skip to main content
OSTI.GOV title logo U.S. Department of Energy
Office of Scientific and Technical Information

Title: Continuous-energy Monte Carlo neutron transport on GPUs in the Shift code

Abstract

Here, 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.

Authors:
ORCiD logo [1]; ORCiD logo [1]
  1. Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
Publication Date:
Research Org.:
Oak Ridge National Laboratory (ORNL), Oak Ridge, TN (United States). Oak Ridge Leadership Computing Facility (OLCF)
Sponsoring Org.:
USDOE Office of Science (SC)
OSTI Identifier:
1492181
Alternate Identifier(s):
OSTI ID: 1547869
Grant/Contract Number:  
AC05-00OR22725
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Annals of Nuclear Energy (Oxford)
Additional Journal Information:
Journal Volume: 128; Journal Issue: C; Journal ID: ISSN 0306-4549
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
73 NUCLEAR PHYSICS AND RADIATION PHYSICS; Radiation transport; Monte Carlo; GPU

Citation Formats

Hamilton, Steven P., and Evans, Thomas M. Continuous-energy Monte Carlo neutron transport on GPUs in the Shift code. United States: N. p., 2019. Web. doi:10.1016/j.anucene.2019.01.012.
Hamilton, Steven P., & Evans, Thomas M. Continuous-energy Monte Carlo neutron transport on GPUs in the Shift code. United States. https://doi.org/10.1016/j.anucene.2019.01.012
Hamilton, Steven P., and Evans, Thomas M. 2019. "Continuous-energy Monte Carlo neutron transport on GPUs in the Shift code". United States. https://doi.org/10.1016/j.anucene.2019.01.012. https://www.osti.gov/servlets/purl/1492181.
@article{osti_1492181,
title = {Continuous-energy Monte Carlo neutron transport on GPUs in the Shift code},
author = {Hamilton, Steven P. and Evans, Thomas M.},
abstractNote = {Here, 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.},
doi = {10.1016/j.anucene.2019.01.012},
url = {https://www.osti.gov/biblio/1492181}, journal = {Annals of Nuclear Energy (Oxford)},
issn = {0306-4549},
number = C,
volume = 128,
place = {United States},
year = {Thu Jan 17 00:00:00 EST 2019},
month = {Thu Jan 17 00:00:00 EST 2019}
}

Journal Article:

Citation Metrics:
Cited by: 21 works
Citation information provided by
Web of Science

Save / Share: