Nikoli Dryden
Title
Cited by
Cited by
Year
Communication quantization for data-parallel training of deep neural networks
N Dryden, T Moon, SA Jacobs, B Van Essen
2016 2nd Workshop on Machine Learning in HPC Environments (MLHPC), 1-8, 2016
1052016
Gluon: A communication-optimizing substrate for distributed heterogeneous graph analytics
R Dathathri, G Gill, L Hoang, HV Dang, A Brooks, N Dryden, M Snir, ...
Proceedings of the 39th ACM SIGPLAN Conference on Programming Language …, 2018
812018
Improving strong-scaling of CNN training by exploiting finer-grained parallelism
N Dryden, N Maruyama, T Benson, T Moon, M Snir, B Van Essen
2019 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2019
242019
Aluminum: An asynchronous, GPU-aware communication library optimized for large-scale training of deep neural networks on HPC systems
N Dryden, N Maruyama, T Moon, T Benson, A Yoo, M Snir, B Van Essen
2018 IEEE/ACM Machine Learning in HPC Environments (MLHPC), 1-13, 2018
242018
Channel and filter parallelism for large-scale CNN training
N Dryden, N Maruyama, T Moon, T Benson, M Snir, B Van Essen
Proceedings of the International Conference for High Performance Computing …, 2019
222019
A lightweight communication runtime for distributed graph analytics
HV Dang, R Dathathri, G Gill, A Brooks, N Dryden, A Lenharth, L Hoang, ...
2018 IEEE International Parallel and Distributed Processing Symposium (IPDPS …, 2018
202018
Deep learning for post-processing ensemble weather forecasts
P Grönquist, C Yao, T Ben-Nun, N Dryden, P Dueben, S Li, T Hoefler
Philosophical Transactions of the Royal Society A 379 (2194), 20200092, 2021
162021
Towards scalable parallel training of deep neural networks
SA Jacobs, N Dryden, R Pearce, B Van Essen
Proceedings of the Machine Learning on HPC Environments, 1-9, 2017
112017
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks
T Hoefler, D Alistarh, T Ben-Nun, N Dryden, A Peste
arXiv preprint arXiv:2102.00554, 2021
102021
PPL: an abstract runtime system for hybrid parallel programming
A Brooks, HV Dang, N Dryden, M Snir
Proceedings of the First International Workshop on Extreme Scale Programming …, 2015
62015
Predicting weather uncertainty with deep convnets
P Grönquist, T Ben-Nun, N Dryden, P Dueben, L Lavarini, S Li, T Hoefler
arXiv preprint arXiv:1911.00630, 2019
52019
The case for strong scaling in deep learning: Training large 3d cnns with hybrid parallelism
Y Oyama, N Maruyama, N Dryden, E McCarthy, P Harrington, J Balewski, ...
IEEE Transactions on Parallel and Distributed Systems, 2020
42020
Neural network based silent error detector
C Wang, N Dryden, F Cappello, M Snir
2018 IEEE International Conference on Cluster Computing (CLUSTER), 168-178, 2018
42018
Pgdb: A debugger for mpi applications
N Dryden
Proceedings of the 2014 Annual Conference on Extreme Science and Engineering …, 2014
42014
Data movement is all you need: A case study on optimizing transformers
A Ivanov, N Dryden, T Ben-Nun, S Li, T Hoefler
Fourth Conference on Machine Learning and Systems, 2021
3*2021
Shapeshifter Networks: Cross-layer Parameter Sharing for Scalable and Effective Deep Learning
BA Plummer, N Dryden, J Frost, T Hoefler, K Saenko
arXiv preprint arXiv:2006.10598, 2020
22020
DiHydrogen
N Maruyama, BV Essen, NJ Dryden, TR Benson, TY Moon, Y Oyama
Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States), 2020
12020
Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging
S Li, T Ben-Nun, G Nadiradze, S Digirolamo, N Dryden, D Alistarh, ...
IEEE Transactions on Parallel and Distributed Systems, 2020
12020
Aluminum GPU-aware communication library
N Dryden, N Maruyama, M Snir, B Van Essen
Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States), 2018
12018
Scaling Deep Learning for Cancer Drug Discovery on HPC Systems
SA Jacobs, N Dryden, T Moon, B Van Essen, S He, J Allen
Lawrence Livermore National Lab.(LLNL), Livermore, CA (United States), 2018
12018
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