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ORCIDNikoli Dryden
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2020 – today
- 2024
[c18]Piotr Teterwak, Soren Nelson, Nikoli Dryden, Dina Bashkirova, Kate Saenko, Bryan A. Plummer:
Learning to Compose SuperWeights for Neural Parameter Allocation Search. WACV 2024: 2739-2748- 2023
- [i18]Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Saleh Ashkboos, Torsten Hoefler:
STen: Productive and Efficient Sparsity in PyTorch. CoRR abs/2304.07613 (2023) - [i17]Julia Bazinska, Andrei Ivanov, Tal Ben-Nun, Nikoli Dryden, Maciej Besta, Siyuan Shen, Torsten Hoefler:
Cached Operator Reordering: A Unified View for Fast GNN Training. CoRR abs/2308.12093 (2023) - [i16]Piotr Teterwak, Soren Nelson, Nikoli Dryden, Dina Bashkirova, Kate Saenko, Bryan A. Plummer:
Learning to Compose SuperWeights for Neural Parameter Allocation Search. CoRR abs/2312.01274 (2023) - 2022
- [c17]Bryan A. Plummer, Nikoli Dryden, Julius Frost, Torsten Hoefler, Kate Saenko:
Neural Parameter Allocation Search. ICLR 2022 - [c16]Oliver Rausch, Tal Ben-Nun, Nikoli Dryden, Andrei Ivanov, Shigang Li, Torsten Hoefler:
A data-centric optimization framework for machine learning. ICS 2022: 36:1-36:13 - [c15]Maciej Besta, Raphael Grob, Cesare Miglioli
, Nicola Bernold, Grzegorz Kwasniewski, Gabriel Gjini, Raghavendra Kanakagiri, Saleh Ashkboos, Lukas Gianinazzi, Nikoli Dryden, Torsten Hoefler:
Motif Prediction with Graph Neural Networks. KDD 2022: 35-45 - [c14]Maciej Besta, Patrick Iff, Florian Scheidl, Kazuki Osawa, Nikoli Dryden, Michal Podstawski, Tiancheng Chen, Torsten Hoefler:
Neural Graph Databases. LoG 2022: 31 - [c13]Saleh Ashkboos, Langwen Huang, Nikoli Dryden, Tal Ben-Nun, Peter Dueben, Lukas Gianinazzi, Luca Kummer, Torsten Hoefler:
ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts. NeurIPS 2022 - [c12]Nikoli Dryden, Torsten Hoefler:
Spatial Mixture-of-Experts. NeurIPS 2022 - [i15]Saleh Ashkboos, Langwen Huang, Nikoli Dryden, Tal Ben-Nun, Peter Dueben, Lukas Gianinazzi, Luca Kummer, Torsten Hoefler:
ENS-10: A Dataset For Post-Processing Ensemble Weather Forecast. CoRR abs/2206.14786 (2022) - [i14]Maciej Besta, Patrick Iff, Florian Scheidl, Kazuki Osawa, Nikoli Dryden, Michal Podstawski, Tiancheng Chen, Torsten Hoefler:
Neural Graph Databases. CoRR abs/2209.09732 (2022) - [i13]Nikoli Dryden, Torsten Hoefler:
Spatial Mixture-of-Experts. CoRR abs/2211.13491 (2022) - 2021
- [j4]Francis J. Alexander, James A. Ang, Jenna A. Bilbrey, Jan Balewski, Tiernan Casey, Ryan Chard, Jong Choi, Sutanay Choudhury, Bert J. Debusschere, Anthony M. DeGennaro, Nikoli Dryden, J. Austin Ellis, Ian T. Foster, Cristina Garcia-Cardona, Sayan Ghosh, Peter Harrington, Yunzhi Huang, Shantenu Jha, Travis Johnston, Ai Kagawa, Ramakrishnan Kannan, Neeraj Kumar, Zhengchun Liu, Naoya Maruyama, Satoshi Matsuoka, Erin McCarthy, Jamaludin Mohd-Yusof, Peter Nugent, Yosuke Oyama, Thomas Proffen, David Pugmire, Sivasankaran Rajamanickam, Vinay Ramakrishnaiah, Malachi Schram, Sudip K. Seal, Ganesh Sivaraman, Christine Sweeney, Li Tan, Rajeev Thakur, Brian Van Essen, Logan T. Ward, Paul M. Welch, Michael Wolf, Sotiris S. Xantheas, Kevin G. Yager, Shinjae Yoo, Byung-Jun Yoon:
Co-design Center for Exascale Machine Learning Technologies (ExaLearn). Int. J. High Perform. Comput. Appl. 35(6): 598-616 (2021) - [j3]Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste:
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. J. Mach. Learn. Res. 22: 241:1-241:124 (2021) - [j2]Yosuke Oyama, Naoya Maruyama, Nikoli Dryden, Erin McCarthy, Peter Harrington, Jan Balewski, Satoshi Matsuoka, Peter Nugent, Brian Van Essen:
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs With Hybrid Parallelism. IEEE Trans. Parallel Distributed Syst. 32(7): 1641-1652 (2021) - [j1]Shigang Li, Tal Ben-Nun, Giorgi Nadiradze, Salvatore Di Girolamo, Nikoli Dryden, Dan Alistarh, Torsten Hoefler:
Breaking (Global) Barriers in Parallel Stochastic Optimization With Wait-Avoiding Group Averaging. IEEE Trans. Parallel Distributed Syst. 32(7): 1725-1739 (2021) - [c11]Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Shigang Li, Torsten Hoefler:
