Wu-chun Feng

About

Wu-chun Feng is a professor in the Department of Computer Science at Virginia Tech. His research interests include high-performance computing and computational science, data analytics systems, computational biology and bioinformatics, and machine learning. He holds a Ph.D. in computer science from the University of Illinois at Urbana-Champaign, obtained in 1996, and has earned a master's degree in computer engineering and a bachelor's degree in electrical and computer engineering along with a B.A. in music from Penn State University. His professional location is Torgersen Hall, RM 2050, at Virginia Tech, and he is involved with multiple research institutes and centers. His contact information includes an email address (feng@cs.vt.edu) and phone number (540-231-1192).

Research topics

• Computer Science
• Artificial Intelligence
• Parallel computing
• Machine Learning
• Programming language
• Embedded system
• Theoretical computer science
• Computer architecture
• Distributed computing
• Operating system

Selected publications

• Identifying multi-hit carcinogenic gene combinations: Scaling up a weighted set cover algorithm using compressed binary matrix representation on a GPU

  Scientific Reports · 2020 · 18 citations

  Despite decades of research, effective treatments for most cancers remain elusive. One reason is that different instances of cancer result from different combinations of multiple genetic mutations (hits). Therefore, treatments that may be effective in some cases are not effective in others. We previously developed an algorithm for identifying combinations of carcinogenic genes with mutations (multi-hit combinations), which could suggest a likely cause for individual instances of cancer. Most can…

  DOI

• Empirical Memory-Access Cost Models in Multicore NUMA Architectures

  OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · 2024-02-07 · 16 citations

  paratextOpen accessSenior author

  Data location is of prime importance when scheduling tasks in a non-uniform memory access (NUMA) architecture. The characteristics of the NUMA architecture must be understood so tasks can be scheduled onto processors that are close to the task's data. However, in modern NUMA architectures, such as AMD Magny-Cours and Intel Nehalem, there may be a relatively large number of memory controllers with sockets that are connected in a non-intuitive manner, leading to performance degradation due to unin…

  PublisherOA PDF

• Edge-Connected Jaccard Similarity for Graph Link Prediction on FPGA

  2022 · 11 citations

  Senior authorCorresponding

  Graph analysis is a critical task in many fields, such as social networking, epidemiology, bioinformatics, and fraud de-tection. In particular, understanding and inferring relationships between graph elements lies at the core of many graph-based workloads. Real-world graph workloads and their associated data structures create irregular computational patterns that compli-cate the realization of high-performance kernels. Given these complications, there does not exist a de facto “best” architectur…

  DOI

• Alleviating Load Imbalance in Data Processing for Large-Scale Deep Learning

  2020 · 8 citations

  Senior authorCorresponding

  Scalable deep learning remains an onerous challenge, as it is constrained by many factors, including those related to load imbalance. For many deep-learning software systems, multiple data-processing components-including neural network training, graph scheduling, input pipeline, and gradient synchronization-execute simultaneously and asynchronously. Such execution can cause the various data-processing components to contend with one another for the hardware resources, leading to severe load imbal…

  DOI

• SamBaS: Sampling-Based Stochastic Block Partitioning

  IEEE Transactions on Network Science and Engineering · 2024-01-25 · 4 citations

  articleOpen accessSenior author

  Community detection is a well-studied problem with applications in domains ranging from networking to bioinformatics. Due to the rapid growth in the volume of real-world data, there is growing interest in accelerating contemporary community detection algorithms. However, the more accurate and statistically robust methods tend to be hard to parallelize. One such method is stochastic block partitioning (SBP) – a community detection algorithm that works well on graphs with complex and heterogeneous…

  PublisherOA PDFDOI

Recent grants

• EAGER: Collaborative Research: Democratizing the Teaching of Parallel Computing Concepts

  NSF · $260k · 2013–2016

• XPS: SDA: Collaborative Research: A Scalable and Distributed System Framework for Compute-Intensive and Data-Parallel Applications

  NSF · $375k · 2013–2017

• Phase-I IUCRC Virginia Tech: Center for Space, High-performance, and Resilient Computing (SHREC)

  NSF · $2.2M · 2018–2025

Frequent coauthors

• Ümit V. Çatalyürek

  44 shared

• David A. Bader

  44 shared

• Quincey Koziol

  Lawrence Berkeley National Laboratory

  44 shared

• Bora Uçar

  44 shared

• Yale N. Patt

  The University of Texas at Austin

  44 shared

• Federico Silla

  Universitat Politècnica de València

  44 shared

• Thomas M. Stricker

  44 shared

• Heshan Lin

  Institute of Oceanography

  42 shared

Education

• Ph.D., Computer Science

  University of Illinois at Urbana-Champaign

  1996

• M.S., Computer Engineering

  The Pennsylvania State University

  1990

• B.S., Computer Engineering

  The Pennsylvania State University

  1988

Awards & honors

• Elizabeth and James E. Turner Jr. '56 Faculty Fellow

Similar researchers at Virginia Tech

• Wenjing Louh-77
• Doug Bowmanh-56
• Siwei Caoh-43