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Measuring energy consumption using EML (energy measurement library)

  • Special Issue Paper
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Computer Science - Research and Development

Abstract

Energy consumption and efficiency is a main issue in high performance computing systems in order to reach exascale computing. Researchers in the field are focusing their effort in reducing the first and increasing the latter while there is no current standard for energy measurement. Current energy measurement tools are specific and architectural dependent and this has to be addressed. By creating a standard tool, it is possible to generate independence between the experiments and the hardware, and thus, researchers effort can be focused in energy, by maximizing the portability of the code used for experimentation with the multiple architectures we have access nowadays. We present the energy measurement library (EML) library, a software library that eases the access to the energy measurement tools and can be easily extended to add new measurement systems. Using EML, it is viable to obtain architectural and algorithmic parameters that affect energy consumption and efficiency. The use of this library is tested in the field of the analytic modeling of the energy consumed by parallel programs.

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References

  1. Almeida F, Blanco V, Cabrera A, Ruiz J (2013) Modeling energy consumption for master-slave applications. J Supercomput 65(3):1137–1149. doi:10.1007/s11227-013-0914-y

    Article  Google Scholar 

  2. Almeida F, Pérez VB, Gonzalez I, Cabrera A, Giménez D (2013) Analytical energy models for mpi communications on a sandy-bridge architecture. In: ICPP. IEEE, pp 868–876

  3. Barrachina S, Barreda M, Catalán S, Dolz MF, Fabregat G, Mayo R, Quintana-Ortí ES (2013) An integrated framework for power-performance analysis of parallel scientific workloads. Energy 2013: the third international conference on smart grids, green communications and it energy-aware technologies, pp 114–119

  4. Bedard D, Lim MY, Fowler R, Porterfield A (2010) Powermon: fine-grained and integrated power monitoring for commodity computer systems. In: IEEE SoutheastCon, pp 479–484

  5. Bellosa F (2000) The benefits of event: driven energy accounting in power-sensitive systems. In: ACM SIGOPS European Workshop. ACM, pp 37–42

  6. Bourdon A, Noureddine A, Rouvoy R, Seinturier L (2013) PowerAPI: a software library to monitor the energy consumed at the process-level. ERCIM News 92:43–44

    Google Scholar 

  7. Browne S, Dongarra J, Garner N, Ho G, Mucci P (2000) A portable programming interface for performance evaluation on modern processors. Int J High Perform Comput Appl 14(3):189–204. doi:10.1177/109434200001400303

    Article  Google Scholar 

  8. Choi J, Vuduc RW (2012) Modeling and analysis for performance and power. In: IPDPS Workshops. IEEE Computer Society, pp 2466–2469

  9. Choi J, Vuduc RW (2013) How much (execution) time and energy does my algorithm cost? ACM Crossroads 19(3):49–51

    Article  Google Scholar 

  10. Choi JW, Vuduc R (2012) A roofline model of energy. Tech. rep., Gerogia Tech. College of Computing

  11. Intel\(^{\textregistered }\) Energy Checker User Guide (2010). https://software.intel.com/sites/default/files/m/3/a/6/6/a/Intel_R__Energy_Checker_SDK--User_Guide.pdf

  12. Corporation I (2013) Intel 64 and ia-32 architectures software developer’s manual. http://www.intel.com/content/dam/www/public/us/en/documents/manuals/64-ia-32-architectures-software-developer-manual-325462.pdf

  13. Corporation N (2013) Nvidia management library (nvml). https://developer.nvidia.com/nvidia-management-library-nvml

  14. Flinn J, Satyanarayanan M (1999) Powerscope: a tool for profiling the energy usage of mobile applications. In: WMCSA. IEEE Computer Society, pp 2–10

  15. Ge R, Cameron K (2007) Power-aware speedup. In: Proceedings of the 21st IEEE International Parallel and Distributed Processing Symposium (IPDPS 07)

