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
  • [42] gPET: a GPU-based, accurate and efficient Monte Carlo simulation tool for PET
    Lai, Youfang
    Zhong, Yuncheng
    Chalise, Ananta
    Shao, Yiping
    Jin, Mingwu
    Jia, Xun
    Chi, Yujie
    PHYSICS IN MEDICINE AND BIOLOGY, 2019, 64 (24):
  • [43] The development of GPU-based parallel PRNG for Monte Carlo applications in CUDA Fortran
    Kargaran, Hamed
    Minuchehr, Abdolhamid
    Zolfaghari, Ahmad
    AIP ADVANCES, 2016, 6 (04)
  • [44] A dynamic accuracy estimation for GPU-based monte carlo simulation in tissue optics
    Cai F.
    Lu W.
    Lu, Wen (wen_lu@yeah.net), 2017, Optical Society of Korea (01) : 551 - 555
  • [45] A Dynamic Accuracy Estimation for GPU-based Monte Carlo Simulation in Tissue Optics
    Cai, Fuhong
    Lu, Wen
    CURRENT OPTICS AND PHOTONICS, 2017, 1 (05) : 551 - 555
  • [46] Study of Thread Divergence in GPU-Based Monte Carlo Particle Transport Simulations
    Panaino, C.
    Tseung, H.
    MEDICAL PHYSICS, 2022, 49 (06) : E495 - E495
  • [47] A GPU-based direct Monte Carlo simulation of time dependence in nuclear reactors
    Molnar, Balazs
    Tolnai, Gabor
    Legrady, David
    ANNALS OF NUCLEAR ENERGY, 2019, 132 : 46 - 63
  • [48] GPU-Based Monte Carlo Treatment Planning System for Electron FLASH Radiotherapy
    Zhou, B.
    Lu, W.
    Lai, Y.
    Rahman, M.
    Zhang, R.
    Jia, X.
    Wang, K.
    MEDICAL PHYSICS, 2022, 49 (06) : E127 - E127
  • [49] GPU-based Monte Carlo simulation for light propagation in complex heterogeneous tissues
    Ren, Nunu
    Liang, Jimin
    Qu, Xiaochao
    Li, Jianfeng
    Lu, Bingjia
    Tian, Jie
    OPTICS EXPRESS, 2010, 18 (07): : 6811 - 6823
  • [50] An Open-Source Tool for GPU-Based Microscopic Monte Carlo Simulation
    Tsai, M.
    Yan, C.
    Tian, Z.
    Qin, N.
    Hung, S.
    Jia, X.
    MEDICAL PHYSICS, 2018, 45 (06) : E697 - E697