Domain decomposition in the GPU-accelerated Shift Monte Carlo code

2022 ◽  
Vol 166 ◽  
pp. 108687
Author(s):  
Steven P. Hamilton ◽  
Thomas M. Evans ◽  
Katherine E. Royston ◽  
Elliott D. Biondo
Keyword(s):  
Monte Carlo ◽  
Domain Decomposition ◽  
Monte Carlo Code

scholarly journals Development of a Spatial Domain Decomposition Scheme for Monte Carlo Neutron Transport

2018 ◽  
Author(s):  
Manuel García ◽  
Diego Ferraro ◽  
Victor Hugo Sanchez-Espinoza ◽  
Luigi Mercatali ◽  
Jaakko Leppänen ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Domain Decomposition ◽  
High Performance ◽  
Large Scale ◽  
Neutron Transport ◽  
Spatial Domain ◽  
Parallel Execution ◽  
Monte Carlo Code ◽  
Processing Scheme ◽  
Low Efficiency

The computing power available nowadays to the average Monte-Carlo-code user is sufficient to perform large-scale neutron transport simulations, such as full-core burnup or high-fidelity multiphysics. In practice however, software limitations in the majority of the available Monte Carlo codes result in a low efficiency when running in High Performance Computing (HPC) environments, the main issues being inadequate memory utilization and poor scalability. The traditional parallel processing scheme based of splitting particle histories among processes requires domain replication across nodes, and therefore the memory demand for each computing node does not scale, and a memory bottleneck appears for large-scale problems. The scalability of this approach usually limits the resources that can be used efficiently to a small number of nodes/processors. Consequently, massively parallel execution is not viable with particle-based parallelism, at least not by itself. In this work we propose a Spatial Domain Decomposition (SDD) approach to develop an efficient and scalable Monte Carlo neutron transport algorithm. Breaking down the geometry into subdomains, a distributed memory scheme can be used to reduce the in-node memory demand, allowing the simulation of large-scale memory-intensive problems. Additionally, with an efficient neutron tracking algorithm the overall speedup can be significantly improved.

A Collision-based Domain Decomposition scheme for large-scale depletion with the Serpent 2 Monte Carlo code

2021 ◽  
Vol 152 ◽  
pp. 108026
Author(s):  
Manuel García ◽  
Jaakko Leppänen ◽  
Victor Sanchez-Espinoza
Keyword(s):  
Monte Carlo ◽  
Domain Decomposition ◽  
Large Scale ◽  
Monte Carlo Code ◽  
Decomposition Scheme

scholarly journals Domain Decomposition Strategy for Pin-wise Full-Core Monte Carlo Depletion Calculation with the Reactor Monte Carlo Code

2016 ◽  
Vol 48 (3) ◽  
pp. 635-641 ◽  
Author(s):  
Jingang Liang ◽  
Kan Wang ◽  
Yishu Qiu ◽  
Xiaoming Chai ◽  
Shenglong Qiang
Keyword(s):  
Monte Carlo ◽  
Domain Decomposition ◽  
Monte Carlo Code ◽  
Full Core

Usage of burnt fuel isotopic compositions from engineering codes in Monte-Carlo code calculations

Kerntechnik ◽  
2015 ◽  
Vol 80 (4) ◽  
pp. 394-401 ◽  
Author(s):  
S. S. Aleshin ◽  
S. S. Gorodkov ◽  
A. I. Shcherenko
Keyword(s):  
Monte Carlo ◽  
Monte Carlo Code ◽  
Isotopic Compositions

scholarly journals FRED: a fast Monte Carlo code on GPU for quality control in Particle Therapy

2020 ◽  
Vol 1548 ◽  
pp. 012020
Author(s):  
M De Simoni ◽  
M Fischetti ◽  
E Gioscio ◽  
M Marafini ◽  
R Mirabelli ◽  
...  
Keyword(s):  
Quality Control ◽  
Monte Carlo ◽  
Particle Therapy ◽  
Monte Carlo Code

Improvement of sensitivity and uncertainty analysis capabilities of generalized response in Monte Carlo code RMC

2021 ◽  
Vol 154 ◽  
pp. 108099
Author(s):  
Guanlin Shi ◽  
Yuchuan Guo ◽  
Conglong Jia ◽  
Zhiyuan Feng ◽  
Kan Wang ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Uncertainty Analysis ◽  
Monte Carlo Code ◽  
Sensitivity And Uncertainty Analysis
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