A Case Study on Coupling OpenFOAM with Different Machine Learning Frameworks

In High-Performance Computing, new use cases are currently emerging in which classical numerical simulations are coupled with machine learning as a surrogate for complex physical models that are expensive to compute. In the context of simulating reactive thermo-fluid systems, the idea to replace current state-of-the-art tabulated chemistry with machine learning inference is an active field of research. For this purpose, a simplified OpenFOAM application is coupled with an artificial neural network. In this work, we present a case study focusing solely on the performance of the coupled OpenFOAM-ML application. Our coupling approach features a heterogeneous cluster architecture combining pure CPU nodes and nodes equipped with two Nvidia V100 GPUs. We evaluate our approach by comparing the inference performance and the communication our approach induces with various machine learning frameworks. Additionally, we also compare the GPUs with NEC Vector Engine Type 10B regarding inference performance.