| Gandiva: Introspective cluster scheduling for deep learning W Xiao, R Bhardwaj, R Ramjee, M Sivathanu, N Kwatra, Z Han, P Patel, ... 13th USENIX Symposium on Operating Systems Design and Implementation (OSDI …, 2018 | 490 | 2018 |
| {HiveD}: Sharing a {GPU} cluster for deep learning with guarantees H Zhao, Z Han, Z Yang, Q Zhang, F Yang, L Zhou, M Yang, FCM Lau, ... 14th USENIX symposium on operating systems design and implementation (OSDI …, 2020 | 71 | 2020 |
| Sdpaxos: Building efficient semi-decentralized geo-replicated state machines H Zhao, Q Zhang, Z Yang, M Wu, Y Dai Proceedings of the ACM Symposium on Cloud Computing, 68-81, 2018 | 28 | 2018 |
| MSD: Multi-self-distillation learning via multi-classifiers within deep neural networks Y Luan, H Zhao, Z Yang, Y Dai arXiv preprint arXiv:1911.09418, 2019 | 23 | 2019 |
| SCHED²: Scheduling Deep Learning Training via Deep Reinforcement Learning Y Luan, X Chen, H Zhao, Z Yang, Y Dai 2019 IEEE Global Communications Conference (GLOBECOM), 1-7, 2019 | 12 | 2019 |
| Scheduling CPU for GPU-based deep learning jobs W Xiao, Z Han, H Zhao, X Peng, Q Zhang, F Yang, L Zhou Proceedings of the ACM Symposium on Cloud Computing, 503-503, 2018 | 11 | 2018 |
| Zoomer: Boosting retrieval on web-scale graphs by regions of interest Y Jiang, Y Cheng, H Zhao, W Zhang, X Miao, Y He, L Wang, Z Yang, ... 2022 IEEE 38th International Conference on Data Engineering (ICDE), 2224-2236, 2022 | 9 | 2022 |
| SiloD: A Co-design of Caching and Scheduling for Deep Learning Clusters H Zhao, Z Han, Z Yang, Q Zhang, M Li, F Yang, Q Zhang, B Li, Y Yang, ... Proceedings of the Eighteenth European Conference on Computer Systems, 883-898, 2023 | 7 | 2023 |
| Infinite-LLM: Efficient LLM Service for Long Context with DistAttention and Distributed KVCache B Lin, T Peng, C Zhang, M Sun, L Li, H Zhao, W Xiao, Q Xu, X Qiu, S Li, ... arXiv preprint arXiv:2401.02669, 2024 | 4 | 2024 |
| Easyscale: Accuracy-consistent elastic training for deep learning M Li, W Xiao, B Sun, H Zhao, H Yang, S Ren, Z Luan, X Jia, Y Liu, Y Li, ... arXiv preprint arXiv:2208.14228, 2022 | 4 | 2022 |
| Instance-wise prompt tuning for pretrained language models Y Jiang, H Yang, J Lin, H Zhao, A Yang, C Zhou, H Yang, Z Yang, B Cui arXiv preprint arXiv:2206.01958, 2022 | 4 | 2022 |
| GoldMiner: Elastic Scaling of Training Data Pre-Processing Pipelines for Deep Learning H Zhao, Z Yang, Y Cheng, C Tian, S Ren, W Xiao, M Yuan, L Chen, K Liu, ... Proceedings of the ACM on Management of Data 1 (2), 1-25, 2023 | 3 | 2023 |
| A Topology-Aware Performance Prediction Model for Distributed Deep Learning on GPU Clusters Z Lin, X Chen, H Zhao, Y Luan, Z Yang, Y Dai 2020 IEEE International Conference on Big Data (Big Data), 2795-2801, 2020 | 2 | 2020 |
| EasyScale: Elastic Training with Consistent Accuracy and Improved Utilization on GPUs M Li, W Xiao, H Yang, B Sun, H Zhao, S Ren, Z Luan, X Jia, Y Liu, Y Li, ... Proceedings of the International Conference for High Performance Computing …, 2023 | 1 | 2023 |
| Building efficient and available distributed transaction with Paxos-based coding consensus S Li, Q Zhang, Z Yang, H Zhao, Y Dai IEEE INFOCOM 2018-IEEE Conference on Computer Communications Workshops …, 2018 | 1 | 2018 |
| ROAM: memory-efficient large DNN training via optimized operator ordering and memory layout H Shu, A Wang, Z Shi, H Zhao, Y Li, L Lu arXiv preprint arXiv:2310.19295, 2023 | | 2023 |
| Dynamic allocation of computing resources Q Zhang, L Zhou, Y Mao, F Yang, H Zhao, Z Han US Patent App. 17/609,700, 2022 | | 2022 |
Lidong ZhouMicrosoft ResearchDirección de correo verificada de microsoft.com
