Skip to main content

Modeling the Productivity of HPC Systems on a Computing Center Scale

Part of the Lecture Notes in Computer Science book series (LNTCS,volume 9137)

Abstract

In pursue of exaflop computing, the expenses of HPC centers increase in terms of acquisition, energy, employment, and programming. Thus, a quantifiable metric for productivity as value per cost gets more important to make an informed decision on how to invest available budgets. In this work, we model overall productivity from a computing center’s perspective. The productivity model uses as value the number of application runs possible during the lifetime of a given supercomputer. The cost is the total cost of ownership (TCO) of an HPC center including costs for administration and programming effort. For the latter, we include techniques for software cost estimation of large codes taken from the domain of software engineering. As tuning effort increases when more performance is required, we further focus on the impact of the 80-20 rule when it comes to development effort. Here, performance can be expressed with respect to Amdahl’s law. Moreover, we include an asymptotic analysis for parameters like number of compute nodes and lifetime. We evaluate our approach on a real-world case: an engineering application in our integrative hosting environment.

Keywords

  • Productivity
  • Cost-benefit ratio
  • Cost efficiency
  • TCO
  • Development effort
  • COCOMO
  • 80-20 rule
  • Pareto principle
  • Computing center
  • Scalability

This is a preview of subscription content, access via your institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • DOI: 10.1007/978-3-319-20119-1_26
  • Chapter length: 18 pages
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
eBook
USD   69.99
Price excludes VAT (USA)
  • ISBN: 978-3-319-20119-1
  • Instant PDF download
  • Readable on all devices
  • Own it forever
  • Exclusive offer for individuals only
  • Tax calculation will be finalised during checkout
Softcover Book
USD   89.99
Price excludes VAT (USA)
Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.
Fig. 13.
Fig. 14.

References

  1. Apon, A., Ahalt, S., Dantuluri, V., Gurdgiev, C., Limayem, M., Ngo, L., Stealey, M.: High performance computing instrumentation and research productivity in US universities. J. Inf. Technol. Impact 10(2), 87–98 (2010)

    Google Scholar 

  2. Boehm, B., Abts, C., Brown, A.W., Chulani, S., Clark, B., Horowitz, E., Madachy, R., Reifer, D., Steece, B.: COCOMO II Model Definition Manual, Version 2.1. Technical report, University of Southern California (2000)

    Google Scholar 

  3. Chew, W.: No-nonsense guide to measuring productivity. Harvard Bus. Rev. 66(1), 110–118 (1988)

    MathSciNet  Google Scholar 

  4. Culler, D.E., Karp, R.M., Patterson, D.A., Sahay, A., Schauser, K.E., Santos, E., Subramonian, R., von Eicken, T.: LogP: Towards a Realistic Model of Parallel Computation. Technical report, Berkeley (1992)

    Google Scholar 

  5. Dongarra, J., Graybill, R., Harrod, W., Lucas, R., Lusk, E., Luszczek, P., Mcmahon, J., Snavely, A., Vetter, J., Yelick, K., Alam, S., Campbell, R., Carrington, L., Chen, T.Y., Khalili, O., Meredith, J., Tikir, M.: DARPA’s HPCS program: history, models, tools, languages. In: Zelkowitz, M.V. (ed.) Advances in COMPUTERS High Performance Computing, Advances in Computers, vol. 72, pp. 1–100. Elsevier (2008)

    Google Scholar 

  6. Ebcioglu, K., Sarkar, V., El-Ghazawi, T., Urbanic, J., Center, P.: An experiment in measuring the productivity of three parallel programming languages. In: Workshop on Productivity and Performance in High-End Computing (P-PHEC), pp. 30–36 (2006)

    Google Scholar 

  7. European Commission: Guide to Financial Issues relating to FP7 Indirect Actions (2013)

    Google Scholar 

  8. Faulk, S., Gustafson, J., Johnson, P., Porter, A., Tichy, W., Votta, L.: Measuring high performance computing productivity. Int. J. High Perform. Comput. Appl. 18(4), 459–473 (2004)

    CrossRef  Google Scholar 

  9. German Science Foundation (DFG): Personalmittelsätze der DFG für das Jahr (2013)

    Google Scholar 

  10. Göbbert, J.H., Gauding, M.: psOpen (2015). http://www.fz-juelich.de/ias/jsc/EN/Expertise/High-Q-Club/psOpen/_node.html

  11. InfiniBand Trade Association (2015). http://www.infinibandta.org/

  12. Intel Corporation: Intel Processor ARK (2015). http://ark.intel.com/

  13. Joseph, E.C., Conway, S., Dekate, C.: Creating Economic Models Showing the Relationship Between Investments in HPC and the Resulting Financial ROI and Innovation and How It Can Impact a Nation’s Competitiveness and Innovation. International Data Corporation (IDC), Technical report (2013)