Data Movement Is All You Need: A Case Study on Optimizing Transformers. MLSys 2021 - [c10]Nikoli Dryden, Roman Böhringer, Tal Ben-Nun, Torsten Hoefler:
Clairvoyant prefetching for distributed machine learning I/O. SC 2021: 92 - [i12]Roman Böhringer, Nikoli Dryden, Tal Ben-Nun, Torsten Hoefler:
Clairvoyant Prefetching for Distributed Machine Learning I/O. CoRR abs/2101.08734 (2021) - [i11]Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste:
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. CoRR abs/2102.00554 (2021) - [i10]Maciej Besta, Raphael Grob, Cesare Miglioli, Nicola Bernold, Grzegorz Kwasniewski, Gabriel Gjini, Raghavendra Kanakagiri, Saleh Ashkboos, Lukas Gianinazzi, Nikoli Dryden, Torsten Hoefler:
Motif Prediction with Graph Neural Networks. CoRR abs/2106.00761 (2021) - [i9]Lukas Gianinazzi, Maximilian Fries, Nikoli Dryden, Tal Ben-Nun, Maciej Besta, Torsten Hoefler:
Learning Combinatorial Node Labeling Algorithms. CoRR abs/2106.03594 (2021) - [i8]Oliver Rausch, Tal Ben-Nun, Nikoli Dryden, Andrei Ivanov, Shigang Li, Torsten Hoefler:
A Data-Centric Optimization Framework for Machine Learning. CoRR abs/2110.10802 (2021) - 2020
- [i7]Shigang Li, Tal Ben-Nun, Dan Alistarh, Salvatore Di Girolamo, Nikoli Dryden, Torsten Hoefler:
Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging. CoRR abs/2005.00124 (2020) - [i6]Peter Grönquist, Chengyuan Yao, Tal Ben-Nun, Nikoli Dryden, Peter Dueben, Shigang Li, Torsten Hoefler:
Deep Learning for Post-Processing Ensemble Weather Forecasts. CoRR abs/2005.08748 (2020) - [i5]Bryan A. Plummer, Nikoli Dryden, Julius Frost, Torsten Hoefler, Kate Saenko:
Shapeshifter Networks: Cross-layer Parameter Sharing for Scalable and Effective Deep Learning. CoRR abs/2006.10598 (2020) - [i4]Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Shigang Li, Torsten Hoefler:
Data Movement Is All You Need: A Case Study on Optimizing Transformers. CoRR abs/2007.00072 (2020) - [i3]Yosuke Oyama, Naoya Maruyama, Nikoli Dryden, Erin McCarthy, Peter Harrington, Jan Balewski, Satoshi Matsuoka, Peter Nugent, Brian Van Essen:
The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism. CoRR abs/2007.12856 (2020)
2010 – 2019
- 2019
- [b1]Nikoli Dryden:
Large-scale training of deep neural networks. University of Illinois Urbana-Champaign, USA, 2019 - [c9]Nikoli Dryden, Naoya Maruyama, Tom Benson, Tim Moon, Marc Snir, Brian Van Essen:
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism. IPDPS 2019: 210-220 - [c8]Nikoli Dryden, Naoya Maruyama, Tim Moon, Tom Benson, Marc Snir, Brian Van Essen:
Channel and filter parallelism for large-scale CNN training. SC 2019: 10:1-10:20 - [i2]Nikoli Dryden, Naoya Maruyama, Tom Benson, Tim Moon, Marc Snir, Brian Van Essen:
Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism. CoRR abs/1903.06681 (2019) - [i1]Peter Grönquist, Tal Ben-Nun, Nikoli Dryden, Peter Dueben, Luca Lavarini, Shigang Li, Torsten Hoefler:
Predicting Weather Uncertainty with Deep Convnets. CoRR abs/1911.00630 (2019) - 2018
- [c7]Chen Wang, Nikoli Dryden, Franck Cappello, Marc Snir:
Neural Network Based Silent Error Detector. CLUSTER 2018: 168-178 - [c6]Hoang-Vu Dang, Roshan Dathathri, Gurbinder Gill, Alex Brooks, Nikoli Dryden, Andrew Lenharth, Loc Hoang, Keshav Pingali, Marc Snir:
A Lightweight Communication Runtime for Distributed Graph Analytics. IPDPS 2018: 980-989 - [c5]Roshan Dathathri, Gurbinder Gill, Loc Hoang, Hoang-Vu Dang, Alex Brooks, Nikoli Dryden, Marc Snir, Keshav Pingali:
Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics. PLDI 2018: 752-768 - 2017
- [c4]Sam Ade Jacobs, Nikoli Dryden, Roger A. Pearce, Brian Van Essen:
Towards Scalable Parallel Training of Deep Neural Networks. MLHPC@SC 2017: 5:1-5:9 - 2016
- [c3]Nikoli Dryden, Tim Moon, Sam Ade Jacobs, Brian Van Essen:
Communication Quantization for Data-Parallel Training of Deep Neural Networks. MLHPC@SC 2016: 1-8 - 2015
- [c2]Alex Brooks, Hoang-Vu Dang, Nikoli Dryden, Marc Snir:
PPL: an abstract runtime system for hybrid parallel programming. ESPM@SC 2015: 2-9 - 2014
- [c1]Nikoli Dryden:
PGDB: A Debugger for MPI Applications. XSEDE 2014: 44:1-44:7
Coauthor Index
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last updated on 2024-10-07 21:21 CEST by the dblp team
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