  The Eulerian fluid simulation is an important HPC application. The neural
network has been applied to accelerate it. The current methods that accelerate
the fluid simulation with neural networks lack flexibility and generalization.
In this paper, we tackle the above limitation and aim to enhance the
applicability of neural networks in the Eulerian fluid simulation. We introduce
Smartfluidnet, a framework that automates model generation and application.
Given an existing neural network as input, Smartfluidnet generates multiple
neural networks before the simulation to meet the execution time and simulation
quality requirement. During the simulation, Smartfluidnet dynamically switches
the neural networks to make the best efforts to reach the user requirement on
simulation quality. Evaluating with 20,480 input problems, we show that
Smartfluidnet achieves 1.46x and 590x speedup comparing with a state-of-the-art
neural network model and the original fluid simulation respectively on an
NVIDIA Titan X Pascal GPU, while providing better simulation quality than the
state-of-the-art model.
