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Nikoli Dryden

ETH Zürich

Learning Combinatorial Node Labeling Algorithms

Jun 15, 2021
Lukas Gianinazzi, Maximilian Fries, Nikoli Dryden, Tal Ben-Nun, Maciej Besta, Torsten Hoefler

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Motif Prediction with Graph Neural Networks

Jun 05, 2021
Maciej Besta, Raphael Grob, Cesare Miglioli, Nicola Bernold, Grzegorz Kwasniewski, Gabriel Gjini, Raghavendra Kanakagiri, Saleh Ashkboos, Lukas Gianinazzi, Nikoli Dryden, Torsten Hoefler

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Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks

Jan 31, 2021
Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste

* 90 pages, 26 figures 

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Clairvoyant Prefetching for Distributed Machine Learning I/O

Jan 21, 2021
Roman Böhringer, Nikoli Dryden, Tal Ben-Nun, Torsten Hoefler

* 15 pages, 11 figures 

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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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Data Movement Is All You Need: A Case Study on Optimizing Transformers

Jul 02, 2020
Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Shigang Li, Torsten Hoefler

* 15 pages, 6 figures; minor clarifications and style updates 

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Shapeshifter Networks: Cross-layer Parameter Sharing for Scalable and Effective Deep Learning

Jun 18, 2020
Bryan A. Plummer, Nikoli Dryden, Julius Frost, Torsten Hoefler, Kate Saenko

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Deep Learning for Post-Processing Ensemble Weather Forecasts

May 18, 2020
Peter Grönquist, Chengyuan Yao, Tal Ben-Nun, Nikoli Dryden, Peter Dueben, Shigang Li, Torsten Hoefler

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Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging

Apr 30, 2020
Shigang Li, Tal Ben-Nun, Dan Alistarh, Salvatore Di Girolamo, Nikoli Dryden, Torsten Hoefler

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Predicting Weather Uncertainty with Deep Convnets

Dec 04, 2019
Peter Grönquist, Tal Ben-Nun, Nikoli Dryden, Peter Dueben, Luca Lavarini, Shigang Li, Torsten Hoefler

* Poster presentation at NeurIPS2019 "Machine Learning and the Physical Sciences" Workshop 

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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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