Monte Carlo Application ToolKit (MCATK)

Adams, Terry R.; Nolen, Steven D.; Sweezy, Jeremy; Zukaitis, Anthony J.; Campbell, Joann M.; Goorley, Tim; Greene, Simon; Aulwes, Rob

doi:10.1016/j.anucene.2014.08.047

Cited by 12 publications

(3 citation statements)

References 21 publications

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“…Many traditionally developed neutron-transport codes are export-controlled (e.g. MCNP (Rising et al, 2023), Shift (Hamilton & Evans, 2019), and MCATK (Adams et al, 2015)) and some are known to be difficult to install, use, and develop in. MC/DC is open-source, and thus, similar to other open-source Monte Carlo neutron-transport codes (e.g., OpenMC (Romano et al, 2015)), it promotes knowledge sharing, collaboration, and inclusive, community-driven development.…”

Section: Statement Of Needmentioning

confidence: 99%

Monte Carlo / Dynamic Code (MC/DC): An accelerated Python package for fully transient neutron transport and rapid methods development

Morgan,

Variansyah,

Pasmann

et al. 2024

JOSS

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Section: Statement Of Needmentioning

confidence: 99%

Monte Carlo / Dynamic Code (MC/DC): An accelerated Python package for fully transient neutron transport and rapid methods development

Morgan,

Variansyah,

Pasmann

et al. 2024

JOSS

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“…Monte Carlo Application Toolkit (MCATK) is a Monte Carlo particle transport code from Los Alamos National Lab (Adams et al, 2015). The current GPU strategy involves moving the ray-tracing component of transport onto the GPU through a package called MonteRay (Sweezy, 2018).…”

Section: Mcatk Gpu Prototype Code Tim Burke Los Alamos National Labor...mentioning

confidence: 99%

A Quasi–Monte Carlo Method With Krylov Linear Solvers for Multigroup Neutron Transport Simulations

Pasmann

Ilham

Kelley

et al. 2023

Nuclear Science and Engineering

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The Monte Carlo Computational Summit was held on the campus of the University of Notre Dame in South Bend, Indiana, USA on 25-26 October 2023. The goals of the summit were to discuss algorithmic and software alterations required for successfully porting respective code bases to exascale-class computing hardware, compare software engineering techniques used by various code teams, and consider the adoption of industry-standard benchmark problems to better facilitate code-to-code performance comparisons. A large portion of the meeting included candid discussions of direct experiences with approaches that have and have not worked. Participants reported that identifying and implementing suitable Monte Carlo algorithms for GPUs continues to be a sticking point. They also report significant difficulty porting existing algorithms between GPU APIs (specifically Nvidia CUDA to AMD ROCm). To better compare code-to-code performance, participants decided to design a C5G7like benchmark problem with a defined figure of merit, with the expectation of adding more benchmarks in the future. Problem specifications and results will eventually be hosted in a public repository and will be open to submissions by all Monte Carlo transport codes capable of running the benchmark problem. The participants also identified the need to explore the intermediate and long-term future of the Monte Carlo neutron transport community and how best to modernize and contextualize Monte Carlo as a useful tool in modern industry. Overall the summit was considered to be a success by the organizers and participants, and the group shared a strong desire for future, potentially larger, Monte Carlo summits.

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“…However, there are some specific use cases-ultra fast reactivity excursions in nuclear reactors or high energy density experiments involving nuclear fusion, for example,-where the detailed time dependence of the particle population is needed. Over the years, there have been timedependent neutron transport codes developed (Buck and Hall, 1999, TART;Cullen, 2000, Mercury;Procassini et al, 2004;MCATK;Shim et al, 2012;Leppänen, 2013;Adams et al, 2015;McCARD), some were optimized for execution on older machines, and others employ approximate treatments of time-dependence to make larger problems solveable in reasonable wall clock times.…”

Section: Introductionmentioning

confidence: 99%

Validation of time-dependent shift using the pulsed sphere benchmarks

Palmer,

Northrop,

Palmer

et al. 2023

Front. Nucl. Eng.

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The detailed behavior of neutrons in a rapidly changing time-dependent physical system is a challenging computational physics problem, particularly when using Monte Carlo methods on heterogeneous high-performance computing architectures. A small number of algorithms and code implementations have been shown to be performant for time-independent (fixed source and k-eigenvalue) Monte Carlo, and there are existing simulation tools that successfully solve the time-dependent Monte Carlo problem on smaller computing platforms. To bridge this gap, a time-dependent version of ORNL’s Shift code has been recently developed. Shift’s history-based algorithm on CPUs, and its event-based algorithm on GPUs, have both been observed to scale well to very large numbers of processors, which motivated the extension of this code to solve time-dependent problems. The validation of this new capability requires a comparison with time-dependent neutron experiments. Lawrence Livermore National Laboratory’s (LLNL) pulsed sphere benchmark experiments were simulated in Shift to validate both the time-independent as well as new time-dependent features recently incorporated into Shift. A suite of pulsed-sphere models was simulated using Shift and compared to the available experimental data and simulations with MCNP. Overall results indicate that Shift accurately simulates the pulsed sphere benchmarks, and that the new time-dependent modifications of Shift are working as intended. Validated exascale neutron transport codes are essential for a wide variety of future multiphysics applications.

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Monte Carlo Application ToolKit (MCATK)

Cited by 12 publications

References 21 publications

Monte Carlo / Dynamic Code (MC/DC): An accelerated Python package for fully transient neutron transport and rapid methods development

Monte Carlo / Dynamic Code (MC/DC): An accelerated Python package for fully transient neutron transport and rapid methods development

A Quasi–Monte Carlo Method With Krylov Linear Solvers for Multigroup Neutron Transport Simulations

Validation of time-dependent shift using the pulsed sphere benchmarks

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