Abstract
In scientific and engineering computing, there are a large number of complex physical simulations involving multiple physical fields. This complex physical simulation in which multiple physical fields superimpose and interact with each other is aiming at solving the multiphysics coupling problem. A typical approach to solving a complex physics problem is decoupling it into multiple separate physical models. These models are solved independently and coupled by explicitly exchanging data with each other. A key to the method is the design of the multiphysics coupler, that transmits data between two physical models with high fidelity and high efficiency. However, current multiphysics data transmission algorithms have scalability and performance bottlenecks caused by communication and computation overhead. In this paper, we take full advantage of modern multi-core hardware to improve the performance of multiphysics data transfer algorithms. At the same time, the scalability of the coupler is improved by optimizing the communication algorithm, search algorithm, and KD-Tree reusing strategies. Experimental results on the ARM multi-core platform show that our improved multiphysics coupling methods achieve more than 10\(\times \) acceleration compared with the original program. The scalability of the our method has also been greatly improved.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Afazov, S.M., Becker, A.A., Hyde, T.: Development of a finite element data exchange system for chain simulation of manufacturing processes. Adv. Eng. Softw. 47(1), 104–113 (2012)
Blanco, J., Rai, P.: Nanoflann: a C++ header-only fork of FLANN, a library for nearest neighbor (NN) with KD. Trees (2014)
Gaston, D.R., et al.: Physics-based multiscale coupling for full core nuclear reactor simulation. Ann. Nucl. Energy 84, 45–54 (2015)
Hart, S.W., Rearden, B.T.: Warthog: coupling status update. Technical report, Technical Report ONRL/LTR-2017/325, Oak Ridge National Laboratory (2017)
Khamayseh, A., Hansen, G.: Use of the spatial KD-tree in computational physics applications. Commun. Comput. Phys. 2(3), 545–576 (2007)
Khamayseh, A., Kuprat, A.: Deterministic point inclusion methods for computational applications with complex geometry. Comput. Sci. Discov. 1(1), 015004 (2008)
Kirk, B.S., Peterson, J.W., Stogner, R.H., Carey, G.F.: libMesh: a c++ library for parallel adaptive mesh refinement/coarsening simulations. Eng. Comput. 22, 237–254 (2006)
Leitão, N.S., Castilho, E., Farinha, M.L.B.: Towards a better understanding of concrete arch dam behavior during the first filling of the reservoir. CivilEng 4(1), 151–173 (2023)
Lindsay, A.D., et al.: 2.0-MOOSE: enabling massively parallel multiphysics simulation. SoftwareX 20, 101202 (2022)
Mahadevan, V.S., et al.: High-resolution coupled physics solvers for analysing fine-scale nuclear reactor design problems. Philos. Trans. Royal Soc. A Math. Phys. Eng. Sci. 372(2021), 20130381 (2014)
Mei, Z., Shi, C., Fan, X., Wang, X.: Coupled simulation for reentry ablative behavior of hypersonic vehicles. In: IOP Conference Series: Materials Science and Engineering, vol. 892, p. 012028. IOP Publishing (2020)
Merzari, E., et al.: Full core multi-physics simulation with offline core deformation. ANL/NE 15, 42 (2015)
Merzari, E., et al.: Multi-physics demonstration problem with the sharp reactor simulation toolkit. Technical report, Argonne National Lab. (ANL), Argonne, IL (United States) (2015)
Miao, Y., Mo, K., Fei, T., Cao, Y.: Multiphysics simulations of self-regulating performance of an optimized molten metal fuel microreactor design. Nucl. Eng. Des. 406, 112244 (2023)
Nieminen, V.: Fluid-structure interaction simulation utilising MpCCI (2015)
Park, H., Yu, Y., Shemon, E., Novak, A.: Progress on demonstration of a moose-based coupled capability for hot channel factors in fast reactors. Technical report, Argonne National Lab. (ANL), Argonne, IL (United States) (2022)
Permann, C.J., et al.: MOOSE: enabling massively parallel multiphysics simulation. SoftwareX 11, 100430 (2020)
Scrimieri, D., Afazov, S.M., Becker, A.A., Ratchev, S.M.: Fast mapping of finite element field variables between meshes with different densities and element types. Adv. Eng. Softw. 67, 90–98 (2014)
Slaughter, A.E., Permann, C.J., Kong, F.: NEAMS-IPL MOOSE framework activities. Technical report, Idaho National Lab. (INL), Idaho Falls, ID (United States) (2016)
Stimpson, C., et al.: CUBIT v. 16. x. Technical report, Sandia National Lab. (SNL-NM), Albuquerque, NM (United States) (2022)
Tautges, T.J., Ernst, C., Stimpson, C., Meyers, R.J., Merkley, K.: MOAB: a mesh-oriented database. Technical report, Sandia National Laboratories (SNL), Albuquerque, NM, and Livermore, CA (2004)
Acknowledgement
This work was supported by the Key-Area Research and Development Program of Guangdong Province 2021B0101190003, the Major Program of Guangdong Basic and Applied Research: 2019B030302002, National Natural Science Foundation of China (NSFC): 62272499 and Guangdong Province Special Support Program for Cultivating High-Level Talents: 2021TQ06X160.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2024 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Shi, W., Hu, N., Du, J., Huang, D., Lu, Y. (2024). Enhancing Multi-physics Coupling on ARM Many-Core Cluster. In: Li, C., Li, Z., Shen, L., Wu, F., Gong, X. (eds) Advanced Parallel Processing Technologies. APPT 2023. Lecture Notes in Computer Science, vol 14103. Springer, Singapore. https://doi.org/10.1007/978-981-99-7872-4_1
Download citation
DOI: https://doi.org/10.1007/978-981-99-7872-4_1
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-99-7871-7
Online ISBN: 978-981-99-7872-4
eBook Packages: Computer ScienceComputer Science (R0)
