Skip to main content

Advertisement

SpringerLink
Log in
Menu
Find a journal Publish with us
Search
Cart
Book cover

European Conference on Parallel Processing

Euro-Par 2011: Euro-Par 2011: Parallel Processing Workshops pp 178–187Cite as

  1. Home
  2. Euro-Par 2011: Parallel Processing Workshops
  3. Conference paper
Auto-tuning for Energy Usage in Scientific Applications

Auto-tuning for Energy Usage in Scientific Applications

  • Ananta Tiwari30,
  • Michael A. Laurenzano30,
  • Laura Carrington30 &
  • …
  • Allan Snavely30 
  • Conference paper
  • 1175 Accesses

  • 21 Citations

  • 3 Altmetric

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 7156)

Abstract

The power wall has become a dominant impeding factor in the realm of exascale system design. It is therefore important to understand how to most effectively create software to minimize its power usage while maintaining satisfactory levels of performance. This work uses existing software and hardware facilities to tune applications to minimize for several combinations of power and performance. The tuning is done with respect to software level performance-related tunables and for processor clock frequency. These tunable parameters are explored via an offline search to find the parameter combinations that are optimal with respect to performance (or delay, D), energy (E), energy×delay (E×D) and energy×delay×delay (E×D 2). These searches are employed on a parallel application that solves Poisson’s equation using stencils. We show that the parameter configuration that minimizes energy consumption can save, on average, 5.4% energy with a performance loss of 4% when compared to the configuration that minimizes runtime.

Keywords

  • Clock Frequency
  • Energy Usage
  • Relaxation Function
  • Power Usage
  • Compiler Optimization

These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Download conference paper PDF

References

  1. CPU Frequency Scaling, https://wiki.archlinux.org/index.php/Cpufrequtils

  2. KeLP, http://cseweb.ucsd.edu/groups/hpcl/scg/KeLP1.4/

  3. WattsUp? Meters, https://www.wattsupmeters.com/secure/products.php?pn=0

  4. Bedard, D., Lim, M.Y., Fowler, R., Porterfield, A.: PowerMon: Fine-grained and integrated power monitoring for commodity computer systems. In: Proceedings of the IEEE SoutheastCon 2010 (SoutheastCon), pp. 479–484 (2010)

    Google Scholar 

  5. Bekas, C., Curioni, A.: A new energy aware performance metric. Computer Science - Research and Development 25, 187–195 (2010)

    CrossRef  Google Scholar 

  6. Brooks, D., Tiwari, V., Martonosi, M.: Wattch: a framework for architectural-level power analysis and optimizations. In: Proceedings of the 27th Annual International Symposium on Computer Architecture, ISCA 2000, pp. 83–94. ACM, New York (2000)

    CrossRef  Google Scholar 

  7. Brooks, D.M., Bose, P., Schuster, S.E., Jacobson, H., Kudva, P.N., Buyuktosunoglu, A., Wellman, J.-D., Zyuban, V., Gupta, M., Cook, P.W.: Power-aware microarchitecture: Design and modeling challenges for next-generation microprocessors. IEEE Micro 20, 26–44 (2000)

    CrossRef  Google Scholar 

  8. Chen, C.: Model-Guided Empirical Optimization for Memory Hierarchy. PhD thesis, University of Southern California (2007)

    Google Scholar 

  9. Chung, I.-H., Hollingsworth, J.: A case study using automatic performance tuning for large-scale scientific programs. In: 2006 15th IEEE International Symposium on High Performance Distributed Computing, pp. 45–56 (2006)

    Google Scholar 

  10. Ciccotti, P., et al.: Characterization of the DARPA Ubiquitous High Performance Computing (UHPC) Challenge Applications. Submission to International Symposium on Workload Characterization, IIWSC (2011)

    Google Scholar 

  11. Flinn, J., Satyanarayanan, M.: Energy-aware adaptation for mobile applications. In: Proceedings of the Seventeenth ACM Symposium on Operating Systems Principles, SOSP 1999, pp. 48–63. ACM, New York (1999)

