Energy-Efficiency Tuning of a Lattice Boltzmann Simulation Using MERIC

  • Enrico CaloreEmail author
  • Alessandro Gabbana
  • Sebastiano Fabio Schifano
  • Raffaele Tripiccione
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12044)


Energy-efficiency is already of paramount importance for High Performance Computing (HPC) systems operation, and tools to monitor power usage and tune relevant hardware parameters are already available and in use at major supercomputing centres. On the other hand, HPC application developers and users still usually focus just on performance, even if they will probably be soon required to look also at the energy-efficiency of their jobs. Only few software tools allow to energy-profile a generic application, and even less are able to tune energy-related hardware parameters from the application itself. In this work we use the MERIC library and the RADAR analyzer, developed within the EU READEX project, to profile and tune for efficiency the execution parameters of a real-life Lattice Boltzmann code. Profiling methodology and details are described, and results are presented and compared with the ones measured in a previous work using different methodologies and tools.


MERIC Optimization Lattice Boltzmann Energy Efficiency 



This work was done in the framework of the COKA, and COSA projects of INFN. We thank Università degli Studi di Ferrara for access to their HPC systems. Enrico Calore was partially founded by “Contributo 5 per mille assegnato all’Università degli Studi di Ferrara - dichiarazione dei redditi dell’anno 2014”.


