Skip to main content

Advertisement

Log in

A new energy aware performance metric

  • Special Issue Paper
  • Published:
Computer Science - Research and Development

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  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

  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 www.netlib.org/scalapack. Accessed 1997

    Google Scholar 

  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

    Article  Google Scholar 

  4. Colella P (2004) Defining software requirements for scientific computing

  5. Higham NJ (1996) Accuracy and stability of numerical algorithms. SIAM, Philadelphia

    MATH  Google Scholar 

  6. IBM Red Book (2009) IBM system Blue Gene solution: performance analysis tools. http://www.redbooks.ibm.com/abstracts/redp4256.html. Last update 6 May 2010

  7. Kogge P (2009) The road to exascale: hardware and software challenges panel, SC09, Nov. 14–19, 2009, Portland, Oregon, USA. Available at: www.exascale.org/mediawiki/images/6/6e/Sc09-exa-panel-kogge.pdf. Accessed 2009

  8. Saad Y (2003) Iterative methods for sparse linear systems, 2nd edn. SIAM, Philadelphia

    MATH  Google Scholar 

  9. The Green 500. www.green500.org. Accessed 2010

  10. Whaley RC, Dongarra J (1998) Automatically tuned linear algebra software. SuperComputing 1998: High performance networking and computing

  11. Wilkinson JH (1963) Rounding errors in algebraic processes. Notes on applied science, vol 32. Her Majesty’s Stationary Office, London

    MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Costas Bekas.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bekas, C., Curioni, A. A new energy aware performance metric. Comput Sci Res Dev 25, 187–195 (2010). https://doi.org/10.1007/s00450-010-0119-z

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00450-010-0119-z

Keywords

Navigation