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A Performance Model of Non-deterministic Particle Transport on Large-Scale Systems

Part of the Lecture Notes in Computer Science book series (LNCS,volume 2659)

Abstract

In this work we present a predictive analytical model that encompasses the performance and scaling characteristics of a non-deterministic particle transport application, MCNP. Previous studies on the scalability of parallel Monte Carlo eigenvalue calculations have been rather general in nature [[1]]. It can be used for the simulation of neutron, photon, electron, or coupled transport, and has found uses in many problem areas. The performance model is validated against measurements on an AlphaServer ES40 system showing high accuracy across many processor / problem combinations. It is parametric with both application characteristics (e.g. problem size), and system characteristics (e.g. communication latency, bandwidth, achieved processing rate) serving as input. The model is used to provide insight into the achievable performance that should be possible on systems containing thousands of processors and to quantify the impact that possible improvements in sub-system performance may have. In addition, the impact on performance of modifying the communication structure of the code is also quantified.

Keywords

  • Communication Cost
  • Alamos National Laboratory
  • Discrete Ordinate
  • Achievable Performance
  • Scatter Phase

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.

Mathis is supported in part by a DOE High-Performance Computer Science Fellowship. Los Alamos National Laboratory is operated by the University of California for the National Nuclear Security Administration of the US Department of Energy.

References

  1. Matsuura, S., Blomquist, R.N., Brown, F.B.: Parallel Monte Carlo Eigenvalue Calculations. Transactions of the American Nuclear Society 71 (1994) 199–202

    Google Scholar 

  2. Briesmeister, J.F.: MCNPTM-A General Purpose Monte Carlo N-Particle Transport Code, Version 4C. Los Alamos National Laboratory. (2000)

    Google Scholar 

  3. Kerbyson, D.J., Hoisie, A., Wasserman, H.J.: Use of Predictive Performance Modeling During Large-Scale System Installation. In: 1st Int. Workshop on Hardware/Software Support for Parallel and Distributed Scientific and Engineering Computing, SPDSEC02, Charlottesville (2002)

    Google Scholar 

  4. Hoisie, A., Lubeck, O., Wasserman, H.J.: Performance and Scalability Analysis of Teraflop-Scale Parallel Architectures Using Multidimensional Wavefront Applications. Int. J. of High Performance Computing Applications 14 (2000) 330–346

    CrossRef  Google Scholar 

  5. Kerbyson, D.J., Hoisie, A., Pautz, S.D.: Performance Modeling of Deterministic Transport Computations. In: Performance Analysis and Grid Computing, Kluwer (2003)

    Google Scholar 

  6. Mathis, M.M., Amato, N.M., Adams, M.L.: A General Performance Model for Parallel Sweeps on Orthogonal Grids for Particle Transport Calculations. In: cs, Santa Fe (2000) 255–263

    Google Scholar 

  7. Kerbyson, D.J., Alme, H.J., Hoisie, A., Petrini, F., Wasserman, H.J., Gittings, M.L.: Predictive Performance and Scalability Modeling of a Large-scale Application. n: Supercomputing, Denver (2001)

    Google Scholar 

  8. Kerbyson, D.J., Wasserman, H.J., Hoisie, A.: Exploring Advanced Architectures using Performance Prediction. In: Innovative Architecture for Future Generation High-Performance Processors and Systems, IEEE CS Press (2002) 27–37

    Google Scholar 

  9. Metropolis, N., Ulam, S.: The Monte Carlo Method. J. Amer. Statist. Assoc. 44 (1949) 335–341

    MATH  CrossRef  MathSciNet  Google Scholar 

  10. Koch, K.R., Baker, R.S., Alcouffe, R.E.: Solution of the first-order form of the 3D discrete ordinates equation on a massively parallel processor. Transactions of the American Nuclear Society 65 (1992) 198–199

    Google Scholar 

  11. Cox, L.J.: DMMP Upgrade for MCNP4C™. Los Alamos National Laboratory Research Note (2001)

    Google Scholar 

  12. Barrett, R., McKay, M.: UPS: Unified Parallel Software User’s Guide and Reference Manual. Los Alamos National Laboratory. (2002)

    Google Scholar 

  13. Petrini, F., Feng, W.C., Hoisie, A., Coll, S., Frachtenberg, E.: The Quadrics Network: High-Performance Clustering Technology. IEEE Micro 22 (2002) 46–57

    CrossRef  Google Scholar 

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© 2003 Springer-Verlag Berlin Heidelberg

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Mathis, M.M., Kerbyson, D.J., Hoisie, A. (2003). A Performance Model of Non-deterministic Particle Transport on Large-Scale Systems. In: Sloot, P.M.A., Abramson, D., Bogdanov, A.V., Gorbachev, Y.E., Dongarra, J.J., Zomaya, A.Y. (eds) Computational Science — ICCS 2003. ICCS 2003. Lecture Notes in Computer Science, vol 2659. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-44863-2_89

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  • DOI: https://doi.org/10.1007/3-540-44863-2_89

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  • Print ISBN: 978-3-540-40196-4

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