  1. 1.
    Ahmad, W.A., et al.: Design of an energy aware petaflops class high performance cluster based on power architecture. In: 2017 IEEE International Parallel and Distributed Processing Symposium Workshops (IPDPSW), pp. 964–973 (2017).
  2. 2.
    Alessi, F., Thoman, P., Georgakoudis, G., Fahringer, T., Nikolopoulos, D.S.: Application-level energy awareness for OpenMP. In: Terboven, C., de Supinski, B.R., Reble, P., Chapman, B.M., Müller, M.S. (eds.) IWOMP 2015. LNCS, vol. 9342, pp. 219–232. Springer, Cham (2015). Scholar
  3. 3.
    Beneventi, F., Bartolini, A., Cavazzoni, C., Benini, L.: Continuous learning of HPC infrastructure models using big data analytics and in-memory processing tools. In: Proceedings of the Conference on Design, Automation & Test in Europe. DATE 2017, pp. 1038–1043 (2017)Google Scholar
  4. 4.
    Biferale, L., Mantovani, F., Sbragaglia, M., Scagliarini, A., Toschi, F., Tripiccione, R.: Reactive Rayleigh-Taylor systems: front propagation and non-stationarity. EPL 94(5), 54004 (2011). Scholar
  5. 5.
    Biferale, L., Mantovani, F., Sbragaglia, M., Scagliarini, A., Toschi, F., Tripiccione, R.: Second-order closure in stratified turbulence: simulations and modeling of bulk and entrainment regions. Phys. Rev. E 84(1), 016305 (2011). Scholar
  6. 6.
  7. 7.
    Calore, E., Gabbana, A., Kraus, J., Pellegrini, E., Schifano, S.F., Tripiccione, R.: Massively parallel lattice-Boltzmann codes on large GPU clusters. Parallel Comput. 58, 1–24 (2016). Scholar
  8. 8.
    Calore, E., Gabbana, A., Kraus, J., Schifano, S.F., Tripiccione, R.: Performance and portability of accelerated lattice Boltzmann applications with OpenACC. Concurr. Computat.: Pract. Exp. 28(12), 3485–3502 (2016). Scholar
  9. 9.
    Calore, E., Gabbana, A., Schifano, S.F., Tripiccione, R.: Evaluation of DVFS techniques on modern HPC processors and accelerators for energy-aware applications. Concurr. Comput.: Pract. Exp. 29(12), 1–19 (2017). Scholar
  10. 10.
    Calore, E., Mantovani, F., Ruiz, D.: Advanced performance analysis of HPC workloads on Cavium ThunderX. In: 2018 International Conference on High Performance Computing Simulation (HPCS), pp. 375–382 (2018).
  11. 11.
    Calore, E., Schifano, S.F., Tripiccione, R.: Energy-performance tradeoffs for HPC applications on low power processors. In: Hunold, S., et al. (eds.) Euro-Par 2015. LNCS, vol. 9523, pp. 737–748. Springer, Cham (2015). Scholar
  12. 12.
    Cesarini, D., Bartolini, A., Bonfà, P., Cavazzoni, C., Benini, L.: COUNTDOWN: a run-time library for application-agnostic energy saving in MPI communication primitives. In: Proceedings of the 2nd Workshop on AutotuniNg and aDaptivity AppRoaches for Energy-efficient HPC Systems. ANDARE 2018, pp. 2:1–2:6 (2018).
  13. 13.
    Dick, B., Vogel, A., Khabi, D., Rupp, M., Küster, U., Wittum, G.: Utilization of empirically determined energy-optimal CPU-frequencies in a numerical simulation code. Comput. Vis. Sci. 17(2), 89–97 (2015). Scholar
  14. 14.
    Dongarra, J., London, K., Moore, S., Mucci, P., Terpstra, D.: Using PAPI for hardware performance monitoring on Linux systems. In: Conference on Linux Clusters: The HPC Revolution, vol. 5. Linux Clusters Institute (2001)Google Scholar
  15. 15.
    Etinski, M., Corbalán, J., Labarta, J., Valero, M.: Understanding the future of energy-performance trade-off via DVFS in HPC environments. J. Parallel Distrib. Comput. 72(4), 579–590 (2012). Scholar
  16. 16.
    Hackenberg, D., Schone, R., Ilsche, T., Molka, D., Schuchart, J., Geyer, R.: An energy efficiency feature survey of the Intel Haswell processor. In: 2015 IEEE International Parallel and Distributed Processing Symposium Workshop (IPDPSW), pp. 896–904 (2015).
  17. 17.
    Kjeldsberg, P.G., et al.: Run-time exploitation of application dynamism for energy-efficient exascale computing. System-Scenario-Based Design Principles and Applications, pp. 113–126. Springer, Cham (2020). Scholar
  18. 18.
    Mantovani, F., Calore, E.: Performance and power analysis of HPC workloads on heterogeneous multi-node clusters. J. Low Power Electron. Appl. 8(2) (2018).
  19. 19.
    Mantovani, F., Pivanti, M., Schifano, S.F., Tripiccione, R.: Performance issues on many-core processors: a D2Q37 lattice Boltzmann scheme as a test-case. Comput. Fluids 88, 743–752 (2013). Scholar
  20. 20.
    McCalpin, J.D.: Memory bandwidth and machine balance in current high performance computers. IEEE Technical Committee on Computer Architecture (TCCA) Newsletter (1995)Google Scholar
  21. 21.
    Oleynik, Y., Gerndt, M., Schuchart, J., Kjeldsberg, P.G., Nagel, W.E.: Run-time exploitation of application dynamism for energy-efficient exascale computing (READEX). In: 2015 IEEE 18th International Conference on Computational Science and Engineering, pp. 347–350 (2015).
  22. 22.
    Sbragaglia, M., Benzi, R., Biferale, L., Chen, H., Shan, X., Succi, S.: Lattice Boltzmann method with self-consistent thermo-hydrodynamic equilibria. J. Fluid Mech. 628, 299–309 (2009). Scholar
  23. 23.
    Scagliarini, A., Biferale, L., Sbragaglia, M., Sugiyama, K., Toschi, F.: Lattice Boltzmann methods for thermal flows: continuum limit and applications to compressible Rayleigh-Taylor systems. Phys. Fluids (1994-present) 22(5), 055101 (2010). Scholar
  24. 24.
    Schuchart, J., et al.: The readex formalism for automatic tuning for energy efficiency. Computing 99(8), 727–745 (2017). Scholar
  25. 25.
    Sensi, D.D., Matteis, T.D., Danelutto, M.: Simplifying self-adaptive and power-aware computing with Nornir. Future Gener. Comput. Syst. 87, 136–151 (2018). Scholar
  26. 26.
    Shafik, R.A., Das, A., Yang, S., Merrett, G., Al-Hashimi, B.M.: Adaptive energy minimization of OpenMP parallel applications on many-core systems. In: Proceedings of the 6th Workshop on Parallel Programming and Run-Time Management Techniques for Many-core Architectures. PARMA-DITAM 2015, pp. 19–24. ACM (2015).
  27. 27.
    Succi, S.: The Lattice-Boltzmann Equation. Oxford University Press, Oxford (2001)zbMATHGoogle Scholar
  28. 28.
    Vysocky, O., Beseda, M., Říha, L., Zapletal, J., Lysaght, M., Kannan, V.: MERIC and RADAR generator: tools for energy evaluation and runtime tuning of HPC applications. In: Kozubek, T., et al. (eds.) HPCSE 2017. LNCS, vol. 11087, pp. 144–159. Springer, Cham (2018). Scholar
  29. 29.
    Wu, Q., et al.: A dynamic compilation framework for controlling microprocessor energy and performance. In: Proceedings of the 38th Annual IEEE/ACM International Symposium on Microarchitecture, pp. 271–282. IEEE Computer Society (2005)Google Scholar

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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.INFN FerraraFerraraItaly
  2. 2.Università degli Studi di FerraraFerraraItaly

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