| Bloom: A 176b-parameter open-access multilingual language model T Le Scao, A Fan, C Akiki, E Pavlick, S Ilić, D Hesslow, R Castagné, ... | 1623 | 2023 |
| 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 | 1204 | 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 | 1111 | 2020 |
| A Practical Dynamic Buffer Overflow Detector. O Ruwase, MS Lam NDSS 2004, 159-169, 2004 | 549 | 2004 |
| Phi-3 technical report: A highly capable language model locally on your phone M Abdin, J Aneja, H Awadalla, A Awadallah, AA Awan, N Bach, A Bahree, ... arXiv preprint arXiv:2404.14219, 2024 | 539 | 2024 |
| Accelerating deep convolutional neural networks using specialized hardware K Ovtcharov, O Ruwase, JY Kim, J Fowers, K Strauss, ES Chung Microsoft Research Whitepaper 2 (11), 1-4, 2015 | 532 | 2015 |
| {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 | 369 | 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 | 321 | 2021 |
| 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 | 278 | 2022 |
| Flexible hardware acceleration for instruction-grain program monitoring S Chen, M Kozuch, T Strigkos, B Falsafi, PB Gibbons, TC Mowry, ... ACM SIGARCH Computer Architecture News 36 (3), 377-388, 2008 | 208 | 2008 |
| 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 | 114 | 2015 |
| Parallelizing dynamic information flow tracking O Ruwase, PB Gibbons, TC Mowry, V Ramachandran, S Chen, M Kozuch, ... Proceedings of the twentieth annual symposium on Parallelism in algorithms …, 2008 | 93 | 2008 |
| Toward accelerating deep learning at scale using specialized hardware in the datacenter K Ovtcharov, O Ruwase, JY Kim, J Fowers, K Strauss, ES Chung 2015 IEEE Hot Chips 27 Symposium (HCS), 1-38, 2015 | 91 | 2015 |
| Ditto: a system for opportunistic caching in multi-hop wireless networks FR Dogar, A Phanishayee, H Pucha, O Ruwase, DG Andersen Proceedings of the 14th ACM international conference on Mobile computing and …, 2008 | 77 | 2008 |
| 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 | 65 | 2017 |
| Page overlays: An enhanced virtual memory framework to enable fine-grained memory management V Seshadri, G Pekhimenko, O Ruwase, O Mutlu, PB Gibbons, MA Kozuch, ... ACM SIGARCH Computer Architecture News 43 (3S), 79-91, 2015 | 64 | 2015 |
| 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 | 53 | 2023 |
| Neural network training performance optimization framework TA Chilimbi, O Ruwase, S Rajbhandari, M Carbin, Y He US Patent App. 14/986,186, 2017 | 44 | 2017 |
| Zero++: Extremely efficient collective communication for giant model training G Wang, H Qin, SA Jacobs, C Holmes, S Rajbhandari, O Ruwase, F Yan, ... arXiv preprint arXiv:2306.10209, 2023 | 36 | 2023 |
| SERF: Efficient scheduling for fast deep neural network serving via judicious parallelism F Yan, O Ruwase, Y He, E Smirni SC'16: Proceedings of the International Conference for High Performance …, 2016 | 35 | 2016 |
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