  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.
