Publications

Complete List in Google Scholar

^† means equal contribution. * means corresponding authors.

Conference

• Peijie Dong, Lujun Li, Zhenheng Tang, Xiang Liu, Zimian Wei, Qiang Wang, Xiaowen Chu, “ParZC: Parametric Zero-Cost Proxies for Efficient NAS”, AAAI Conference on Artificial Intelligence (AAAI), 2025.
• Qingsong Yan, Qiang Wang, Kaiyong Zhao, Jie Chen, Bo Li, Xiaowen Chu, Fei Deng, “SphereFusion: Efficient Panorama Depth Estimation via Gated Fusion”, International Conference on 3D Vision (3DV), 2025.
• Lujun Li, Peijie Dong, Zhenheng Tang, Xiang Liu, Qiang Wang, Wenhan Luo, Wei Xue, Qifeng Liu, Xiaowen Chu, Yike Guo, “Discovering Sparsity Allocation for Layer-wise Pruning of Large Language Models “, Annual Conference on Neural Information Processing Systems (NeurIPS), 2024.
• Peijie Dong, Lujun Li, Xiang Liu, Zhenheng Tang, Xuebo Liu, Qiang Wang, Xiaowen Chu, “LPZero: Language Model Zero-cost Proxy Search from Zero”, Empirical Methods in Natural Language Processing (EMNLP) Findings, 2024.
• Guoqing Zhu, Honghu Pan, Qiang Wang*, Chao Tian, Chao Yang, Zhenyu He, “Data Generation Scheme for Thermal Modality with Edge-Guided Adversarial Conditional Diffusion Model”, ACM Multimedia (ACMMM), 2024.
• Penglei Sun, Yaoxian Song, Xiang Liu, Xiaofei Yang, Qiang Wang*, Tiefeng Li, Yang Yang, Xiaowen Chu, “3D Question Answering for City Scene Understanding”, ACM Multimedia (ACMMM), 2024.
• Penglei Sun, Yaoxian Song, Xinglin Pan, Peijie Dong, Xiaofei Yang, Qiang Wang*, Zhixu Li, Tiefeng Li, Xiaowen Chu, “Multi-Task Domain Adaptation for Language Grounding with 3D Objects”, European Conference on Computer Vision (ECCV), 2024.
• Peijie Dong, Lujun Li, Zhenheng Tang, Xiang Liu, Xinglin Pan, Qiang Wang*, Xiaowen Chu, “Pruner-Zero: Evolving Symbolic Pruning Metric From Scratch for Large Language Models,” International Conference on Machine Learning (ICML), 2024.
• Qiang Wang, Laiyi Li, Weile Luo, Yijia Zhang, Bingqiang Wang, “DSO: A GPU Energy Efficiency Optimizer by Fusing Dynamic and Static Information,” IEEE/ACM 30th International Symposium on Quality of Service (IWQoS), 2024.
• Yizhou Luo, Qiang Wang*, Shaohuai Shi, Jiaxin Lai, Shuhan Qi, Jiajia Zhang, and Xuan Wang, “Scheduling Deep Learning Jobs in Multi-Tenant GPU Clusters via Wise Resource Sharing,” IEEE/ACM 30th International Symposium on Quality of Service (IWQoS), 2024.
• Shaohuai Shi, Xinglin Pan, Qiang Wang*, Chengjian Liu, Xiaozhe Ren, Zhongzhe Hu, Yu Yang, Bo Li, and Xiaowen Chu, “ScheMoE: An Extensible Mixture-of-Experts Distributed Training System with Tasks Scheduling”, EuroSys 2024, Athens, Greece, April 22-25, 2024.
• Weile Luo, Ruibo Fan, Zongpeng Li, Dayou Du, Qiang Wang*, Xiaowen Chu, “Benchmarking and Dissecting the Nvidia Hopper GPU Architecture”, IEEE International Parallel and Distributed Processing Symposium (IPDPS), 2024.
• Qingsong Yan, Qiang Wang*, Kaiyong Zhao, Jie Chen, Bo Li, Xiaowen Chu, Fei Deng, “CF-NeRF: Camera Parameter Free Neural Radiance Fields with Incremental Learning”, AAAI Conference on Artificial Intelligence (AAAI), 2024.
• Yijia Zhang, Qiang Wang*, Zhe Lin, Pengxiang Xu, Bingqiang Wang, “Improving GPU Energy Efficiency through an Application-transparent Frequency Scaling Policy with Performance Assurance”, The European Conference on Computer Systems (EuroSys), 2024.
• Zhenheng Tang, Yuxin Wang, Xin He, Longteng Zhang, Xinglin Pan, Qiang Wang, Rongfei Zeng, Shaohuai Shi, Bingsheng He, and Xiaowen Chu, “FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs,” Symposium on Large Language Models (LLM 2023) with IJCAI 2023, Macao, China, August 21, 2023.
• Ruiqi Zhang, Jie Chen, Qiang Wang, “Explicify Neural Implicit Fields for Efficient Dynamic Human Avatar Modeling via a Neural Explicit Surface”, ACM International Conference on Multimedia (ACMMM), 2023.
• Qingsong Yan^†, Qiang Wang^†, Kaiyong Zhao, Bo Li, Xiaowen Chu, Fei Deng, “Rethinking Disparity: A depth range free Multi-View Stereo based on Disparity”, AAAI Conference on Artificial Intelligence (AAAI), 2023.
• Qingsong Yan, Qiang Wang, Kaiyong Zhao, Bo Li, Xiaowen Chu, Fei Deng, “SphereDepth: Panorama Depth Estimation from Spherical Domain”, International Conference on 3D Vision (3DV), 2022. (CCF C)
• Qiang Wang, Shaohuai Shi, Kaiyong Zhao and Xiaowen Chu, “EASNet:Searching Elastic and Accurate Network Architecture for Stereo Matching,” European Conference on Computer Vision (ECCV), 2022.
• Qiang Wang, Shizhen Zheng, Qingsong Yan, Fei Deng, Kaiyong Zhao, and Xiaowen 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%)
• Songyan Zhang, Zhicheng Wang, Qiang Wang, Jinshuo Zhang, Gang Wei, and Xiaowen 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.
• Yuxin Wang, Qiang Wang, and Xiaowen Chu, “Energy-efficient Inference Service of Transformer-based Deep Learning Models on GPUs,” IEEE GreenCom, Greece, 2020. (Best Paper Award)
• Qiang Wang, Shaohuai Shi, Shizhen Zheng, Kaiyong Zhao, and Xiaowen Chu, “FADNet: A Fast and Accurate Network for Disparity Estimation”. International Conference on Robotics and Automation (ICRA), 2020.
• Shaohuai Shi, Zhenheng Tang, Qiang Wang, Kaiyong Zhao, and Xiaowen 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.
• Shaohuai Shi, Qiang Wang, Xiaowen Chu, Bo Li, Yang Qin, Ruihao Liu, and Xinxiao Zhao, “Communication-Efficient Distributed Deep Learning with Merged Gradient Sparsification on GPUs,” IEEE INFOCOM 2020, Beijing, China, May 2020.
• Shaohuai Shi, Qiang Wang, and Xiaowen Chu, “Efficient Sparse-Dense Matrix-Matrix Multiplication on GPUs Using the Customized Sparse Storage Format,” IEEE ICPADS 2020, Hong Kong, China, Dec 2020.
• Yuxin Wang, Qiang Wang, Shaohuai Shi, Xin He, Zhenheng Tang, Kaiyong Zhao, and Xiaowen 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.
• Qiang Wang, Chengjian Liu, and Xiaowen Chu, “GPGPU Performance Estimation for Frequency Scaling Using Cross-Benchmarking” Proceedings of the 13th Workshop on General Purpose Processing Using GPUs (GPGPU), 2020.
• Zhenheng Tang, Yuxin Wang, Qiang Wang, and Xiaowen 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)
• Shaohuai Shi, Kaiyong Zhao, Qiang Wang, Zhenheng Tang, and Xiaowen Chu, “A Convergence Analysis of Distributed SGD with Communication-Efficient Gradient Sparsification,” IJCAI 2019, Macau, P.R.C., August 2019.
• Shaohuai Shi, Qiang Wang, Kaiyong Zhao, Zhenheng Tang, Yuxin Wang, Xiang Huang, and Xiaowen Chu, “A Distributed Synchronous SGD Algorithm with Global Top-k Sparsification for Low Bandwidth Networks,” IEEE ICDCS 2019, Dallas, Texas, USA, July 2019.
• Shaohuai Shi, Qiang Wang, Xiaowen Chu, and Bo 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.
• Shaohuai Shi, Qiang Wang, and Xiaowen Chu, “Performance Modeling and Evaluation of Distributed Deep Learning Frameworks on GPUs,” IEEE DataCom 2018, Athens, Greece, August 2018. (Best Paper Award)
• Qiang Wang and Xiaowen 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.]
• Qiang Wang, Pengfei Xu, Yatao Zhang, and Xiaowen 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)
• Shaohuai Shi, Qiang Wang, Pengfei Xu, and Xiaowen 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.