    CrossRef  Google Scholar 

  12. Freeh, V.W., Kappiah, N., Lowenthal, D.K., Bletsch, T.K.: Just-in-time dynamic voltage scaling: Exploiting inter-node slack to save energy in mpi programs. J. Parallel Distrib. Comput. 68, 1175–1185 (2008)

    CrossRef  Google Scholar 

  13. Ge, R., Feng, X., Song, S., Chang, H.-C., Li, D., Cameron, K.: PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications. IEEE Transactions on Parallel and Distributed Systems 21(5), 658–671 (2010)

    CrossRef  Google Scholar 

  14. Horowitz, M., Indermaur, T., Gonzalez, R.: Low-power digital design. In: IEEE Symposium on Low Power Electronics, Digest of Technical Papers 1994, pp. 8–11 (October 1994)

    Google Scholar 

  15. Hotta, Y., Sato, M., Kimura, H., Matsuoka, S., Boku, T., Takahashi, D.: Profile-based optimization of power performance by using dynamic voltage scaling on a pc cluster. In: Proceedings of the 20th International Conference on Parallel and Distributed Processing, IPDPS 2006, p. 298. IEEE Computer Society, Washington, DC (2006)

    Google Scholar 

  16. Hsu, C.-H., Feng, W.-C.: A Power-Aware Run-Time System for High-Performance Computing. In: Proceedings of the 2005 ACM/IEEE Conference on Supercomputing, SC 2005, p. 1. IEEE Computer Society, Washington, DC (2005)

    Google Scholar 

  17. Kadayif, I., Kandemir, M., Vijaykrishnan, N., Irwin, M., Sivasubramaniam, A.: Eac: a compiler framework for high-level energy estimation and optimization. In: Proceedings of Design, Automation and Test in Europe Conference and Exhibition, 2002, pp. 436–442 (2002)

    Google Scholar 

  18. Kandemir, M., Vijaykrishnan, N., Irwin, M.J., Ye, W.: Influence of compiler optimizations on system power. IEEE Trans. Very Large Scale Integr. Syst. 9, 801–804 (2001)

    CrossRef  Google Scholar 

  19. Laurenzano, M.A., Meswani, M., Carrington, L., Snavely, A., Tikir, M.M., Poole, S.: Reducing Energy Usage with Memory and Computation-Aware Dynamic Frequency Scaling. In: Jeannot, E., Namyst, R., Roman, J. (eds.) Euro-Par 2011, Part I. LNCS, vol. 6852, pp. 79–90. Springer, Heidelberg (2011)

    CrossRef  Google Scholar 

  20. Li, D., de Supinski, B., Schulz, M., Cameron, K., Nikolopoulos, D.: Hybrid MPI/OpenMP power-aware computing. In: 2010 IEEE International Symposium on Parallel Distributed Processing (IPDPS), pp. 1–12 (April 2010)

    Google Scholar 

  21. Olschanowsky, C., Carrington, L., Tikir, M., Laurenzano, M., Rosing, T.S., Snavely, A.: Fine-grained energy consumption characterization and modeling. In: DOD High Performance Computing Modernization Program User Group Conference (2010)

    Google Scholar 

  22. Pillai, P., Shin, K.G.: Real-time dynamic voltage scaling for low-power embedded operating systems. SIGOPS Oper. Syst. Rev. 35, 89–102 (2001)

    CrossRef  Google Scholar 

  23. Rahman, S.F., Guo, J., Yi, Q.: Automated empirical tuning of scientific codes for performance and power consumption. In: Proceedings of the 6th International Conference on High Performance and Embedded Architectures and Compilers, HiPEAC 2011, pp. 107–116. ACM, New York (2011)

    CrossRef  Google Scholar 

  24. Rivera, G., Tseng, C.-W.: Tiling optimizations for 3D scientific computations. In: Proceedings of the 2000 ACM/IEEE Conference on Supercomputing (CDROM), Supercomputing 2000. IEEE Computer Society, Washington, DC (2000)

    Google Scholar 

  25. Seng, J.S., Tullsen, D.M.: The Effect of Compiler Optimizations on Pentium 4 Power Consumption. In: Proceedings of the Seventh Workshop on Interaction between Compilers and Computer Architectures, INTERACT 2003, p. 51. IEEE Computer Society, Washington, DC (2003)

