observed and simulated temperature variability in the sierra de Guadarrama. Atmosphere 11(9), 985 (2020). https://doi.org/10.3390/atmos11090985

  32. Yoo, A.B., Jette, M.A., Grondona, M.: SLURM: simple Linux utility for resource management. In: Feitelson, D., Rudolph, L., Schwiegelshohn, U. (eds.) JSSPP 2003. LNCS, vol. 2862, pp. 44–60. Springer, Heidelberg (2003). https://doi.org/10.1007/10968987_3

    Chapter  Google Scholar 

  33. Zhang, C., Yuan, X.: Processor affinity and MPI performance on SMP-CMP clusters. In: IEEE International Symposium Parallel and Distributed Processing, Atlanta, USA, pp. 1–8 (2010). https://doi.org/10.1109/IPDPSW.2010.5470774

Download references

Acknowledgments

This work was partially funded by the Comunidad de Madrid CABAHLA-CM project (S2018/TCS-4423), the ADMIRE project Grant Agreement number 956748 (H2020-JTI-EuroHPC-2019-1), and the ENERXICO project Grant Agreement number 828947 (H2020-FETHPC-2018).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andres Bustos .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Bustos, A. et al. (2022). Energy Consumption Studies of WRF Executions with the LIMITLESS Monitor. In: Gitler, I., Barrios Hernández, C.J., Meneses, E. (eds) High Performance Computing. CARLA 2021. Communications in Computer and Information Science, vol 1540. Springer, Cham. https://doi.org/10.1007/978-3-031-04209-6_2

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-04209-6_2

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-04208-9

  • Online ISBN: 978-3-031-04209-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics