Qiang Wang
Qiang Wang
Department of Computer Science, Hong Kong Baptist University
Verified email at comp.hkbu.edu.hk - Homepage
Title
Cited by
Cited by
Year
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
2922016
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
542018
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
542017
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
392019
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
272020
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
182019
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
172019
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
132020
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
132018
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
132018
Benchmarking the Performance and Power of AI Accelerators for AI Training
Y Wang, Q Wang, S Shi, X He, Z Tang, K Zhao, X Chu
3rd High Performance Machine Learning Workshop (HPML 2020), 2020
10*2020
EPPMiner: An Extended Benchmark Suite for Energy, Power and Performance Characterization of Heterogeneous Architecture
Q Wang, P Xu, Y Zhang, X Chu
Proceedings of the Eighth International Conference on Future Energy Systems …, 2017
102017
GPGPU Power Estimation with Core and Memory Frequency Scaling
Q Wang, X Chu
ACM SIGMETRICS Performance Evaluation Review 45 (2), 73-78, 2017
82017
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
72020
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
5*2021
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
52020
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
42020
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
32020
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
32017
Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format
S Shi, Q Wang, X Chu
2020 International Conference on Parallel and Distributed Systems, 2020
22020
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