Dr. Philip J. Rhodes
Associate Professor and Undergraduate Coordinator
213 Weir Hall
Dr. Philip J. Rhodes joined the faculty in 2004 after receiving his Ph.D. from the University of New Hampshire. He is chief architect and implementer of the Granite system, a library that provides efficient and convenient access to spatial datasets such as those produced by two and three dimensional simulations. At the graduate level, Rhodes teaches scientific data representation and analysis, scientific visualization, computer graphics, and cloud and parallel computing.
Dr. Rhodes’s research focuses on the unique problems presented by spatial data and computation. Grid and Cloud computing have been a subject of intense interest in recent years, and his recent research addresses the notoriously difficult problem of providing efficient access to spatial datasets stored in far away locations. He has developed visualization methods that avoid disk and network latency costs, vastly improving performance and the size of feasible visualizations.
Other work performs automatic partitioning of spatial datasets among nodes in a cluster, using advance knowledge of the computation access pattern to optimize performance. Recent work also includes investigation of efficient spatial data access methods for GPU computing environments.
- “Decentralized Storage for Scientific Data”, by Shirish Patel and Philip J. Rhodes, 6th Annual IEEE International Workshop on Big Spatial Data in Proc. IEEE Big Data 2021, 10 pages, 2021 (available here)
- “Delaunay Triangulation of Large-scale Datasets using Two-level Parallelism”, by Cuong M. Nguyen and Philip J. Rhodes, Parallel Computing, vol. 98, October 2020 (journal, preprint)
- “TIPP: Parallel Delaunay Triangulation for Large-Scale Datasets”, Cuong Nguyen and Philip J. Rhodes, 30th International Conference on Scientific and Statistical Database Management (SSDBM ’18), 12 pages, 2018.
- “Accelerating Range Queries for Large-scale Unstructured Meshes”, Cuong Nguyen and Philip J. Rhodes, Proceedings of the IEEE International Conference on Big Data 2016 (IEEE BigData 2016), 10 pages, 2016.
- “A location service for partial spatial replicas implementing an R-tree in a relational database “, Yun Tian and Philip J. Rhodes, Journal of Parallel and Distributed Computing, April 2016, pp. 9-21.
- “Towards an Efficient Storage and Retrieval Mechanism For Large Unstructured Grids”, Oyindamola Akande and Philip J. Rhodes, Future Generation Computer Systems (2015), pp. 53-69 DOI information: 10.1016/j.future.2014.10.024.
- “Multilevel Partitioning of Large Unstructured Grids”, Oyindamola Akande and Philip J. Rhodes, Proceedings of the IEEE International Conference on Big Data 2014 (IEEE BigData 2014), 6 pages, 2014.
- “Iteration Aware Prefetching for Unstructured Grids”, Oyindamola Akande and Philip J. Rhodes, Proceedings of the IEEE International Conference on Big Data 2013 (IEEE BigData 2013), 9 pages, 2013.
- “Partial Replica Selection for Spatial Datasets”, Yun Tian and Philip J. Rhodes, Proceedings of the 8th IEEE International Conference on eScience (eScience), 10 pages, 2012.
- “A Fast Location Service for Partial Spatial Replicas” , Yun Tian and Philip J. Rhodes, Proceedings of the 2011 12th IEEE/ACM International Conference on Grid Computing (GRID), pp. 190-197, 2011.
- “IDEA—An API for Parallel Computing with Large Spatial Datasets”, Baoqiang Yan and Philip J. Rhodes, Proceedings of the 2011 International Conference on Parallel Processing (ICPP), pp. 355 – 364, 2011.
- “The Globus Toolkit R-tree for partial spatial replica selection” , Yun Tian and Philip J. Rhodes, Proceedings of the 2010 11th IEEE/ACM International Conference on Grid Computing (GRID), pp. 169 – 176, 2010.
- “Optimizing Memory Access on GPUs Using Morton Order Indexing”, Anthony E. Nocentino and Philip J. Rhodes, Proceedings of the 48th Annual Southeast Regional Conference (ACMSE), 2010.
- “Toward Automatic Parallelization of Spatial Computation for Computing Clusters,” by Baoqiang Yan and Philip J. Rhodes, Journal of the Mississippi Academy of Sciences, Vol. 53, No. 1, p. 98, January 2008. Best High Performance Computing Presentation, Math and Computer Science Division.
- “Spatial Prefetching for Out-of-Core Visualisation of Multidimensional Data” by Dan R. Lipsa, Philip J. Rhodes, R. Daniel Bergeron, and Ted M. Sparr, Proc. Visualization and Data Analysis 2007.
- “Out of core visualization using Iterator Aware Multidimensional Prefetching ” , Philip J. Rhodes, Xuan Tang, R. Daniel Bergeron, and Ted M. Sparr, Proceedings SPIE Vol. 5669, Visualization and Data Analysis 2005 , pp. 295-306.
- “A Data Model for Adaptive Multiresolution Scientific Data”, Philip J. Rhodes, R. Daniel Bergeron, and Ted M. Sparr, Data Visualization: The State of the Art 2003 , pp. 257-272.
- “Uncertainty Visualization Methods in Isosurface Rendering” , Philip J. Rhodes, Robert S. Laramee, R. Daniel Bergeron, and Ted M. Sparr, Eurographics 2003 Short Papers , M. Chover, H. Hagen and D. Tost Editors, pp. 83-88, September 1-5 2003, Granada, Spain.
- “A Data Model for Distributed Multiresolution Multisource Scientific Data”, by Philip J. Rhodes, R. Daniel Bergeron, and Ted M. Sparr, Hierarchical and Geometrical Methods in Scientific Visualization , G. Farin, H. Hagen, and B. Hamann, eds. Springer-Verlag, Heidelberg, Germany, 2002.