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Onodera, Naoyuki; Idomura, Yasuhiro; Hasegawa, Yuta; Yamashita, Susumu; Shimokawabe, Takashi*; Aoki, Takayuki*
Proceedings of International Conference on High Performance Computing in Asia-Pacific Region (HPC Asia 2021) (Internet), p.120 - 128, 2021/01
Times Cited Count：0 Percentile：0.01(Computer Science, Hardware & Architecture)We develop a multigrid preconditioned conjugate gradient (MG-CG) solver for the pressure Poisson equation in a two-phase flow CFD code JUPITER. The MG preconditioner is constructed based on the geometric MG method with a three-stage V-cycle, and a RB-SOR smoother and its variant with cache-reuse optimization (CR-SOR) are applied at each stage. The numerical experiments are conducted for two-phase flows in a fuel bundle of a nuclear reactor. The MG-CG solvers with the RB-SOR and CR-SOR smoothers reduce the number of iterations to less than 15% and 9% of the original preconditioned CG method, leading to 3.1- and 5.9-times speedups, respectively. The obtained performance indicates that the MG-CG solver designed for the block-structured grid is highly efficient and enables large-scale simulations of two-phase flows on GPU based supercomputers.
Idomura, Yasuhiro; Onodera, Naoyuki; Yamada, Susumu; Yamashita, Susumu; Ina, Takuya*; Imamura, Toshiyuki*
Supa Kompyuteingu Nyusu, 22(5), p.18 - 29, 2020/09
A communication avoiding multigrid preconditioned conjugate gradient method (CAMGCG) is applied to the pressure Poisson equation in a multiphase CFD code JUPITER, and its computational performance and convergence property are compared against the conventional Krylov methods. The CAMGCG solver has robust convergence properties regardless of the problem size, and shows both communication reduction and convergence improvement, leading to higher performance gain than CA Krylov solvers, which achieve only the former. The CAMGCG solver is applied to extreme scale multiphase CFD simulations with 90 billion DOFs, and its performance is compared against the preconditioned CG solver. In this benchmark, the number of iterations is reduced to 
, and 
speedup is achieved with keeping excellent strong scaling up to 8,000 nodes on the Oakforest-PACS.
Oka, Yoshiaki*; Koshizuka, Seiichi*; Okano, Yasushi*
PNC TY9602 96-001, 133 Pages, 1996/03
no abstracts in English
*; Yokokawa, Mitsuo;
JAERI-Data/Code 96-012, 43 Pages, 1996/03
JAERI-Data-Code-96-012.pdf:1.26MB
no abstracts in English
Ogura, Koichi; ; Shibata, Takemasa
Journal of Nuclear Science and Technology, 30(12), p.1248 - 1255, 1993/12
Times Cited Count：14 Percentile：76.61(Nuclear Science & Technology)no abstracts in English
Ogura, Koichi; ; Shibata, Takemasa
JAERI-M 92-141, 21 Pages, 1992/09
no abstracts in English
Onodera, Naoyuki; Idomura, Yasuhiro; Asahi, Yuichi; Hasegawa, Yuta; Shimokawabe, Takashi*; Aoki, Takayuki*
no journal, ,
This paper presents performance studies of a multigrid (MG) Poisson solver on a block-structured adaptive mesh refinement (block-AMR) method on CPU and GPU supercomputers. The block-AMR method is efficient solutions of the nuclear reactor which is composed of complicated structures. We implement a three-stage V-cycle MG method and the calculation is accelerated by using a mixed precision techniques. For a large-scale Poisson problem with 
cells, the developed MG-CG method reduced the number of iterations to less than 30% and achieved 2 times speedup compared with the original preconditioned CG method on the GPU-supercomputer TSUBAME. This kind of performance studies are useful for designing advanced preconditioners in terms of robustness, computational precision, thread parallelization, and cache size on each architecture.
