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Exploring Energy Efficiency for GPU-Accelerated POWER Servers

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High Performance Computing (ISC High Performance 2016)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 9945))

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Abstract

Modern servers provide different features for managing the amount of energy that is needed to execute a given work-load. In this article we focus on a new generation of GPU-accelerated servers with POWER8 processors. For different scientific applications, which have in common that they have been written for massively-parallel computers, we measure energy-to-solution for different system configurations. By combining earlier developed performance models and a simple power model, we derive an energy model that can help to optimise for energy efficiency.

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Notes

  1. 1.

    https://github.com/open-power/amester.

  2. 2.

    Comparison with nvidia-smi indicates an overhead of roughly 40 W measured with idle system.

  3. 3.

    Less than 15 % of the mean value in pathological cases.

  4. 4.

    Linux is a registered trademark of Linus Torvalds in the United States, other countries, or both.

  5. 5.

    Trademark of IBM in USA and/or other countries.

  6. 6.

    We also performed fits with \(\gamma \) as a free parameter, where we found \(\gamma \simeq 3\).

References

  1. Abraham, M.J., Murtola, T., Schulz, R., Páll, S., Smith, J.C., Hess, B., Lindahl, E.: GROMACS: high performance molecular simulations through multi-level parallelism from laptops to supercomputers. SoftwareX 1, 19–25 (2015)

    Article  Google Scholar 

  2. Alonso, P., Dolz, M.F., Mayo, R., Quintana-Ortí, E.S.: Modeling power and energy of the task-parallel Cholesky factorization on multicore processors. Comput. Sci. Res. Dev. 29(2), 105–112 (2012). doi:10.1007/s00450-012-0227-z

    Article  Google Scholar 

  3. Baumeister, P.F., Hater, T., Kraus, J., Pleiter, D., Wahl, P.: A performance model for GPU-accelerated FDTD applications. In: 2015 IEEE 22nd International Conference on High Performance Computing (HiPC), pp. 185–193, December 2015

    Google Scholar 

  4. Beeby, J.: The density of electrons in a perfect or imperfect lattice. Proc. R. Soc. Lond. A Math. Phys. Eng. Sci. 302(1468), 113–136 (1967). The Royal Society

    Article  Google Scholar 

  5. Bilardi, G., Pietracaprina, A., Pucci, G., Schifano, F., Tripiccione, R.: The potential of on-chip multiprocessing for QCD machines. In: Bader, D.A., Parashar, M., Sridhar, V., Prasanna, V.K. (eds.) HiPC 2005. LNCS, vol. 3769, pp. 386–397. Springer, Heidelberg (2005). doi:10.1007/11602569_41

    Chapter  Google Scholar 

  6. Bui, V., Norris, B., Huck, K., McInnes, L.C., Li, L., Hernandez, O., Chapman, B.: A component infrastructure for performance and power modeling of parallel scientific applications. In: Proceedings of the 2008 compFrame/HPC-GECO Workshop on Component Based High Performance, CBHPC 2008, pp. 6:1–6:11. (2008). http://doi.acm.org/10.1145/1456190.1456199

  7. Cabrera, A., Almeida, F., Blanco, V., Giménez, D.: Analytical modeling of the energy consumption for the High Performance Linpack. In: 2013 21st Euromicro International Conference on Parallel, Distributed and Network-Based Processing (PDP), pp. 343–350, February 2013

    Google Scholar 

  8. Caldeira, A.B., et al.: IBM Power System S824L technical overview and introduction (2014). http://www.redbooks.ibm.com/Redbooks.nsf/RedbookAbstracts/redp5139.html

  9. David, H., Gorbatov, E., Hanebutte, U.R., Khanna, R., Le, C.: RAPL: memory power estimation and capping. In: 2010 ACM/IEEE International Symposium on Low-Power Electronics and Design (ISLPED), pp. 189–194, August 2010

