activity
20172021
most citedUsing Machine Learning to Augment Coarse-Grid Computational Fluid Dynamics Simulations

20 citations · 69 across the 7 of their papers we have counts for

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5 papers · 1 filter

cs.DC2020

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

Aymen Al Saadi, Dario Alfe, Yadu Babuji +33

The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2-3 billion to deliver one new drug. This is both too expensive…

cs.DC2020

Time-Based Roofline for Deep Learning Performance Analysis

Yunsong Wang, Charlene Yang, Steven Farrell +3

Deep learning applications are usually very compute-intensive and require a long run time for training and inference. This has been tackled by researchers from both hardware and so…

cs.DC2020

Hierarchical Roofline Performance Analysis for Deep Learning Applications

Charlene Yang, Yunsong Wang, Steven Farrell +2

This paper presents a practical methodology for collecting performance data necessary to conduct hierarchical Roofline analysis on NVIDIA GPUs. It discusses the extension of the Em…

cs.DC2018

Exascale Deep Learning for Climate Analytics

Thorsten Kurth, Sean Treichler, Joshua Romero +9

We extract pixel-level masks of extreme weather patterns using variants of Tiramisu and DeepLabv3+ neural networks. We describe improvements to the software frameworks, input pipel…

cs.DC201710 cited

Scaling GRPC Tensorflow on 512 nodes of Cori Supercomputer

Amrita Mathuriya, Thorsten Kurth, Vivek Rane +5

We explore scaling of the standard distributed Tensorflow with GRPC primitives on up to 512 Intel Xeon Phi (KNL) nodes of Cori supercomputer with synchronous stochastic gradient de…