Data decomposition of Monte Carlo particle transport simulations via tally servers
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....
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| Format: | Article |
| Language: | en_US |
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Elsevier
2017
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| Online Access: | http://hdl.handle.net/1721.1/107672 https://orcid.org/0000-0002-1147-045X https://orcid.org/0000-0003-1459-7672 https://orcid.org/0000-0003-2497-4312 |
| _version_ | 1826206683401027584 |
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| author | Siegel, Andrew R. Romano, Paul Kollath Forget, Benoit Robert Yves Smith, Kord S. |
| author2 | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering |
| author_facet | Massachusetts Institute of Technology. Department of Nuclear Science and Engineering Siegel, Andrew R. Romano, Paul Kollath Forget, Benoit Robert Yves Smith, Kord S. |
| author_sort | Siegel, Andrew R. |
| collection | MIT |
| description | 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. |
| first_indexed | 2024-09-23T13:36:38Z |
| format | Article |
| id | mit-1721.1/107672 |
| institution | Massachusetts Institute of Technology |
| language | en_US |
| last_indexed | 2024-09-23T13:36:38Z |
| publishDate | 2017 |
| publisher | Elsevier |
| record_format | dspace |
| spelling | mit-1721.1/1076722022-09-28T15:02:45Z Data decomposition of Monte Carlo particle transport simulations via tally servers Siegel, Andrew R. Romano, Paul Kollath Forget, Benoit Robert Yves Smith, Kord S. Massachusetts Institute of Technology. Department of Nuclear Science and Engineering Romano, Paul Kollath Forget, Benoit Robert Yves Smith, Kord S. 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. United States. Dept. of Energy. Naval Reactors Division. Rickover Fellowship Program in Nuclear Engineering United States. Dept. of Energy. Office of Advanced Scientific Computing Research (Contract DE-AC02-06CH11357) United States. Dept. of Energy (Consortium for Advanced Simulation of Light Water Reactors. Contract DE-AC05-00OR22725) 2017-03-23T19:01:26Z 2017-03-23T19:01:26Z 2013-06 2013-04 Article http://purl.org/eprint/type/JournalArticle 00219991 http://hdl.handle.net/1721.1/107672 Romano, Paul K. et al. “Data Decomposition of Monte Carlo Particle Transport Simulations via Tally Servers.” Journal of Computational Physics 252 (2013): 20–36. https://orcid.org/0000-0002-1147-045X https://orcid.org/0000-0003-1459-7672 https://orcid.org/0000-0003-2497-4312 en_US http://dx.doi.org/10.1016/j.jcp.2013.06.011 Journal of Computational Physics Creative Commons Attribution-NonCommercial-NoDerivs License http://creativecommons.org/licenses/by-nc-nd/4.0/ application/pdf Elsevier Prof. Forget via Chris Sherratt |
| spellingShingle | Siegel, Andrew R. Romano, Paul Kollath Forget, Benoit Robert Yves Smith, Kord S. Data decomposition of Monte Carlo particle transport simulations via tally servers |
| title | Data decomposition of Monte Carlo particle transport simulations via tally servers |
| title_full | Data decomposition of Monte Carlo particle transport simulations via tally servers |
| title_fullStr | Data decomposition of Monte Carlo particle transport simulations via tally servers |
| title_full_unstemmed | Data decomposition of Monte Carlo particle transport simulations via tally servers |
| title_short | Data decomposition of Monte Carlo particle transport simulations via tally servers |
| title_sort | data decomposition of monte carlo particle transport simulations via tally servers |
| url | http://hdl.handle.net/1721.1/107672 https://orcid.org/0000-0002-1147-045X https://orcid.org/0000-0003-1459-7672 https://orcid.org/0000-0003-2497-4312 |
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