Evaluation of Single-Node Performance of Parallel Algorithms for Multigroup Monte Carlo Particle Transport Methods

Abstract

Monte Carlo (MC) methods have been widely used to solve the particle transport equation due to their high accuracy and capability of processing complex geometries. History-based and event-based algorithms that are applicable to different architectures are two methods for parallelizing the MC code. There is a large work on evaluating and optimizing parallel algorithms with continuous-energy schemes. In this work, we evaluate the single-node performance of history-based and event-based algorithms for multigroup MC methods on both CPUs and GPUs with Quicksilver, a multigroup MC transport code that has already implemented the history-based algorithms. We first implement and optimize the event-based algorithm based on Quicksilver and then perform the evaluation work extensively on the Coral2 benchmark. Numerical results indicate that contrary to continuous-energy schemes, the history-based approach with multigroup schemes outperforms the event-based algorithm on both architectures in all cases. We summarize that the performance loss of the event-based algorithm is mainly due to: 1) extra operations to reorganize particles, 2) batched atomic operations, and 3) poor particle data locality. Despite the poor performance, the event-based algorithm achieves higher memory bandwidth utilization. We further discuss the impact of memory access patterns and calculation of cross sections (xs) on the performance of the GPU. Built on the analytics, and shed light on the algorithm choice and optimizations for paralleling the MC transport code on different architectures.