SCI Publications
2007
C.R. Johnson, R. Ross, S. Ahern, J. Ahrens, W. Bethel, K.L. Ma, M. Papka, J. van Rosendale, H.W. Shen, J. Thomas.
“DOE Visualization and Knowledge Discovery,” Note: Report from the DOE/ASCR Workshop on Visual Analysis and Data Exploration at Extreme Scale, October, 2007.
M. Jolley, J.G. Stinstra, D.M. Weinstein, S. Pieper, R.S.J. Estepar, G. Kindlmann, R.S. MacLeod, D.H. Brooks, J.K. Triedman.
“Open-Source Environment for Interactive Finite Element Modeling of Optimal ICD Electrode Placement,” In Functional Imaging and Modeling of the Heart, Lecture Notes in Computer Science (LCNS), Vol. 4466/2007, pp. 373--382. 2007.
ISBN: 978-3-540-72906-8
B.W. Jones, R.E. Marc, C.B. Watt, K. Kinardi, D. DeMill, J.H. Yang, T. Tasdizen, P. Koshevoy, E. Jurrus, R.T. Whitaker.
“Structure and Function of Microneuromas in Retinal Remodeling,” In The Association for Research in Vision and Ophthalmology (ARVO) Conference, Note: (abstract), 2007.
C. Jones, K.-L. Ma, A.R. Sanderson, L. Myers.
“Visual Interrogation of Gyrokinetic Particle Simulations,” In Journal of Physics, Conference Series, Vol. 78, pp. 012033 (6pp). 2007.
G. Kindlmann, D.B. Ennis, R.T. Whitaker, C.-F. Westin.
“Diffusion Tensor Analysis With Invariant Gradients and Rotation Tangents,” In IEEE Transactions on Medical Imaging, Vol. 26, No. 11, pp. 1483--1499. 2007.
R.M. Kirby, Z. Yosibash, G.E. Karniadakis.
“Towards Stable Coupling Methods for High-Order Discretizations of Fluid-Structure Interaction: Algorithms and Observations,” In Journal of Computational Physics, Vol. 223, No. 2, pp. 489--518. 2007.
J.M. Kniss, W. Hunt, K. Potter, P. Sen.
“IStar: A Raster Representation for Scalable Image and Volume Data,” In IEEE Transactions on Visualization and Computer Graphics (TVCG), Vol. 13, No. 6, pp. 1424--1431. Nov, 2007.
DOI: 10.1109/TVCG.2007.70572
Topology has been an important tool for analyzing scalar data and flow fields in visualization. In this work, we analyze the topology of multivariate image and volume data sets with discontinuities in order to create an efficient, raster-based representation we call IStar. Specifically, the topology information is used to create a dual structure that contains nodes and connectivity information for every segmentable region in the original data set. This graph structure, along with a sampled representation of the segmented data set, is embedded into a standard raster image which can then be substantially downsampled and compressed. During rendering, the raster image is upsampled and the dual graph is used to reconstruct the original function. Unlike traditional raster approaches, our representation can preserve sharp discontinuities at any level of magnification, much like scalable vector graphics. However, because our representation is raster-based, it is well suited to the real-time rendering pipeline. We demonstrate this by reconstructing our data sets on graphics hardware at real-time rates.
A. Knoll, C.D. Hansen, I. Wald.
“Coherent Multiresolution Isosurface Ray Tracing,” SCI Institute Technical Report, No. UUSCI-2007-001, University of Utah, 2007.
A. Knoll, Y. Hijazi, C.D. Hansen, I. Wald, H. Hagen.
“Interactive Ray Tracing of Arbitrary Implicit Functions,” SCI Institute Technical Report, No. UUSCI-2007-002, University of Utah, 2007.
A. Knoll, Y. Hijazi, C.D. Hansen, I. Wald, H. Hagen.
“Interactive Ray Tracing of Arbitrary Implicits with SIMD Interval Arithmetic,” In Proceedings of 2nd IEEE/EG Symposium on Interactive Ray Tracing 2007, Ulm, Germany, pp. 11--18. 2007.
