Designed especially for neurobiologists, FluoRender is an interactive tool for multi-channel fluorescence microscopy data visualization and analysis.
Deep brain stimulation
BrainStimulator is a set of networks that are used in SCIRun to perform simulations of brain stimulation such as transcranial direct current stimulation (tDCS) and magnetic transcranial stimulation (TMS).
Developing software tools for science has always been a central vision of the SCI Institute.

SCI Publications

2011


S. Williams, M. Petersen, P.-T. Bremer, M. Hecht, V. Pascucci, J. Ahrens, M. Hlawitschka, B. Hamann. “Adaptive Extraction and Quantification of Geophysical Vortices,” In IEEE Transactions on Visualization and Computer Graphics, Proceedings of the 2011 IEEE Visualization Conference, Vol. 17, No. 12, pp. 2088--2095. 2011.


2010


M. Berger, L.G. Nonato, V. Pascucci, C.T. Silva. “Fiedler Trees for Multiscale Surface Analysis,” In Computer & Graphics, Vol. 34, No. 3, Note: Special Issue of Sha, pp. 272--281. June, 2010.
DOI: 10.1016/j.cag.2010.03.009

ABSTRACT

In this work we introduce a new hierarchical surface decomposition method for multiscale analysis of surface meshes. In contrast to other multiresolution methods, our approach relies on spectral properties of the surface to build a binary hierarchical decomposition. Namely, we utilize the first nontrivial eigenfunction of the Laplace–Beltrami operator to recursively decompose the surface. For this reason we coin our surface decomposition the Fiedler tree. Using the Fiedler tree ensures a number of attractive properties, including: mesh-independent decomposition, well-formed and nearly equi-areal surface patches, and noise robustness. We show how the evenly distributed patches can be exploited for generating multiresolution high quality uniform meshes. Additionally, our decomposition permits a natural means for carrying out wavelet methods, resulting in an intuitive method for producing feature-sensitive meshes at multiple scales.



T. Etiene, L.G. Nonato, C.E. Scheidegger, J. Tierny, T.J. Peters, V. Pascucci, R.M. Kirby, C.T. Silva. “Topology Verification for Isosurface Extraction,” SCI Technical Report, No. UUSCI-2010-003, SCI Institute, University of Utah, 2010.



S. Gerber, P.-T. Bremer, V. Pascucci, R.T. Whitaker. “Visual Exploration of High Dimensional Scalar Functions,” In IEEE Transactions on Visualization and Computer Graphics, IEEE Transactions on Visualization and Computer Graphics, Vol. 16, No. 6, IEEE, pp. 1271--1280. Nov, 2010.
DOI: 10.1109/TVCG.2010.213
PubMed ID: 20975167
PubMed Central ID: PMC3099238



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.



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, Wiley-Blackwell, pp. 1073--1082. Aug, 2010.
DOI: 10.1111/j.1467-8659.2009.01704.x

ABSTRACT

We propose a new framework design for exploiting multi-core architectures in the context of visualization dataflow systems. Recent hardware advancements have greatly increased the levels of parallelism available with all indications showing this trend will continue in the future. Existing visualization dataflow systems have attempted to take advantage of these new resources, though they still have a number of limitations when deployed on shared memory multi-core architectures. Ideally, visualization systems should be built on top of a parallel dataflow scheme that can optimally utilize CPUs and assign resources adaptively to pipeline elements. We propose the design of a flexible dataflow architecture aimed at addressing many of the shortcomings of existing systems including a unified execution model for both demand-driven and event-driven models; a resource scheduler that can automatically make decisions on how to allocate computing resources; and support for more general streaming data structures which include unstructured elements. We have implemented our system on top of VTK with backward compatibility. In this paper, we provide evidence of performance improvements on a number of applications.


2009


E.W. Bethel, C.R. Johnson, S. Ahern, J. Bell, P.-T. Bremer, H. Childs, E. Cormier-Michel, M. Day, E. Deines, P.T. Fogal, C. Garth, C.G.R. Geddes, H. Hagen, B. Hamann, C.D. Hansen, J. Jacobsen, K.I. Joy, J. Krüger, J. Meredith, P. Messmer, G. Ostrouchov, V. Pascucci, K. Potter, Prabhat, D. Pugmire, O. Rubel, A.R. Sanderson, C.T. Silva, D. Ushizima, G.H. Weber, B. Whitlock, K. Wu. “Occam's Razor and Petascale Visual Data Analysis,” In Journal of Physics: Conference Series, Journal of Physics: Conference Series, Vol. 180, No. 012084, pp. (published online). 2009.
DOI: 10.1088/1742-6596/180/1/012084

ABSTRACT

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. Johhson. “Visualization of Uncertainty and Ensemble Data: Exploration of Climate Modeling and Weather Forecast Data with Integrated ViSUS-CDAT Systems,” In J. Phys.: Conf. Ser., Vol. 180, No. 012089, IOP Publishing, pp. 012089. July, 2009.
DOI: 10.1088/1742-6596/180/1/012089



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.



J. Tierny, A. Gyulassy, E. Simon, V. Pascucci. “Loop Surgery for Volumetric Meshes: Reeb Graphs Reduced to Contour Trees,” In IEEE Transactions on Visualization and Computer Graphics, Proceedings of the 2009 IEEE Visualization Conference, Vol. 15, No. 6, pp. 1177--1184. Sept/Oct, 2009.
DOI: 10.1109/TVCG.2009.163



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.


2008


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.


2007


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.