Nikoli Dryden

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 38

Top Topics

Weather Forecast

Top Venues

IEEE Trans. Parallel Distributed Syst.

Publications

Piotr Teterwak, Soren Nelson, Nikoli Dryden, Dina Bashkirova, Kate Saenko, Bryan A. Plummer
Learning to Compose SuperWeights for Neural Parameter Allocation Search. WACV (2024)
Piotr Teterwak, Soren Nelson, Nikoli Dryden, Dina Bashkirova, Kate Saenko, Bryan A. Plummer
Learning to Compose SuperWeights for Neural Parameter Allocation Search. CoRR (2023)
Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Saleh Ashkboos, Torsten Hoefler
STen: Productive and Efficient Sparsity in PyTorch. CoRR (2023)
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 (2023)
Nikoli Dryden, Torsten Hoefler
Spatial Mixture-of-Experts. CoRR (2022)
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)
Maciej Besta, Patrick Iff, Florian Scheidl, Kazuki Osawa, Nikoli Dryden, Michal Podstawski, Tiancheng Chen, Torsten Hoefler
Neural Graph Databases. CoRR (2022)
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)
Nikoli Dryden, Torsten Hoefler
Spatial Mixture-of-Experts. NeurIPS (2022)
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 (2022)
Bryan A. Plummer, Nikoli Dryden, Julius Frost, Torsten Hoefler, Kate Saenko
Neural Parameter Allocation Search. ICLR (2022)
Oliver Rausch, Tal Ben-Nun, Nikoli Dryden, Andrei Ivanov, Shigang Li, Torsten Hoefler
A data-centric optimization framework for machine learning. ICS (2022)
Maciej Besta, Patrick Iff, Florian Scheidl, Kazuki Osawa, Nikoli Dryden, Michal Podstawski, Tiancheng Chen, Torsten Hoefler
Neural Graph Databases. LoG (2022)
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 (2021)
Roman Böhringer, Nikoli Dryden, Tal Ben-Nun, Torsten Hoefler
Clairvoyant Prefetching for Distributed Machine Learning I/O. CoRR (2021)
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 (2021)
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) (2021)
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) (2021)
Oliver Rausch, Tal Ben-Nun, Nikoli Dryden, Andrei Ivanov, Shigang Li, Torsten Hoefler
A Data-Centric Optimization Framework for Machine Learning. CoRR (2021)
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 (2021)
Francis J. Alexander, James 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 Ramakrishniah, 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) (2021)
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)
Nikoli Dryden, Roman Böhringer, Tal Ben-Nun, Torsten Hoefler
Clairvoyant prefetching for distributed machine learning I/O. SC (2021)
Lukas Gianinazzi, Maximilian Fries, Nikoli Dryden, Tal Ben-Nun, Maciej Besta, Torsten Hoefler
Learning Combinatorial Node Labeling Algorithms. CoRR (2021)
Peter Grönquist, Chengyuan Yao, Tal Ben-Nun, Nikoli Dryden, Peter Dueben, Shigang Li, Torsten Hoefler
Deep Learning for Post-Processing Ensemble Weather Forecasts. CoRR (2020)
Bryan A. Plummer, Nikoli Dryden, Julius Frost, Torsten Hoefler, Kate Saenko
Shapeshifter Networks: Cross-layer Parameter Sharing for Scalable and Effective Deep Learning. CoRR (2020)
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 (2020)
Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Shigang Li, Torsten Hoefler
Data Movement Is All You Need: A Case Study on Optimizing Transformers. CoRR (2020)
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 (2020)
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 (2019)
Peter Grönquist, Tal Ben-Nun, Nikoli Dryden, Peter Dueben, Luca Lavarini, Shigang Li, Torsten Hoefler
Predicting Weather Uncertainty with Deep Convnets. CoRR (2019)
Nikoli Dryden, Naoya Maruyama, Tim Moon, Tom Benson, Marc Snir, Brian Van Essen
Channel and filter parallelism for large-scale CNN training. SC (2019)
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)
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)
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)
Chen Wang, Nikoli Dryden, Franck Cappello, Marc Snir
Neural Network Based Silent Error Detector. CLUSTER (2018)
Sam Ade Jacobs, Nikoli Dryden, Roger A. Pearce, Brian Van Essen
Towards Scalable Parallel Training of Deep Neural Networks. MLHPC@SC (2017)
Nikoli Dryden, Tim Moon, Sam Ade Jacobs, Brian Van Essen
Communication Quantization for Data-Parallel Training of Deep Neural Networks. MLHPC@SC (2016)
Alex Brooks, Hoang-Vu Dang, Nikoli Dryden, Marc Snir
PPL: an abstract runtime system for hybrid parallel programming. ESPM@SC (2015)
Nikoli Dryden
PGDB: A Debugger for MPI Applications. XSEDE (2014)