Nikoli Dryden - RankMe

46 papers A* 5A 7B 1C 1Journal 25Unranked 6

Year	Rank	Type	Title / Venue / Authors
2025	B	`conf`	A General and Scalable GCN Training Framework on CPU Supercomputers. PPoPP Chen Zhuang, Peng Chen, Xin Liu, Rio Yokota, Nikoli Dryden, Lingqi Zhang, Toshio Endo, Satoshi Matsuoka, Mohamed Wahib
2025	J	`jnl`	Polar Sparsity: High Throughput Batched LLM Inferencing with Scalable Contextual Sparsity. CoRR Susav Shrestha, Brad Settlemyer, Nikoli Dryden, Narasimha Reddy
2025	A	`conf`	Scaling Large-scale GNN Training to Thousands of Processors on CPU-based Supercomputers. ICS Chen Zhuang, Lingqi Zhang, Du Wu, Peng Chen, Jiajun Huang, Xin Liu, Rio Yokota, Nikoli Dryden, Toshio Endo, Satoshi Matsuoka, Mohamed Wahib
2024	A	`conf`	Learning to Compose SuperWeights for Neural Parameter Allocation Search. WACV Piotr Teterwak, Soren Nelson, Nikoli Dryden, Dina Bashkirova, Kate Saenko, Bryan A. Plummer
2024	J	`jnl`	Lion Cub: Minimizing Communication Overhead in Distributed Lion. CoRR Satoki Ishikawa, Tal Ben-Nun, Brian Van Essen, Rio Yokota, Nikoli Dryden
2024	J	`jnl`	SuperGCN: General and Scalable Framework for GCN Training on CPU-powered Supercomputers. CoRR Chen Zhuang, Peng Chen, Xin Liu, Rio Yokota, Nikoli Dryden, Toshio Endo, Satoshi Matsuoka, Mohamed Wahib
2023	J	`jnl`	Cached Operator Reordering: A Unified View for Fast GNN Training. CoRR Julia Bazinska, Andrei Ivanov, Tal Ben-Nun, Nikoli Dryden, Maciej Besta, Siyuan Shen, Torsten Hoefler
2023	J	`jnl`	Learning to Compose SuperWeights for Neural Parameter Allocation Search. CoRR Piotr Teterwak, Soren Nelson, Nikoli Dryden, Dina Bashkirova, Kate Saenko, Bryan A. Plummer
2023	J	`jnl`	STen: Productive and Efficient Sparsity in PyTorch. CoRR Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Saleh Ashkboos, Torsten Hoefler
2022	A	`conf`	A data-centric optimization framework for machine learning. ICS Oliver Rausch, Tal Ben-Nun, Nikoli Dryden, Andrei Ivanov, Shigang Li, Torsten Hoefler
2022	J	`jnl`	ENS-10: A Dataset For Post-Processing Ensemble Weather Forecast. CoRR Saleh Ashkboos, Langwen Huang, Nikoli Dryden, Tal Ben-Nun, Peter Dueben, Lukas Gianinazzi, Luca Kummer, Torsten Hoefler
2022	A*	`conf`	ENS-10: A Dataset For Post-Processing Ensemble Weather Forecasts. NeurIPS Saleh Ashkboos, Langwen Huang, Nikoli Dryden, Tal Ben-Nun, Peter Dueben, Lukas Gianinazzi, Luca Kummer, Torsten Hoefler
2022	A*	`conf`	Motif Prediction with Graph Neural Networks. KDD Maciej Besta, Raphael Grob, Cesare Miglioli, Nicola Bernold, Grzegorz Kwasniewski, Gabriel Gjini, Raghavendra Kanakagiri, Saleh Ashkboos, Lukas Gianinazzi, Nikoli Dryden, Torsten Hoefler
2022	—	`conf`	Neural Graph Databases. LoG Maciej Besta, Patrick Iff, Florian Scheidl, Kazuki Osawa, Nikoli Dryden, Michal Podstawski, Tiancheng Chen, Torsten Hoefler
2022	J	`jnl`	Neural Graph Databases. CoRR Maciej Besta, Patrick Iff, Florian Scheidl, Kazuki Osawa, Nikoli Dryden, Michal Podstawski, Tiancheng Chen, Torsten Hoefler
2022	A*	`conf`	Neural Parameter Allocation Search. ICLR Bryan A. Plummer, Nikoli Dryden, Julius Frost, Torsten Hoefler, Kate Saenko
2022	A*	`conf`	Spatial Mixture-of-Experts. NeurIPS Nikoli Dryden, Torsten Hoefler
2022	J	`jnl`	Spatial Mixture-of-Experts. CoRR Nikoli Dryden, Torsten Hoefler
2021	J	`jnl`	A Data-Centric Optimization Framework for Machine Learning. CoRR Oliver Rausch, Tal Ben-Nun, Nikoli Dryden, Andrei Ivanov, Shigang Li, Torsten Hoefler
2021	J	`jnl`	Breaking (Global) Barriers in Parallel Stochastic Optimization With Wait-Avoiding Group Averaging. IEEE Trans. Parallel Distributed Syst. Shigang Li, Tal Ben-Nun, Giorgi Nadiradze, Salvatore Di Girolamo, Nikoli Dryden, Dan Alistarh, Torsten Hoefler
2021	J	`jnl`	Clairvoyant Prefetching for Distributed Machine Learning I/O. CoRR Roman Böhringer, Nikoli Dryden, Tal Ben-Nun, Torsten Hoefler
2021	A	`conf`	Clairvoyant prefetching for distributed machine learning I/O. SC Nikoli Dryden, Roman Böhringer, Tal Ben-Nun, Torsten Hoefler
