Cross-facility science with the Superfacility Project at LBNL

Enders, Bjoern; Bard, Debbie; Snavely, C.; Gerhardt, L.; Lee, Jason; Totzke, Becci; Antypas, Katie; Byna, Suren; Cheema, Ravi; Cholia, Shreyas; Day, Mark R.; Gaur, Aditi; Greiner, Annette; Groves, Taylor; Kiran, Mariam; Koziol, Quincey; Rowland, Kelly; Samuel, Chris; Selvarajan, Ashwin; Sim, Alex; Skinner, David; Thomas, R. C.; Torok, Gabor

doi:10.1109/xloop51963.2020.00006

Cited by 28 publications

(17 citation statements)

References 10 publications

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“…In order to achieve this goal, several projects have made progress in resource allocation across multiple machines, data streaming over large areas, resiliency, security, etc. Once such effort is the National Energy Research Scientific Computing Center (NERSC) Superfacility [49] project that aims to provide an ecosystem of connected facilities and software for the NERSC computing center. Its main focus is on providing a vision for making resource reservations using an API that can be used to connect to the center's HPC systems.…”

Section: F Workflow Automationmentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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Data science and technology offer transformative tools and methods to science. This review article highlights latest development and progress in the interdisciplinary field of data-driven plasma science (DDPS). A large amount of data and machine learning algorithms go hand in hand. Most plasma data, whether experimental, observational or computational, are generated or collected by machines today. It is now becoming impractical for humans to analyze all the data manually. Therefore, it is imperative to train machines to analyze and interpret (eventually) such data as intelligently as humans but far more efficiently in quantity. Despite the recent impressive progress in applications of data science to plasma science and technology, the emerging field of DDPS is still in its infancy. Fueled by some of the most challenging problems such as fusion energy, plasmaprocessing of materials, and fundamental understanding of the universe through observable plasma phenomena, it is expected that DDPS continues to benefit significantly from the interdisciplinary marriage between plasma science and data science into the foreseeable future. CONTENTSI. Introduction 6 II. Fundamental Data Science 6 A. Introduction 6 B. Data Reduction/Compression 7 C. Dimensional reduction and sparse modeling 8 D. ML-enhanced modeling and simulation 9 E. ML Hardware and integration with models 10 F. Workflow Automation 11 G. Uncertainty Quantification 12 H. Visualization and Data Understanding 13 I. ML Control Theory 15 III. Basic Plasma Physics and Laboratory Experiments A. Introduction B. Spectroscopy, imaging and tomography C. Sparse measurement and noise D. Synthetic instruments and data E. Experimental data visualization F. High-rep rate laser experiments G. Charged particle beams H. Control and Optimisation of Plasma Accelerator Experiments I. Dusty and complex plasmas J. Physics and machine learning K. Challenges and outlook 5 IV. Magnetic Confinement Fusion 36 A. Introduction 36 B. Data-Driven Physics Models 36 C. Optimizing experimental workflows with data-driven methods 37 D. Diagnostics and Fusion Data Streams 38 E. Prediction of Tokamak Disruption 39 F. Surrogate models of fusion plasma 40 G. Magnetic Fusion Energy Data Challenges and Solutions 41 H. Data Science for extreme scale simulation 43 I. Challenges and outlook 44 V. Inertial confinement fusion and high-energy-density physics 45 A. Introduction 45 B. Representation learning for multimodal data 45 C. Transfer learning for simulation and experimen 47 D. Uncertainty quantification and Bayesian inference 47 E. High-performance computing and simulation acceleration 49 F. Design exploration and optimization 49 G. Self-driving experimental facilities 51 H. Challenges and outlook 52 VI. Space and astronomical plasmas 52 A. Introduction 52 B. Space and ground instruments 53 C. Space weather prediction 55 D. Transfer learning to improve historic data 56 E. Surrogate models of fluid closures using machine learning 56 F. Magnetic reconnection 58 G. Challenges and outlook 60 VII. Plasma tec...

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Section: F Workflow Automationmentioning

confidence: 99%

2022 Review of Data-Driven Plasma Science

Archibald¹,

Asif²,

Becker³

et al. 2022

Preprint

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“…The LBNL Superfacility Project [105] was designed to leverage and integrate work being done across NERSC, ESnet and research divisions at LBNL to provide a coordinated and coherent approach to supporting experiments at DOE facilities. The project aims to provide an integrated, scalable and sustainable framework for experiment science, working closely with a range of science teams on design and requirements, and using industry standard and open source tools wherever possible.…”

Section: Snowmass2021 Cosmic Frontiermentioning

confidence: 99%

Snowmass2021 Cosmic Frontier: Modeling, statistics, simulations, and computing needs for direct dark matter detection

Kahn¹,

Monzani²,

Palladino³

et al. 2022

Preprint

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“…The LBNL Superfacility Project [8] was designed to leverage and integrate work being done across NERSC, ESnet and research divisions at LBNL to provide a coordinated and coherent approach to supporting experimental science at DOE facilities. The principles behind the project were to provide an integrated, scalable and sustainable framework for experiment science, working closely with a range of science teams to get the design right and using existing, industry standard and open source tools wherever possible.…”

Section: Key Challenge: Scaling User Supportmentioning

confidence: 99%