Using HPC infrastructures for deep learning applications in fusion research
- 10 June 2021
- journal article
- research article
- Published by IOP Publishing in Plasma Physics and Controlled Fusion
- Vol. 63 (8), 084006
- https://doi.org/10.1088/1361-6587/ac0a3b
Abstract
In the fusion community, the use of high performance computing (HPC) has been mostly dominated by heavy-duty plasma simulations, such as those based on particle-in-cell and gyrokinetic codes. However, there has been a growing interest in applying machine learning for knowledge discovery on top of large amounts of experimental data collected from fusion devices. In particular, deep learning models are especially hungry for accelerated hardware, such as graphics processing units (GPUs), and it is becoming more common to find those models competing for the same resources that are used by simulation codes, which can be either CPU- or GPU-bound. In this paper, we give examples of deep learning models -- such as convolutional neural networks, recurrent neural networks, and variational autoencoders -- that can be used for a variety of tasks, including image processing, disruption prediction, and anomaly detection on diagnostics data. In this context, we discuss how deep learning can go from using a single GPU on a single node to using multiple GPUs across multiple nodes in a large-scale HPC infrastructure.Keywords
This publication has 32 references indexed in Scilit:
- Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic dataPhysics of Plasmas, 2020
- Fast modeling of turbulent transport in fusion plasmas using neural networksPhysics of Plasmas, 2020
- Predicting disruptive instabilities in controlled fusion plasmas through deep learningNature, 2019
- High performance PIC plasma simulation with modern GPUsJournal of Physics: Conference Series, 2018
- A fast low-to-high confinement mode bifurcation dynamics in the boundary-plasma gyrokinetic code XGC1Physics of Plasmas, 2018
- MARCONI-FUSION: The new high performance computing facility for European nuclear fusion modellingFusion Engineering and Design, 2018
- Exploratory Machine Learning Studies for Disruption Prediction Using Large Databases on DIII-DFusion Science and Technology, 2018
- Progress in understanding disruptions triggered by massive gas injection via 3D non-linear MHD modelling with JOREKPlasma Physics and Controlled Fusion, 2016
- The global version of the gyrokinetic turbulence code GENEJournal of Computational Physics, 2011
- Generalized gyrokineticsPlasma Physics, 1981