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Onodera, Naoyuki; Idomura, Yasuhiro; Hasegawa, Yuta; Shimokawabe, Takashi*; Aoki, Takayuki*
Keisan Kogaku Koenkai Rombunshu (CD-ROM), 26, 3 Pages, 2021/05
We develop a mixed-precision preconditioner for the pressure Poisson equation in a two-phase flow CFD code JUPITER-AMR. The multi-grid (MG) preconditioner is constructed based on the geometric MG method with a three- stage V-cycle, and a cache-reuse SOR (CR-SOR) method at each stage. The numerical experiments are conducted for two-phase flows in a fuel bundle of a nuclear reactor. The MG-CG solver in single-precision shows the same convergence histories as double-precision, which is about 75% of the computational time in double-precision. In the strong scaling test, the MG-CG solver in single-precision is accelerated by 1.88 times between 32 and 96 GPUs.
Ina, Takuya; Idomura, Yasuhiro; Imamura, Toshiyuki*; Yamashita, Susumu; Onodera, Naoyuki
no journal, ,
We have developed mixed-precision preprocessing for the preconditioned conjugate gradients (PCG) method in the multi-phase multi-component thermal-hydraulic code JUPITER. The preconditioner employs a hybrid mixed-precision approach which combines FP16 data and FP32 operations. The roundoff errors are reduced by converting FP16 data to FP32 on cache, holding the intermediate result in FP32, converting the final result to FP16, and returning it to the memory. The developed preconditioner was tested for large-scale problems with 3D structured grids of 3,200
2,00014,160. The convergence of the PCG method was maintained even when the FP16 data format was used for ill-condition matrices, and the computational speed was dramatically increased by reducing the memory access. The hybrid FP16/32 mixed-precision implementation achieved 1.79 speedup from the FP64 implementation at 2,000 nodes on Fugaku.