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

News

  • ( 12/28/17 )
    Paper Accepted to IEEE TPDS
    Hou, Wang, and Feng, have their paper — A Framework for the Automatic Vectorization of Parallel Sort on x86-based Processors — accepted for publication in a future issue of IEEE Transactions on Parallel and Distributed Systems (TPDS).

  • ( 12/14/17 )
    Paper Accepted to IEEE IPDPS 2018
    Hou, Wang, and Feng, in collaboration with Vetter and Lee of Oak Ridge National Laboratory, have their paper — Highly Efficient Compensation-based Parallelism for Wavefront Loops on GPUs — accepted for publication in the IEEE International Parallel and Distributed Processing Symposium (IPDPS) to be held in Vancouver, British Columbia, Canada in May 2018.

  • ( 10/12/17 )
    Paper Accepted to IEEE BigData 2017
    Yu, Hou, Wang, and Feng have their paper — Robotomata: A Framework for Approximate Pattern Matching of Big Data on an Automata Processor — accepted as a regular paper at the IEEE Big Data to be held in Boston, MA, USA on December 11-14, 2017.

  • ( 09/04/17 )
    Poster Accepted to ACM/IEEE SC | 17
    Cui, and Feng, in collaboration with Scogland, de Supinski, at Lawrence Livermore National Laboratory, have their poster — Performance Evaluation of the NVIDIA Tesla P100: Our Directive-Based Partitioning and Pipelining vs. NVIDIA’s Unified Memory — accepted for publication at SC|17: The International Conference for High Performance Computing, Networking, Storage and Analysis to be held in Denver, Colorado, USA in November 2017.

  • ( 09/01/17 )
    Paper Accepted to IEEE ICPADS 2017
    Pumma, and Feng, in collaboration with Si and Balaji at Argonne National Laboratory have their paper — Parallel I/O Optimizations for Scalable Deep Learning — accepted for publication at the IEEE International Conference on Parallel and Distributed Systems (ICPADS) to be held in Shenzhen, China in December 2017.

  • ( 08/17/17 )
    Papers Accepted to IEEE HPCC 2017
    Pumma, and Feng, in collaboration with Si and Balaji at Argonne National Laboratory have their paper — Towards Scalable Deep Learning via I/O Analysis and Optimization — accepted for publication in the IEEE International Conference on High Performance Computing and Communications (HPCC) to be held in Bangkok, Thailand in December 2017. Likewise, Dash, Verma, and Feng, in collaboration with Prof. Chris North of CS@VT, for getting their paper — Portable Parallel Design of Weighted Multi-Dimensional Scaling for Real-Time Data Analysis — accepted for publication in the IEEE International Conference on High Performance Computing and Communications (HPCC) to be held in Bangkok, Thailand in December 2017

  • ( 08/17/17 )
    Best Paper Finalist at IEEE HPCC 2017
    Dash, Verma, and Feng, in collaboration with Prof. Chris North of CS@VT, have their paper — Portable Parallel Design of Weighted Multi-Dimensional Scaling for Real-Time Data Analysis — selected as one of five Best Paper Finalists at the IEEE International Conference on High Performance Computing and Communications (HPCC) to be held in Bangkok, Thailand in December 2017.

More ...