Abstract: In recent years, deep learning has been widely recognized as a reliable approach to accele- rate reacting flow simulations. In recent work, this paper has developed an open-source platform named DeepFlame, which supports machine learning libraries and algorithms during the simulation of reacting flows. Leveraging DeepFlame, this paper has successfully employed deep neural networks (DNNs) to compute chemical reaction source terms. This paper focus on optimizing the platform for high-performance. Firstly, to fully harness the acceleration potential of DNNs this paper implements support for multi-GPU parallel inference in DeepFlame, developing intra-node partitioning algorithms and a master-slave communication structure, and complete the migration to Graphics Processing Units (GPUs) and Deep Computing Units (DCUs). Furthermore, this paper implements the solution of partial differential equations and the construction of discrete sparse matrices on GPUs based on the Nvidia AmgX library. Finally, this paper evaluates the computational performance of the updated DeepFlame on a CPU-GPU/DCU heterogeneous architecture. The results indicate that using a single GPU card alone can achieve a maximum speedup of up to 15 times when simulating a reactive Taylor Green Vortex (TGV). 

Key words: computational fluid dynamics, reacting flows, deep neural network, graphics computing unit, partial differential equations