  16. Ge R, Feng X, Song S, Chang HC, Li D, Cameron KW (2010) Powerpack: Energy profiling and analysis of high-performance systems and applications. IEEE Trans Parallel Distrib Syst 21(5):658–671

    Article  Google Scholar 

  17. Korthikanti VA, Agha G (2009) Analysis of parallel algorithms for energy conservation in scalable multicore architectures. In: ICPP, IEEE Computer Society, pp 212–219

  18. Meuer H, Strohmaier E, Dongarra J, Simon H Top500 list. http://www.top500.org/

  19. Pierson JM, Costa GD, Dittmann L (eds) (2013) Energy efficiency in large scale distributed systems: COST IC0804 European Conference, EE-LSDS 2013, Vienna, Austria, April 22–24, 2013, Revised Selected Papers, Lecture Notes in Computer Science, vol 8046. Springer, New York

  20. Popa P (2006) Managing server energy consumption using ibm powerexecutive. Tech. rep, IBM Systems and Technology Group

  21. Ryffel S (2009) Lea\(^2\)p: The linux energy attribution and accounting platform. Master’s thesis, Swiss Federal Institute of Technology

  22. Schneider Elecric A (2013) Metered-by-outlet rack pdus. http://www.apc.com

  23. Schuermans A (2012) Schleifenbauer products bv. http://www.schleifenbauer.eu/en/home.htm

  24. Shin D, Shim H, Joo Y, Yun HS, Kim J, Chang N (2002) Energy-monitoring tool for low-power embedded programs. IEEE Design Test Comput 19(4):7–17

    Article  Google Scholar 

  25. Song S, Su CY, Ge R, Vishnu A, Cameron KW (2011) Iso-energy-efficiency: an approach to power-constrained parallel computation. In: Proceedings of the 2011 IEEE International Parallel and Distributed Processing Symposium, IPDPS ’11. IEEE Computer Society, Washington, DC, USA, pp 128–139. doi:10.1109/IPDPS.2011.22

  26. Stathopoulos T, McIntire D, Kaiser WJ (2008) The energy endoscope: Real-time detailed energy accounting for wireless sensor nodes. In: IPSN. IEEE Computer Society, pp 383–394

  27. Treibig J, Hager G, Wellein G (2010) Likwid: A lightweight performance-oriented tool suite for x86 multicore environments. In: Proceedings of PSTI2010, the First International Workshop on Parallel Software Tools and Tool Infrastructures. San Diego

  28. Weaver V, Johnson M, Kasichayanula K, Ralph J, Luszczek P, Terpstra D, Moore S (2012) Measuring energy and power with papi. In: International Workshop on Power-Aware Systems and Architectures. Pittsburgh

  29. Williams S, Waterman A, Patterson DA (2009) Roofline: an insightful visual performance model for multicore architectures. Commun ACM 52(4):65–76

    Article  Google Scholar 

  30. Xu Z, Hwang K (1996) Modeling communication overhead: Mpi and mpl performance on the ibm sp2. IEEE Parallel Distrib Technol 4(1):9–23. doi:10.1109/88.481662

    Article  Google Scholar 

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Acknowledgments

This work was supported by the Spanish Ministry of Education and Science through the TIN2011-24598 project and the Spanish network CAPAP-H4.

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Authors and Affiliations

  1. HPC Group, ETS de Ingeniería Informática, Universidad de La Laguna, ULL, La Laguna, 38270, Tenerife, Spain

    Alberto Cabrera, Francisco Almeida, Javier Arteaga & Vicente Blanco

Authors
  1. Alberto Cabrera
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  2. Francisco Almeida
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  3. Javier Arteaga
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  4. Vicente Blanco
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Correspondence to Alberto Cabrera.

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Cabrera, A., Almeida, F., Arteaga, J. et al. Measuring energy consumption using EML (energy measurement library). Comput Sci Res Dev 30, 135–143 (2015). https://doi.org/10.1007/s00450-014-0269-5

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  • DOI: https://doi.org/10.1007/s00450-014-0269-5

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