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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.
Mayumi, Akie; Idomura, Yasuhiro; Yamada, Susumu; Ina, Takuya; Yamashita, Susumu
no journal, ,
In this work, we implemented the Communication-Avoiding CG (CA-CG) method to the Poisson solver in the JUPITER code, which analyzes a multi-phase thermal-hydraulic problem, and evaluated its convergence property and computational performance. We analyzed the degradation of the convergence property due to accumulation of numerical errors associated with CA procedures, and applied quad-precision computation to a part of the CA-CG method to improve the convergence property.
Onodera, Naoyuki; Idomura, Yasuhiro; Ali, Y.*; Yamashita, Susumu; Ina, Takuya*; Imamura, Toshiyuki*
no journal, ,
Transient heat flow analysis of nuclear reactors is very important from the view point of efficient design and safety. We have developed the stencil-based CFD code JUPITER for simulating three-dimensional multiphase flows. We extended the JUPITER with GPU-accelerated Poisson solvers based on the P-CG and P-CBCG methods. All main kernels were implemented using CUDA, and the GPU kernel function is well tuned to achieve high performance on the latest Volta-core GPUs. The developed solvers showed good strong scaling up to 2,048 GPUs/CPUs on the Summit (NVIDIA TESLA V100), the ABCI (NVIDIA TESLA V100), the Oakforest-PACS (Intel Knights Landing). Finally, the performance gain from the Oakforest-PACS ranges from 1.2 
1.6x on the Summit and 1.4 1.7x on the ABCI, respectively.