2012

J. Tierny, V. Pascucci.
**“Generalized Topological Simplification of Scalar Fields on Surfaces,”** In *IEEE Transactions on Visualization and Computer Graphics (TVCG)*, Vol. 18, No. 12, pp. 2005--2013. Dec, 2012.

DOI: 10.1109/TVCG.2012.228

We present a combinatorial algorithm for the general topological simplification of scalar fields on surfaces. Given a scalar field f, our algorithm generates a simplified field g that provably admits only critical points from a constrained subset of the singularities of f, while guaranteeing a small distance ||f - g||

W. Widanagamaachchi, C. Christensen, P.-T. Bremer, V. Pascucci.
**“Interactive Exploration of Large-scale Time-varying Data using Dynamic Tracking Graphs,”** In *2012 IEEE Symposium on Large Data Analysis and Visualization (LDAV)*, pp. 9--17. 2012.

DOI: 10.1109/LDAV.2012.6378962

Exploring and analyzing the temporal evolution of features in large-scale time-varying datasets is a common problem in many areas of science and engineering. One natural representation of such data is tracking graphs, i.e., constrained graph layouts that use one spatial dimension to indicate time and show the “tracks” of each feature as it evolves, merges or disappears. However, for practical data sets creating the corresponding optimal graph layouts that minimize the number of intersections can take hours to compute with existing techniques. Furthermore, the resulting graphs are often unmanageably large and complex even with an ideal layout. Finally, due to the cost of the layout, changing the feature definition, e.g. by changing an iso-value, or analyzing properly adjusted sub-graphs is infeasible. To address these challenges, this paper presents a new framework that couples hierarchical feature definitions with progressive graph layout algorithms to provide an interactive exploration of dynamically constructed tracking graphs. Our system enables users to change feature definitions on-the-fly and filter features using arbitrary attributes while providing an interactive view of the resulting tracking graphs. Furthermore, the graph display is integrated into a linked view system that provides a traditional 3D view of the current set of features and allows a cross-linked selection to enable a fully flexible spatio-temporal exploration of data. We demonstrate the utility of our approach with several large-scale scientific simulations from combustion science.

P.C. Wong, H. Shen, V. Pascucci.
**“Extreme-Scale Visual Analytics,”** In *IEEE Computer Graphics and Applications*, Vol. 32, No. 4, pp. 23--25. 2012.

DOI: 10.1109/MCG.2012.73

Extreme-scale visual analytics (VA) is about applying VA to extreme-scale data. The articles in this special issue examine advances related to extreme-scale VA problems, their analytical and computational challenges, and their real-world applications.

2011

S. Ahern, A. Shoshani, K.L. Ma, A. Choudhary, T. Critchlow, S. Klasky, V. Pascucci.
**“Scientific Discovery at the Exascale: Report from the (DOE) (ASCR) 2011 Workshop on Exascale Data Management, Analysis, and Visualization,”** Note: *Office of Scientific and Technical Information (OSTI)*, January, 2011.

DOI: 10.2172/1011053

J.C. Bennett, V. Krishnamoorthy, S. Liu, R.W. Grout, E.R. Hawkes, J.H. Chen, J. Shepherd, V. Pascucci, P.-T. Bremer.
**“Feature-Based Statistical Analysis of Combustion Simulation Data,”** In *IEEE Transactions on Visualization and Computer Graphics, Proceedings of the 2011 IEEE Visualization Conference*, Vol. 17, No. 12, pp. 1822--1831. 2011.

H. Bhatia, S. Jadhav, P.-T. Bremer, G. Chen, J.A. Levine, L.G. Nonato, V. Pascucci.
**“Edge Maps: Representing Flow with Bounded Error,”** In *Proceedings of IEEE Pacific Visualization Symposium 2011*, Hong Kong, China, Note: *Won Best Paper Award!*, pp. 75--82. March, 2011.

DOI: 10.1109/PACIFICVIS.2011.5742375

H. Bhatia, S. Jadhav, P.-T. Bremer, G. Chen, J.A. Levine, L.G. Nonato, V. Pascucci.
**“Flow Visualization with Quantified Spatial and Temporal Errors using Edge Maps,”** In *IEEE Transactions on Visualization and Computer Graphics (TVCG)*, Vol. 18, No. 9, IEEE Society, pp. 1383--1396. 2011.

DOI: 10.1109/TVCG.2011.265

A. Cuadros-Vargas, L.G. Nonato, V. Pascucci.
**“Combinatorial Laplacian Image Cloning,”** In *Proceedings of XXIV Sibgrapi – Conference on Graphics, Patterns and Images*, pp. 236--241. 2011.

