Professor C.L. Wang

BS Nat. Taiwan; MS, PhD S. Calif
BEng(CE) Programme Coordinator; Professor

Tel: (+852) 2857 8458
Fax: (+852) 2559 8447

Professor Cho-Li Wang received his B.S. degree in Computer Science and Information Engineering from National Taiwan University in 1985. He obtained his M.S. and Ph.D. degrees in Computer Engineering from University of Southern California in 1990 and 1995 respectively. He is currently a professor at the Department of Computer Science. Professor Wang's research interests include parallel architecture (multicore, GPU, AI chips), software systems for Cloud Computing and large-scale Distributed Deep Learning system. Professor Wang has published more than 150 papers in various peer reviewed journals and conference proceedings. He is/was on the editorial boards of  several international journals , including IEEE Transactions on Computers (TC), Multiagent and Grid Systems (MGS), Journal of Information Science and Engineering (JISE), International Journal of Pervasive Computing and Communications (JPCC), ICST Transactions on Scalable Information Systems (SIS). He was the program chair for Cluster'03, CCGrid'09, InfoScale'09, and ICPADS'09, ISPA'11, FCST'11, FutureTech'12, and Cluster2012; and the General Chair for IPDPS2012. He has also served as program committee members for numerous international conferences , including IPDPS, CCGrid, Cloud, CloudCom, Grid, HiPC, ICPP, and ICPADS. Professor Wang is the primary investigator of China 863 project " Hong Kong University Grid Point '' (2006-2011). The HKU Grid point  offers parallel computing services for the  China National Grid (CNGrid) and is used as a testbed for Cloud-related systems development. He has been invited to give  keynote and plenary talk s related to Distributed JVM design and  Cloud Computing at various international conferences.

Research Interests

Operating Systems, Virtual Machines and Cloud Computing, System Software on Manycore, GPU and AI chips, Large-scale Distributed Deep/Machine Learning Systems.

Selected Publications

  • Hao Wu, Weizhi Liu and Cho-Li Wang, A Performance Model for Fine-grained Concurrent Kernel Execution of GPGPU Computing, to appear in ACM Transactions on Architecture and Code Optimization (TACO).

  • Huanxin Lin, Cho-Li Wang. On-GPU Thread-Data Remapping for Nested Branch Divergence, to appear in Journal of Parallel and Distributed Computing.

  • Huanxin Lin, Cho-Li Wang, Efficient Low-Latency Packet Processing Using On-GPU Thread-Data Remapping, Journal of Parallel and Distributed Computing (JPDC), Volume 133, November 2019, Pages 51-62.

  • Xin Yao, Xueyu  Wu, Cho-Li Wang, "FluentPS: A Parameter Server Design with Low-frequency Synchronization for Distributed Deep Learning",  2019 IEEE International Conference on Cluster Computing (Cluster 2019), Sept 23-26. 2019, Albuquerque, NM, USA.  

  • Xin Yao, Mingzhe Zhang, Cho-Li Wang, EC-Shuffle: Dynamic Erasure Coding Optimization for Efficient and Reliable Shuffle in Spark, The 19th Annual IEEE/ACM International Symposium in Cluster, Cloud, and Grid Computing (CCGrid 2019), Larnaca, Cyprus, May 14-17, 2019.

  • Huanxin Lin, Cho-Li Wang, Hongyuan Liu, “On-GPU Thread-Data Remapping for Branch Divergence Reduction,” ACM Transactions on Architecture and Code Optimization (TACO), Vol. 15, No. 3, Oct. 2018.

  • Mingzhe Zhang, King Tin Lam, Xin Yao, Cho-Li Wang, SIMPO: A Scalable In-Memory Persistent Object Framework Using NVRAM for Reliable Big Data Computing, ACM Transactions on Architecture and Code Optimization (TACO), Volume 15 Issue 1, April 2018.

  • Hongyuan Liu, King Tin Lam, Huanxin Lin, Cho-Li Wang, Junchao Ma, Lightweight Dependency Checking for Parallelizing Loops with Non-Deterministic Dependency on GPU, The 22nd IEEE International Conference on Parallel and Distributed Systems (ICPADS 2016), Dec. 13-16, 2016, Wuhan, China. [Best Paper Awards]

  • Zhiquan Lai, King Tin Lam, Cho-Li Wang, and Jinshu Su, PoweRock: Power Modelling and Flexible Dynamic Power Management for Many-core Architectures, IEEE Systems Journal, Issue: 99, pp. 1-13, 20 January 2016.

  • Sheng Di, Cho-Li Wang, Franck Cappello, Adaptive Algorithm for Minimizing Cloud Task Length with Prediction Errors, , IEEE Transactions on Cloud Computing, Vol.2, No.2, pp 194 - 207, April-June 2014

  • S. Di and C.L. Wang, Dynamic Optimization of Multi-Attribute Resource Allocation in Self-Organizing Clouds, IEEE Transactions on Parallel and Distributed Systems (TPDS), 14 May 2012
  • S. Di and C.L. Wang, Decentralized Proactive Resource Allocation for Maximizing Throughput of P2P Grid, Journal of Parallel and Distributed Computing (JPDC), Vol. 72, No. 2, February 2012, pp. 308–321

Recent Research Grants

  • Co-PI: Hong Kong RGC Research Impact Fund (RIF) project entitled “Edge Learning: the Enabling Technology for Distributed Big Data Analytics in Cloud-Edge Environment” (Ref: R5060-19), led by Prof. Jiannong Cao from PolyU.
  • Co-PI: CRF Equipment Fund 2019/20, “X-GPU: An Extreme GPU Cluster for Interdisciplinary Research on Molecular Dynamics Simulations and Genomics Studies”, led by Dr. Xuhui Huang from HKUST.
  • Co-PI: Hong Kong RGC Collaborative Research Fund (2018/2019) entitled ``Big Data Analytics on Complex Systems: Methodologies and Applications', led by Prof. Guo Song from PolyU. 
  • RGC's General Research Fund (2016-2019): Big-Little Heterogeneous Computing with Polymorphic GPU Kernels
  • RGC's General Research Fund (2015-2018): Software Architecture for Fault-Tolerant Multicore Computing with Hybridized Non-Volatile Memories
  • Huawei research grant (2015-2017): Big Data Acceleration on GPU-based Heterogeneous Architecture
  • RGC's General Research Fund (2012-2015): Scalable Cloud-on-Chip Runtime Support with Software Coherence for Future 1000-Core Tiled Architectures.
  • Huawei research grant (2012-2013): A New Multikernel OS for High Throughput Computing on Manycore Systems
  • RGC's General Research Fund (2011-2013): Transparent Runtime and Memory Coherence Support for GPU Based Heterogeneous Many-Core Architecture.
  • China 863 Project (2006-2010): 香港大学网格自适应服务技术研究 (CNGrid HKU Grid Point).