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Scalable Reduced-Order Modeling for Three-Dimensional Turbulent Flow

DescriptionA neural network-based reduced order modeling method for three-dimensional turbulent flow simulation is proposed. This method was implemented as the scalable distributed learning on Fugaku. Our method constitutes a dimensional reduction using a convolutional-autoencoder-like neural network and the time evolution prediction using long short-term memory networks. The time evolution of the turbulent three-dimensional flow (e.g., Re=2.8×10^6) could be simulated at a significantly lower cost (approximately four orders of magnitude) without a major loss in accuracy. Using the single core memory group, our implementation shows 370 GFLOPS (24.28% of the peak performance) for the entire training loop and 753 GFLOPS (24.28% of the peak performance) for the convolution kernel. Our implementation scales up to 25,250 computational nodes (1,212,000 cores). Thus it shows 72.9 % of weak scaling performance (7.8 PFLOPS) for the entire training loop. On the other hand, the convolution routine shows 100.8% of weak scaling performance (113 PFLOPS).

Authors

Kazuto Ando

RIKEN Center for Computational Science

Kobe University

Rahul Bale

RIKEN Center for Computational Science

Kobe University

Akiyoshi Kuroda

RIKEN Center for Computational Science

Makoto Tsubokura

RIKEN Center for Computational Science

Kobe University