title: Adaptive Neural Network-Based Approximation to Accelerate Eulerian Fluid Simulation
authors: Wenqian Dong Jie Liu Zhen Xie Dong Li
text: 
  
  
  
    University of California, Merced
  
  wdong5@ucmerced.edu
  
  
  
    University of California, Merced
  
  jliu279@ucmerced.edu
  
  
  
    University of California, Merced
   
  zxie10@ucmerced.edu
  
  
  
    University of California, Merced
   
   dli35@ucmerced.edu
   
  
  
   
  
  
  
  
  
  
  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 Smart-fluidnet, a framework that automates model generation and application. Given an existing neural network as input, Smart-fluidnet generates multiple neural networks before the simulation to meet the execution time and simulation quality requirement. During the simulation, Smart-fluidnet dynamically switches the neural networks to make best efforts to reach the user’s requirement on simulation quality. Evaluating with 20,480 input problems, we show that Smart-fluidnet 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.
  
  
  
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  <concept>
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  [500]Computing methodologies Parallel algorithms
  [500]Computing methodologies Neural networks
  [500]Computing methodologies Model development and analysis
  
  
  
  Adaptive Neural Network-Based Approximation to  Accelerate Eulerian Fluid Simulation
      Dong Li
      Received: date / Accepted: date
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  ontent/intro
  ontent/background
  ontent/overflow
  ontent/model_candidates
  ontent/mlp
  ontent/runtime_management
  ontent/evaluation
  ontent/related_work
  ontent/conclusion
  
  unsrt
  
  
  
  