Thorsten Kurth

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Berkeley Lab

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IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads

The drug discovery process currently employed in the pharmaceutical indu...
10/13/2020 ∙ by Aymen Al Saadi, et al. ∙ 0 ∙ share

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Using Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations

Simulation of turbulent flows at high Reynolds number is a computational...
09/30/2020 ∙ by Jaideep Pathak, et al. ∙ 0 ∙ share

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Hierarchical Roofline Performance Analysis for Deep Learning Applications

This paper presents a practical methodology for collecting performance d...
09/11/2020 ∙ by Charlene Yang, et al. ∙ 0 ∙ share

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Time-Based Roofline for Deep Learning Performance Analysis

Deep learning applications are usually very compute-intensive and requir...
09/09/2020 ∙ by Yunsong Wang, et al. ∙ 0 ∙ share

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Highly-scalable, physics-informed GANs for learning solutions of stochastic PDEs

Uncertainty quantification for forward and inverse problems is a central...
10/29/2019 ∙ by Liu Yang, et al. ∙ 42 ∙ share

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Exascale Deep Learning for Climate Analytics

We extract pixel-level masks of extreme weather patterns using variants ...
10/03/2018 ∙ by Thorsten Kurth, et al. ∙ 0 ∙ share

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Simulating the weak death of the neutron in a femtoscale universe with near-Exascale computing

The fundamental particle theory called Quantum Chromodynamics (QCD) dict...
10/03/2018 ∙ by Evan Berkowitz, et al. ∙ 0 ∙ share

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Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer

We explore scaling of the standard distributed Tensorflow with GRPC prim...
12/26/2017 ∙ by Amrita Mathuriya, et al. ∙ 0 ∙ share

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Deep Neural Networks for Physics Analysis on low-level whole-detector data at the LHC

There has been considerable recent activity applying deep convolutional ...
11/09/2017 ∙ by Wahid Bhimji, et al. ∙ 0 ∙ share

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Deep Learning at 15PF: Supervised and Semi-Supervised Classification for Scientific Data

This paper presents the first, 15-PetaFLOP Deep Learning system for solv...
08/17/2017 ∙ by Thorsten Kurth, et al. ∙ 0 ∙ share

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