| 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 Journal of Machine Learning Research 22, 1-124, 2021 | 876 | 2021 |
| 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 | 249 | 2016 |
| 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 | 192 | 2021 |
| 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 | 162 | 2018 |
| 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 | 161 | 2021 |
| Clairvoyant Prefetching for Distributed Machine Learning I/O N Dryden, R Böhringer, T Ben-Nun, T Hoefler International Conference for High Performance Computing, Network, Storage …, 2021 | 72 | 2021 |
| 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 | 64 | 2019 |
| 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 | 57 | 2019 |
| 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 | 57 | 2018 |
| 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 32 (7), 1641-1652, 2020 | 53 | 2020 |
| Motif prediction with graph neural networks M Besta, R Grob, C Miglioli, N Bernold, G Kwasniewski, G Gjini, ... Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and …, 2022 | 42 | 2022 |
| 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 | 33 | 2018 |
| 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 | 30 | 2018 |
| Neural parameter allocation search BA Plummer, N Dryden, J Frost, T Hoefler, K Saenko International Conference on Learning Representations (ICLR), 2022 | 28* | 2022 |
| Ens-10: A dataset for post-processing ensemble weather forecasts S Ashkboos, L Huang, N Dryden, T Ben-Nun, P Dueben, L Gianinazzi, ... Advances in Neural Information Processing Systems 35, 21974-21987, 2022 | 27 | 2022 |
| Neural graph databases M Besta, P Iff, F Scheidl, K Osawa, N Dryden, M Podstawski, T Chen, ... Learning on Graphs Conference, 31: 1-31: 38, 2022 | 21 | 2022 |
| Learning combinatorial node labeling algorithms L Gianinazzi, M Fries, N Dryden, T Ben-Nun, M Besta, T Hoefler arXiv preprint arXiv:2106.03594, 2021 | 20 | 2021 |
| Co-design center for exascale machine learning technologies (exalearn) FJ Alexander, J Ang, JA Bilbrey, J Balewski, T Casey, R Chard, J Choi, ... The International Journal of High Performance Computing Applications 35 (6 …, 2021 | 19 | 2021 |
| 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 | 19 | 2017 |
| Breaking (global) barriers in parallel stochastic optimization with wait-avoiding group averaging S Li, T Ben-Nun, G Nadiradze, S Di Girolamo, N Dryden, D Alistarh, ... IEEE Transactions on Parallel and Distributed Systems 32 (7), 1725-1739, 2020 | 15 | 2020 |
Torsten HoeflerProfessor of Computer Science at ETH ZurichVerifierad e-postadress på inf.ethz.ch