S. Jadhav, H. Bhatia, P.-T. Bremer, J.A. Levine, L.G. Nonato, V. Pascucci. Consistent Approximation of Local Flow Behavior for 2D Vector Fields using Edge Maps, SCI Technical Report, No. UUSCI-2010-004, SCI Institute, University of Utah, 2010.
S. Kumar, V. Vishwanath, P. Carns, V. Pascucci, R. Latham, T. Peterka, M. Papka, R. Ross. Towards Efficient Access of Multi-dimensional, Multi-resolution Scientific Data, In Proceedings of the 5th Petascale Data Storage Workshop, Supercomputing 2010, pp. (in press). 2010.
J. Tierny, J. Daniels II, L.G. Nonato, V. Pascucci, C.T. Silva. Interactive Quadrangulation with Reeb Atlases and Connectivity Textures, SCI Technical Report, No. UUSCI-2010-006, SCI Institute, University of Utah, 2010.
H.T. Vo, D.K. Osmari, B. Summa, J.L.D. Comba, V. Pascucci, C.T. Silva. Streaming-Enabled Parallel Dataflow Architecture for Multicore Systems, In Computer Graphics Forum, Vol. 29, No. 3, pp. 1073--1082. 2010.
One of the central challenges facing visualization research is how to effectively enable knowledge discovery. An effective approach will likely combine application architectures that are capable of running on today's largest platforms to address the challenges posed by large data with visual data analysis techniques that help find, represent, and effectively convey scientifically interesting features and phenomena.
C.D. Hansen, C.R. Johnson, V. Pascucci, C.T. Silva. Visualization for Data-Intensive Science, In The Fourth Paradigm: Data-Intensive Science, Edited by S. Tansley and T. Hey and K. Tolle, Microsoft Research, pp. 153--164. 2009.
K. Potter, A. Wilson, P.-T. Bremer, D. Williams, C. Doutriaux, V. Pascucci, C.R. Johnson. Ensemble-Vis: A Framework for the Statistical Visualization of Ensemble Data, In Proceedings of the 2009 IEEE International Conference on Data Mining Workshops, pp. 233--240. 2009.
H.T. Vo, D.K. Osmari, B. Summa, J.L.D. Comba, V. Pascucci, C.T. Silva. Parallel Dataflow Scheme for Streaming (Un)Structured Data, SCI Technical Report, No. UUSCI-2009-004, SCI Institute, University of Utah, 2009.
E.W. Bethel, H. Childs, A. Mascarenhas, V. Pascucci, Prabhat. Scientific Data Management Challenges in High Performance Visual Data Analysis, In Scientific Data Management: Challenges, Existing Technology, and Deployment, Chapman Hall/CRC Press, 2008.
J. Bennett, V. Pascucci, K.I. Joy. Genus Oblivious Cross Parameterization: Robust Topological Management of Intersurface Maps, In Proceedings of Pacific Graphics 2007, 2007.
E.W. Bethel, C.R. Johnson, K. Joy, S. Ahern, V. Pascucci, H. Childs, J. Cohen, M. Duchaineau, B. Hamann, C.D. Hansen, D. Laney, P. Lindstrom, J. Meredith, G. Ostrouchov, S.G. Parker, C.T. Silva, A.R. Sanderson, X. Tricoche. SciDAC Visualization and Analytics Center for Enabling Technology, In Journal of Physics, Conference Series, Vol. 78, No. 012032, pp. (published online). 2007.
E.W. Bethel, C.R. Johnson, C. Aragon, Prabhat, O. Rübel, G. Weber, V. Pascucci, H. Childs, P.-T. Bremer, B. Whitlock, S. Ahern, J. Meredith, G. Ostrouchov, K. Joy, B. Hamann, C. Garth, M. Cole, C.D. Hansen, S.G. Parker, A.R. Sanderson, C.T. Silva, X. Tricoche. DOE's SciDAC Visualization and Analytics Center for Enabling Technologies - Strategy for Petascale Visual Data Analysis Success, In CTWatch Quarterly, Vol. 3, No. 4, 2007.
P.-T. Bremer, E.M. Bringa, M.A. Duchaineau, A. Gyulassy, D. Laney, A. Mascarenhas, V. Pascucci. Topological Feature Extraction and Tracking, In Proceedings of SciDAC 2007 -- Scientific Discovery Through Advanced Computing, Boston, MA, USA, Vol. 78, Journal of Physics Conference Series, pp. 012032 (5pp). June, 2007.
Computing and analyzing the topology of scalar fields has proven to be a powerful tool in a wide variety of applications. In recent years the field has evolved from computing contour trees of two-dimensional functions to Reeb graphs of general two-manifolds, analyzing the topology of time-dependent volumes, and finally to creating Morse-Smale complexes of two and three dimensional functions. However, apart from theoretical advances practical applications depend on the development of robust and easy to implement algorithms. The progression from initial to practical algorithms is evident, for example, in the contour tree computation where the latest algorithms consist of no more than a couple of dozens lines of pseudo-code. In this paper we describe a similarly simple approach to compute progressive Morse-Smale complexes of functions over two-manifolds. We discuss compact and transparent data-structures used to compute and store Morse-Smale complexes and demonstrate how they can be used to implement interactive topology based simplification. In particular, we show how special cases arising, for example, from manifolds with boundaries or highly quantized functions are handled effectively. Overall the new algorithm is easier to implement and more efficient both run-time and storage wise than previous approaches by avoiding to refine a given triangulation.
S.P. Callahan, L. Bavoil, V. Pascucci, C.T. Silva. Progressive Volume Rendering of Large Unstructured Grids, In IEEE Transactions on Visualization and Computer Graphics, Vol. 13, No. 1, pp. 1307-1314. 2007.
A. Gyulassy, V. Natarajan, B. Hamann, V. Pascucci. Efficient Computation of Morse-Smale Complexes for Three-dimensional Scalar Functions, In IEEE Transactions on Visualization and Computer Graphics, Note: (presented at IEEE VIS 2007), 2007.