Deep-learning enhancement of large scale numerical simulations

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 experience...

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Bibliographic Details
Published in:arXiv.org 2020-03
Main Authors: Caspar van Leeuwen, Podareanu, Damian, Codreanu, Valeriu, Cai, Maxwell X, Berg, Axel, Simon Portegies Zwart, Stoffer, Robin, Veerman, Menno, Chiel van Heerwaarden, Otten, Sydney, Caron, Sascha, Geng, Cunliang, Ambrosetti, Francesco, Alexandre M J J Bonvin
Format: Article
Language:English
Subjects:
Computation
Computer simulation
Deep learning
Domains
Guidelines
High performance computing
Machine learning
Mathematical models
Simulation
Online Access:Get full text
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Summary: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.
ISSN:2331-8422