| 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 | 486 | 2020 |
| 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 | 479 | 2022 |
| 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 | 379 | 2020 |
| Planck: millisecond-scale monitoring and control for commodity networks J Rasley, B Stephens, C Dixon, E Rozner, W Felter, K Agarwal, J Carter, ... Proceedings of the 2014 ACM conference on SIGCOMM, 407-418, 2014 | 241 | 2014 |
| 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 | 136 | 2021 |
| Efficient queue management for cluster scheduling J Rasley, K Karanasos, S Kandula, R Fonseca, M Vojnovic, S Rao Proceedings of the Eleventh European Conference on Computer Systems, 1-15, 2016 | 135 | 2016 |
| 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 | 79 | 2022 |
| Retaining sandbox containment despite bugs in privileged memory-safe code J Cappos, A Dadgar, J Rasley, J Samuel, I Beschastnikh, C Barsan, ... Proceedings of the 17th ACM conference on Computer and communications …, 2010 | 63 | 2010 |
| Hyperdrive: Exploring hyperparameters with pop scheduling J Rasley, Y He, F Yan, O Ruwase, R Fonseca Proceedings of the 18th ACM/IFIP/USENIX Middleware Conference, 1-13, 2017 | 58 | 2017 |
| 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 | 38 | 2022 |
| Crowdsourcing from scratch: A pragmatic experiment in data collection by novice requesters A Papoutsaki, H Guo, D Metaxa-Kakavouli, C Gramazio, J Rasley, W Xie, ... Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 3 …, 2015 | 28 | 2015 |
| Wes Felter, Kanak Agarwal, John Carter, and Rodrigo Fonseca. 2014. Planck: Millisecond-scale Monitoring and Control for Commodity Networks J Rasley, B Stephens, C Dixon, E Rozner Proc. of SIGCOMM 10 (2619239.2626310), 0 | 13 | |
| Accelerating Large Scale Deep Learning Inference through {DeepCPU} at Microsoft M Zhang, S Rajbandari, W Wang, E Zheng, O Ruwase, J Rasley, J Li, ... 2019 USENIX Conference on Operational Machine Learning (OpML 19), 5-7, 2019 | 9 | 2019 |
| Detecting latent cross-platform api violations J Rasley, E Gessiou, T Ohmann, Y Brun, S Krishnamurthi, J Cappos 2015 IEEE 26th International Symposium on Software Reliability Engineering …, 2015 | 6 | 2015 |
| MCR-DL: Mix-and-Match Communication Runtime for Deep Learning Q Anthony, AA Awan, J Rasley, Y He, A Shafi, M Abduljabbar, ... arXiv preprint arXiv:2303.08374, 2023 | 1 | 2023 |
| Deepspeed inference: Enabling efficient inference of transformer models at unprecedented scale R Yazdani Aminabadi, S Rajbhandari, M Zhang, AA Awan, C Li, D Li, ... arXiv e-prints, arXiv: 2207.00032, 2022 | 1 | 2022 |
| DeepSpeed-Chat: Easy, Fast and Affordable RLHF Training of ChatGPT-like Models at All Scales Z Yao, RY Aminabadi, O Ruwase, S Rajbhandari, X Wu, AA Awan, ... arXiv preprint arXiv:2308.01320, 2023 | | 2023 |
| Seattle: The Internet as a Testbed J Rasley, M Muhammad, A Hanson, S Morgan, A Loh, J Cappos | | 2011 |
He YuxiongMicrosoft ResearchVerified email at microsoft.com
