Domain decomposition in the GPU-accelerated Shift Monte Carlo code

Hamilton, Steven P.; Evans, Thomas M.; Royston, Katherine; Biondo, Elliott

doi:10.1016/j.anucene.2021.108687

Cited by 6 publications

(4 citation statements)

References 13 publications

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“…The final threat source emission spectra for the basic isotopes, medical isotopes, industrial isotopes, and SNMs have been formatted for three transport codes: MCNP, SCALE/MAVRIC, and Omnibus/Shift [26]. The MCNP files use the *.i extension, the MAVRIC files use the *.dist.txt extension, and the Shift files use the *.omn extension.…”

Section: Discussionmentioning

confidence: 99%

Threat Sources for Creating Synthetic Urban Search Data

Peplow,

Archer,

Nicholson

et al. 2024

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Section: Discussionmentioning

confidence: 99%

Threat Sources for Creating Synthetic Urban Search Data

Peplow,

Archer,

Nicholson

et al. 2024

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“…Shift was originally written as a historybased CPU code with a rich set of features for modeling entire reactor systems. Many of these features have been ported to a separate GPU event-based code (Hamilton et al, 2021). Within the event-based scheme, the CPU essentially acts as an event dispatcher, calling events and passing the relevant data to the GPU.…”

Section: Presentationsmentioning

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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“…Like other GPU-enabled MC transport codes such as Shift [20,[22][23][24], the low-level component code used by transport kernels is designed so that each particle track corresponds to a single thread. There is no cooperation between individual threads, facilitating the dual host/device annotation of most of Celeritas.…”

Section: Code Architecturementioning

confidence: 99%

Celeritas: GPU-accelerated particle transport for detector simulation in High Energy Physics experiments

Tognini¹,

Canal²,

Evans³

et al. 2022

Preprint

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Within the next decade, experimental High Energy Physics (HEP) will enter a new era of scientific discovery through a set of targeted programs recommended by the Particle Physics Project Prioritization Panel (P5), including the upcoming High Luminosity Large Hadron Collider (LHC) HL-LHC upgrade and the Deep Underground Neutrino Experiment (DUNE). These efforts in the Energy and Intensity Frontiers will require an unprecedented amount of computational capacity on many fronts including Monte Carlo (MC) detector simulation. In order to alleviate this impending computational bottleneck, the Celeritas MC particle transport code is designed to leverage the new generation of heterogeneous computer architectures, including the exascale computing power of U.S. Department of Energy (DOE) Leadership Computing Facilities (LCFs), to model targeted HEP detector problems at the full fidelity of Geant4. This paper presents the planned roadmap for Celeritas, including its proposed code architecture, physics capabilities, and strategies for integrating it with existing and future experimental HEP computing workflows.

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Domain decomposition in the GPU-accelerated Shift Monte Carlo code

Cited by 6 publications

References 13 publications

Threat Sources for Creating Synthetic Urban Search Data

Threat Sources for Creating Synthetic Urban Search Data

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

Celeritas: GPU-accelerated particle transport for detector simulation in High Energy Physics experiments

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