Skip to main content

Platforms

  • Chapter
  • First Online:
Energy-Efficient High Performance Computing

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

  • 897 Accesses

Abstract

The ability to quantify power and energy use is critical to understanding how power and energy are currently being used. A measurement capability is also necessary to measure the effect of tuning or modification of platform parameters, CPU frequency, and network bandwidth for example. In later chapters, these effects will be evaluated based on energy savings versus performance impact. Simply stated, power and energy measurement first require hardware support. This chapter will outline the hardware architectures and some of the significant systems software of the platforms used in the experiments detailed in this book.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Notes

  1. 1.

    Sandia’s most recent effort is the Kitten light-weight kernel [2].

References

  1. S.M. Kelly, R.B. Brightwell, Software Architecture of the Light Weight Kernel, Catamount, in Cray User Group, CUG, 2005

    Google Scholar 

  2. Kitten Light Weight Kernel, Sandia National Laboratories. Available https://software.sandia.gov/trac/kitten

  3. J.H. Laros III, A Software and Hardware Architecture for a Modular, Portable, Extensible Reliability Availability and Serviceability System, in IEEE Proceedings of the Workshop on High Performance Computing Reliability Issues, 2006

    Google Scholar 

  4. Cielo, Sandia National Laboratories and Los Alamos Laboratory. Available http://www.lanl.gov/orgs/hpc/cielo/

  5. R. Ge, X. Feng, K.W. Cameron, Performance-Constrained Distributed DVS Scheduling for Scientific Applications on Power-aware Clusters, in Proceedings of the International Conference on High Performance Computing, Networking, Storage, and Analysis (SC), ACM/IEEE, 2005

    Google Scholar 

  6. K.B. Ferreira, R. Brightwell, P.G. Bridges, Characterizing Application Sensitivity to OS Interference Using Kernel-Level Noise Injection, in Proceedings of the International Conference on High Performance Computing, Networking, Storage, and Analysis (SC), ACM/IEEE, 2008

    Google Scholar 

  7. F. Petrini, D. Kerbyson, S. Pakin, The Case of the Missing Supercomputer Performance: Achieving Optimal Performance on the 8,192 Processors of ASCI Q, in Proceedings of the International Conference on High Performance Computing, Networking, Storage, and Analysis (SC), ACM/IEEE, 2003

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to James H. Laros III .

Rights and permissions

Reprints and permissions

Copyright information

© 2013 James H. Laros III

About this chapter

Cite this chapter

Laros III, J.H. et al. (2013). Platforms. In: Energy-Efficient High Performance Computing. SpringerBriefs in Computer Science. Springer, London. https://doi.org/10.1007/978-1-4471-4492-2_2

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-4492-2_2

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-4491-5

  • Online ISBN: 978-1-4471-4492-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics