Benchmarking state-of-the-art deep learning software tools S Shi, Q Wang, P Xu, X Chu
2016 7th International conference on cloud computing and big data (CCBD), 99-104, 2016
467 2016 Highly scalable deep learning training system with mixed-precision: Training imagenet in four minutes X Jia, S Song, W He, Y Wang, H Rong, F Zhou, L Xie, Z Guo, Y Yang, L Yu, ...
NeurIPS Workshop on Systems for ML and Open Source Software, 2018
464 2018 A Distributed Synchronous SGD Algorithm with Global Top- S Shi, Q Wang, K Zhao, Z Tang, Y Wang, X Huang, X Chu
IEEE ICDCS 2019, 2019
176 2019 Communication-efficient distributed deep learning: A comprehensive survey Z Tang, S Shi, W Wang, B Li, X Chu
arXiv preprint arXiv:2003.06307, 2020
162 2020 Performance modeling and evaluation of distributed deep learning frameworks on gpus S Shi, Q Wang, X Chu
2018 IEEE 16th Intl Conf on Dependable, Autonomic and Secure Computing, 16th …, 2018
133 2018 MG-WFBP: Efficient data communication for distributed synchronous SGD algorithms S Shi, X Chu, B Li
IEEE INFOCOM 2019-IEEE International Conference on Computer Communications …, 2019
114 2019 Understanding top-k sparsification in distributed deep learning S Shi, X Chu, KC Cheung, S See
arXiv preprint arXiv:1911.08772, 2019
112 2019 Benchmarking the performance and energy efficiency of AI accelerators for AI training Y Wang, Q Wang, S Shi, X He, Z Tang, K Zhao, X Chu
2020 20th IEEE/ACM International Symposium on Cluster, Cloud and Internet …, 2020
102 * 2020 Virtual Homogeneity Learning: Defending against Data Heterogeneity in Federated Learning Z Tang, Y Zhang, S Shi, X He, B Han, X Chu
ICML 2022, 2022
101 2022 Lora-fa: Memory-efficient low-rank adaptation for large language models fine-tuning L Zhang, L Zhang, S Shi, X Chu, B Li
arXiv preprint arXiv:2308.03303, 2023
94 2023 FADNet: A Fast and Accurate Network for Disparity Estimation Q Wang, S Shi, S Zheng, K Zhao, X Chu
International Conference on Robotics and Automation (ICRA) 2020, 2020
94 2020 GossipFL: A Decentralized Federated Learning Framework with Sparsified and Adaptive Communication Z Tang, S Shi, B Li, X Chu
IEEE Transactions on Parallel and Distributed Systems 34 (3), 909 - 922, 2023
92 2023 A Convergence Analysis of Distributed SGD with Communication-Efficient Gradient Sparsification S Shi, K Zhao, Q Wang, Z Tang, X Chu
IJCAI, 3411-3417, 2019
88 2019 Performance evaluation of deep learning tools in docker containers P Xu, S Shi, X Chu
2017 3rd International Conference on Big Data Computing and Communications …, 2017
77 2017 Communication-efficient distributed deep learning with merged gradient sparsification on gpus S Shi, Q Wang, X Chu, B Li, Y Qin, R Liu, X Zhao
IEEE INFOCOM 2020-IEEE International Conference on Computer Communications, 2020
75 2020 Towards Scalable Distributed Training of Deep Learning on Public Cloud Clusters S Shi, X Zhou, S Song, X Wang, Z Zhu, X Huang, X Jiang, F Zhou, Z Guo, ...
Fourth Conference on Machine Learning and Systems (MLSys 2021), 2021
69 2021 Communication-efficient decentralized learning with sparsification and adaptive peer selection Z Tang, S Shi, X Chu
2020 IEEE 40th International Conference on Distributed Computing Systems …, 2020
65 2020 Benchmarking deep learning models and automated model design for COVID-19 detection with chest CT scans X He, S Wang, S Shi, X Chu, J Tang, X Liu, C Yan, J Zhang, G Ding
MedRxiv, 2020.06. 08.20125963, 2020
64 2020 Speeding up convolutional neural networks by exploiting the sparsity of rectifier units S Shi, X Chu
arXiv preprint arXiv:1704.07724, 2017
55 2017 A Quantitative Survey of Communication Optimizations in Distributed Deep Learning S Shi, Z Tang, X Chu, C Liu, W Wang, B Li
IEEE Network 35 (3), 230 - 237, 2020
52 2020 
Xiaowen ChuIEEE Fellow, Professor, Data Science and Analytics, HKUST(GZ)Correu electrònic verificat a ust.hk
