Nikoli Dryden

According to our database, Nikoli Dryden authored at least 31 papers between 2014 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

Learning to Compose SuperWeights for Neural Parameter Allocation Search.

Piotr Teterwak, Soren Nelson, Nikoli Dryden, Dina Bashkirova, Kate Saenko, Bryan A. Plummer

Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision, 2024

2023

Cached Operator Reordering: A Unified View for Fast GNN Training.

Julia Bazinska, Andrei Ivanov, Tal Ben-Nun, Nikoli Dryden, Maciej Besta, Siyuan Shen, Torsten Hoefler

CoRR, 2023

STen: Productive and Efficient Sparsity in PyTorch.

Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Saleh Ashkboos, Torsten Hoefler

CoRR, 2023

2022

ENS-10: A Dataset For Post-Processing Ensemble Weather Forecast.

Saleh Ashkboos, Langwen Huang, Nikoli Dryden, Tal Ben-Nun, Peter Dueben, Lukas Gianinazzi, Luca Kummer, Torsten Hoefler

CoRR, 2022

Spatial Mixture-of-Experts.

Nikoli Dryden, Torsten Hoefler

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts.

Saleh Ashkboos, Langwen Huang, Nikoli Dryden, Tal Ben-Nun, Peter Dueben, Lukas Gianinazzi, Luca Kummer, Torsten Hoefler

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

Neural Graph Databases.

Maciej Besta, Patrick Iff, Florian Scheidl, Kazuki Osawa, Nikoli Dryden, Michal Podstawski, Tiancheng Chen, Torsten Hoefler

Proceedings of the Learning on Graphs Conference, 2022

Motif Prediction with Graph Neural Networks.

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

Proceedings of the KDD '22: The 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, August 14, 2022

A data-centric optimization framework for machine learning.

Oliver Rausch, Tal Ben-Nun, Nikoli Dryden, Andrei Ivanov, Shigang Li, Torsten Hoefler

Proceedings of the ICS '22: 2022 International Conference on Supercomputing, Virtual Event, June 28, 2022

Neural Parameter Allocation Search.

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

Proceedings of the Tenth International Conference on Learning Representations, 2022

2021

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

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

IEEE Trans. Parallel Distributed Syst., 2021

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

Shigang Li, Tal Ben-Nun, Giorgi Nadiradze, Salvatore Di Girolamo, Nikoli Dryden, Dan Alistarh, Torsten Hoefler

IEEE Trans. Parallel Distributed Syst., 2021

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

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

J. Mach. Learn. Res., 2021

Co-design Center for Exascale Machine Learning Technologies (ExaLearn).

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

Int. J. High Perform. Comput. Appl., 2021

Learning Combinatorial Node Labeling Algorithms.

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

CoRR, 2021

Clairvoyant prefetching for distributed machine learning I/O.

Nikoli Dryden, Roman Böhringer, Tal Ben-Nun, Torsten Hoefler

Proceedings of the International Conference for High Performance Computing, 2021

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

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

Proceedings of the Fourth Conference on Machine Learning and Systems, 2021

2020

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

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

CoRR, 2020

Deep Learning for Post-Processing Ensemble Weather Forecasts.

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

CoRR, 2020

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

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

CoRR, 2020

2019

Large-scale training of deep neural networks

Nikoli Dryden

PhD thesis, 2019

Predicting Weather Uncertainty with Deep Convnets.

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

CoRR, 2019

Channel and filter parallelism for large-scale CNN training.

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

Proceedings of the International Conference for High Performance Computing, 2019

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

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

Proceedings of the 2019 IEEE International Parallel and Distributed Processing Symposium, 2019

2018

Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics.

Roshan Dathathri, Gurbinder Gill, Loc Hoang, Hoang-Vu Dang, Alex Brooks, Nikoli Dryden, Marc Snir, Keshav Pingali

Proceedings of the 39th ACM SIGPLAN Conference on Programming Language Design and Implementation, 2018

A Lightweight Communication Runtime for Distributed Graph Analytics.

Hoang-Vu Dang, Roshan Dathathri, Gurbinder Gill, Alex Brooks, Nikoli Dryden, Andrew Lenharth, Loc Hoang, Keshav Pingali, Marc Snir

Proceedings of the 2018 IEEE International Parallel and Distributed Processing Symposium, 2018

Neural Network Based Silent Error Detector.

Chen Wang, Nikoli Dryden, Franck Cappello, Marc Snir

Proceedings of the IEEE International Conference on Cluster Computing, 2018

2017

Towards Scalable Parallel Training of Deep Neural Networks.

Sam Ade Jacobs, Nikoli Dryden, Roger A. Pearce, Brian Van Essen

Proceedings of the Machine Learning on HPC Environments, 2017

2016

Communication Quantization for Data-Parallel Training of Deep Neural Networks.

Nikoli Dryden, Tim Moon, Sam Ade Jacobs, Brian Van Essen

Proceedings of the 2nd Workshop on Machine Learning in HPC Environments, 2016

2015

PPL: an abstract runtime system for hybrid parallel programming.

Alex Brooks, Hoang-Vu Dang, Nikoli Dryden, Marc Snir

Proceedings of the First International Workshop on Extreme Scale Programming Models and Middleware, 2015

2014

PGDB: A Debugger for MPI Applications.

Nikoli Dryden

Proceedings of the Annual Conference of the Extreme Science and Engineering Discovery Environment, 2014