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Biographical Sketch
David Camp is a computer systems engineer at Lawrence Berkeley National Laboratory, where he is a member of the Scientific Visualization group. Camp has two years experience at LBNL and sixteen years experience as a software developer in the commercial software industry. Camp's research interests include computer graphics algorithms, visualization and analytics software architecture, distributed visualization algorithms, hybrid-parallelism designs, parallel algorithm techniques, and high performance computing. The goal of his work is to increasing scientific productivity and understanding through increase parallel algorithm performance and data analysis using graphic techniques. Camp received a BS in Computer Science from the University of California, Davis in 1994, a MS in Computer Science from the University of California, Davis in 2000 and a PhD in Computer Science from the University of California, Davis in 2012 under Dr. Kenneth I. Joy and Hank Childs.
Research Highlights
Journal Articles
David Camp, Christoph Garth, Hank Childs, Dave Pugmire, Kenneth I. Joy, "Streamline Integration Using MPI-Hybrid Parallelism on a Large Multicore Architecture", IEEE Transactions on Visualization and Computer Graphics, November 2011, 17:1702-1713, LBNL 4563E, doi: http://doi.ieeecomputersociety.org/10.1109/TVCG.2010.259
- Download File: LBNL-4563E.pdf (pdf: 3.5 MB)
Conference Papers
David Camp, Hari Krishnan, David Pugmire, Christoph Garth, Ian Johnson, E. Wes Bethel, Kenneth I. Joy, and Hank Childs., "GPU Acceleration of Particle Advection Workloads in a Parallel, Distributed Memory Setting", Proceedings of Eurographics Symposium on Parallel Graphics and Visualization (EGPGV), May 5, 2013,
David Camp, Hank Childs, Christoph Garth, David Pugmire, Kenneth I. Joy, "Parallel Stream Surface Computation for Large Data Sets", Proceedings of IEEE Symposium on Large Data Analysis and Visualization (LDAV), October 2012, 39--47, LBNL 5776E,
David Camp, Hank Childs, Amit Chourasia, Christoph Garth, Kenneth Joy, "Evaluating the Benefits of An Extended Memory Hierarchy for Parallel Streamline Algorithms", Proceedings IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV) 2011, Providence, RI, USA, IEEE Press, October 2011, 57-64, LBNL 5503E,
- Download File: LBNL-camp.pdf (pdf: 1.1 MB)