Journal

• Qiang Wang, Xinxin Mei, Xiaowen Chu, Hai Liu, Yiu Wing Leung, and Zongpeng Li, “Energy-aware Non-preemptive Task Scheduling with Deadline Constraint in DVFS-enabled Heterogeneous Clusters,” IEEE Transactions on Parallel and Distributed Systems (TPDS), 2022.
• Wenpeng Xing, Jie Chen, Zaifeng Yang, Qiang Wang, Yike Guo, “Scale-Consistent Fusion: From Heterogeneous Local Sampling to Global Immersive Rendering”, IEEE Transactions on Image Processing (TIP), 2022.
• Yuxin Wang, Qiang Wang and Xiaowen Chu, “Energy-efficient Online Scheduling of Transformer Inference Services on GPU Servers,” IEEE Transactions on Green Communications and Networking (TGCN), 2022.
• Qiang Wang and Xiaowen 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.
• Chengjian Liu^†, Qiang Wang^† and Xiaowen Chu, “ESetStore: an Erasure-coded Storage System with Fast Data Recovery,” IEEE Transactions on Parallel and Distributed Systems (TPDS). 2020.
• Chengjian Liu, Qiang Wang, Xiaowen Chu, and Yiu Wing Leung, “G-CRS: GPU Accelerated Cauchy Reed-Solomon Coding,” IEEE Transactions on Parallel and Distributed Systems (TPDS), Vol. 29, No. 7, pages 1482-1498, July 2018.
• Qiang Wang and Xiaowen Chu, “GPGPU Power Estimation with Core and Memory Frequency Scaling,” ACM SIGMETRICS Performance Evaluation Review, October 2017.
• Xinxin Mei, Qiang Wang, and Xiaowen Chu, “A Survey and Measurement Study of GPU DVFS on Energy Conservation,” Digital Communications and Networks, 2017.

Preprint

• Qiang Wang, Shaohuai Shi, Shizhen Zheng, Kaiyong Zhao, Xiaowen Chu, “FADNet++: Real-Time and Accurate Disparity Estimation with Configurable Networks”, arXiv preprint arXiv:2110.02582.
• Qiang Wang, Shaohuai Shi, Canhui Wang, Xiaowen Chu, “Communication Contention Aware Scheduling of Multiple Deep Learning Training Jobs”, arXiv preprint arXiv:2002.10105.