    Google Scholar 

  14. Kennedy, K., Koelbel, C., Schreiber, R.: Defining and measuring the productivity of programming languages. Int. J. High Perform. Comput. Appl. 18(4), 441–448 (2004)

    CrossRef  Google Scholar 

  15. Kepner, J.: High performance computing productivity model synthesis. Int. J. High Perform. Comput. Appl. 18(4), 505–516 (2004)

    CrossRef  Google Scholar 

  16. McConnell, S.: Software Estimation: Demystifying the Black Art. Redmond, Wa. Microsoft Press (2006)

    Google Scholar 

  17. McCracken, M., Wolter, N., Snavely, A.: Beyond performance tools: Measuring and modeling productivity in HPC. In: Third International Workshop on Software Engineering for High Performance Computing Applications, SE-HPC 2007, pp. 4–4 (2007)

    Google Scholar 

  18. Murphy, D., Nash, T., Lawrence Votta, J., Kepner, J.: A System-wide Productivity Figure of Merit. CT Watch Quarterly 2(4B) (2006)

    Google Scholar 

  19. Newman, M.: Power laws, Pareto distributions and Zipf’s law. Contemp. Phys. 46(5), 323–351 (2005)

    CrossRef  Google Scholar 

  20. Pekurovsky, D.: P3DFFT: a framework for parallel computations of Fourier transforms in three dimensions. SIAM J. Sci. Comput. 34(4), C192–C209 (2012)

    MATH  MathSciNet  CrossRef  Google Scholar 

  21. Sadowski, C., Shewmaker, A.: The Last Mile: Parallel Programming and Usability. In: Proceedings of the FSE/SDP Workshop on Future of Software Engineering Research, FoSER 2010, pp. 309–314. ACM, New York (2010)

    Google Scholar 

  22. Snir, M., Bader, D.A.: A framework for measuring supercomputer productivity. Int. J. High Perform. Comput. Appl. 18(4), 417–432 (2004)

    CrossRef  Google Scholar 

  23. Sterling, T.: Productivity metrics and models for high performance computing. Int. J. High Perform. Comput. Appl. 18(4), 433–440 (2004)

    MathSciNet  CrossRef  Google Scholar 

  24. Wang, L., Khan, S.: Review of performance metrics for green data centers: a taxonomy study. J. Supercomputing 63(3), 639–656 (2011)

    CrossRef  Google Scholar 

  25. Wienke, S., Iliev, H., Hahnfeld, J., an Mey, D., Müller, M.S.: \(aixH(PC)^2\) - Aachen HPC Productivity Calculator (2015). http://www.hpc.rwth-aachen.de/research/tco/

  26. Wienke, S., an Mey, D., Müller, M.S.: Accelerators for technical computing: is it worth the pain? A TCO perspective. In: Kunkel, J.M., Ludwig, T., Meuer, H.W. (eds.) ISC 2013. LNCS, vol. 7905, pp. 330–342. Springer, Heidelberg (2013)

    CrossRef  Google Scholar 

  27. Williams, S., Waterman, A., Patterson, D.: Roofline: an insightful visual performance model for multicore architectures. Commun. ACM 52(4), 65–76 (2009)

    CrossRef  Google Scholar 

  28. Zelkowitz, M., Basili, V., Asgari, S., Hochstein, L., Hollingsworth, J., Nakamura, T.: Measuring productivity on high performance computers. In: IEEE International Symposium on Software Metrics, p. 6 (2005). http://doi.ieeecomputersociety.org/10.1109/METRICS.2005.33

  29. Zelkowitz, M., Hollingsworth, J., Basili, V., Asgari, S., Shull, F., Carver, J., Hochstein, L.: Parallel Programmer Productivity: A Case Study of Novice Parallel Programmers. SC Conference 35 (2005). http://dx.doi.org/10.1109/SC.2005.53

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Sandra Wienke .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and Permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Wienke, S., Iliev, H., an Mey, D., Müller, M.S. (2015). Modeling the Productivity of HPC Systems on a Computing Center Scale. In: Kunkel, J., Ludwig, T. (eds) High Performance Computing. ISC High Performance 2015. Lecture Notes in Computer Science(), vol 9137. Springer, Cham. https://doi.org/10.1007/978-3-319-20119-1_26

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-20119-1_26

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-20118-4

  • Online ISBN: 978-3-319-20119-1

  • eBook Packages: Computer ScienceComputer Science (R0)