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Visualization

Visualization, sometimes referred to as visual data analysis, uses the graphical representation of data as a means of gaining understanding and insight into the data. Visualization research at SCI has focused on applications spanning computational fluid dynamics, medical imaging and analysis, biomedical data analysis, healthcare data analysis, weather data analysis, poetry, network and graph analysis, financial data analysis, etc.

Research involves novel algorithm and technique development to building tools and systems that assist in the comprehension of massive amounts of (scientific) data. We also research the process of creating successful visualizations.

We strongly believe in the role of interactivity in visual data analysis. Therefore, much of our research is concerned with creating visualizations that are intuitive to interact with and also render at interactive rates.

Visualization at SCI includes the academic subfields of Scientific Visualization, Information Visualization and Visual Analytics.


chuck

Charles Hansen

Volume Rendering
Ray Tracing
Graphics
pascucci

Valerio Pascucci

Topological Methods
Data Streaming
Big Data
chris

Chris Johnson

Scalar, Vector, and
Tensor Field Visualization,
Uncertainty Visualization

mike

Mike Kirby

Uncertainty Visualization
ross

Ross Whitaker

Topological Methods
Uncertainty Visualization
miriah

Miriah Meyer

Information Visualization

yarden

Yarden Livnat

Information Visualization

alex lex

Alex Lex

Information Visualization

Visualization Project Sites:


Associated Labs:


Publications in Visualization:




TOD-Tree: Task-Overlapped Direct send Tree Image Compositing for Hybrid MPI Parallelism and GPUs
A. V. P. Grosset, M. Prasad, C. Christensen, A. Knoll, C. Hansen. In IEEE Transactions on Visualization and Computer Graphics, IEEE, pp. 1--1. 2016.
DOI: 10.1109/tvcg.2016.2542069

Modern supercomputers have thousands of nodes, each with CPUs and/or GPUs capable of several teraflops. However, the network connecting these nodes is relatively slow, on the order of gigabits per second. For time-critical workloads such as interactive visualization, the bottleneck is no longer computation but communication. In this paper, we present an image compositing algorithm that works on both CPU-only and GPU-accelerated supercomputers and focuses on communication avoidance and overlapping communication with computation at the expense of evenly balancing the workload. The algorithm has three stages: a parallel direct send stage, followed by a tree compositing stage and a gather stage. We compare our algorithm with radix-k and binary-swap from the IceT library in a hybrid OpenMP/MPI setting on the Stampede and Edison supercomputers, show strong scaling results and explain how we generally achieve better performance than these two algorithms. We developed a GPU-based image compositing algorithm where we use CUDA kernels for computation and GPU Direct RDMA for inter-node GPU communication. We tested the algorithm on the Piz Daint GPU-accelerated supercomputer and show that we achieve performance on par with CPUs. Lastly, we introduce a workflow in which both rendering and compositing are done on the GPU.



Dynamically Scheduled Region-Based Image Compositing
A.V. P. Grosset, A. Knoll, C.D. Hansen. In Eurographics Symposium on Parallel Graphics and Visualization, June, 2016.

Algorithms for sort-last parallel volume rendering on large distributed memory machines usually divide a dataset equally across all nodes for rendering. Depending on the features that a user wants to see in a dataset, all the nodes will rarely finish rendering at the same time. Existing compositing algorithms do not often take this into consideration, which can lead to significant delays when nodes that are compositing wait for other nodes that are still rendering. In this paper, we present an image compositing algorithm that uses spatial and temporal awareness to dynamically schedule the exchange of regions in an image and progressively composite images as they become available. Running on the Edison supercomputer at NERSC, we show that a scheduler-based algorithm with awareness of the spatial contribution from each rendering node can outperform traditional image compositing algorithms.



Kernel Partial Least Squares Regression for Relating Functional Brain Network Topology to Clinical Measures of Behavior
E. Wong, S. Palande, Bei Wang, B. Zielinski, J. Anderson, P. T. Fletcher. In 2016 IEEE 13th International Symposium on Biomedical Imaging (ISBI), IEEE, April, 2016.
DOI: 10.1109/isbi.2016.7493506

In this paper we present a novel method for analyzing the relationship between functional brain networks and behavioral phenotypes. Drawing from topological data analysis, we first extract topological features using persistent homology from functional brain networks that are derived from correlations in resting-state fMRI. Rather than fixing a discrete network topology by thresholding the connectivity matrix, these topological features capture the network organization across all continuous threshold values. We then propose to use a kernel partial least squares (kPLS) regression to statistically quantify the relationship between these topological features and behavior measures. The kPLS also provides an elegant way to combine multiple image features by using linear combinations of multiple kernels. In our experiments we test the ability of our proposed brain network analysis to predict autism severity from rs-fMRI. We show that combining correlations with topological features gives better prediction of autism severity than using correlations alone.



