Advertisement

Efficient and Scalable Pareto Front Generation for Energy and Makespan in Heterogeneous Computing Systems

  • Kyle M. TarpleeEmail author
  • Ryan Friese
  • Anthony A. Maciejewski
  • Howard Jay Siegel
Chapter
Part of the Studies in Computational Intelligence book series (SCI, volume 580)

Abstract

The rising costs and demand of electricity for high-performancecomputing systems pose difficult challenges to system administrators that are trying to simultaneously reduce operating costs and offer state-of-the-art performance. However, system performance and energy consumption are often conflicting objectives. Algorithms are necessary to help system administrators gain insight into this energy/performance trade-off. Through the use of intelligent resource allocation techniques, system administrators can examine this trade-off space to quantify how much a given performance level will cost in electricity, or see what kind of performance can be expected when given an energy budget. A novel algorithm is presented that efficiently computes tight lower bounds and high quality solutions for energy and makespan. These solutions are used to bound the Pareto front to easily trade-off energy and performance. These new algorithms are shown to be highly scalable in terms of solution quality and computation time compared to existing algorithms.

Keywords

High performance computing Scheduling Bag-of-tasks Scalable Efficient Heterogeneous computing 

Notes

Acknowledgments

This work was supported by the Sjostrom Family Scholarship, Numerica Corporation, the National Science Foundation (NSF) under grants CNS-0905399 and CCF-1302693, the NSF Graduate Research Fellowship, and by the Colorado State University George T. Abell Endowment. Any opinion, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the National Science Foundation. A preliminary version of portions of this work have been previously presented in [25].

