Advertisement

Formal Metrics for Large-Scale Parallel Performance

  • Kenneth MorelandEmail author
  • Ron Oldfield
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9137)

Abstract

Performance measurement of parallel algorithms is well studied and well understood. However, a flaw in traditional performance metrics is that they rely on comparisons to serial performance with the same input. This comparison is convenient for theoretical complexity analysis but impossible to perform in large-scale empirical studies with data sizes far too large to run on a single serial computer. Consequently, scaling studies currently rely on ad hoc methods that, although effective, have no grounded mathematical models. In this position paper we advocate using a rate-based model that has a concrete meaning relative to speedup and efficiency and that can be used to unify strong and weak scaling studies.

Keywords

Processing Element Problem Size Parallel Algorithm Parallel Performance Serial Performance 
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.

Notes

Acknowledgment

This material is based in part upon work supported by the U.S. Department of Energy, Office of Science, Office of Advanced Scientific Computing Research, Scientific Discovery through Advanced Computing (SciDAC) program under Award Number 12-015215.

Sandia National Laboratories is a multi-program laboratory managed and operated by Sandia Corporation, a wholly owned subsidiary of Lockheed Martin Corporation, for the U.S. Department of Energy’s National Nuclear Security Administration under contract DE-AC04-94AL85000. SAND 2015-2890 C

References

  1. 1.
    Amdahl, G.M.: Validity of the single processor approach to achieving large scale computing capabilities. In: Proceedings of the AFIPS 1967, pp. 483–485, April 1967. doi: 10.1145/1465482.1465560
  2. 2.
    Bernaschi, M., Bisson, M., Fatica, M., Melchionna, S.: 20 Petaflops simulation of proteins suspensions in crowding conditions. In: Proceedings of the SC 2013, November 2013. doi: 10.1145/2503210.2504563
  3. 3.
    Bussmann, M., et al.: Radiative signatures of the relativistic Kelvin-Helmholtz instability. In: Proceedings of the SC 2013, November 2013. doi: 10.1145/2503210.2504564
  4. 4.
    Cameron, K.W., Ge, R.: Generalizing Amdahl’s law for power and energy. IEEE Comput. 45(3), 75–77 (2012). doi: 10.1109/MC.2012.92 CrossRefGoogle Scholar
  5. 5.
    Faber, V., Lubeck, O.M., White Jr., A.B.: Superlinear speedup of an efficient sequential algorithm is not possible. Parallel Comput. 3(3), 259–260 (1986). doi: 10.1016/0167-8191(86)90024-4 zbMATHMathSciNetCrossRefGoogle Scholar
  6. 6.
    Grama, A.Y., Gupta, A., Kuma, V.: Isoefficiency: measuring the scalability of parallel algorithms and architectures. IEEE Parallel Distrib. Technol.: Syst. Appl. 1(3), 12–21 (1993). doi: 10.1109/88.242438 CrossRefGoogle Scholar
  7. 7.
    Gustafson, J.L.: Reevaluating Amdahl’s law. Commun. ACM 31(5), 532–533 (1988). doi: 10.1145/42411.42415 CrossRefGoogle Scholar
  8. 8.
    Gustafson, J.L.: Fixed time, tiered memory, and superlinear speedup. In: Proceedings of the Fifth Distributed Memory Computing Conference, pp. 1255–1260 April 1990. doi: 10.1109/DMCC.1990.556383
  9. 9.
    Habib, S., et al.: HACC: Extreme scaling and performance across diverse architectures. In: Proceedings of the SC 2013, November 2013. doi: 10.1145/2503210.2504566
  10. 10.
    JáJá, J.: An Introduction to Parallel Algorithms. Addison Wesley, Boston (1992). ISBN 0-201-54856-9zbMATHGoogle Scholar
  11. 11.
    Kaminsky, A.: Big CPU, Big Data: Solving the World’s Toughest Computational Problems with Parallel Computing. Unpublished manuscript (2015), retrieved from http://www.cs.rit.edu/ark/bcbd
  12. 12.
    Karp, A.H., Flatt, H.P.: Measuring parallel processor performance. Commun. ACM 33(5), 539–543 (1990). doi: 10.1145/78607.78614 CrossRefGoogle Scholar
  13. 13.
    Oldfield, R.A., Moreland, K., Fabian, N., Rogers, D.: Evaluation of methods to integrate analysis into a large-scale shock physics code. In: Proceedings of the ICS 2014, pp. 83–92. June 2014. doi: 10.1145/2597652.2597668
  14. 14.
    Quinn, M.J.: Parallel Programming in C with MPI and OpenMP. McGraw-Hill, New York (2004). ISBN 978-0-07-282256-4Google Scholar
  15. 15.
    Rossinelli, D., et al.: 11 PFLOP/s simulations of cloud cavitation collapse. In: Proceedings of the SC 2013, November 2013. doi: 10.1145/2503210.2504565

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Sandia National LaboratoriesAlbuquerqueUSA

Personalised recommendations