Wu-chun Feng

Wu-chun Feng (a.k.a. "Wu")

Director
SyNeRGy Laboratory

Professor
Dept. of CS
Dept. of ECE
Health Sciences

Other Affiliations
CHREC | VBI |
Wireless @ VT

Profiles
Google Scholar

Contact

Office:
Torgersen Hall 2050
620 Drillfield Drive (Alumni Mall)
Blacksburg, VA.
24061. [map]
Phone:
(540) 231-1192
Fax:
(540) 231-9218
Email:
feng [at] cs.vt.edu

Virginia Tech

Publications

For electronic version of publications please visit the Synergy Lab. web site.

  • Making a Case for Green High-Performance Visualization via Embedded Graphics Processors.
    Vignesh Adhinarayanan, Bishwajit Dutta, Wu-chun Feng.
    In Proceedings of the 14th Workshop on High-Performance, Power-Aware Computing (HPPAC) held in conjunction with the 32nd International Parallel and Distributed Processing Symposium (IPDPS), Vancouver, British Columbia, Canada, May 2018.
       
  • GPU Power Prediction via Ensemble Machine Learning for DVFS Space Exploration.
    Bishwajit Dutta, Vignesh Adhinarayanan, Wu-chun Feng.
    In Proceedings of the 2018 ACM International Conference on Computing Frontiers (CF), Ischia, Italy, May 2018.
       
  • Taming Irregular Applications via Advanced Dynamic Parallelism on GPUs.
    Jing Zhang, Ashwin M. Aji, Michael L. Chu, Hao Wang, Wu-chun Feng.
    In Proceedings of the 2018 ACM International Conference on Computing Frontiers (CF), Ischia, Italy, May 2018.
       
  • A Framework for the Automatic Vectorization of Parallel Sort on x86-based Processors.
    Kaixi Hou, Hao Wang, Wu-chun Feng.
    In IEEE Transactions on Parallel and Distributed Systems, 29 (5): 958-972, May 2018.
       
  • Parallel I/O Optimizations for Scalable Deep Learning.
    Sarunya Pumma, Min Si, Wu-chun Feng, Pavan Balaji.
    In Proceedings of the IEEE International Conference on Parallel and Distributed Systems (ICPADS), Shenzhen, China, December 2017.
       
  • Portable Parallel Design of Weighted Multi-Dimensional Scaling for Real-Time Data Analysis.
    Sajal Dash, Anshuman Verma, Chris North, Wu-chun Feng.
    In Proceedings of the IEEE International Conference on High Performance Computing and Communications (HPCC), Bangkok, Thailand, December 2017.
    Best Paper Finalist
       
  • Robotomata: A Framework for Approximate Pattern Matching of Big Data on an Automata Processor.
    Xiaodong Yu, Kaixi Hou, Hao Wang, Wu-chun Feng.
    In Proceedings of the IEEE International Conference on Big Data, Boston, MA, December 2017.
       

More ...