论文标题
基于自适应神经网络的近似，以加速Eulerian流体模拟
Adaptive Neural Network-Based Approximation to Accelerate Eulerian Fluid Simulation
论文作者
论文摘要
Eulerian流体模拟是重要的HPC应用。神经网络已应用于加速它。使用神经网络加速流体模拟的当前方法缺乏灵活性和泛化。在本文中，我们应对上述限制，并旨在增强神经网络在Eulerian流体模拟中的适用性。我们介绍了SmartFluidnet，这是一个自动化模型生成和应用的框架。鉴于现有的神经网络作为输入，SmartFluidNet在模拟之前会生成多个神经网络，以满足执行时间和仿真质量要求。在模拟过程中，SmartFluidNet会动态切换神经网络，以尽最大努力达到用户对仿真质量的需求。通过20,480个输入问题评估，我们表明SmartFluidNet与最先进的神经网络模型和原始流体模拟相比，SmartFluidNet在NVIDIA X Pascal GPU上分别获得了1.46倍和590倍的速度，同时提供了比州立模型更好的模拟质量。
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.