References

  1. 1.
    Koomey, J.: Growth in data center electricity use 2005 to 2010, pp. 1. Analytics Press (2011)Google Scholar
  2. 2.
    Cameron, K.W.: Energy oddities, part 2: why green computing is odd. Computer 46(3), 90–93 (2013)CrossRefGoogle Scholar
  3. 3.
    Friese, R., Brinks, T., Oliver, C., Siegel, H.J., Maciejewski, A.A.: Analyzing the trade-offs between minimizing makespan and minimizing energy consumption in a heterogeneous resource allocation problem. In: INFOCOMP, The Second International Conference on Advanced Communications and Computation. 81–89 (2012)Google Scholar
  4. 4.
    Braun, T.D., Siegel, H.J., Beck, N., Bölöni, L.L., Maheswaran, M., Reuther, A.I., Robertson, J.P., Theys, M.D., Yao, B., Hensgen, D., Freund, R.F.: A comparison of eleven static heuristics for mapping a class of independent tasks onto heterogeneous distributed computing systems. J. Parallel Distrib. Comput. 61(6), 810–837 (2001)CrossRefGoogle Scholar
  5. 5.
    Bharadwaj, V., Robertazzi, T.G., Ghose, D.: Scheduling Divisible Loads in Parallel and Distributed Systems. IEEE Computer Society Press, Los Alamitos (1996)Google Scholar
  6. 6.
    Al-Qawasmeh, A.M., Maciejewski, A.A., Wang, H., Smith, J., Siegel, H.J., Potter, J.: Statistical measures for quantifying task and machine heterogeneities. J. Supercomput. 57(1), 34–50 (2011)CrossRefGoogle Scholar
  7. 7.
    Friese, R., Khemka, B., Maciejewski, A.A., Siegel, H.J., Koenig, G.A., Powers, S., Hilton, M., Rambharos, J., Okonski, G., Poole, S.W.: An analysis framework for investigating the trade-offs between system performance and energy consumption in a heterogeneous computing environment. In: IEEE 27th International Parallel and Distributed Processing Symposium Workshops (IPDPSW), Heterogeneity in Computing Workshop, IEEE, pp. 19–30 (2013)Google Scholar
  8. 8.
    Friese, R., Brinks, T., Oliver, C., Siegel, H.J., Maciejewski, A.A., Pasricha, S.: A machine-by-machine analysis of a bi-objective resource allocation problem. In: International Conference on Parallel and Distributed Processing Technologies and Applications (PDPTA) (2013)Google Scholar
  9. 9.
    Bertsimas, D., Tsitsiklis, J.N.: Introduction to Linear Optimization. Optimization and Neural Computation. Athena Scientific (1997)Google Scholar
  10. 10.
    Graham, R.: Bounds on multiprocessing timing anomalies. SIAM J. Appl. Math. 17(2), 416–429 (1969)CrossRefzbMATHMathSciNetGoogle Scholar
  11. 11.
    Phoronix Media: Intel core i7 3770k power consumption, thermal. http://openbenchmarking.org/result/1204229-SU-CPUMONITO81 (May 2013)
  12. 12.
    Tarplee, K.M.: Energy and makespan bi-objective optimization data. http://goo.gl/3Ik8eC (November 2013)
  13. 13.
    Hall, J.: Coin-or clp. https://projects.coin-or.org/Clp (March 2013)
  14. 14.
    Lenstra, J., Shmoys, D., Tardos, É.: Approximation algorithms for scheduling unrelated parallel machines. Math. Program. 46(1–3), 259–271 (1990)CrossRefzbMATHMathSciNetGoogle Scholar
  15. 15.
    Pareto, V.: Cours d’economie Politique. F. Rouge, Lausanne (1896)Google Scholar
  16. 16.
    Ehrgott, M.: Multicriteria Optimization. Springer-Verlag New York Inc, Secaucus (2005)zbMATHGoogle Scholar
  17. 17.
    Benson, H.: An outer approximation algorithm for generating all efficient extreme points in the outcome set of a multiple objective linear programming problem. J. Global Optim. 13(1), 1–24 (1998)CrossRefzbMATHMathSciNetGoogle Scholar
  18. 18.
    Löhne, A.: Vector Optimization with Infimum and Supremum. Vector Optimization. Springer, Berlin Heidelberg (2011)CrossRefGoogle Scholar
  19. 19.
    Eichfelder, G.: Adaptive Scalarization Methods in Multiobjective Optimization. Springer (2008)Google Scholar
  20. 20.
    Jansen, K., Porkolab, L.: Improved approximation schemes for scheduling unrelated parallel machines. Math. Oper. Res. 26(2), 324–338 (2001)CrossRefzbMATHMathSciNetGoogle Scholar
  21. 21.
    Deb, K., Pratap, A., Agarwal, S., Meyarivan, T.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  22. 22.
    Ali, S., Siegel, H.J., Maheswaran, M., Hensgen, D., Ali, S.: Representing task and machine heterogeneities for heterogeneous computing systems. Tamkang J. Sci. Eng. 3(3), 195–208 (2000)Google Scholar
  23. 23.
    Shmoys, D.B., Tardos, É.: Scheduling unrelated machines with costs. In: Fourth Annual ACM-SIAM Symposium on Discrete algorithms, Society for Industrial and Applied Mathematics. 448–454 (1993)Google Scholar
  24. 24.
    Li, D., Wu, J.: Energy-aware scheduling for frame-based tasks on heterogeneous multiprocessor platforms. In: 41st International Conference on Parallel Processing (ICPP), pp. 430–439 (2012)Google Scholar
  25. 25.
    Tarplee, K.M., Friese, R., Maciejewski, A.A., Siegel, H.J.: Efficient and scalable computation of the energy and makespan pareto front for heterogeneous computing systems. In: Federated Conference on Computer Science and Information Systems, Workshop on Computational Optimization. 401–408 (2013)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Kyle M. Tarplee
    • 1
    Email author
  • Ryan Friese
    • 1
  • Anthony A. Maciejewski
    • 1
  • Howard Jay Siegel
    • 1
    • 2
  1. 1.Department of Electrical and Computer EngineeringColorado State UniversityFort CollinsUSA
  2. 2.Department of Computer ScienceColorado State UniversityFort CollinsUSA

Personalised recommendations