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Primeur weekly 2017-12-18

Exascale supercomputing

Mont-Blanc 2020 project will pave the way to a European scalable, modular and power efficient High Performance Computing processor ...

A supercomputer will discover our future medicines ...

Quantum computing

Jülich Supercomputing Centre to achieve world record: Quantum computer with 46 qubits simulated ...

DENSO and Toyota Tsusho to conduct a test applying a quantum computer to analyze IoT data with a commercial application ...

IBM announces collaboration with Fortune 500 companies, academic institutions and national research labs to accelerate quantum computing ...

Complete design of a silicon quantum computer chip unveiled ...

Error-free into the quantum computer age ...

Quantum memory with record-breaking capacity based on laser-cooled atoms ...

Physicists from Konstanz, Princeton and Maryland create a stable quantum gate as a basic element for the quantum computer ...

Physicists say rare earth metals could help quantum computers communicate ...

Focus on Europe

Lenovo and Intel to deliver powerful, energy-efficient SuperMUC-NG, next generation supercomputer at Leibniz Supercomputing Centre ...

North Rhine-Westphalia supports expansion of German national supercomputer infrastructure ...

ISC 2018 and PASC18 Conferences to announce partnership ...

Hardware

Intersect360 Research invites to participate in tenth HPC budget map survey ...

BP supercomputer now world's most powerful for commercial research ...

MareNostrum 4, chosen as the most beautiful data centre in the world ...

NCI welcomes $70 million investment in HPC capability ...

Applications

Physicists win supercomputing time to study fusion and the cosmos ...

Artificial intelligence helps accelerate progress toward efficient fusion reactions ...

Johns Hopkins scientists chart how brain signals connect to neurons ...

Artificial Intelligence and supercomputers to help alleviate urban traffic problems ...

ESnet's Petascale DTN project speeds up data transfers between leading HPC centres ...

A computer system needing less time and memory to simulate mechanical systems ...

Drug discovery could accelerate hugely with Machine Learning ...

The Cloud

Murex to offer MX.3 risk, trading and post-trade solutions on the AWS Cloud ...

Fujitsu develops WAN acceleration technology utilizing FPGA accelerators ...

Meituan.com selects Mellanox interconnect solutions to accelerate its artificial intelligence, Big Data and Cloud data centres ...

Mellanox interconnect solutions accelerate Tencent Cloud high-performance computing and artificial intelligence infrastructure ...

Artificial intelligence helps accelerate progress toward efficient fusion reactions


Image of plasma disruption in experiment on JET, left, and disruption-free experiment on JET, right. Training the FRNN neural network to predict disruptions calls for assigning weights to the data flow along the connections between nodes. Data from new experiments is then put through the network, which predicts "disruption" or "non-disruption". The ultimate goal is at least 95 percent correct predictions of disruption events. Image and explanation courtesy of Eliot Feibush.
14 Dec 2017 Plainsboro - Before scientists can effectively capture and deploy fusion energy, they must learn to predict major disruptions that can halt fusion reactions and damage the walls of doughnut-shaped fusion devices called tokamaks. Timely prediction of disruptions, the sudden loss of control of the hot, charged plasma that fuels the reactions, will be vital to triggering steps to avoid or mitigate such large-scale events.

Today, researchers at the U.S. Department of Energy's (DOE) Princeton Plasma Physics Laboratory (PPPL) and Princeton University are employing artificial intelligence to improve predictive capability. Researchers led by William Tang, a PPPL physicist and a lecturer with the rank and title of professor at Princeton University, are developing the code for predictions for ITER, the international experiment under construction in France to demonstrate the practicality of fusion energy.

The new predictive software, called the Fusion Recurrent Neural Network (FRNN) code, is a form of "deep learning" - a newer and more powerful version of modern machine-learning software, an application of artificial intelligence. "Deep learning represents an exciting new avenue toward the prediction of disruptions", William Tang stated. "This capability can now handle multi-dimensional data."

FRNN is a deep-learning architecture that has proven to be the best way to analyze sequential data with long-range patterns. Members of the PPPL and Princeton University machine-learning team are the first to systematically apply a deep learning approach to the problem of disruption forecasting in tokamak fusion plasmas.

Chief architect of FRNN is Julian Kates-Harbeck, a graduate student at Harvard University and a DOE-Office of Science Computational Science Graduate Fellow. Drawing upon expertise gained while earning a master's degree in computer science at Stanford University, he has led the building of the FRNN software.

Using this approach, the team has demonstrated the ability to predict disruptive events more accurately than previous methods have done. By drawing from the huge data base at the Joint European Torus (JET) facility located in the United Kingdom - the largest and most powerful tokamak in operation - the researchers have significantly improved upon predictions of disruptions and reduced the number of false positive alarms. EUROfusion, the European Consortium for the Development of Fusion Energy, manages JET research.

