Data decomposition of Monte Carlo particle transport simulations via tally servers
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Forget_Data decomposition.pdf
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Author(s)
Siegel, Andrew R.
Romano, Paul Kollath
Forget, Benoit Robert Yves
Smith, Kord S.
Date Issued
June 2013
Journal
Journal of Computational Physics
Publisher
Elsevier
Citation
Romano, Paul K. et al. “Data Decomposition of Monte Carlo Particle Transport Simulations via Tally Servers.” Journal of Computational Physics 252 (2013): 20–36.
Version
Original manuscript
Abstract
An algorithm for decomposing large tally data in Monte Carlo particle transport simulations is developed, analyzed, and implemented in a continuous-energy Monte Carlo code, OpenMC. The algorithm is based on a non-overlapping decomposition of compute nodes into tracking processors and tally servers. The former are used to simulate the movement of particles through the domain while the latter continuously receive and update tally data. A performance model for this approach is developed, suggesting that, for a range of parameters relevant to LWR analysis, the tally server algorithm should perform with minimal overhead on contemporary supercomputers. An implementation of the algorithm in OpenMC is then tested on the Intrepid and Titan supercomputers, supporting the key predictions of the model over a wide range of parameters. We thus conclude that the tally server algorithm is a successful approach to circumventing classical on-node memory constraints en route to unprecedentedly detailed Monte Carlo reactor simulations.
MIT Department
Massachusetts Institute of Technology. Department of Nuclear Science and Engineering
Terms of Use
Creative Commons Attribution-NonCommercial-NoDerivs License
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1016/j.jcp.2013.06.011