Analyzing Simulation-Based PRA Data Through Traditional and Topological Clustering: A BWR Station Blackout Case Study
D. Maljovec, S. Liu, Bei Wang, V. Pascucci, P. T. Bremer, D. Mandelli, C. Smith.. In Reliability Engineering & System Safety, Vol. 145, Elsevier, pp. 262--276. January, 2016.
DOI: 10.1016/j.ress.2015.07.001

Dynamic probabilistic risk assessment (DPRA) methodologies couple system simulator codes (e.g., RELAP, MELCOR) with simulation controller codes (e.g., RAVEN, ADAPT). Whereas system simulator codes model system dynamics deterministically, simulation controller codes introduce both deterministic (e.g., system control logic, operating procedures) and stochastic (e.g., component failures, parameter uncertainties) elements into the simulation. Typically, a DPRA is performed by sampling values of a set of parameters, and simulating the system behavior for that specific set of parameter values. For complex systems, a major challenge in using DPRA methodologies is to analyze the large number of scenarios generated, where clustering techniques are typically employed to better organize and interpret the data. In this paper, we focus on the analysis of two nuclear simulation datasets that are part of the risk-informed safety margin characterization (RISMC) boiling water reactor (BWR) station blackout (SBO) case study. We provide the domain experts a software tool that encodes traditional and topological clustering techniques within an interactive analysis and visualization environment, for understanding the structures of such high-dimensional nuclear simulation datasets. We demonstrate through our case study that both types of clustering techniques complement each other in bringing enhanced structural understanding of the data.



muView: A Visual Analysis System for Exploring Uncertainty in Myocardial Ischemia Simulations
P. Rosen, B. Burton, K. Potter, C.R. Johnson. In Visualization in Medicine and Life Sciences III, Springer Nature, pp. 49--69. 2016.
DOI: 10.1007/978-3-319-24523-2_3

In this paper we describe the Myocardial Uncertainty Viewer (muView or µView) system for exploring data stemming from the simulation of cardiac ischemia. The simulation uses a collection of conductivity values to understand how ischemic regions effect the undamaged anisotropic heart tissue. The data resulting from the simulation is multi-valued and volumetric, and thus, for every data point, we have a collection of samples describing cardiac electrical properties. µView combines a suite of visual analysis methods to explore the area surrounding the ischemic zone and identify how perturbations of variables change the propagation of their effects. In addition to presenting a collection of visualization techniques, which individually highlight different aspects of the data, the coordinated view system forms a cohesive environment for exploring the simulations.We also discuss the findings of our study, which are helping to steer further development of the simulation and strengthening our collaboration with the biomedical engineers attempting to understand the phenomenon.



Approximating the Generalized Voronoi Diagram of Closely Spaced Objects
J. Edwards, E. Daniel, V. Pascucci, C. Bajaj. In Computer Graphics Forum, Vol. 34, No. 2, Wiley-Blackwell, pp. 299-309. May, 2015.
DOI: 10.1111/cgf.12561

Generalized Voronoi Diagrams (GVDs) have far-reaching applications in robotics, visualization, graphics, and simulation. However, while the ordinary Voronoi Diagram has mature and efficient algorithms for its computation, the GVD is difficult to compute in general, and in fact, has only approximation algorithms for anything but the simplest of datasets. Our work is focused on developing algorithms to compute the GVD efficiently and with bounded error on the most difficult of datasets -- those with objects that are extremely close to each other.



