Quicksilver: A Proxy App for the Monte Carlo Transport Code Mercury

Richards, David F.; Bleile, Ryan; Brantley, Patrick S.; Dawson, Shawn; McKinley, Michael Scott; O’Brien, Matthew J.

doi:10.1109/cluster.2017.121

Cited by 28 publications

(11 citation statements)

References 3 publications

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“…A discussion of the process that led to Thin-Threads as well as the key features of the OpenMP 4.5 and CUDA implementations are presented by Richards et. al [16].…”

Section: Related Workmentioning

confidence: 98%

Thin-Threads: An Approach for History-Based Monte Carlo on GPUs

Bleile

Brantley

Richards

et al. 2019

Self Cite

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A graphics processing unit (GPU) has become a core technology for modern supercomputers. Applications that once ran on supercomputers are being forced to make significant changes to their designs to utilize these new machines. This paper introduces the concept of Thin-Threads as a method for history-based Monte Carlo transport applications on GPUs. The key principles behind Thin-Threads are light memory usage and communication and managing data race issues via atomics. We show that we can achieve a 10x speedup when moving from the traditional method to Thin-Threads on GPUs. Additionally, we demonstrate the viability of the Thin-Threads model at scale for GPU and CPU platforms.

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“…A discussion of the process that led to Thin-Threads as well as the key features of the OpenMP 4.5 and CUDA implementations are presented by Richards et. al [16].…”

Section: Related Workmentioning

confidence: 98%

Thin-Threads: An Approach for History-Based Monte Carlo on GPUs

Bleile

Brantley

Richards

et al. 2019

Self Cite

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“…This article is aimed at providing a detailed analysis of the single-node performance difference between different parallel algorithms with different cross section schemes on both CPUs and GPUs. The studies were performed using Quicksilver (Richards et al, 2017), a proxy application for the MC transport code Mercury (LLNL, 2017). It implements the history-based algorithm on both CPUs and GPUs through a thin-threads approach (Bleile et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

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

Yang

Zhang

et al. 2021

Front. Energy Res.

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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.

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“…An initial implementation of the Thin-Threads model was added to Quicksilver in order to provide a feasible method for GPU computing. A discussion of the process that led to Thin-Threads as well as the key features of the OpenMP 4.5 and CUDA implementations are presented by Richards et al (2017).…”

Section: Introductionmentioning

confidence: 99%

Enhancing Monte Carlo Particle Transport for Modern Many-Core Architectures

Bleile

2021

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Quicksilver: A Proxy App for the Monte Carlo Transport Code Mercury

Cited by 28 publications

References 3 publications

Thin-Threads: An Approach for History-Based Monte Carlo on GPUs

Thin-Threads: An Approach for History-Based Monte Carlo on GPUs

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

Enhancing Monte Carlo Particle Transport for Modern Many-Core Architectures

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