    Google Scholar 

  10. Demmel, J., Gearhart, A.: Instrumenting linear algebra energy consumption via on-chip energy counters. Technical report, UCB/EECS-2012-168, EECS Department, University of California, Berkeley, June 2012. http://www.eecs.berkeley.edu/Pubs/TechRpts/2012/EECS-2012-168.html

  11. Dongarra, J., Ltaief, H., Luszczek, P., Weaver, V.M.: Energy footprint of advanced dense numerical linear algebra using tile algorithms on multicore architecture. In: The 2nd International Conference on Cloud and Green Computing, November 2012

    Google Scholar 

  12. Eyerman, S., Eeckhout, L.: A counter architecture for online DVFS profitability estimation. IEEE Trans. Comput. 59(11), 1576–1583 (2010)

    Article  MathSciNet  Google Scholar 

  13. Feng, W.C., et al.: Green500 list, November 2015. http://green500.org

  14. Flinn, J., Satyanarayanan, M.: PowerScope: a tool for profiling the energy usage of mobile applications. In: Proceedings of the Second IEEE Workshop on Mobile Computer Systems and Applications, WMCSA 1999, p. 2 (1999). http://dl.acm.org/citation.cfm?id=520551.837522

  15. Floyd, M., et al.: Introducing the adaptive energy management features of the POWER7 chip. IEEE Micro 31(2), 60–75 (2011)

    Article  Google Scholar 

  16. Freund, R.W., Nachtigal, N.: QMR: a quasi-minimal residual method for non-Hermitian linear systems. Numer. Math. 60(1), 315–339 (1991)

    Article  MathSciNet  MATH  Google Scholar 

  17. Friedrich, J., Le, H., Starke, W., Stuechli, J., Sinharoy, B., Fluhr, E., Dreps, D., Zyuban, V., Still, G., Gonzalez, C., Hogenmiller, D., Malgioglio, F., Nett, R., Puri, R., Restle, P., Shan, D., Deniz, Z., Wendel, D., Ziegler, M., Victor, D.: The POWER8\(^{\rm {TM}}\) processor: designed for big data, analytics, and cloud environments. In: 2014 IEEE International Conference on IC Design Technology (ICICDT), pp. 1–4, May 2014

    Google Scholar 

  18. Ge, R., Feng, X., Song, S., Chang, H.C., Li, D., Cameron, K.W.: PowerPack: energy profiling and analysis of high-performance systems and applications. IEEE Trans. Parallel Distrib. Syst. 21(5), 658–671 (2010)

    Article  Google Scholar 

  19. Ghosh, S., Chandrasekaran, S., Chapman, B.: Statistical modeling of power/energy of scientific kernels on a multi-GPU system. In: 2013 International Green Computing Conference (IGCC), pp. 1–6, June 2013

    Google Scholar 

  20. Hackenberg, D., Ilsche, T., Schuchart, J., Schöne, R., Nagel, W.E., Simon, M., Georgiou, Y.: HDEEM: high definition energy efficiency monitoring. In: Energy Efficient Supercomputing Workshop, E2SC 2014, pp. 1–10, November 2014

    Google Scholar 

  21. Isci, C., Martonosi, M.: Runtime power monitoring in high-end processors: methodology and empirical data. In: 2003 Proceedings of the 36th Annual IEEE/ACM International Symposium on Microarchitecture, MICRO-36, pp. 93–104, December 2003

    Google Scholar 

  22. Klavík, P., Malossi, A.C.I., Bekas, C., Curioni, A.: Changing computing paradigms towards power efficiency. Philos. Trans. R. Soc. Lond. A: Math. Phys. Eng. Sci. 372(2018), 20130278 (2014)

    Article  Google Scholar 

  23. Knobloch, M., Foszczynski, M., Homberg, W., Pleiter, D., Böttiger, H.: Mapping fine-grained power measurements to HPC application runtime characteristics on IBM POWER7. Comput. Sci. Res. Dev. 29(3), 211–219 (2013). doi:10.1007/s00450-013-0245-5

