| Optimus: an efficient dynamic resource scheduler for deep learning clusters Y Peng, Y Bao, Y Chen, C Wu, C Guo Proceedings of the Thirteenth EuroSys Conference, 1-14, 2018 | 346 | 2018 |
| A generic communication scheduler for distributed dnn training acceleration Y Peng, Y Zhu, Y Chen, Y Bao, B Yi, C Lan, C Wu, C Guo Proceedings of the 27th ACM Symposium on Operating Systems Principles, 16-29, 2019 | 258 | 2019 |
| Deep learning-based job placement in distributed machine learning clusters Y Bao, Y Peng, C Wu IEEE INFOCOM 2019-IEEE conference on computer communications, 505-513, 2019 | 100 | 2019 |
| Online job scheduling in distributed machine learning clusters Y Bao, Y Peng, C Wu, Z Li IEEE INFOCOM 2018-IEEE Conference on Computer Communications, 495-503, 2018 | 83 | 2018 |
| Dl2: A deep learning-driven scheduler for deep learning clusters Y Peng, Y Bao, Y Chen, C Wu, C Meng, W Lin IEEE Transactions on Parallel and Distributed Systems 32 (8), 1947-1960, 2021 | 52 | 2021 |
| Preemptive all-reduce scheduling for expediting distributed DNN training Y Bao, Y Peng, Y Chen, C Wu IEEE INFOCOM 2020-IEEE Conference on Computer Communications, 626-635, 2020 | 39 | 2020 |
| Dynamic scaling of virtualized, distributed service chains: A case study of IMS J Duan, C Wu, F Le, AX Liu, Y Peng IEEE Journal on Selected Areas in Communications 35 (11), 2501-2511, 2017 | 36 | 2017 |
| deTector: a Topology-aware Monitoring System for Data Center Networks Y Peng, J Yang, C Wu, C Guo, C Hu, Z Li 2017 USENIX Annual Technical Conference (USENIX ATC 17), 55-68, 2017 | 30 | 2017 |
| Elastic parameter server load distribution in deep learning clusters Y Chen, Y Peng, Y Bao, C Wu, Y Zhu, C Guo Proceedings of the 11th ACM Symposium on Cloud Computing, 507-521, 2020 | 18 | 2020 |
| {BGL}:{GPU-Efficient}{GNN} Training by Optimizing Graph Data {I/O} and Preprocessing T Liu, Y Chen, D Li, C Wu, Y Zhu, J He, Y Peng, H Chen, H Chen, C Guo 20th USENIX Symposium on Networked Systems Design and Implementation (NSDI …, 2023 | 11 | 2023 |
| Multi-resource interleaving for deep learning training Y Zhao, Y Liu, Y Peng, Y Zhu, X Liu, X Jin Proceedings of the ACM SIGCOMM 2022 Conference, 428-440, 2022 | 7 | 2022 |
| dPRO: A Generic Performance Diagnosis and Optimization Toolkit for Expediting Distributed DNN Training H Hu, C Jiang, Y Zhong, Y Peng, C Wu, Y Zhu, H Lin, C Guo Proceedings of Machine Learning and Systems 4, 623-637, 2022 | 2 | 2022 |
| SP-GNN: Learning structure and position information from graphs Y Chen, J You, J He, Y Lin, Y Peng, C Wu, Y Zhu Neural Networks 161, 505-514, 2023 | 1 | 2023 |
| Deep Learning-Based Job Placement in Distributed Machine Learning Clusters With Heterogeneous Workloads Y Bao, Y Peng, C Wu IEEE/ACM Transactions on Networking, 2022 | 1 | 2022 |
| dPRO: A Generic Profiling and Optimization System for Expediting Distributed DNN Training H Hu, C Jiang, Y Zhong, Y Peng, C Wu, Y Zhu, H Lin, C Guo arXiv preprint arXiv:2205.02473, 2022 | | 2022 |
| SAPipe: Staleness-Aware Pipeline for Data Parallel DNN Training Y Chen, C Xie, M Ma, J Gu, Y Peng, H Lin, C Wu, Y Zhu Advances in Neural Information Processing Systems, 2022 | | 2022 |
| 分布式深度学习训练的通信加速 Y Jiang, Y Peng, Y Zhu, C Guo Communications of the CCF 17 (9), 18-25, 2021 | | 2021 |
| Accelerating distributed DNN training in AI clouds Y Peng HKU Theses Online (HKUTO), 2020 | | 2020 |
Chuan WuProfessor of Computer Science, The University of Hong KongVerified email at cs.hku.hk
