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Journal Articles

Communication Reduced Multi-time-step Algorithm for Real-time Wind Simulation on GPU-based Supercomputers

Onodera, Naoyuki; Idomura, Yasuhiro; Ali, Y.*; Shimokawabe, Takashi*

Proceedings of 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA 2018) (Internet), p.9 - 16, 2018/11

 Times Cited Count:2 Percentile:14.22

We develop a communication reduced multi-time- step (CRMT) algorithm for a Lattice Boltzmann method (LBM) based on a block-structured adaptive mesh refinement (AMR). This algorithm is based on the temporal blocking method, and can improve computational efficiency by replacing a communication bottleneck with additional computation. The proposed method is implemented on an extreme scale airflow simulation code CityLBM, and its impact on the scalability is tested on GPU based supercomputers, TSUBAME and Reedbush. Thanks to the CRMT algorithm, the communication cost is reduced by $$sim 64%$$, and weak and strong scalings are improved up to $$sim 200$$ GPUs. The obtained performance indicates that real time airflow simulations for about 2km square area with the wind speed of $$5m/s$$ is feasible using 1m resolution.

Journal Articles

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

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

Proceedings of 9th Workshop on Latest Advances in Scalable Algorithms for Large-Scale Systems (ScalA 2018) (Internet), p.17 - 24, 2018/11

 Times Cited Count:0 Percentile:100

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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