    Google Scholar 

  24. Kohn, W., Rostoker, N.: Solution of the Schrödinger equation in periodic lattices with an application to metallic Lithium. Phys. Rev. 94, 1111–1120 (1954)

    Article  MATH  Google Scholar 

  25. Korringa, J.: On the calculation of the energy of a Bloch wave in a metal. Physica 13(6), 392–400 (1947)

    Article  MathSciNet  Google Scholar 

  26. Kraus, J.: Increase performance with GPU boost and K80 autoboost (2014). https://devblogs.nvidia.com/parallelforall/increase-performance-gpu-boost-k80-autoboost/

  27. Lee, K.H., Ahmed, I., Goh, R.S., Khoo, E.H., Li, E.P., Hung, T.G.: Implementation of the FDTD method based on Lorentz-Drude dispersive model on GPU for plasmonics applications. Prog. Electromagnet. Res. 116, 441–456 (2011)

    Article  Google Scholar 

  28. Lefurgy, C., Wang, X., Ware, M.: Server-level power control. In: 2007 Fourth International Conference on Autonomic Computing, ICAC 2007, p. 4, June 2007

    Google Scholar 

  29. Lindahl, E.: Molecular simulation with GROMACS on CUDA GPUs (2013). http://on-demand.gputechconf.com/gtc/2013/webinar/gromacs-kepler-gpus-gtc-express-webinar.pdf

  30. Pleiter, D.: Parallel computer architectures. In: 45th IFF Spring School 2014 “Computing Solids Models, ab-initio methods and supercomputing”. Schriften des Forschungszentrums Jülich, Reihe Schlüsseltechnologien, vol. 74 (2014)

    Google Scholar 

  31. Rountree, B., Lowenthal, D.K., Schulz, M., de Supinski, B.R.: Practical performance prediction under dynamic voltage frequency scaling. In: 2011 International Green Computing Conference and Workshops (IGCC), pp. 1–8, July 2011

    Google Scholar 

  32. Ryffel, S.: LEA\(^2\)P: the Linux energy attribution and accounting platform. Master’s thesis, Swiss Federal Institute of Technology (ETH) (2009). http://ftp.tik.ee.ethz.ch/pub/students/2009-FS/MA-2009-04.pdf

  33. Shahmansouri, A., Rashidian, B.: GPU implementation of split-field finite-difference time-domain method for Drude-Lorentz dispersive media. Prog. Electromagnet. Res. 125, 55–77 (2012)

    Article  Google Scholar 

  34. Song, S., Su, C., Rountree, B., Cameron, K.W.: A simplified and accurate model of power-performance efficiency on emergent GPU architectures. In: 2013 IEEE 27th International Symposium on Parallel Distributed Processing (IPDPS), pp. 673–686, May 2013

    Google Scholar 

  35. Song, S.L., Barker, K., Kerbyson, D.: Unified performance and power modeling of scientific workloads. In: Proceedings of the 1st International Workshop on Energy Efficient Supercomputing, E2SC 2013, pp. 4:1–4:8. (2013). http://doi.acm.org/10.1145/2536430.2536435

  36. Subramaniam, B., Feng, W.C.: Statistical power and performance modeling for optimizing the energy efficiency of scientific computing. In: Green Computing and Communications (GreenCom), pp. 139–146, December 2010

    Google Scholar 

  37. Taflove, A., Hagness, S.C.: Others: Computational Electrodynamics: The Finite-Difference Time-Domain Method. Artech House, Norwood (1995)

    MATH  Google Scholar 

  38. Tan, L., Kothapalli, S., Chen, L., Hussaini, O., Bissiri, R., Chen, Z.: A survey of power and energy efficient techniques for high performance numerical linear algebra operations. Parallel Comput. 40(10), 559–573 (2014)

