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CEMSE Dean's Distinguished Lecture: Deep Learning Acceleration of Progress toward Delivery of Fusion Energy

Start Date: September 16, 2018
End Date: September 16, 2018

By Professor William Tang (Princeton University, USA)
 


Accelerated progress in producing accurate predictions in science and industry have been accomplished by engaging modern big-data-driven statistical methods featuring machine/deep learning/artificial intelligence (ML/DL/AI). Associated techniques being formulated and adapted have enabled new avenues of data-driven discovery in key scientific applications areas such as the quest to deliver Fusion Energy – identified by the 2015 CNN “Moonshots for the 21st Century” series as one of 5 prominent grand challenges. An especially time-urgent and very challenging problem facing the development of a fusion energy reactor is the need to reliably predict and avoid large-scale major disruptions in magnetically-confined tokamak systems such as the EUROFUSION Joint European Torus (JET) today and the burning plasma ITER device in the near future. Significantly improved methods of prediction with better than 95% predictive accuracy are required to provide sufficient advanced warning for disruption avoidance or mitigation strategies to be effectively applied before critical damage can be done to ITER-- a ground-breaking $25B international burning plasma experiment with the potential capability to exceed “breakeven” fusion power by a factor of 10 or more. This truly formidable task demands accuracy beyond the near-term reach of hypothesis-driven /”first-principles” extreme-scale computing (HPC) simulations that dominate current research and development in the field. Recent HPC-relevant advances in the deployment of deep learning recurrent and convolutional neural networks in Princeton’s new Deep Learning Code -- "FRNN (Fusion Recurrent Neural Nets) Code on modern GPU systems. This is clearly a “big-data” project in that it has direct access to the huge JET disruption data-base of over a half-petabyte to drive these studies. FRNN implements a distributed data parallel synchronous stochastic gradient approach with Tensorflow libraries at the backend and MPI forcommunication. This deep learning software has demonstrated excellent scaling up to 6000 GPU's on “Titan” at the Oak Ridge National Laboratory – an achievement that has helped establish the practical feasibility of using leadership class supercomputers to greatly enhance training of neural nets to enable transformational impact on key discovery science application domains such as Fusion Energy Science. Powerful systems on which FRNN is currently deployed include: (1) Japan’s TSUBAME 3 – where over1000 Pascal P100 GPU's have already enabled impressive hyper-parameter tuning production runs; and (2) ORNL’s SUMMIT featuring the new VOLTA GPU’s on which FRNN’s new “half-precision” algorithmic capability has produced attractive scaling results. Summarily, statistical Deep Learning software trained on very large data sets hold exciting promise for delivering much-needed predictive tools capable of accelerating scientific knowledge discovery in HPC. The associated creative methods being developed also has significant potential for cross-cutting benefit to a number of important application areas in science and industry.
 
Bio: William Tang is Principal Research Physicist at the Princeton Plasma Physics Laboratory, Lecturer with Rank & Title of Professor in the University’s Dept. of Astrophysical Sciences, member of the Executive Board and PI for the Intel Parallel Computing Center (IPCC) at the University’s interdisciplinary “Princeton Institute for Computational Science and Engineering, and Distinguished Visiting Professor at Shanghai Jiao Tong University’s Center for High Performance Computing and NVIDIA Center of Excellence. He is a Fellow of the American Physical Society, was U.S. PI for the G8 Research Council’s “Exascale Computing for Global Scale Issues” Project in Fusion Energy (2011-14), and received the Distinguished Achievement Award from the Chinese Institute of Engineers-USA (2005), the High Performance Computing Innovation Excellence Award from the International Data Corporation (2013), and the NVIDIA 2018 Global Impact Award. Most recently, he is PI of the new project on “Accelerated Deep Learning Discovery in Fusion Energy Science” that has been selected as one of the DOE-ALCF-21 Early Science Projects: https://www.alcf.anl.gov/articles/alcf-selects-data-and-learning-projects-aurora-early-science-program
 

More Information:

For more info contact: Prof. David E Keyes : email: david.keyes@kaust.edu.sa
 
Date: Sunday 16th Sep 2018
Time:12:00 PM - 01:00 PM
Location: Engineering Science Hall (bldg.9), Level 2, Hall 2
Refreshments: Light Lunch will be available at 11:45 am