    CrossRef  Google Scholar 

  26. Singh, K., Bhadauria, M., McKee, S.A.: Prediction-based power estimation and scheduling for cmps. In: Proceedings of the 23rd International Conference on Supercomputing, ICS 2009, pp. 501–502. ACM, New York (2009)

    CrossRef  Google Scholar 

  27. Tiwari, A., Chen, C., Chame, J., Hall, M., Hollingsworth, J.: A Scalable Auto-Tuning Framework for Compiler Optimization. In: 23rd IEEE International Parallel & Distributed Processing Symposium, Rome, Italy (May 2009)

    Google Scholar 

  28. Vuduc, R., Demmel, J.W., Yelick, K.A.: Oski: A library of automatically tuned sparse matrix kernels. Journal of Physics: Conference Series 16, 521–530 (2005)

    CrossRef  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Performance Modeling and Characterization Laboratory, San Diego Supercomputer Center, USA

    Ananta Tiwari, Michael A. Laurenzano, Laura Carrington & Allan Snavely

Authors
  1. Ananta Tiwari
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Michael A. Laurenzano
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Laura Carrington
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Allan Snavely
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Scilytics, Koellnerhofgasse 3/15A, 1010, Vienna, Austria

    Michael Alexander

  2. ICAR-CNR, Via P. Castellino, 111, 80131, Napoli, Italy

    Pasqua D’Ambra

  3. University of Amsterdam, 1090, Amsterdam, Netherlands

    Adam Belloum

  4. Innovative Computing Laboratory, The University of Tennessee, US

    George Bosilca

  5. Department of Experimental Medicine and Clinic, University Magna Græcia, 88100, Catanzaro, Italy

    Mario Cannataro

  6. Computer Science Department, University of Pisa, Italy

    Marco Danelutto

  7. Second University of Naples, Italy

    Beniamino Di Martino

  8. TUMünchen,, Boltzmannstr. 3, ,, 85748, Garching, Germany

    Michael Gerndt

  9. Equipe Runtime, INRIA Bordeaux Sud-Ouest, 33405, Talence Cedex, France

    Emmanuel Jeannot & Raymond Namyst & 

  10. Equipe HIEPACS, INRIA Bordeaux Sud-Ouest, 33405, Talence Cedex, France

    Jean Roman

  11. Computer Science and Mathematics Division, Oak Ridge National Laboratory, 37831-6164, Oak Ridge, TN, USA

    Stephen L. Scott

  12. Department of Scientific Computing, University of Vienna, Nordbergstr. 15/3C, 1090, Vienna, Austria

    Jesper Larsson Traff

  13. Computer Science and Mathematics Division, Oak Ridge National Laboratory, 37831, Oak Ridge, TN, USA

    Geoffroy Vallée

  14. Technische Universität München, Germany

    Josef Weidendorfer

Rights and permissions

Reprints and Permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Tiwari, A., Laurenzano, M.A., Carrington, L., Snavely, A. (2012). Auto-tuning for Energy Usage in Scientific Applications. In: Alexander, M., et al. Euro-Par 2011: Parallel Processing Workshops. Euro-Par 2011. Lecture Notes in Computer Science, vol 7156. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29740-3_21

Download citation

  • .RIS
  • .ENW
  • .BIB
  • DOI: https://doi.org/10.1007/978-3-642-29740-3_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-29739-7

  • Online ISBN: 978-3-642-29740-3

  • eBook Packages: Computer ScienceComputer Science (R0)

Share this paper

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

Search

Navigation

  • Find a journal
  • Publish with us

Discover content

  • Journals A-Z
  • Books A-Z

Publish with us

  • Publish your research
  • Open access publishing

Products and services

  • Our products
  • Librarians
  • Societies
  • Partners and advertisers

Our imprints

  • Springer
  • Nature Portfolio
  • BMC
  • Palgrave Macmillan
  • Apress
  • Your US state privacy rights
  • Accessibility statement
  • Terms and conditions
  • Privacy policy
  • Help and support

167.114.118.210

Not affiliated

Springer Nature

© 2023 Springer Nature