| Demystifying parallel and distributed deep learning: An in-depth concurrency analysis T Ben-Nun, T Hoefler ACM Computing Surveys (CSUR) 52 (4), 1-43, 2019 | 894 | 2019 |
| 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 (241), 1-124, 2021 | 877 | 2021 |
| Gptq: Accurate post-training quantization for generative pre-trained transformers E Frantar, S Ashkboos, T Hoefler, D Alistarh arXiv preprint arXiv:2210.17323, 2022 | 756 | 2022 |
| Graph of thoughts: Solving elaborate problems with large language models M Besta, N Blach, A Kubicek, R Gerstenberger, M Podstawski, ... Proceedings of the AAAI Conference on Artificial Intelligence 38 (16), 17682 …, 2024 | 689 | 2024 |
| The convergence of sparsified gradient methods D Alistarh, T Hoefler, M Johansson, N Konstantinov, S Khirirat, C Renggli Advances in Neural Information Processing Systems 31, 2018 | 614 | 2018 |
| MPI: A Message-Passing Interface Standard MPI Forum Technical Report, 2012 | 460* | 2012 |
| Slim fly: A cost effective low-diameter network topology M Besta, T Hoefler SC'14: proceedings of the international conference for high performance …, 2014 | 361 | 2014 |
| Scientific benchmarking of parallel computing systems: twelve ways to tell the masses when reporting performance results T Hoefler, R Belli Proceedings of the international conference for high performance computing …, 2015 | 330 | 2015 |
| Characterizing the influence of system noise on large-scale applications by simulation T Hoefler, T Schneider, A Lumsdaine SC'10: Proceedings of the 2010 ACM/IEEE International Conference for High …, 2010 | 322 | 2010 |
| Neural code comprehension: A learnable representation of code semantics T Ben-Nun, AS Jakobovits, T Hoefler Advances in neural information processing systems 31, 2018 | 316 | 2018 |
| Generic topology mapping strategies for large-scale parallel architectures T Hoefler, M Snir Proceedings of the international conference on Supercomputing, 75-84, 2011 | 309 | 2011 |
| The PERCS high-performance interconnect B Arimilli, R Arimilli, V Chung, S Clark, W Denzel, B Drerup, T Hoefler, ... 2010 18th IEEE Symposium on High Performance Interconnects, 75-82, 2010 | 300 | 2010 |
| Implementation and performance analysis of non-blocking collective operations for MPI T Hoefler, A Lumsdaine, W Rehm Proceedings of the 2007 ACM/IEEE conference on Supercomputing, 1-10, 2007 | 284 | 2007 |
| Augment your batch: Improving generalization through instance repetition E Hoffer, T Ben-Nun, I Hubara, N Giladi, T Hoefler, D Soudry Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2020 | 263 | 2020 |
| LogGOPSim: simulating large-scale applications in the LogGOPS model T Hoefler, T Schneider, A Lumsdaine Proceedings of the 19th ACM International Symposium on High Performance …, 2010 | 230 | 2010 |
| The digital revolution of Earth-system science P Bauer, PD Dueben, T Hoefler, T Quintino, TC Schulthess, NP Wedi Nature Computational Science 1 (2), 104-113, 2021 | 226 | 2021 |
| Kilometer-scale climate models: Prospects and challenges C Schär, O Fuhrer, A Arteaga, N Ban, C Charpilloz, S Di Girolamo, ... Bulletin of the American Meteorological Society 101 (5), E567-E587, 2020 | 209* | 2020 |
| OPTQ: Accurate quantization for generative pre-trained transformers E Frantar, S Ashkboos, T Hoefler, D Alistarh The Eleventh International Conference on Learning Representations, 2022 | 208 | 2022 |
| Spqr: A sparse-quantized representation for near-lossless llm weight compression T Dettmers, R Svirschevski, V Egiazarian, D Kuznedelev, E Frantar, ... arXiv preprint arXiv:2306.03078, 2023 | 202 | 2023 |
| Using automated performance modeling to find scalability bugs in complex codes A Calotoiu, T Hoefler, M Poke, F Wolf Proceedings of the International Conference on High Performance Computing …, 2013 | 196 | 2013 |
Maciej BestaETH Zurichinf.ethz.ch의 이메일 확인됨