2021	J	`jnl`	Co-design Center for Exascale Machine Learning Technologies (ExaLearn). Int. J. High Perform. Comput. Appl. 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 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
2021	—	`conf`	Data Movement Is All You Need: A Case Study on Optimizing Transformers. MLSys Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Shigang Li, Torsten Hoefler
2021	J	`jnl`	Learning Combinatorial Node Labeling Algorithms. CoRR Lukas Gianinazzi, Maximilian Fries, Nikoli Dryden, Tal Ben-Nun, Maciej Besta, Torsten Hoefler
2021	J	`jnl`	Motif Prediction with Graph Neural Networks. CoRR Maciej Besta, Raphael Grob, Cesare Miglioli, Nicola Bernold, Grzegorz Kwasniewski, Gabriel Gjini, Raghavendra Kanakagiri, Saleh Ashkboos, Lukas Gianinazzi, Nikoli Dryden, Torsten Hoefler
2021	J	`jnl`	Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. CoRR Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste
2021	J	`jnl`	Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks. J. Mach. Learn. Res. Torsten Hoefler, Dan Alistarh, Tal Ben-Nun, Nikoli Dryden, Alexandra Peste
2021	J	`jnl`	The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs With Hybrid Parallelism. IEEE Trans. Parallel Distributed Syst. Yosuke Oyama, Naoya Maruyama, Nikoli Dryden, Erin McCarthy, Peter Harrington, Jan Balewski, Satoshi Matsuoka, Peter Nugent, Brian Van Essen
2020	J	`jnl`	Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging. CoRR Shigang Li, Tal Ben-Nun, Dan Alistarh, Salvatore Di Girolamo, Nikoli Dryden, Torsten Hoefler
2020	J	`jnl`	Data Movement Is All You Need: A Case Study on Optimizing Transformers. CoRR Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun, Shigang Li, Torsten Hoefler
2020	J	`jnl`	Deep Learning for Post-Processing Ensemble Weather Forecasts. CoRR Peter Grönquist, Chengyuan Yao, Tal Ben-Nun, Nikoli Dryden, Peter Dueben, Shigang Li, Torsten Hoefler
2020	J	`jnl`	Shapeshifter Networks: Cross-layer Parameter Sharing for Scalable and Effective Deep Learning. CoRR Bryan A. Plummer, Nikoli Dryden, Julius Frost, Torsten Hoefler, Kate Saenko
2020	J	`jnl`	The Case for Strong Scaling in Deep Learning: Training Large 3D CNNs with Hybrid Parallelism. CoRR Yosuke Oyama, Naoya Maruyama, Nikoli Dryden, Erin McCarthy, Peter Harrington, Jan Balewski, Satoshi Matsuoka, Peter Nugent, Brian Van Essen
2019	A	`conf`	Channel and filter parallelism for large-scale CNN training. SC Nikoli Dryden, Naoya Maruyama, Tim Moon, Tom Benson, Marc Snir, Brian Van Essen
2019	A	`conf`	Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism. IPDPS Nikoli Dryden, Naoya Maruyama, Tom Benson, Tim Moon, Marc Snir, Brian Van Essen
2019	J	`jnl`	Improving Strong-Scaling of CNN Training by Exploiting Finer-Grained Parallelism. CoRR Nikoli Dryden, Naoya Maruyama, Tom Benson, Tim Moon, Marc Snir, Brian Van Essen
2019	—	`—`	Large-scale training of deep neural networks Nikoli Dryden
2019	J	`jnl`	Predicting Weather Uncertainty with Deep Convnets. CoRR Peter Grönquist, Tal Ben-Nun, Nikoli Dryden, Peter Dueben, Luca Lavarini, Shigang Li, Torsten Hoefler
2018	A	`conf`	A Lightweight Communication Runtime for Distributed Graph Analytics. IPDPS Hoang-Vu Dang, Roshan Dathathri, Gurbinder Gill, Alex Brooks, Nikoli Dryden, Andrew Lenharth, Loc Hoang, Keshav Pingali, Marc Snir
2018	A*	`conf`	Gluon: a communication-optimizing substrate for distributed heterogeneous graph analytics. PLDI Roshan Dathathri, Gurbinder Gill, Loc Hoang, Hoang-Vu Dang, Alex Brooks, Nikoli Dryden, Marc Snir, Keshav Pingali
2018	C	`conf`	Neural Network Based Silent Error Detector. CLUSTER Chen Wang, Nikoli Dryden, Franck Cappello, Marc Snir
2017	—	`conf`	Towards Scalable Parallel Training of Deep Neural Networks. MLHPC@SC Sam Ade Jacobs, Nikoli Dryden, Roger A. Pearce, Brian Van Essen
2016	—	`conf`	Communication Quantization for Data-Parallel Training of Deep Neural Networks. MLHPC@SC Nikoli Dryden, Tim Moon, Sam Ade Jacobs, Brian Van Essen
2015	—	`conf`	PPL: an abstract runtime system for hybrid parallel programming. ESPM@SC Alex Brooks, Hoang-Vu Dang, Nikoli Dryden, Marc Snir
2014	—	`conf`	PGDB: A Debugger for MPI Applications. XSEDE Nikoli Dryden