EDUCATION
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Ph.D. in Electrical Engineering, Stanford University, Stanford, California, 2003
M.S. in Electrical Engineering, Stanford University, Stanford, California, 1997
B.S. in Electrical Engineering and Computer Sciences, Highest Honors, University of California, Berkeley, California, 1995
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PROFESSIONAL EXPERIENCE
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Associate Professor, University of California, Davis, Electrical & Computer Engineering, 2008-
Assistant Professor, University of California, Davis, Electrical & Computer Engineering, 2003-2008
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AFFILIATION
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Institute of Data Analysis and Visualization
SciDAC Institute for Ultra-Scale Visualization
Visiting Scientist, Los Alamos National Laboratory
Graduate Groups in Electrical & Computer Engineering and Computer Science
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RESEARCH INTERESTS
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Parallel computing: fundamental algorithms, data structures, and programming models for data-parallel processors and particularly the graphics processor (GPUs); data-parallel and GPU computing; graphics hardware; general-purpose programmability of graphics hardware (GPGPU).
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RESEARCH ACTIVITIES
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My research group investigates parallel computing systems, recently concentrating on the use of the graphics processing unit (GPU) as a general-purpose processor. GPUs can potentially deliver substantially greater performance on a broad range of problems than their CPU counterparts, but effectively mapping problems to a parallel programming model with an immature programming environment is a significant and important research problem. As the computing industry moves to parallel hardware and software, the lessons learned from the GPU, the first commodity parallel processor, are even more important. The lessons learned from our field of "general-purpose computation on the GPU" (GPGPU) (also called "GPU computing") have had a substantial and growing impact on mainstream computing. We are primarily interested in the intersection between hardware and software: how to build software that best utilizes the hardware, how to build hardware that will be programmable and a good target for software, and how to characterize a programming model that connects the two.
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DISTINCTIONS
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Department of Energy Early Career Principal Investigator Award, 2004
NVIDIA Faculty Teaching Fellowship, 2006
Best Paper, Graphics Hardware 2007
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SELECTED PUBLICATIONS
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John D. Owens, Mike Houston, David Luebke, Simon Green, John E. Stone, and James C. Phillips. GPU Computing. Proceedings of the IEEE, 96(5):879--899, May 2008. Pub Link
Adam Moerschell and John D. Owens. Distributed Texture Memory in a Multi-GPU Environment. Computer Graphics Forum, 27(1):130--151, March 2008. Pub Link
Shubhabrata Sengupta, Mark Harris, Yao Zhang, and John D. Owens. Scan Primitives for GPU Computing. In Graphics Hardware 2007, pages 97--106, August 2007. Best Paper Award. Pub Link
John D. Owens, David Luebke, Naga Govindaraju, Mark Harris, Jens Krüger, Aaron E. Lefohn, and Tim Purcell. A Survey of General-Purpose Computation on Graphics Hardware. Computer Graphics Forum, 26(1):80--113, March 2007. Pub Link
Aaron E. Lefohn, Shubhabrata Sengupta, Joe Kniss, Robert Strzodka, and John D. Owens. Glift: Generic Data Structures for the GPU. In Proceedings of the 2006 Workshop on Edge Computing Using New Commodity Architectures, pages D-15--16, May 2006. Pub Link
All publications can be found at
http://www.ece.ucdavis.edu/~jowens/pubs.html.
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