  Traditional simulations on High-Performance Computing (HPC) systems typically
involve modeling very large domains and/or very complex equations. HPC systems
allow running large models, but limits in performance increase that have become
more prominent in the last 5-10 years will likely be experienced. Therefore new
approaches are needed to increase application performance. Deep learning
appears to be a promising way to achieve this. Recently deep learning has been
employed to enhance solving problems that traditionally are solved with
large-scale numerical simulations using HPC. This type of application, deep
learning for high-performance computing, is the theme of this whitepaper. Our
goal is to provide concrete guidelines to scientists and others that would like
to explore opportunities for applying deep learning approaches in their own
large-scale numerical simulations. These guidelines have been extracted from a
number of experiments that have been undertaken in various scientific domains
over the last two years, and which are described in more detail in the
Appendix. Additionally, we share the most important lessons that we have
learned.