DOI: 10.1109/SIBGRAPI.2011.7

Seamless image cloning has become one of the most important editing operation for photomontage. Recent coordinate-based methods have lessened considerably the computational cost of image cloning, thus enabling interactive applications. However, those techniques still bear severe limitations as to concavities and dynamic shape deformation. In this paper we present novel methodology for image cloning that turns out to be highly efficient in terms of computational times while still being more flexible than existing techniques. Our approach builds on combinatorial Laplacian and fast Cholesky factorization to ensure interactive image manipulation, handling holes, concavities, and dynamic deformations during the cloning process. The provided experimental results show that the proposed technique outperforms existing methods in requisites such as accuracy and flexibility.

T. Etiene, L.G. Nonato, C. Scheidegger, J. Tierny, T.J. Peters, V. Pascucci, R.M. Kirby, C.T. Silva.
**“Topology Verfication for Isosurface Extraction,”** In *IEEE Transactions on Visualization and Computer Graphics*, pp. (accepted). 2011.

The broad goals of verifiable visualization rely on correct algorithmic implementations. We extend a framework for verification of isosurfacing implementations to check topological properties. Specifically, we use stratified Morse theory and digital topology to design algorithms which verify topological invariants. Our extended framework reveals unexpected behavior and coding mistakes in popular publicly-available isosurface codes.

Attila Gyulassy, J.A. Levine, V. Pascucci.
**“Visualization of Discrete Gradient Construction (Multimedia submission),”** In *Proceedings of the 27th Symposium on Computational Geometry*, Paris, France, ACM, pp. 289--290. June, 2011.

DOI: 10.1145/1998196.1998241

This video presents a visualization of a recent algorithm to compute discrete gradient fields on regular cell complexes [3]. Discrete gradient fields are used in practical methods that robustly translate smooth Morse theory to combinatorial domains. We describe the stages of the algorithm, highlighting both its simplicity and generality.

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,”** In *Mathematics and Visualization*, Springer, pp. 141--159. Nov, 2011.

DOI: 10.1007/978-3-642-23175-9 10

Typically, vector fields are stored as a set of sample vectors at discrete locations. Vector values at unsampled points are defined by interpolating some subset of the known sample values. In this work, we consider two-dimensional domains represented as triangular meshes with samples at all vertices, and vector values on the interior of each triangle are computed by piecewise linear interpolation.

Many of the commonly used techniques for studying properties of the vector field require integration techniques that are prone to inconsistent results. Analysis based on such inconsistent results may lead to incorrect conclusions about the data. For example, vector field visualization techniques integrate the paths of massless particles (streamlines) in the flow or advect a texture using line integral convolution (LIC). Techniques like computation of the topological skeleton of a vector field, require integrating separatrices, which are streamlines that asymptotically bound regions where the flow behaves differently. Since these integrations may lead to compound numerical errors, the computed streamlines may intersect, violating some of their fundamental properties such as being pairwise disjoint. Detecting these computational artifacts to allow further analysis to proceed normally remains a significant challenge.

S. Kumar, V. Vishwanath, P. Carns, B. Summa, G. Scorzelli, V. Pascucci, R. Ross, J. Chen, H. Kolla, R. Grout.
**“PIDX: Efficient Parallel I/O for Multi-resolution Multi-dimensional Scientific Datasets,”** In *Proceedings of The IEEE International Conference on Cluster Computing*, pp. 103--111. September, 2011.

Valerio Pascucci, Xavier Tricoche, Hans Hagen, Julien Tierny.
**“Topological Methods in Data Analysis and Visualization: Theory, Algorithms, and Applications (Mathematics and Visualization),”** Springer, 2011.

ISBN: 978-3642150135

T. Peterka, R. Ross, A. Gyulassy, V. Pascucci, W. Kendall, H.-W. Shen, T.-Y. Lee, A. Chaudhuri.
**“Scalable Parallel Building Blocks for Custom Data Analysis,”** In *Proceedings of the 2011 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV)*, pp. 105--112. October, 2011.

DOI: 10.1109/LDAV.2011.6092324

We present a set of building blocks that provide scalable data movement capability to computational scientists and visualization researchers for writing their own parallel analysis. The set includes scalable tools for domain decomposition, process assignment, parallel I/O, global reduction, and local neighborhood communicationtasks that are common across many analysis applications. The global reduction is performed with a new algorithm, described in this paper, that efficiently merges blocks of analysis results into a smaller number of larger blocks. The merging is configurable in the number of blocks that are reduced in each round, the number of rounds, and the total number of resulting blocks. We highlight the use of our library in two analysis applications: parallel streamline generation and parallel Morse-Smale topological analysis. The first case uses an existing local neighborhood communication algorithm, whereas the latter uses the new merge algorithm.