A. Knoll, Y. Hijazi, A. Kensler, M. Schott, C.D. Hansen, H. Hagen.
“Fast and Robust Ray Tracing of General Implicits on the GPU,” SCI Institute Technical Report, No. UUSCI-2007-014, University of Utah, 2007.
P.A. Koshevoy, T. Tasdizen, R.T. Whitaker.
“Automatic Assembly of TEM Mosaics and Mosaic Stacks Using Phase Correlation,” SCI Institute Technical Report, No. UUSCI-2007-004, University of Utah, 2007.
L. Krishnan, J.B. Hoying, H. Ngyuen, H. Song, J.A. Weiss.
“Interaction of Angiogenic Microvessels with the Extracellular Matrix,” In American Journal of Physiology: Heart and Circulation Physiology, Vol. 293, No. 6, pp. H3650--H36588. 2007.
D. Laney, P.-T. Bremer, A. Mascarenhas, P. Miller, V. Pascucci.
“Understanding the Structure of the Turbulent Mixing Layer in Hydrodynamic Instabilities,” In IEEE Transactions on Visualization and Computer Graphics, Vol. 13, No. 1, pp. 1053--1060. January, 2007.
S. Lew, C. Wolters, A. Anwander, S. Makeig, R.S. MacLeod.
“Low Resolution Conductivity Estimation to Improve Source Localization,” In New Frontiers in Biomagnetism. Proceedings of the 15th International Conference on Biomagnetism, Vancouver, BC, Canada, August 21-25, 2006., International Congress Series, Vol. 1300, pp. 149--152. June, 2007.
G.-S. Li, X. Tricoche, C.D. Hansen.
“Visualizing Unsteady Flows on Surfaces Using Spherical Parameterization,” SCI Institute Technical Report, No. UUSCI-2007-013, University of Utah, 2007.
Y. Livnat, S.G. Parker, C.R. Johnson.
“Fast Isosurface Extraction Methods for Large Image Data Sets,” In Handbook of Medical Imaging: Processing and Analysis, 2nd Edition, Ch. 44, Edited by Isaac Bankman, Academic Press, 2007.
J. Luitjens, M. Berzins, T.C. Henderson.
“Parallel Space Filling Curve Generation Through Sorting,” In Journal of Concurrency and Computation, Vol. 19, No. 10, pp. 1387--1402. 2007.
J. Luitjens, B. Worthen, M. Berzins, T.C. Henderson.
“Scalable Parallel AMR for the Uintah Multiphysics Code,” In Petascale Computing Algorithms and Applications, Edited by D. Bader, Chapman and Hall/CRC, 2007.
J. Luitjens, M. Berzins, T. Henderson.
“Parallel space-filling curve generation through sorting,” In Concurrency and Computation: Practice and Experience, Vol. 19, No. 10, pp. 1387--1402. July, 2007.
DOI: 10.1002/cpe.1179
In this paper we consider the scalability of parallel space-filling curve generation as implemented through parallel sorting algorithms. Multiple sorting algorithms are studied and results show that space-filling curves can be generated quickly in parallel on thousands of processors. In addition, performance models are presented that are consistent with measured performance and offer insight into performance on still larger numbers of processors. At large numbers of processors, the scalability of adaptive mesh refined codes depends on the individual components of the adaptive solver. One such component is the dynamic load balancer. In adaptive mesh refined codes, the mesh is constantly changing resulting in load imbalance among the processors requiring a load-balancing phase. The load balancing may occur often, requiring the load balancer to perform quickly. One common method for dynamic load balancing is to use space-filling curves. Space-filling curves, in particular the Hilbert curve, generate good partitions quickly in serial. However, at tens and hundreds of thousands of processors serial generation of space-filling curves will hinder scalability. In order to avoid this issue we have developed a method that generates space-filling curves quickly in parallel by reducing the generation to integer sorting.