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

Steven P. Hamilton; Thomas M. Evans

doi:10.1016/j.anucene.2019.01.012

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

Steven P. Hamilton, Thomas M. Evans

HPC Methods for Nuclear Applications

Research output: Contribution to journal › Article › peer-review

51 Scopus citations

Abstract

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.

Original language	English
Pages (from-to)	236-247
Number of pages	12
Journal	Annals of Nuclear Energy
Volume	128
DOIs	https://doi.org/10.1016/j.anucene.2019.01.012
State	Published - Jun 2019

Funding

This research was sponsored by the Laboratory Directed Research and Development Program of Oak Ridge National Laboratory , managed by UT-Battelle, LLC, for the U.S. Department of Energy. This research was supported by the Exascale Computing Project (ECP), project number 17-SC-20-SC. The ECP is a collaborative effort of two DOE organizations, the Office of Science and the National Nuclear Security Administration, that are responsible for the planning and preparation of a capable exascale ecosystem—including software, applications, hardware, advanced system engineering, and early testbed platforms—to support the nation’s exascale computing imperative. This research used resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

Keywords

GPU
Monte Carlo
Radiation transport

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10.1016/j.anucene.2019.01.012

Cite this

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title = "Continuous-energy Monte Carlo neutron transport on GPUs in the Shift code",

abstract = "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.",

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AU - Evans, Thomas M.

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AB - 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.

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

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