Computer Science - Research and Development

, Volume 25, Issue 3–4, pp 187–195 | Cite as

A new energy aware performance metric

  • Costas BekasEmail author
  • Alessandro Curioni
Special Issue Paper


Energy aware algorithms are the wave of the future. The development of exascale systems made it clear that extrapolations of current technologies, algorithmic practices and performance metrics are simply inadequate. The community reacted by introducing the FLOPS/WATT metric in order to promote energy awareness. In this work we take a step forward and argue what one should aim for is the total reduction of the spent energy in conjunction with minimization of time to solution. Thus, we propose to use f(time to solution)⋅energy (FTTSE) as the performance metric, where f(⋅) is an application dependent function of time. In this paper, we introduce our ideas and showcase them with a recently developed framework for solving large dense linear systems.


Energy aware Performance metrics Analysis Fault tolerance 


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  1. 1.
    Bekas C, Curioni A, Fedulova I (November 2009) Low cost high performance uncertainty quantification. Workshop on high performance computational finance, SC09, Portland, OR, USA Google Scholar
  2. 2.
    Blackford L, Choi J, Cleary A, D’Azevedo E, Demmel J, Dhillon I, Dongarra J, Hammarling S, Henry G, Petitet A, Stanley K, Walker D, Whaley R (1997) ScaLAPACK user’s guide. SIAM, Philadelphia. See also Accessed 1997 Google Scholar
  3. 3.
    Buttari A, Dongarra J, Langou J, Langou J, Luszczek P, Kurzak J (2007) Mixed precision iterative refinement techniques for the solution of dense linear systems. Int J High Perform Comput Appl 21(4):457–466 CrossRefGoogle Scholar
  4. 4.
    Colella P (2004) Defining software requirements for scientific computing Google Scholar
  5. 5.
    Higham NJ (1996) Accuracy and stability of numerical algorithms. SIAM, Philadelphia zbMATHGoogle Scholar
  6. 6.
    IBM Red Book (2009) IBM system Blue Gene solution: performance analysis tools. Last update 6 May 2010
  7. 7.
    Kogge P (2009) The road to exascale: hardware and software challenges panel, SC09, Nov. 14–19, 2009, Portland, Oregon, USA. Available at: Accessed 2009
  8. 8.
    Saad Y (2003) Iterative methods for sparse linear systems, 2nd edn. SIAM, Philadelphia zbMATHGoogle Scholar
  9. 9.
    The Green 500. Accessed 2010
  10. 10.
    Whaley RC, Dongarra J (1998) Automatically tuned linear algebra software. SuperComputing 1998: High performance networking and computing Google Scholar
  11. 11.
    Wilkinson JH (1963) Rounding errors in algebraic processes. Notes on applied science, vol 32. Her Majesty’s Stationary Office, London zbMATHGoogle Scholar

Copyright information

© Springer-Verlag 2010

Authors and Affiliations

  1. 1.IBM Research – ZurichRüschlikonSwitzerland

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