兰州大学机构库 >核科学与技术学院
Optimizing the GPU based method calculating energy deposition of beams coupling with discrete materials in dynamical and thermal simulations for higher computing efficiency
Hao, Changwei1,3,5; Tian, Yuan2,3,5; Lin, Ping3,4,5; Du, Yunzhen1,3,5; Yang, Lijuan3,4; Zhang, Sheng3,4,5,6; Yang, L(杨磊)3,4,5; Zhou, QG(周庆国)2; Duan, Wenshan1
2022-09-01
Source PublicationCOMPUTER PHYSICS COMMUNICATIONS
ISSN0010-4655
Volume278
AbstractIn order to obtain more accurate energy deposition simulation results of beams coupling with discrete materials especially the granular materials in dynamical and thermal simulations, a discrete energy deposition calculation method was proposed previously to replace the equivalent homogenization method and accelerated with GPUs (Tian et al., 2021 [10]). However, it was found that the computing efficiency drops so severely with the increasing of the energy space size and the time required for simulations increases greatly unacceptably. In this work, for higher computing performance, based on the bottleneck analyses of CUDA (Compute Unified Device Architecture) kernels of the previous method, two improvements were made to the space cell marking phase and the energy deposition phase. In the space cell marking phase, a new proposed Lagrange-Euler Mapping Method searching the fixed space cells through flowing grains replaced the previous Euler Searching Method searching the flowing grains from fixed space cells. In the energy deposition phase, a warp aggregation method was used, assigning fewer threads to perform atomic operations. In the simulation of a granular-flow target bombarded by a 1 GeV proton beam setting a large energy computing space with a number of cells up to similar to 10(8), the improved algorithm can effectively reduce the number of memory access instructions of CUDA warps. As a result, the computing performance was improved by 85% on A100 GPUs and 206% on K80 GPUs, making the dynamical and thermal simulations of granular-flow targets more efficient. Our method also could be beneficial for simulating the interactions between arbitrary source (or field) with a fixed spatial distribution and discrete materials.(C) 2022 Elsevier B.V. All rights reserved.
KeywordBeam-target coupling Energy deposition simulations Granular-flow targets Algorithm optimizations GPUs
PublisherELSEVIER
DOI10.1016/j.cpc.2022.108426
Indexed BySCIE
Language英语
WOS Research AreaComputer Science ; Physics
WOS SubjectComputer Science, Interdisciplinary Applications ; Physics, Mathematical
WOS IDWOS:000809733300004
Original Document TypeArticle
Citation statistics
Document Type期刊论文
Identifierhttps://ir.lzu.edu.cn/handle/262010/480939
Collection核科学与技术学院
信息科学与工程学院
Affiliation1.Northwest Normal Univ, Coll Phys & Elect Engn, Lanzhou 730070, Peoples R China;
2.Lanzhou Univ, Sch Informat Sci & Engn, Lanzhou 730000, Peoples R China;
3.Chinese Acad Sci, Inst Modern Phys, Lanzhou 730000, Peoples R China;
4.Univ Chinese Acad Sci, Sch Nucl Sci & Technol, Beijing 100043, Peoples R China;
5.Adv Energy Sci & Technol Guangdong Lab, Huizhou 516000, Peoples R China;
6.Nanjing Univ Sci & Technol, Ctr Basic Teaching & Expt, Nanjing 214443, Peoples R China
First Author AffilicationSchool of Nuclear Science and Technology
Recommended Citation
GB/T 7714
Hao, Changwei,Tian, Yuan,Lin, Ping,et al. Optimizing the GPU based method calculating energy deposition of beams coupling with discrete materials in dynamical and thermal simulations for higher computing efficiency[J]. COMPUTER PHYSICS COMMUNICATIONS,2022,278.
APA Hao, Changwei.,Tian, Yuan.,Lin, Ping.,Du, Yunzhen.,Yang, Lijuan.,...&Duan, Wenshan.(2022).Optimizing the GPU based method calculating energy deposition of beams coupling with discrete materials in dynamical and thermal simulations for higher computing efficiency.COMPUTER PHYSICS COMMUNICATIONS,278.
MLA Hao, Changwei,et al."Optimizing the GPU based method calculating energy deposition of beams coupling with discrete materials in dynamical and thermal simulations for higher computing efficiency".COMPUTER PHYSICS COMMUNICATIONS 278(2022).
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