Journal
ANNALS OF NUCLEAR ENERGY
Volume 128, Issue -, Pages 236-247Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.anucene.2019.01.012
Keywords
Radiation transport; Monte Carlo; GPU
Categories
Funding
- Laboratory Directed Research and Development Program of Oak Ridge National Laboratory
- Exascale Computing Project (ECP) [17-SC-20-SC]
- Office of Science of the U.S. Department of Energy [DE-AC05-00OR22725]
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A continuous-energy Monte Carlo neutron transport solver executing on GPUs has been developed within the Shift code. Several algorithmic approaches are considered, including both history-based and event-based implementations. Unlike in previous work involving multigroup Monte Carlo transport, it is demonstrated that event-based algorithms significantly outperform a history-based approach for continuous-energy transport as a result of increased device occupancy and reduced thread divergence. Numerical results are presented for detailed full-core models of a small modular reactor (SMR), including a model containing depleted fuel materials. These results demonstrate the substantial gains in performance that are possible with the latest-generation of GPUs. On the depleted SMR core configuration, an NVIDIA P100 GPU with 56 streaming multiprocessors provides performance equivalent to 90 CPU cores, and the latest V100 GPU with 80 multiprocessors offers the performance of more than 150 CPU cores. (C) 2019 Elsevier Ltd. All rights reserved.
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