STAR 2:
A Survey of General-Purpose Computation on Graphics Hardware
WEDNESDAY, AUGUST 31st, 2005. 14:30 - 16:00,
VENUE: Walton Theatre Beckett Rooms.
SESSION CHAIR: Mike Doggett, ATI.
| AUTHORS: | |
| John D. Owens | University of California at Davis |
| David Luebke | University of Virginia |
| Naga Govindaraju | Univerity of North Carolina, Chapel Hill |
| Mark Harris | NVIDIA |
| Jens Krüger | Technische Universität München |
| Aaron E. Lefohn | University of California at Davis |
| Tim Purcell | NVIDIA |
Outline
The rapid increases in the performance of graphics hardware, coupled with the recent improvements in their
programmability, have made graphics hardware a compelling platform for computationally demanding tasks in
a wide variety of application domains. In this report, we describe and analyze the latest research in mapping
general-purpose computation to graphics hardware.
We begin with the technical motivations that underlie general-purpose computation on graphics processors
(GPGPU) and describe the hardware and software developments that have led to the recent interest in this field.
We then aim the main body of this report at two separate audiences. First, we survey and categorize the latest
developments in general-purpose application development on graphics hardware. This survey should be of par-ticular
interest to those researchers who are interested in using the latest GPGPU applications in their systems of
interest. Second, we describe the techniques used in mapping general-purpose computation to graphics hardware.
We believe these techniques will be generally useful for researchers who plan to develop the next generation of
GPGPU algorithms and techniques.
Presenters
John Owens is an assistant professor of electrical and computer
engineering at the University of California at Davis. At Davis, he is
affiliated with the Institute of Data Analysis and Visualization and
leads research groups in projects in graphics hardware and sensor
networks. He graduated from Stanford University in 2003 with a Ph.D.
in electrical engineering and the University of California, Berkeley,
in 1995 with a B.S. in electrical engineering and computer sciences.
David Luebke is an Assistant Professor of Computer Science at the
University of Virginia. Besides GPGPU, his principal research
interests include real-time rendering techniques such as level of
detail and visibility calculation, 3D scanning and surface
reconstruction, and sampling and reconstruction strategies for
interactive ray tracing. He earned his Ph.D. in Computer Science at
the University of North Carolina after studying chemistry at the
Colorado College.
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