Dr. WANG, Qiang
Dr. WANG, Qiang

王強博士
B.Eng., Ph.D.
Research Assistant Professor, Department of Computer Science
https://www.comp.hkbu.edu.hk/~qiangwang/
 

About

Dr. Wang received his B.Eng. degree in Computer Science and Technology from South China University of Technology, China, in 2014, and his PhD degree at the Department of Computer Science, Hong Kong Baptist University in 2020. He was an awardee of Hong Kong PhD Fellowship in 2015. His research interests include GPU Computing, Parallel Computing, Energy Efficiency of GPU, Deep Learning System and Stereo Matching.


Research Interests

  • GPU Computing
  • Parallel Computing
  • GPU Energy Efficiency
  • Deep Learning Systems
 

Selected Publications

  • Q. Wang and X.-W. Chu, “GPGPU Performance Estimation with Core and Memory Frequency Scaling,” IEEE Transactions on Parallel and Distributed Systems, Vol. 31, No. 12, pages 2865-2881, Dec 2020.
  • Q. Wang, S. Shi, S. Zheng, K. Zhao, and X.-W. Chu, “FADNet: A Fast and Accurate Network for Disparity Estimation,” IEEE ICRA 2020, Paris, France, May-June 2020.
  • Q. Wang, C. Liu, and X.-W. Chu, “GPGPU Performance Estimation for Frequency Scaling Using Cross-Benchmarking,” 13th Workshop on General Purpose Processing Using GPU (GPGPU 2020), co-located with ACM PPoPP 2020, San Diego, USA, Feb 2020.
  • C. Liu, Q. Wang, and X.-W Chu, “ESetStore: An Erasure-coded Storage System with Fast Data Recovery,” IEEE Transactions on Parallel and Distributed Systems, March 2020.
  • S. Shi, Q. Wang, K. Zhao, Z. Tang, Y. Wang, X. Huang, and X.-W. Chu, “A Distributed Synchronous SGD Algorithm with Global Top-k Sparsification for Low Bandwidth Networks,” The 39th IEEE International Conference on Distributed Computing Systems (ICDCS), Texas, USA, July 2019.
  • S. Shi, Q. Wang, and X.-W. Chu, “Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs,” The Fourth IEEE International Conference on Big Data Intelligence and Computing (DataCom) 2018, Athens, Greece, August 2018.
  • Q. Wang and X.-W. Chu, “GPGPU Power Estimation with Core and Memory Frequency Scaling,” [J] ACM Performance Evaluation Review, Vol. 45, No. 2, pages 73-78, October 2017.
  • Q. Wang, P. Xu, Y. Zhang, and X.-W Chu, “EPPMiner: An Extended Benchmark Suite for Energy, Power and Performance Characterization of Heterogeneous Architecture,” ACM e-Energy, 2017.