CUDA Spotlight: Compute The Cure

Compute The Cure

Find other great interviews in our CUDA Spotlights Archive.

This week's Spotlight is on Dr. Wu-chun Feng, an associate professor at Virginia Tech and director of the Synergy Lab.

Virginia Tech has been named the inaugural research partner for the NVIDIA Foundation's Compute the Cure initiative. The goal of Compute the Cure is to target ways GPU computing can help find a cure for cancer.

NVIDIA: Wu, congrats on the Compute the Cure award. What will you and your team focus on? 
Wu: We plan to use the philanthropic award from the NVIDIA Foundation to fundamentally change the way cancer biologists conduct their science. We will do this by delivering a framework and toolkit of personal desktop supercomputing solutions for the analysis of genomic changes in next-generation sequencing data, as a first step towards computing the cure for cancer.

NVIDIA: Tell us about the Synergy Lab at Virginia Tech. 
Wu: The Synergy Lab conducts basic and applied research to deliver computational models, algorithms and tools to domain scientists and engineers so they can concentrate on their science and engineering rather than oncomputer science and engineering.

To that end, I explore a breadth of activities that span multiple dimensions of high-performance computing -- from traditional supercomputers down to embedded systems along one dimension and from systems software up to tools and application software along another dimension -- in order to empower the cyber-scientists and cyber-engineers of tomorrow.

NVIDIA: How does GPU computing play a role in your work? 
Wu: I work at the intersection of systems software, middleware and application software and tools. The power of GPU computing is critical to my work and to the domain scientists and engineers with whom I collaborate.

NVIDIA: What are the advantages of CUDA? 
Wu: CUDA provides an easily accessible, general-purpose programming model for the GPU (i.e., it has a relatively short learning curve). With CUDA, more people can get up and running faster to solve problems that lend themselves to parallel programming.

NVIDIA: In addition to Compute the Cure, what else are you excited about? 
Wu: Overall, I can see more and more real-world applications on GPUs that range from traditional supercomputers for large-scale scientific computing down to embedded devices that support consumer electronics and defense applications.

NVIDIA: As computing becomes faster, what will we be able to do in the future that we are not able to do today? 
Wu: GPU computing and, more generally, heterogeneous computing (the synergistic combination of GPU- and CPU-based systems), are accelerating the way that we compute. Supercomputing is no longer just the domain of "big-iron" supercomputers but also of personal desktop or deskside supercomputers. The domain area that I can foresee really benefiting the most from heterogeneous computing is the area of personalized medicine, which tailors healthcare to individual patients. Advances in heterogeneous computing promise to spur accelerated discovery and innovation in this area.

Bio for Wu-Chun Feng 
Dr. Wu-chun Feng — or more simply, "Wu" — is an associate professor of computer science and electrical & computer engineering at Virginia Tech, where he directs the Systems, Networking, and Renaissance Grokking (Synergy) Laboratory. His research interests span many areas of high-performance networking and computing from hardware to applications software.

Dr. Feng received a B.S. in Electrical & Computer Engineering and Music (Honors) and an M.S. in Computer Engineering from Penn State University in 1988 and 1990, respectively. He earned a Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign in 1996.

His previous professional stints include IBM T.J. Watson Research Center, NASA Ames Research Center, Vosaic, University of Illinois at Urbana-Champaign, Purdue University, The Ohio State University, Orion Multisystems, and Los Alamos National Laboratory.

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