Publications
Complete List in Google Scholar
Machine Learning and Deep Learning
- Q. Wang†, S. Zheng†, Q. Yan†, F. Deng, K. Zhao, and X.-W. Chu, “IRS: A Large Naturalistic Indoor Robotics Stereo Dataset to Train Deep Models for Disparity and Surface Normal Estimation”, IEEE International Conference on Multimedia and Expo (ICME) 2021. (Oral:15%)
- S. Zhang, Z. Wang, Q. Wang, J. Zhang, G. Wei, and X.-W Chu, “EDNet: Efficient Disparity Estimation with Cost Volume Combination and Attention-based Spatial Residual “, Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2021.
- Y. Wang, Q. Wang, and X.-W. Chu, “Energy-efficient Inference Service of Transformer-based Deep Learning Models on GPUs,” IEEE GreenCom 2020, Greece, 2020. (Best Paper Award)
- Q. Wang†, S. Shi†, S. Zheng, K. Zhao, and X.-W Chu, “FADNet: A Fast and Accurate Network for Disparity Estimation”. 2020 International Conference on Robotics and Automation (ICRA). IEEE, 2020.
- S. Shi, Z. Tang, Q. Wang, K. Zhao, and X.-W. Chu, “Layer-wise Adaptive Gradient Sparsification for Distributed Deep Learning with Convergence Guarantees,” The 24th European Conference on Artificial Intelligence (ECAI), Santiago de Compostela, Spain, June 2020.
- S. Shi, Q. Wang, X-W. Chu, B. Li, Y. Qin, R. Liu, and X. Zhao, “Communication-Efficient Distributed Deep Learning with Merged Gradient Sparsification on GPUs,” IEEE INFOCOM 2020, Beijing, China, May 2020.
- S. Shi, K. Zhao, Q. Wang, Z. Tang, and X.-W. Chu, “A Convergence Analysis of Distributed SGD with Communication-Efficient Gradient Sparsification,” IJCAI 2019, Macau, P.R.C., August 2019.
- 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,” IEEE ICDCS 2019, Dallas, Texas, USA, July 2019.
- S. Shi, Q. Wang, X.-W. Chu, and B. Li, “A DAG Model of Synchronous Stochastic Gradient Descent in Distributed Deep Learning,” IEEE International Conference on Parallel and Distributed Systems (ICPADS) 2018, Singapore, Dec 2018.
- S. Shi, Q. Wang, and X.-W. Chu, “Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs,” IEEE DataCom 2018, Athens, Greece, August 2018. (Best Paper Award)
- S. Shi, Q. Wang, P. Xu, and X.-W. Chu, “Benchmarking State-of-the-Art Deep Learning Software Tools,” the 7th International Conference on Cloud Computing and Big Data (CCBD 2016), Macau, China, Nov 2016.
GPU Computing/Distributed Systems
- S. Shi, Q. Wang, and X.-W. Chu, “Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format,” IEEE ICPADS 2020, Hong Kong, China, Dec 2020.
- 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.
- Y. Wang, Q. Wang, S. Shi, X. He, Z. Tang, K. Zhao, and X.-W Chu. “Benchmarking the Performance and Power of AI Accelerators for AI Training.”, 3rd High Performance Machine Learning Workshop (HPML 2020), co-located with IEEE CCGrid 2020, Melbourne, Australia, 2020.
- Q. Wang, C. Liu, and X.-W Chu, “GPGPU Performance Estimation for Frequency Scaling Using Cross-Benchmarking” Proceedings of the 13th Workshop on General Purpose Processing Using GPUs. 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. 2020.
- Z. Tang, Y. Wang, Q. Wang, and X.-W. Chu, “The Impact of GPU DVFS on the Energy and Performance of Deep Learning: an Empirical Study,” ACM e-Energy 2019, Phoenix, AZ, USA, June 2019. (notes paper)
- Q. Wang and X.-W. Chu, “GPGPU Performance Estimation with Core and Memory Frequency Scaling,” IEEE International Conference on Parallel and Distributed Systems (ICPADS) 2018, Singapore, Dec 2018. [A poster of this work has been presented at The International Conference for High Performance Computing, Networking, Storage, and Analysis (SC18), Dallas, USA, Nov 2018.]
- C. Liu, Q. Wang, X.-W. Chu, and Y.-W. Leung, “G-CRS: GPU Accelerated Cauchy Reed-Solomon Coding,” IEEE Transactions on Parallel and Distributed Systems, Vol. 29, No. 7, pages 1482-1498, July 2018.
- Q. Wang and X.-W. Chu, “GPGPU Power Estimation with Core and Memory Frequency Scaling,” ACM SIGMETRICS Performance Evaluation Review, 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, Hong Kong, May 2017. (Best Paper Finalist)
- X. Mei, Q. Wang, and X.-W. Chu, “A Survey and Measurement Study of GPU DVFS on Energy Conservation,” Digital Communications and Networks, 2017.
Preprint
- Q. Wang, S. Shi, C. Wang, X-W. Chu, “Communication Contention Aware Scheduling of Multiple Deep Learning Training Jobs”, arXiv preprint arXiv:2002.10105.