| On the Opportunities and Risks of Foundation Models R Bommasani, DA Hudson, E Adeli, R Altman, S Arora, S von Arx, ... arXiv preprint arXiv:2108.07258, 2021 | 4830 | 2021 |
| BLOOM: A 176B-Parameter Open-Access Multilingual Language Model TL Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... arXiv preprint arXiv:2211.05100, 2022 | 1790 | 2022 |
| Holistic Evaluation of Language Models P Liang, R Bommasani, T Lee, D Tsipras, D Soylu, M Yasunaga, Y Zhang, ... arXiv preprint arXiv:2211.09110, 2022 | 1224 | 2022 |
| PipeDream: Generalized Pipeline Parallelism for DNN Training D Narayanan, A Harlap, A Phanishayee, V Seshadri, NR Devanur, ... 27th ACM Symposium on Operating Systems Principles, 1-15, 2019 | 937 | 2019 |
| Efficient Large-Scale Language Model Training on GPU Clusters using Megatron-LM D Narayanan, M Shoeybi, J Casper, P LeGresley, M Patwary, ... Proceedings of the International Conference for High Performance Computing …, 2021 | 748 | 2021 |
| DAWNBench: An End-to-End Deep Learning Benchmark and Competition C Coleman, D Narayanan, D Kang, T Zhao, J Zhang, L Nardi, P Bailis, ... NeurIPS Workshop on Systems for Machine Learning, 2017 | 400 | 2017 |
| MLPerf Training Benchmark P Mattson, C Cheng, C Coleman, G Diamos, P Micikevicius, D Patterson, ... Third Conference on Machine Learning and Systems, 2020 | 360 | 2020 |
| PipeDream: Fast and Efficient Pipeline Parallel DNN Training A Harlap, D Narayanan, A Phanishayee, V Seshadri, N Devanur, ... arXiv preprint arXiv:1806.03377, 2018 | 288 | 2018 |
| Heterogeneity-Aware Cluster Scheduling Policies for Deep Learning Workloads D Narayanan, K Santhanam, F Kazhamiaka, A Phanishayee, M Zaharia 14th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2020 | 258 | 2020 |
| Memory-Efficient Pipeline-Parallel DNN Training D Narayanan, A Phanishayee, K Shi, X Chen, M Zaharia International Conference on Machine Learning, 7937-7947, 2021 | 243 | 2021 |
| MacroBase: Prioritizing Attention in Fast Data P Bailis, E Gan, S Madden, D Narayanan, K Rong, S Suri Proceedings of the 2017 ACM International Conference on Management of Data …, 2017 | 187 | 2017 |
| Weld: A Common Runtime for High Performance Data Analytics S Palkar, JJ Thomas, A Shanbhag, D Narayanan, H Pirk, M Schwarzkopf, ... Conference on Innovative Data Systems Research (CIDR), 2017 | 187 | 2017 |
| Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark C Coleman, D Kang, D Narayanan, L Nardi, T Zhao, J Zhang, P Bailis, ... ACM SIGOPS Operating Systems Review 53 (1), 14-25, 2019 | 148 | 2019 |
| Holistic Evaluation of Text-to-Image Models T Lee, M Yasunaga, C Meng, Y Mai, JS Park, A Gupta, Y Zhang, ... arXiv preprint arXiv:2311.04287, 2023 | 130 | 2023 |
| Evaluating End-to-End Optimization for Data Analytics Applications in Weld S Palkar, J Thomas, D Narayanan, P Thaker, R Palamuttam, P Negi, ... Proceedings of the VLDB Endowment 11 (9), 1002-1015, 2018 | 106 | 2018 |
| MegaBlocks: Efficient Sparse Training with Mixture-of-Experts T Gale, D Narayanan, C Young, M Zaharia Proceedings of Machine Learning and Systems 5, 2023 | 87 | 2023 |
| Nemotron-4 340B Technical Report B Adler, N Agarwal, A Aithal, DH Anh, P Bhattacharya, A Brundyn, ... arXiv preprint arXiv:2406.11704, 2024 | 68 | 2024 |
| Accelerating Deep Learning Workloads through Efficient Multi-Model Execution D Narayanan, K Santhanam, A Phanishayee, M Zaharia NeurIPS Workshop on Systems for Machine Learning, 2018 | 63 | 2018 |
| An Empirical Study of Mamba-based Language Models R Waleffe, W Byeon, D Riach, B Norick, V Korthikanti, T Dao, A Gu, ... arXiv preprint arXiv:2406.07887, 2024 | 61 | 2024 |
| Solving Large-Scale Granular Resource Allocation Problems Efficiently with POP D Narayanan, F Kazhamiaka, F Abuzaid, P Kraft, A Agrawal, S Kandula, ... 28th ACM Symposium on Operating Systems Principles, 2021 | 60 | 2021 |
Matei ZahariaUC Berkeley and Databricksכתובת אימייל מאומתת בדומיין berkeley.edu