Practical methods for GPU-based whole-core Monte Carlo depletion calculation

被引:1
|
作者
Kim, Kyung Min [1 ]
Choi, Namjae [1 ,2 ]
Lee, Han Gyu [1 ]
Joo, Han Gyu [1 ]
机构
[1] Seoul Natl Univ, 1 Gwanak ro, Seoul 08826, South Korea
[2] Idaho Natl Lab, 1955 N Fremont Ave, Idaho Falls, ID 83415 USA
基金
新加坡国家研究基金会;
关键词
PRAGMA; Multilevel spectral collapse; Chebyshev rational approximation method; Vectorized Gauss -Seidel; Consumer -grade GPUs; TRANSPORT; SHIFT; CAPABILITIES; CODE;
D O I
10.1016/j.net.2023.04.021
中图分类号
TL [原子能技术]; O571 [原子核物理学];
学科分类号
0827 ; 082701 ;
摘要
Several practical methods for accelerating the depletion calculation in a GPU-based Monte Carlo (MC) code PRAGMA are presented including the multilevel spectral collapse method and the vectorized Chebyshev rational approximation method (CRAM). Since the generation of microscopic reaction rates for each nuclide needed for the construction of the depletion matrix of the Bateman equation requires either enormous memory access or tremendous physical memory, both of which are quite burdensome on GPUs, a new method called multilevel spectral collapse is proposed which combines two types of spectra to generate microscopic reaction rates: an ultrafine spectrum for an entire fuel pin and coarser spectra for each depletion region. Errors in reaction rates introduced by this method are mitigated by a hybrid usage of direct online reaction rate tallies for several important fissile nuclides. The linear system to appear in the solution process adopting the CRAM is solved by the Gauss-Seidel method which can be easily vectorized on GPUs. With the accelerated depletion methods, only about 10% of MC calculation time is consumed for depletion, so an accurate full core cycle depletion calculation for a commercial power reactor (BEAVRS) can be done in 16 h with 24 consumer-grade GPUs.& COPY; 2023 Korean Nuclear Society, Published by Elsevier Korea LLC. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
引用
收藏
页码:2516 / 2533
页数:18
相关论文
共 50 条
  • [31] A GPU-Based Monte Carlo Tool for Computing DRRs with Multiple Scattering
    Jia, X.
    Folkerts, M.
    Choi, D.
    Majumdar, A.
    Jiang, S.
    MEDICAL PHYSICS, 2011, 38 (06)
  • [32] Expanding a GPU-Based Monte Carlo Simulation Package for Proton Therapy
    Zhong, Y.
    Cheng, X.
    Hu, K.
    Xiong, Z.
    Shao, Y.
    MEDICAL PHYSICS, 2019, 46 (06) : E167 - E168
  • [33] Study of a GPU-based parallel computing method for the Monte Carlo program
    Luo Zhi-Fei
    Qiu Rui
    Li Ming
    Wu Zhen
    Zeng Zhi
    Li Jun-Li
    NUCLEAR SCIENCE AND TECHNIQUES, 2014, 25
  • [34] Study of a GPU-based parallel computing method for the Monte Carlo program
    罗志飞
    邱睿
    李明
    武祯
    曾志
    李君利
    NuclearScienceandTechniques, 2014, 25(S1) (S1) : 31 - 34
  • [35] Fast GPU-based Monte Carlo simulations for LDR prostate brachytherapy
    Bonenfant, Eric
    Magnoux, Vincent
    Hissoiny, Sami
    Ozell, Benoit
    Beaulieu, Luc
    Despres, Philippe
    PHYSICS IN MEDICINE AND BIOLOGY, 2015, 60 (13): : 4973 - 4986
  • [36] GPU-Based Monte Carlo Methods For Accelerating Radiographic and CT Imaging Dose Calculations: Feasibility and Scalability
    Liu, T.
    Ding, A.
    Xu, X.
    MEDICAL PHYSICS, 2012, 39 (06) : 3876 - 3876
  • [37] A hybrid phase-space and histogram source model for GPU-based Monte Carlo radiotherapy dose calculation
    Townson, Reid W.
    Zavgorodni, Sergei
    PHYSICS IN MEDICINE AND BIOLOGY, 2014, 59 (24): : 7919 - 7935
  • [38] Development of GPMC V2.0, a GPU-Based Monte Carlo Dose Calculation Package for Proton Radiotherapy
    Jia, X.
    Schuemann, J.
    Paganetti, H.
    Jiang, S.
    MEDICAL PHYSICS, 2013, 40 (06)
  • [39] Acceleration and Real Variance Reduction in Continuous-Energy Monte Carlo Whole-Core Calculation via p-CMFD Feedback
    Jo, YuGwon
    Cho, Nam Zin
    NUCLEAR SCIENCE AND ENGINEERING, 2018, 189 (01) : 26 - 40
  • [40] Fast GPU-based Monte Carlo dose calculations for permanent prostate implants
    Bonenfant, E.
    Magnoux, V.
    Hissoiny, S.
    Ozell, B.
    Beaulieu, L.
    Despres, P.
    RADIOTHERAPY AND ONCOLOGY, 2014, 111 : S151 - S152