21 Dec Winning whitepaper on deep learning for high-performance computing
The research group on Structural Biology is involved in a collaborative project for “Deep Learning Enhancement of Large-Scale Numerical Simulations” that received the prestigious 2020 HPC Innovation and ROI Award. This project is led by Caspar van Leeuwen & Axel Berg from SURF Sara and gathers a consortium of Dutch universities (WUR, Radboud, Utrecht and Leiden universities) and the SURF Open Innovation Lab. In this context, this consortium has received a 2020 HPC Innovation and ROI Award.
Performance increase
Traditional simulations on High Performance Computing (HPC) systems typically involve modelling 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 the winning whitepaper.
Guidelines
The team aims is to provide concrete guidelines to scientists and others that would like to explore opportunities of 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. Additionally, the team shares the most important lessons learned.
Read the whitepaper Deep Learning Enhancement of Large-Scale Numerical Simulations