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Energy consumption model in multicore architectures with variable frequency

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Abstract

Models extending Amdahl’s law have been developed to study the behavior of parallel programs energy consumption. In addition, it has been shown that energy consumption of those programs also relies on the layout of the resources on the chip, such as power supply. Other extensions over Amdahl’s law have been conducted to study the behavior of parallel programs speedup for frequency variable processors. Previous models have focused on the use of Turbo Boost in the parallel regions of a program, without considering that Turbo Boost also affects the sequential regions. Hence, we present a model to analyze energy consumption of parallel programs executed on Intel multicore processors with Turbo Boost frequencies to cover this gap. The model is an extension to Amdahl’s law, and it is validated with a double-precision matrix multiplication running on Intel multicore processors that enable Turbo Boost technology.

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References

  1. Clerici A, Assayag M (2013) Recursos energéticos globales. Encuesta 2013: Resumen. Tech. rep., World Energy Council, for sustainable energy

  2. Commission WEC (1993) Energy for tomorrow’s world: the realities, the real options and the agenda for achievement. St. Martin’s Press, New York

    Google Scholar 

  3. Nakicenovic N, Jefferson M (1995) Global energy perspectives to 2050 and beyond. Global energy perspectives to 2050 and beyond. Tech. rep

  4. Poizot P, Dolhem F (2011) Clean energy new deal for a sustainable world: from non-CO2 generating energy sources to greener electrochemical storage devices. Energy Environ Sci 4(6):2003–2019

    Article  Google Scholar 

  5. Chu S, Majumdar A (2012) Opportunities and challenges for a sustainable energy future. Nature 488(7411):294–303

    Article  Google Scholar 

  6. Chow J, Kopp RJ, Portney PR (2003) Energy resources and global development. Science 302(5650):1528–1531

    Article  Google Scholar 

  7. Robinson S (2009) Cellphone energy gap: desperately seeking solutions. Strateg Anal

  8. D’Andrea R (2014) Guest editorial can drones deliver? IEEE Trans Autom Sci Eng 11(3):647–648

    Article  Google Scholar 

  9. Mei Y, Lu YH, Hu YC, Lee CG (2004) Energy-efficient motion planning for mobile robots. In: Proceedings, ICRA’04 2004 IEEE International Conference on Robotics and Automation 2004, vol 5, pp 4344–4349

  10. de Santos PG, Garcia E, Ponticelli R, Armada M (2009) Minimizing energy consumption in hexapod robots. Adv Robot 23(6):681

    Article  Google Scholar 

  11. Chyba M, Haberkorn T, Singh S, Smith R, Choi S (2009) Increasing underwater vehicle autonomy by reducing energy consumption. Ocean Eng 36(1):62

    Article  Google Scholar 

  12. Geller T (2011) Supercomputing’s exaflop target. Commun ACM 54(8):16–18

    Article  Google Scholar 

  13. Hsu J (2012) Supercomputer ‘Titans’ face huge energy costs. Blog on LiveScience. https://www.livescience.com/18072-rise-titans-exascale-supercomputers-leap-power-hurdle.html

  14. Tarkoma S, Siekkinen M, Lagerspetz E, Xiao Y (2014) Smartphone energy consumption: modeling and optimization. Cambridge University Press, Cambridge

    Book  Google Scholar 

  15. Meneses-Viveros A, Hernandez-Rubio E, Mendoza S, Rodriguez J, Quintos ABM (2018) Energy saving strategies in the design of mobile device applications. Sustain Comput Inform Syst 19:86–95

    Google Scholar 

  16. Xu Q, Mytkowicz T, Kim NS (2016) Approximate computing: a survey. IEEE Des Test 33(1):8–22

    Article  Google Scholar 

  17. Pant YV, Abbas H, Nischal K, Kelkar P, Kumar D, Devietti J, Mangharam R (2015) Power-efficient algorithms for autonomous navigation. In: 2015 International Conference on Complex Systems Engineering (ICCSE), pp 1–6

  18. Gunther S, Deval A, Burton T, Kumar R (2010) Energy-efficient computing: power management system on the nehalem family of processors. Intel Technol J 14(3):50–65

