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The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism


Jul 25, 2020
Yosuke Oyama, Naoya Maruyama, Nikoli Dryden, Erin McCarthy, Peter Harrington, Jan Balewski, Satoshi Matsuoka, Peter Nugent, Brian Van Essen

* 12 pages, 10 figures 

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Merlin: Enabling Machine Learning-Ready HPC Ensembles


Dec 05, 2019
J. Luc Peterson, Rushil Anirudh, Kevin Athey, Benjamin Bay, Peer-Timo Bremer, Vic Castillo, Francesco Di Natale, David Fox, Jim A. Gaffney, David Hysom, Sam Ade Jacobs, Bhavya Kailkhura, Joe Koning, Bogdan Kustowski, Steven Langer, Peter Robinson, Jessica Semler, Brian Spears, Jayaraman Thiagarajan, Brian Van Essen, Jae-Seung Yeom

* 10 pages, 5 figures; Submitted to IPDPS 2020 

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Parallelizing Training of Deep Generative Models on Massive Scientific Datasets


Oct 05, 2019
Sam Ade Jacobs, Brian Van Essen, David Hysom, Jae-Seung Yeom, Tim Moon, Rushil Anirudh, Jayaraman J. Thiagaranjan, Shusen Liu, Peer-Timo Bremer, Jim Gaffney, Tom Benson, Peter Robinson, Luc Peterson, Brian Spears


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Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism


Mar 15, 2019
Nikoli Dryden, Naoya Maruyama, Tom Benson, Tim Moon, Marc Snir, Brian Van Essen

* To appear at IPDPS 2019 

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Large-Scale Deep Learning on the YFCC100M Dataset


Feb 11, 2015
Karl Ni, Roger Pearce, Kofi Boakye, Brian Van Essen, Damian Borth, Barry Chen, Eric Wang


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