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Communication avoiding multigrid preconditioned conjugate gradient method for extreme scale multiphase CFD simulations

大規模多相流体CFDシミュレーション向け省通信マルチグリッド前処理付共役勾配法

井戸村 泰宏; 伊奈 拓也*; 山下 晋; 小野寺 直幸; 山田 進; 今村 俊幸*

Idomura, Yasuhiro; Ina, Takuya*; Yamashita, Susumu; Onodera, Naoyuki; Yamada, Susumu; Imamura, Toshiyuki*

多相流体CFDコードJUPITERの圧力ポアソン方程式に省通信マルチグリッド前処理付共役勾配(CAMGCG)法を適用し、省通信クリロフ部分空間法と計算性能と収束特性を比較した。JUPITERコードにおいてCAMGCGソルバ問題サイズによらずロバーストな収束特性を有し、通信削減と収束特性向上を両立することから、通信削減のみを実現する省通信クリロフ部分空間法に対する優位性が高い。CAMGCGソルバを$$sim 900$$億自由度の大規模多相流体CFDシミュレーションに適用して反復回数を前処理付CG法の$$sim 1/800$$に削減し、Oakforest-PACSにおける8,000ノードまでの良好な強スケーリングとCG法の$$sim 11.6$$倍の性能向上を達成した。

A communication avoiding (CA) 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 CA Krylov methods. In the JUPITER code, 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 $$sim 90$$ billion DOFs, and it is shown that compared with a preconditioned CG solver, the number of iterations is reduced to $$sim 1/800$$, and $$sim 11.6times$$ speedup is achieved with keeping excellent strong scaling up to 8,000 nodes on the Oakforest-PACS.

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