    Google Scholar 

  19. Meneses-Viveros A, Paredes-López M, Gitler I (2018) In: International Conference on Supercomputing in Mexico, Springer, pp 87–96

  20. Verner U, Mendelson A, Schuster A (2017) Extending Amdahl’s Law for Multicores with Turbo Boost. IEEE Comput Archit Lett 16(1):30–33

    Article  Google Scholar 

  21. Le Sueur E, Heiser G (2010) In: Proceedings of the 2010 International Conference on Power Aware Computing and Systems, USENIX Association, Berkeley, HotPower’10, pp 1–8

  22. Haj-Yahya J, Mendelson A, Asher YB, Chattopadhyay A (2018) In: Energy Efficient High Performance Processors, Springer, pp 57–72

  23. Conway P, Hughes B (2007) The AMD Opteron Northbridge architecture. IEEE Micro 27(2):10–21. https://doi.org/10.1109/MM.2007.43

    Article  Google Scholar 

  24. Rotem E, Naveh A, Ananthakrishnan A, Weissmann E, Rajwan D (2012) Power-management architecture of the intel microarchitecture code-named Sandy bridge. IEEE Micro 32(2):20. https://doi.org/10.1109/MM.2012.12

    Article  Google Scholar 

  25. Charles J, Jassi P, Ananth NS (2009) In: Proceedings of IEEE International Symposium on Workload Characterization, 2009. IISWC 2009, IEEE, pp 188–197

  26. Fuller SH, Miller LE (2011) The National Academies Press pp 31–38

  27. Song W, Mukhopadhyay S, Yalamanchili S (2012) In: Dark Silicon Workshop

  28. Cebrian JM, Natvig L, Meyer JC (2012) In: 2012 SC Companion: High Performance Computing, Networking Storage and Analysis, IEEE, pp 675–684

  29. Sun XH, Chen Y (2010) Reevaluating Amdahl’s Law in the Multicore Era. J Parallel Distrib Comput 70(2):183

    Article  Google Scholar 

  30. Woo DH, Lee HHS (2008) Extending Amdahl’s law for energy-efficient computing in the Many-Core Era. Computer 41(12):24–31

    Article  MathSciNet  Google Scholar 

  31. Hill MD, R MM (2008) Amdahl’s law in the multicore Era. Computer 41(7):33–38

    Article  Google Scholar 

  32. Londoño SM, de Gyvez JP (2010) In: 2010 International Conference on Energy Aware Computing (ICEAC), IEEE, pp 1–4

  33. Cho S, Melhem RG (2010) On the interplay of parallelization, program performance, and energy consumption. IEEE Trans Parallel Distrib Syst 21:342–353

    Article  Google Scholar 

  34. Isidro-Ramirez R, Viveros AM, Rubio EH (2015) Energy consumption model over parallel programs implemented on multicore architectures. Int J Adv Comput Sci Appl 6(6):21

    Google Scholar 

  35. Pei S, Zhang J, Xiong N, Kim MS, Gaudiot JL (2018) Energy efficiency of heterogeneous multicore system based on the enhanced Amdahl’s law. IJHPCN 12(3):261–269

    Article  Google Scholar 

  36. Hsu CH, Poole SW (2013) In: 2013 42nd International Conference on Parallel Processing, IEEE, pp 834–840

  37. Hsu CH, Poole SW (2015) In: Proceedings of the 6th ACM/SPEC International Conference on Performance Engineering, pp 235–240

  38. Ruiu P, Fiandrino C, Giaccone P, Bianco A, Kliazovich D, Bouvry P (2017) On the energy-proportionality of data center networks. IEEE Trans Sustain Comput 2(2):197–210

    Article  Google Scholar 

  39. Jiang C, Wang Y, Ou D, Luo B, Shi W (2017) In: 2017 IEEE 37th International Conference on Distributed Computing Systems (ICDCS), IEEE, pp 1649–1660

  40. Malla S, Christensen K (2020) The effect of server energy proportionality on data center power oversubscription. Future Gener Comput Syst 104:119–130