    Article  MathSciNet  Google Scholar 

  39. Thiess, A., et al.: Massively parallel density functional calculations for thousands of atoms: KKRnano. Phys. Rev. B 85, 235103 (2012)

    Article  Google Scholar 

  40. Tiwari, A., Laurenzano, M.A., Carrington, L., Snavely, A.: Modeling power and energy usage of HPC kernels. In: 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops PhD Forum (IPDPSW), pp. 990–998, May 2012

    Google Scholar 

  41. Wahl, P., Ly-Gagnon, D., Debaes, C., Miller, D., Thienpont, H.: B-CALM: an open-source GPU-based 3D-FDTD with multi-pole dispersion for plasmonics. In: 2011 11th International Conference on Numerical Simulation of Optoelectronic Devices (NUSOD), pp. 11–12, September 2011

    Google Scholar 

  42. Weaver, V.M., Johnson, M., Kasichayanula, K., Ralph, J., Luszczek, P., Terpstra, D., Moore, S.: Measuring energy and power with PAPI. In: 2012 41st International Conference on Parallel Processing Workshops (ICPPW), pp. 262–268, September 2012

    Google Scholar 

  43. Wittmann, M., Hager, G., Zeiser, T., Treibig, J., Wellein, G.: Chip-level and multi-node analysis of energy-optimized lattice Boltzmann CFD simulations. Concur. Comput. Pract. Exper. 28, 2295–2315 (2016). doi:10.1002/cpe.3489

    Article  Google Scholar 

  44. Wu, G., Greathouse, J.L., Lyashevsky, A., Jayasena, N., Chiou, D.: GPGPU performance and power estimation using machine learning. In: 2015 IEEE 21st International Symposium on High Performance Computer Architecture (HPCA), pp. 564–576, February 2015

    Google Scholar 

  45. Zyuban, V., Taylor, S.A., Christensen, B., Hall, A.R., Gonzalez, C.J., Friedrich, J., Clougherty, F., Tetzloff, J., Rao, R.: IBM POWER7+ design for higher frequency at fixed power. IBM J. Res. Dev. 57(6), 1:1–1:18 (2013)

    Article  Google Scholar 

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Acknowledgements

This work has been carried out in the context of the POWER Acceleration and Design Center, a joined project between IBM, Forschungszentrum Jülich and NVIDIA. We acknowledge generous support from IBM by providing early access to GPU-accelerated POWER8 systems.

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Authors and Affiliations

  1. Forschungszentrum Jülich, JSC, 52425, Jülich, Germany

    Thorsten Hater, Paul Baumeister & Dirk Pleiter

  2. IBM Deutschland Research and Development GmbH, 71032, Böblingen, Germany

    Benedikt Anlauf & Markus Bühler

  3. NVIDIA GmbH, Würselen, Germany

    Jiri Kraus

Authors
  1. Thorsten Hater
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  2. Benedikt Anlauf
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  3. Paul Baumeister
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  4. Markus Bühler
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  5. Jiri Kraus
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  6. Dirk Pleiter
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Corresponding author

Correspondence to Dirk Pleiter .

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Editors and Affiliations

  1. University of Delaware, Newark, Delaware, USA

    Michela Taufer

  2. Forschungszentrum Jülich, Jülich, Germany

    Bernd Mohr

  3. DKRZ, Hamburg, Germany

    Julian M. Kunkel

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Hater, T., Anlauf, B., Baumeister, P., Bühler, M., Kraus, J., Pleiter, D. (2016). Exploring Energy Efficiency for GPU-Accelerated POWER Servers. In: Taufer, M., Mohr, B., Kunkel, J. (eds) High Performance Computing. ISC High Performance 2016. Lecture Notes in Computer Science(), vol 9945. Springer, Cham. https://doi.org/10.1007/978-3-319-46079-6_15

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  • DOI: https://doi.org/10.1007/978-3-319-46079-6_15

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