| Zero: Memory optimizations toward training trillion parameter models S Rajbhandari, J Rasley, O Ruwase, Y He SC20: International Conference for High Performance Computing, Networking …, 2020 | 2198 | 2020 |
| Deepspeed: System optimizations enable training deep learning models with over 100 billion parameters J Rasley, S Rajbhandari, O Ruwase, Y He Proceedings of the 26th ACM SIGKDD international conference on knowledge …, 2020 | 1987 | 2020 |
| Using deepspeed and megatron to train megatron-turing nlg 530b, a large-scale generative language model S Smith, M Patwary, B Norick, P LeGresley, S Rajbhandari, J Casper, ... arXiv preprint arXiv:2201.11990, 2022 | 821 | 2022 |
| Zeroquant: Efficient and affordable post-training quantization for large-scale transformers Z Yao, R Yazdani Aminabadi, M Zhang, X Wu, C Li, Y He Advances in neural information processing systems 35, 27168-27183, 2022 | 686 | 2022 |
| Deepspeed-inference: enabling efficient inference of transformer models at unprecedented scale RY Aminabadi, S Rajbhandari, AA Awan, C Li, D Li, E Zheng, O Ruwase, ... SC22: International Conference for High Performance Computing, Networking …, 2022 | 634 | 2022 |
| {Zero-offload}: Democratizing {billion-scale} model training J Ren, S Rajbhandari, RY Aminabadi, O Ruwase, S Yang, M Zhang, D Li, ... 2021 USENIX Annual Technical Conference (USENIX ATC 21), 551-564, 2021 | 617 | 2021 |
| Zero-infinity: Breaking the gpu memory wall for extreme scale deep learning S Rajbhandari, O Ruwase, J Rasley, S Smith, Y He Proceedings of the international conference for high performance computing …, 2021 | 518 | 2021 |
| Deepspeed-moe: Advancing mixture-of-experts inference and training to power next-generation ai scale S Rajbhandari, C Li, Z Yao, M Zhang, RY Aminabadi, AA Awan, J Rasley, ... International conference on machine learning, 18332-18346, 2022 | 481 | 2022 |
| Graph query processing using plurality of engines S ELNIKETY, Y He, S Sakr US Patent 9,053,210, 2015 | 335 | 2015 |
| Deepspeed ulysses: System optimizations for enabling training of extreme long sequence transformer models SA Jacobs, M Tanaka, C Zhang, M Zhang, SL Song, S Rajbhandari, Y He arXiv preprint arXiv:2309.14509, 2023 | 191 | 2023 |
| Swayam: distributed autoscaling to meet slas of machine learning inference services with resource efficiency A Gujarati, S Elnikety, Y He, KS McKinley, BB Brandenburg Proceedings of the 18th ACM/IFIP/USENIX middleware conference, 109-120, 2017 | 166 | 2017 |
| Learning intrinsic sparse structures within long short-term memory W Wen, Y He, S Rajbhandari, M Zhang, W Wang, F Liu, B Hu, Y Chen, ... arXiv preprint arXiv:1709.05027, 2017 | 162 | 2017 |
| Few-to-many: Incremental parallelism for reducing tail latency in interactive services ME Haque, YH Eom, Y He, S Elnikety, R Bianchini, KS McKinley ACM Sigplan Notices 50 (4), 161-175, 2015 | 161 | 2015 |
| Provably-efficient job scheduling for energy and fairness in geographically distributed data centers S Ren, Y He, F Xu 2012 IEEE 32nd International Conference on Distributed Computing Systems, 22-31, 2012 | 160 | 2012 |
| The Cilkview scalability analyzer Y He, CE Leiserson, WM Leiserson Proceedings of the twenty-second annual ACM symposium on Parallelism in …, 2010 | 155 | 2010 |
| Adaptive work-stealing with parallelism feedback K Agrawal, CE Leiserson, Y He, WJ Hsu ACM Transactions on Computer Systems (TOCS) 26 (3), 1-32, 2008 | 148 | 2008 |
| Accelerating training of transformer-based language models with progressive layer dropping M Zhang, Y He Advances in neural information processing systems 33, 14011-14023, 2020 | 133 | 2020 |
| Improving approximate nearest neighbor search through learned adaptive early termination C Li, M Zhang, DG Andersen, Y He Proceedings of the 2020 ACM SIGMOD International Conference on Management of …, 2020 | 132 | 2020 |
| Performance modeling and scalability optimization of distributed deep learning systems F Yan, O Ruwase, Y He, T Chilimbi Proceedings of the 21th ACM SIGKDD International Conference on Knowledge …, 2015 | 132 | 2015 |
| {DeepCPU}: Serving {RNN-based} deep learning models 10x faster M Zhang, S Rajbhandari, W Wang, Y He 2018 USENIX Annual Technical Conference (USENIX ATC 18), 951-965, 2018 | 127 | 2018 |
