Threadsafe Dynamic Neighbor Lists for Monte Carlo Ray Tracing

Abstract

Monte Carlo (MC) transport codes offer high-fidelity modeling of particle transport physics, but their high computational cost makes them impractical for many applications. For some applications such as multiphysics and depletion that use finely discretized geometries, a large portion of this computational cost is attributable to ray tracing. Neighbor lists are a well-known method for accelerating ray-tracing calculations in a MC code, but despite their prevalence, little work has been published on the details of their implementation. The fine details can have a significant impact on performance, particularly when using shared-memory parallelism. This paper addresses these details of implementation with a discussion of different neighbor list schemes and their impact on software runtime.

Performance tests were run by using OpenMC on a pin-cell problem discretized with up to 200 axial regions. The results demonstrate that switching from surface-based to cell-based neighbor lists leads to a 10$\times$ faster calculation rate for the most fine discretization. Furthermore, using a threadsafe shared-memory data structure results in a 20% faster calculation rate versus simple threadprivate neighbor lists. Results here show that a data structure that is contiguous in memory improves performance by only 1% to 2% over noncontiguous linked lists.

Keywords:

• Neighbor lists
• Monte Carlo, OpenMC

Acknowledgments

The research work presented in this paper was supported by the Idaho National Laboratory under the U.S. Department of Energy (DOE) Idaho Operations Office contract DE-AC07-05ID14517. This material also was based upon work supported by the DOE Office of Science under contract DE-AC02-06CH11357. This research was also partially supported by the Exascale Computing Project (17-SC-20-SC), a collaborative effort of two DOE organizations (Office of Science and the National Nuclear Security Administration) responsible for the planning and preparation of a capable exascale ecosystem, including software, applications, hardware, advanced system engineering, and early testbed platforms, in support of the nation’s exascale computing imperative.