Wenqian Dong

Wenqian Dong, Jie Liu, Zhen Xie et al.

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.

Wenqian Dong, Zhen Xie, Gokcen Kestor et al.

The optimal power flow (OPF) problem is one of the most important optimization problems for the operation of the power grid. It calculates the optimum scheduling of the committed generation units. In this paper, we develop a neural network approach to the problem of accelerating the current optimal power flow (AC-OPF) by generating an intelligent initial solution. The high quality of the initial solution and guidance of other outputs generated by the neural network enables faster convergence to the solution without losing optimality of final solution as computed by traditional methods. Smart-PGSim generates a novel multitask-learning neural network model to accelerate the AC-OPF simulation. Smart-PGSim also imposes the physical constraints of the simulation on the neural network automatically. Smart-PGSim brings an average of 49.2% performance improvement (up to 91%), computed over 10,000 problem simulations, with respect to the original AC-OPF implementation, without losing the optimality of the final solution.

Wenqian Dong, Anzheng Guolu, Dong Li

Machine learning, as a tool to learn and model complicated (non)linear relationships between input and output data sets, has shown preliminary success in some HPC problems. Using machine learning, scientists are able to augment existing simulations by improving accuracy and significantly reducing latencies. Our ongoing research work is to create a general framework to apply neural network-based models to HPC applications. In particular, we want to use the neural network to approximate and replace code regions within the HPC application to improve performance (i.e., reducing the execution time) of the HPC application. In this paper, we present our preliminary study and results. Using two applications (the Newton-Raphson method and the Lennard-Jones (LJ) potential in LAMMP) for our case study, we achieve up to 2.7x and 2.46x speedup, respectively.