    Article  Google Scholar 

  41. Martin AJ (2001) Towards an energy complexity of computation. Inf Process Lett 77(2–4):181–187

    Article  MathSciNet  Google Scholar 

  42. Tran VNN, Ha PH (2016) In: 2016 IEEE 22nd International Conference on Parallel and Distributed Systems (ICPADS), IEEE, pp 1041–1048

  43. Roy S, Rudra A, Verma A (2013) In: Proceedings of the 4th conference on Innovations in Theoretical Computer Science, ACM, pp 283–304

  44. Swapnoneel R, Rudra A, Verma A (2013) In: 4th Conference on Innovations in Theoretical Computer Science ITCS ’13, pp 283–304

  45. Basmadjian R, de Meer H (2012) In: Future Energy Systems: Where Energy, Computing and Communications Meet (e-energy), pp 1–10

  46. Wu F, Chen J, Dong Y, Zheng W, Pan X, Sun Y (2018) In: 2018 IEEE 20th International Conference on High Performance Computing and Communications; IEEE 16th International Conference on Smart City; IEEE 4th International Conference on Data Science and Systems (HPCC/SmartCity/DSS), IEEE, pp 960–967

  47. Amdahl GM (1967) In: AFIPS Conference, vol 30, pp 483–485

  48. Kim SH, Kim D, Lee C, Jeong WS, Ro WW, Gaudiot JL (2014) A performance-energy model to evaluate single thread execution acceleration. IEEE Comput Archit Lett 14(2):99–102

    Article  Google Scholar 

  49. Acun B, Miller P, Kale LV (2016) In: Proceedings of the 2016 International Conference on Supercomputing, pp 1–12

  50. Marathe A, Zhang Y, Blanks G, Kumbhare N, Abdulla G, Rountree B (2017) In: Proceedings of the 5th International Workshop on Energy Efficient Supercomputing, pp 1–8

  51. Hackenberg D, Schöne R, Ilsche T, Molka D, Schuchart J, Geyer R (2015) In: 2015 IEEE International Parallel and Distributed Processing Symposium Workshop, IEEE, pp 896–904

  52. Wang B, Schmidl D (2015) In International Workshop on OpenMP. Springer, Switzerland, pp 233–246

    Google Scholar 

  53. Marques SMV, Medeiros TS, Rossi FD, Luizelli MC, Girardi AG, Beck ACS, Lorenzon AF (2019) In: 2019 IFIP/IEEE 27th International Conference on Very Large Scale Integration (VLSI-SoC), IEEE, pp 149–154

  54. Jarus M, Varrette S, Oleksiak A, Bouvry P (2013) In: Revised Selected Papers of the COST IC0804 European Conference on Energy Efficiency in Large Scale Distributed Systems - Volume 8046, Springer, Berlin, EE-LSDS 2013, pp 182–200. https://doi.org/10.1007/978-3-642-40517-4$_$16

  55. Kambadur M, Kim MA (2014) In: Proceedings of the 2014 ACM International Conference on Object Oriented Programming Systems Languages and Applications, pp 329–344

  56. Porterfield AK, Olivier SL, Bhalachandra S, Prins JF (2013) In: 2013 IEEE International Symposium on Parallel and Distributed Processing, Workshops and PhD Forum, IEEE, pp 884–891

  57. Hackenberg D, Oldenburg R, Molka D, Schöne R (2013) In: 2013 International Green Computing Conference Proceedings, IEEE, pp 1–9

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Acknowledgements

The authors thank financial support given by the Mexican National Council of Science and Technology (CONACyT), as well as ABACUS: Laboratory of Applied Mathematics and High-Performance Computing of the Mathematics Department of CINVESTAV-IPN. The authors acknowledge both, the Center for Research and Advance Studies of the National Polytechnic Institute (CINVESTAV-IPN) and the Section of Research and Graduate Studies (SEPI) of ESCOM-IPN, for encouragement and facilities provided to accomplish this publication.

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Correspondence to Amilcar Meneses-Viveros.

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Meneses-Viveros, A., Paredes-López, M., Hernández-Rubio, E. et al. Energy consumption model in multicore architectures with variable frequency. J Supercomput 77, 2458–2485 (2021). https://doi.org/10.1007/s11227-020-03349-0

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