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

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


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.


  • 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.


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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.

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-40196-4

  • Online ISBN: 978-3-540-44863-1

  • eBook Packages: Springer Book Archive

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