検索対象:     
報告書番号:
※ 半角英数字
 年 ~ 
 年

大規模多相流体解析向け省通信型マルチグリッド前処理付き共役勾配法

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

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

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

多相多成分熱流動解析コードJUPITERの圧力ポアソン方程式に省通信型マルチグリッド前処理付き共役勾配(CAMGCG)法を適用し、従来のクリロフ部分空間法と計算性能と収束特性を比較した。CAMGCGソルバは問題サイズによらずロバーストな収束特性を示し、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900億自由度の大規模多相流体解析に適用し、前処理付共役勾配法ソルバと処理性能を比較した。このベンチマークにおいて、反復回数は約1/800に削減され、Oakforest-PACS上で8,000ノードに至る良好な強スケーリングを維持しつつ約11.6倍の性能向上を達成した。

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 $$sim 1/800$$, and $$sim 11.6times$$ speedup is achieved with keeping excellent strong scaling up to 8,000 nodes on the Oakforest-PACS.

Access

:

- Accesses

InCites™

:

Altmetrics

:

[CLARIVATE ANALYTICS], [WEB OF SCIENCE], [HIGHLY CITED PAPER & CUP LOGO] and [HOT PAPER & FIRE LOGO] are trademarks of Clarivate Analytics, and/or its affiliated company or companies, and used herein by permission and/or license.