A Study on the Performance of Distributed Training of Data-driven CFD Simulations
For researchers applying data-driven methods to CFD, this work provides a practical comparison of training configurations to reduce training time, though the findings are incremental.
This paper evaluates distributed training strategies (CPU, multi-GPU, distributed) for a deep learning model predicting fluid simulation states, showing that distributed GPU training achieves high accuracy in a fraction of the time needed by traditional CFD solvers.
Data-driven methods for computer simulations are blooming in many scientific areas. The traditional approach to simulating physical behaviors relies on solving partial differential equations (PDE). Since calculating these iterative equations is highly both computationally demanding and time-consuming, data-driven methods leverage artificial intelligence (AI) techniques to alleviate that workload. Data-driven methods have to be trained in advance to provide their subsequent fast predictions, however, the cost of the training stage is non-negligible. This paper presents a predictive model for inferencing future states of a specific fluid simulation that serves as a use case for evaluating different training alternatives. Particularly, this study compares the performance of only CPU, multiGPU, and distributed approaches for training a time series forecasting deep learning (DL) model. With some slight code adaptations, results show and compare, in different implementations, the benefits of distributed GPU-enabled training for predicting high-accuracy states in a fraction of the time needed by the computational fluid dynamics (CFD) solver.