Exascale applications: skin in the game

Alexander, Francis J.; Almgren, Ann S.; Bell, John B.; Bhattacharjee, A.; Chen, Jacqueline; Colella, P.; Daniel, David; Deslippe, Jack; Diachin, Lori Freitag; Draeger, Erik W.; Dubey, Anshu; Dunning, Thom H.; Evans, Thomas M.; Foster, Ian; François, Marianne M.; Germann, Timothy C.; Gordon, Mark S.; Habib, Salman; Halappanavar, Mahantesh; Hamilton, Steven P.; Hart, William E.; Huang, Zhenyu; Hungerford, Aimee L.; Kasen, Daniel; Kent, Paul; Kolev, Tzanio; Kothe, Douglas B.; Kronfeld, A. S.; Luo, Ye; Mackenzie, Paul B.; McCallen, David; Messer, O. E. Bronson; Mniszewski, Susan M.; Oehmen, Chris; Perazzo, A.; Pérez, Danny; Richards, David F.; Rider, William J.; Rieben, Robert N.; Roche, Kenneth J.; Siegel, A.; Sprague, Michael; Steefel, Carl I.; Stevens, Rick; Syamlal, Madhava; Taylor, Mark A.; Turner, John A.; Vay, Jean‐Luc; Voter, Artur F; Windus, Theresa L.; Yelick, Katherine

doi:10.1098/rsta.2019.0056

Cited by 81 publications

(55 citation statements)

References 55 publications

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“…A new paradigm of exascale computing has been introduced recently that aims to build computing solutions catering to such expensive problems. 56 Exascale computing is ideally suited for simultaneously resolving multiple length scales, such as performing DFT or ab initio calculations for length and time scales approaching continuum behavior or simulating electrochemical interactions of large 3D porous electrodes (∼100 μm thick and ∼1000 × 1000 μm 2 cross-section) with pore-scale resolution. Alternatively, an appropriate combination of ML and physics-based simulations may offer a computationally less expensive solution where physics-based simulations work at different scales and these scales are coupled through ML.…”

Section: Estimating Properties From Experimentsmentioning

confidence: 99%

How Machine Learning Will Revolutionize Electrochemical Sciences

et al. 2021

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Electrochemical systems function via interconversion of electric charge and chemical species and represent promising technologies for our cleaner, more sustainable future. However, their development time is fundamentally limited by our ability to identify new materials and understand their electrochemical response. To shorten this time frame, we need to switch from the trial-and-error approach of finding useful materials to a more selective process by leveraging model predictions. Machine learning (ML) offers data-driven predictions and can be helpful. Herein we ask if ML can revolutionize the development cycle from decades to a few years. We outline the necessary characteristics of such ML implementations. Instead of enumerating various ML algorithms, we discuss scientific questions about the electrochemical systems to which ML can contribute.

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Section: Estimating Properties From Experimentsmentioning

confidence: 99%

How Machine Learning Will Revolutionize Electrochemical Sciences

et al. 2021

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“…Suggested Partners/Experts: This vision can be achieved by partnering with ORNL (Scott Painter, the USGS (Allison Appling, David Lesmes), and the University of Texas Austin (Alexander Sun) to develop a comprehensive multi-fidelity approach for both hydrology and biogeochemistry. To take advantage of exascale computing resources, we can partner with ORNL (Ethan Coon, Scott Painter) and LANL (David Moulton) on GPU computing (Amanzi-ATS on heterogeneous architectures via ExaSheds project), as well as with the Exascale Computing Project, where groundbreaking work on linear and nonlinear solvers for GPU systems is underway (Alexander et al 2020). The effort will make full use of DOE facilities like ARM, SAIL, and NERSC.…”

Section: Expected Resultsmentioning

confidence: 99%

On Demand Machine Learning for Multi-Fidelity Biogeochemistry in River Basins Impacted by Climate Extremes

Steefel

2021

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Focal Area 2: Predictive modeling of watershed to river basin scale biogeochemistry through the use of AI techniques and AI-derived model components and the use of AI to design a prediction system comprised of a hierarchy of multi-fidelity models, including AI driven model/component/parameterization selection. Science Challenge: A full treatment of water flow and biogeochemical reactive transport at watershed to river basin scales is not currently possible, and yet there is a critical need to provide improved estimates of fluxes of nutrients and contaminants for both scientific and water management purposes at these scales. The use of hybrid multi-fidelity predictive models that take advantage of ML techniques offers an attractive option to overcome the obstacles associated with computational expense, especially insofar as it is possible to maintain process fidelity for heterogeneously distributed biogeochemical processes interacting with the hydrological cycle-a situation that is expected to be particularly pronounced during climate extremes.

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“…1 3 10 18 flops) through the US DOE Exascale Computing Initiative (ECI). The ECI is aimed at accelerating the delivery of an exascale computing ecosystem that delivers on the order of 50 times more computational science and data analytic application power than available on today's most advanced high-performance computing (HPC) platforms (Alexander et al, 2020). The Exascale Computing Project (ECP) (https://www.exascaleproject.org) is a major element of the DOE's exascale initiative with three significant, closely integrated components as follows:…”

Section: The Promise Of Exascale Computer Platforms For Applications In Earthquake Science and Engineeringmentioning

confidence: 99%

EQSIM—A multidisciplinary framework for fault-to-structure earthquake simulations on exascale computers part I: Computational models and workflow

et al. 2020

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Computational simulations have become central to the seismic analysis and design of major infrastructure over the past several decades. Most major structures are now “proof tested” virtually through representative simulations of earthquake-induced response. More recently, with the advancement of high-performance computing (HPC) platforms and the associated massively parallel computational ecosystems, simulation is beginning to play a role in increased understanding and prediction of ground motions for earthquake hazard assessments. However, the computational requirements for regional-scale geophysics-based ground motion simulations are extreme, which has restricted the frequency resolution of direct simulations and limited the ability to perform the large number of simulations required to numerically explore the problem parametric space. In this article, recent developments toward an integrated, multidisciplinary earth science-engineering computational framework for the regional-scale simulation of both ground motions and resulting structural response are described with a particular emphasis on advancing simulations to frequencies relevant to engineered systems. This multidisciplinary computational development is being carried out as part of the US Department of Energy (DOE) Exascale Computing Project with the goal of achieving a computational framework poised to exploit emerging DOE exaflop computer platforms scheduled for the 2022–2023 timeframe.

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Exascale applications: skin in the game

Abstract: One contribution of 15 to a discussion meeting issue 'Numerical algorithms for high-performance computational science'. Subject Areas:algorithmic information theory, computer modelling and simulation

Cited by 81 publications

References 55 publications

How Machine Learning Will Revolutionize Electrochemical Sciences

How Machine Learning Will Revolutionize Electrochemical Sciences

On Demand Machine Learning for Multi-Fidelity Biogeochemistry in River Basins Impacted by Climate Extremes

EQSIM—A multidisciplinary framework for fault-to-structure earthquake simulations on exascale computers part I: Computational models and workflow

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