The team now aims to reach the challenging goals that ITER will require. These include producing 95 percent correct predictions when disruptions occur, while providing fewer than 3 percent false alarms when there are no disruptions. "On the test data sets examined, the FRNN has improved the curve for predicting true positives while reducing false positives", stated Eliot Feibush, a computational scientist at PPPL, referring to what is called the "Receiver Operating Characteristic" curve that is commonly used to measure machine learning accuracy. "We are working on bringing in more training data to do even better."

The process is highly demanding. "Training deep neural networks is a computationally intensive task that requires engagement of high-performance computing hardware", stated Alexey Svyatkovskiy, a Princeton University Big Data researcher. "That is why a large part of what we do is developing and distributing new algorithms across many processors to achieve highly efficient parallel computing. Such computing will handle the increasing size of problems drawn from the disruption-relevant data base from JET and other tokamaks."

The deep learning code runs on graphic processing units (GPUs) that can compute thousands of copies of a programme at once, far more than older central processing units (CPUs). Tests performed on modern GPU clusters, and on world-class machines such as Titan, currently the fastest and most powerful U.S. supercomputer at the Oak Ridge Leadership Computing Facility, a DOE Office of Science User Facility at Oak Ridge National Laboratory, have demonstrated excellent linear scaling. Such scaling reduces the computational run time in direct proportion to the number of GPUs used - a major requirement for efficient parallel processing.

Princeton University's Tiger cluster of modern GPUs was the first to conduct deep learning tests, using FRNN to demonstrate the improved ability to predict fusion disruptions. The code has since run on Titan and other leading supercomputing GPU clusters in the United States, Europe and Asia, and have continued to show excellent scaling with the number of GPUs engaged.

Going forward, the researchers seek to demonstrate that this powerful predictive software can run on tokamaks around the world and eventually on ITER. Also planned is enhancement of the speed of disruption analysis for the increasing problem sizes associated with the larger data sets prior to the onset of a disruptive event. Support for this project has primarily come to date from the Laboratory Directed Research and Development funds provided by PPPL.

Source: Princeton Plasma Physics Laboratory - PPPL

Back to Table of contents

Primeur weekly 2017-12-18

Exascale supercomputing

Mont-Blanc 2020 project will pave the way to a European scalable, modular and power efficient High Performance Computing processor ...

A supercomputer will discover our future medicines ...

Quantum computing

Jülich Supercomputing Centre to achieve world record: Quantum computer with 46 qubits simulated ...

DENSO and Toyota Tsusho to conduct a test applying a quantum computer to analyze IoT data with a commercial application ...

IBM announces collaboration with Fortune 500 companies, academic institutions and national research labs to accelerate quantum computing ...

Complete design of a silicon quantum computer chip unveiled ...

Error-free into the quantum computer age ...

Quantum memory with record-breaking capacity based on laser-cooled atoms ...

Physicists from Konstanz, Princeton and Maryland create a stable quantum gate as a basic element for the quantum computer ...

Physicists say rare earth metals could help quantum computers communicate ...

Focus on Europe

Lenovo and Intel to deliver powerful, energy-efficient SuperMUC-NG, next generation supercomputer at Leibniz Supercomputing Centre ...

North Rhine-Westphalia supports expansion of German national supercomputer infrastructure ...

ISC 2018 and PASC18 Conferences to announce partnership ...

Hardware

Intersect360 Research invites to participate in tenth HPC budget map survey ...

BP supercomputer now world's most powerful for commercial research ...

MareNostrum 4, chosen as the most beautiful data centre in the world ...

NCI welcomes $70 million investment in HPC capability ...

Applications

Physicists win supercomputing time to study fusion and the cosmos ...

Artificial intelligence helps accelerate progress toward efficient fusion reactions ...

Johns Hopkins scientists chart how brain signals connect to neurons ...

Artificial Intelligence and supercomputers to help alleviate urban traffic problems ...

ESnet's Petascale DTN project speeds up data transfers between leading HPC centres ...

A computer system needing less time and memory to simulate mechanical systems ...

Drug discovery could accelerate hugely with Machine Learning ...

The Cloud

Murex to offer MX.3 risk, trading and post-trade solutions on the AWS Cloud ...

Fujitsu develops WAN acceleration technology utilizing FPGA accelerators ...

Meituan.com selects Mellanox interconnect solutions to accelerate its artificial intelligence, Big Data and Cloud data centres ...

Mellanox interconnect solutions accelerate Tencent Cloud high-performance computing and artificial intelligence infrastructure ...