S. Philip, B. Summa, P.-T. Bremer, and V. Pascucci.
**“Parallel Gradient Domain Processing of Massive Images,”** In *Proceedings of the 2011 Eurographics Symposium on Parallel Graphics and Visualization*, pp. 11--19. 2011.

Gradient domain processing remains a particularly computationally expensive technique even for relatively small images. When images become massive in size, giga or terapixel, these problems become particularly troublesome and the best serial techniques take on the order of hours or days to compute a solution. In this paper, we provide a simple framework for the parallel gradient domain processing. Specifically, we provide a parallel out-of-core method for the seamless stitching of gigapixel panoramas in a parallel MPI environment. Unlike existing techniques, the framework provides both a straightforward implementation, maintains strict control over the required/allocated resources, and makes no assumptions on the speed of convergence to an acceptable image. Furthermore, the approach shows good weak/strong scaling from several to hundreds of cores and runs on a variety of hardware.

S. Philip, B. Summa, P-T Bremer, V. Pascucci.
**“Hybrid CPU-GPU Solver for Gradient Domain Processing of Massive Images,”** In *Proceedings of 2011 International Conference on Parallel and Distributed Systems (ICPADS)*, pp. 244--251. 2011.

Gradient domain processing is a computationally expensive image processing technique. Its use for processing massive images, giga or terapixels in size, can take several hours with serial techniques. To address this challenge, parallel algorithms are being developed to make this class of techniques applicable to the largest images available with running times that are more acceptable to the users. To this end we target the most ubiquitous form of computing power available today, which is small or medium scale clusters of commodity hardware. Such clusters are continuously increasing in scale, not only in the number of nodes, but also in the amount of parallelism available within each node in the form of multicore CPUs and GPUs. In this paper we present a hybrid parallel implementation of gradient domain processing for seamless stitching of gigapixel panoramas that utilizes MPI, threading and a CUDA based GPU component. We demonstrate the performance and scalability of our implementation by presenting results from two GPU clusters processing two large data sets.

M. Schulz, J.A. Levine, P.-T. Bremer, T. Gamblin, V. Pascucci.
**“Interpreting Performance Data Across Intuitive Domains,”** In *International Conference on Parallel Processing*, Taipei, Taiwan, IEEE, pp. 206--215. 2011.

DOI: 10.1109/ICPP.2011.60

B. Summa, G. Scorzelli, M. Jiang, P.-T. Bremer, V. Pascucci.
**“Interactive Editing of Massive Imagery Made Simple: Turning Atlanta into Atlantis,”** In *ACM Transactions on Graphics*, Vol. 30, No. 2, pp. 7:1--7:13. April, 2011.

DOI: 10.1145/1944846.1944847

This article presents a simple framework for progressive processing of high-resolution images with minimal resources. We demonstrate this framework's effectiveness by implementing an adaptive, multi-resolution solver for gradient-based image processing that, for the first time, is capable of handling gigapixel imagery in real time. With our system, artists can use commodity hardware to interactively edit massive imagery and apply complex operators, such as seamless cloning, panorama stitching, and tone mapping.

We introduce a progressive Poisson solver that processes images in a purely coarse-to-fine manner, providing near instantaneous global approximations for interactive display (see Figure 1). We also allow for data-driven adaptive refinements to locally emulate the effects of a global solution. These techniques, combined with a fast, cache-friendly data access mechanism, allow the user to interactively explore and edit massive imagery, with the illusion of having a full solution at hand. In particular, we demonstrate the interactive modification of gigapixel panoramas that previously required extensive offline processing. Even with massive satellite images surpassing a hundred gigapixels in size, we enable repeated interactive editing in a dynamically changing environment. Images at these scales are significantly beyond the purview of previous methods yet are processed interactively using our techniques. Finally our system provides a robust and scalable out-of-core solver that consistently offers high-quality solutions while maintaining strict control over system resources.

D. Thompson, J.A. Levine, J.C. Bennett, P.-T. Bremer, A. Gyulassy, V. Pascucci, P.P. Pebay.
**“Analysis of Large-Scale Scalar Data Using Hixels,”** In *Proceedings of the 2011 IEEE Symposium on Large-Scale Data Analysis and Visualization (LDAV)*, Providence, RI, pp. 23--30. 2011.

DOI: 10.1109/LDAV.2011.6092313

H.T. Vo, J. Bronson, B. Summa, J.L.D. Comba, J. Freire, B. Howe, V. Pascucci, C.T. Silva.
**“Parallel Visualization on Large Clusters using MapReduce,”** SCI Technical Report, No. UUSCI-2011-002, *SCI Institute, University of Utah*, 2011.