| 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 | 364 | 2016 |
| 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 | 89 | 2018 |
| A Distributed Synchronous SGD Algorithm with Global Top- Sparsification for Low Bandwidth Networks S Shi, Q Wang, K Zhao, Z Tang, Y Wang, X Huang, X Chu IEEE ICDCS 2019, 2019 | 82 | 2019 |
| A survey and measurement study of GPU DVFS on energy conservation X Mei, Q Wang, X Chu Digital Communications and Networks 3 (2), 89-100, 2017 | 69 | 2017 |
| GPGPU performance estimation with core and memory frequency scaling Q Wang, X Chu IEEE Transactions on Parallel and Distributed Systems 31 (12), 2865-2881, 2020 | 44 | 2020 |
| 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 | 40 | 2019 |
| FADNet: A Fast and Accurate Network for Disparity Estimation Q Wang, S Shi, S Zheng, K Zhao, X Chu 2020 International Conference on Robotics and Automation (ICRA), 2020 | 36 | 2020 |
| The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study Z Tang, Y Wang, Q Wang, X Chu Proceedings of the Tenth ACM International Conference on Future Energy …, 2019 | 33 | 2019 |
| 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 Conference on Computer Communications, 406-415, 2020 | 30 | 2020 |
| 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 | 30* | 2020 |
| IRS: A Large Naturalistic Indoor Robotics Stereo Dataset to Train Deep Models for Disparity and Surface Normal Estimation Q Wang, S Zheng, Q Yan, F Deng, K Zhao, X Chu IEEE International Conference on Multimedia and Expo (ICME) 2021, 2021 | 16* | 2021 |
| A dag model of synchronous stochastic gradient descent in distributed deep learning S Shi, Q Wang, X Chu, B Li 2018 IEEE 24th International Conference on Parallel and Distributed Systems …, 2018 | 16 | 2018 |
| G-CRS: GPU Accelerated Cauchy Reed-Solomon Coding C Liu, Q Wang, X Chu, YW Leung IEEE Transactions on Parallel and Distributed Systems 29 (7), 1484-1498, 2018 | 15 | 2018 |
| GPGPU Power Estimation with Core and Memory Frequency Scaling Q Wang, X Chu ACM SIGMETRICS Performance Evaluation Review 45 (2), 73-78, 2017 | 14 | 2017 |
| Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees S Shi, Z Tang, Q Wang, K Zhao, X Chu The 24th European Conference on Artificial Intelligence, Santiago de …, 2020 | 12 | 2020 |
| EPPMiner: An Extended Benchmark Suite for Energy, Power and Performance Characterization of Heterogeneous Architecture Q Wang, P Xu, Y Zhang, X Chu e-Energy [Best Paper Finalist] - Proceedings of the Eighth International …, 2017 | 11 | 2017 |
| ESetStore: An Erasure-Coded Storage System With Fast Data Recovery C Liu, Q Wang, X Chu, YW Leung, H Liu IEEE Transactions on Parallel and Distributed Systems 31 (9), 2001-2016, 2020 | 8 | 2020 |
| Communication Contention Aware Scheduling of Multiple Deep Learning Training Jobs Q Wang, S Shi, C Wang, X Chu arXiv preprint arXiv:2002.10105, 2020 | 7 | 2020 |
| Energy-Aware Non-Preemptive Task Scheduling With Deadline Constraint in DVFS-Enabled Heterogeneous Clusters Q Wang, X Mei, H Liu, YW Leung, Z Li, X Chu IEEE Transactions on Parallel and Distributed Systems, 2022 | 4* | 2022 |
| An optimal parallel implementation of Markov Clustering based on the coordination of CPU and GPU L He, L Lu, Q Wang Journal of Intelligent & Fuzzy Systems 32 (5), 3609-3617, 2017 | 4 | 2017 |
Xiaowen ChuProfessor, HKUST(GZ)Verified email at ust.hk
