Implementation, capabilities, and benchmarking of Shift, a massively parallel Monte Carlo radiation transport code

This work discusses the implementation, capabilities, and validation of Shift, a massively parallel Monte Carlo radiation transport package authored at Oak Ridge National Laboratory. Shift has been developed to scale well from laptops to small computing clusters to advanced supercomputers and includ...

Full description

Saved in:
Bibliographic Details
Published in:Journal of computational physics 2016-03, Vol.308, p.239-272
Main Authors: Pandya, Tara M., Johnson, Seth R., Evans, Thomas M., Davidson, Gregory G., Hamilton, Steven P., Godfrey, Andrew T.
Format: Article
Language:English
Subjects:
Algorithms
Benchmarks
Computation
Computer simulation
Laptop computers
Light water reactors
Monte Carlo methods
Neutron transport
Parallel computation
Radiation transport
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This work discusses the implementation, capabilities, and validation of Shift, a massively parallel Monte Carlo radiation transport package authored at Oak Ridge National Laboratory. Shift has been developed to scale well from laptops to small computing clusters to advanced supercomputers and includes features such as support for multiple geometry and physics engines, hybrid capabilities for variance reduction methods such as the Consistent Adjoint-Driven Importance Sampling methodology, advanced parallel decompositions, and tally methods optimized for scalability on supercomputing architectures. The scaling studies presented in this paper demonstrate good weak and strong scaling behavior for the implemented algorithms. Shift has also been validated and verified against various reactor physics benchmarks, including the Consortium for Advanced Simulation of Light Water Reactors' Virtual Environment for Reactor Analysis criticality test suite and several Westinghouse AP1000® problems presented in this paper. These benchmark results compare well to those from other contemporary Monte Carlo codes such as MCNP5 and KENO.
ISSN:0021-9991
1090-2716
DOI:10.1016/j.jcp.2015.12.037