Continuous-energy Monte Carlo neutron transport on GPUs in the Shift code. (June 2019)

• Record Type: Journal Article
• Authors: Hamilton, Steven P.; Evans, Thomas M.
• Is Part Of: Annals of nuclear energy. Volume 128(2019)
• Page Start: 236
• Page End: 247
• Publication Date: 2019-06
• Subjects: Radiation transport -- Monte Carlo -- GPU; Nuclear energy -- Periodicals; Nuclear engineering -- Periodicals; 621.4805
• DOI: 10.1016/j.anucene.2019.01.012
• Languages: English
• ISSNs: 0306-4549

Abstract

Highlights: Comparison of several algorithmic approaches for continuous-energy neutron transport. Performance results and analysis on NVIDIA K20X, K40, P100, and V100 GPUs. Performance equivalent to 150 CPU cores per GPU on NVIDIA V100 GPUs on full depleted reactor model. Nearly ideal parallel scaling on Oak Ridge National Laboratory's Summit supercomputer. 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.