Paint and Click: Unified Interactions for Image Boundaries
B. Summa, A. A. Gooch, G. Scorzelli, V. Pascucci. In Computer Graphics Forum, Vol. 34, No. 2, Wiley-Blackwell, pp. 385--393. May, 2015.
DOI: 10.1111/cgf.12568

Image boundaries are a fundamental component of many interactive digital photography techniques, enabling applications such as segmentation, panoramas, and seamless image composition. Interactions for image boundaries often rely on two complementary but separate approaches: editing via painting or clicking constraints. In this work, we provide a novel, unified approach for interactive editing of pairwise image boundaries that combines the ease of painting with the direct control of constraints. Rather than a sequential coupling, this new formulation allows full use of both interactions simultaneously, giving users unprecedented flexibility for fast boundary editing. To enable this new approach, we provide technical advancements. In particular, we detail a reformulation of image boundaries as a problem of finding cycles, expanding and correcting limitations of the previous work. Our new formulation provides boundary solutions for painted regions with performance on par with state-of-the-art specialized, paint-only techniques. In addition, we provide instantaneous exploration of the boundary solution space with user constraints. Finally, we provide examples of common graphics applications impacted by our new approach.



An Introduction to Verification of Visualization Techniques
T. Etiene, R.M. Kirby, C. Silva. Morgan & Claypool Publishers, 2015.



Visualization
C.R. Johnson. In Encyclopedia of Applied and Computational Mathematics, Edited by Björn Engquist, Springer, pp. 1537-1546. 2015.
ISBN: 978-3-540-70528-4
DOI: 10.1007/978-3-540-70529-1_368



View-Dependent Streamline Deformation and Exploration
X. Tong, J. Edwards, C. Chen, H. Shen, C. R. Johnson, P. Wong. In Transactions on Visualization and Computer Graphics, Vol. PP, No. 99, IEEE, pp. 1-1. 2015.
ISSN: 1077-2626
DOI: 10.1109/TVCG.2015.2502583

Occlusion presents a major challenge in visualizing 3D flow and tensor fields using streamlines. Displaying too many streamlines creates a dense visualization filled with occluded structures, but displaying too few streams risks losing important features. We propose a new streamline exploration approach by visually manipulating the cluttered streamlines by pulling visible layers apart and revealing the hidden structures underneath. This paper presents a customized view-dependent deformation algorithm and an interactive visualization tool to minimize visual clutter in 3D vector and tensor fields. The algorithm is able to maintain the overall integrity of the fields and expose previously hidden structures. Our system supports both mouse and direct-touch interactions to manipulate the viewing perspectives and visualize the streamlines in depth. By using a lens metaphor of different shapes to select the transition zone of the targeted area interactively, the users can move their focus and examine the vector or tensor field freely.

Keywords: Context;Deformable models;Lenses;Shape;Streaming media;Three-dimensional displays;Visualization;Flow visualization;deformation;focus+context;occlusion;streamline;white matter tracts



Evaluating Alignment of Shapes by Ensemble Visualization
M. Raj, M. Mirzargar, R. Kirby, R. Whitaker, J. Preston. In IEEE Computer Graphics and Applications, IEEE, 2015.

The visualization of variability in 3D shapes or surfaces, which is a type of ensemble uncertainty visualization for volume data, provides a means of understanding the underlying distribution for a collection or ensemble of surfaces. While ensemble visualization for surfaces is already described in the literature, we conduct an expert-based evaluation in a particular medical imaging application: the construction of atlases or templates from a population of images. In this work, we extend contour boxplots to 3D, allowing us to evaluate it against an enumeration-style visualization of the ensemble members and also other conventional visualizations used by atlas builders, namely examining the atlas image and the corresponding images/data provided as part of the construction process. We present feedback from domain experts on the efficacy of contour boxplots compared to other modalities when used as part of the atlas construction and analysis stages of their work.



Interstitial and Interlayer Ion Diffusion Geometry Extraction in Graphitic Nanosphere Battery Materials
A. Gyulassy, A. Knoll, K. C. Lau, Bei Wang, PT. Bremer, M.l E. Papka, L. A. Curtiss, V. Pascucci. In Proceedings IEEE Visualization Conference, 2015.

Large-scale molecular dynamics (MD) simulations are commonly used for simulating the synthesis and ion diffusion of battery materials. A good battery anode material is determined by its capacity to store ion or other diffusers. However, modeling of ion diffusion dynamics and transport properties at large length and long time scales would be impossible with current MD codes. To analyze the fundamental properties of these materials, therefore, we turn to geometric and topological analysis of their structure. In this paper, we apply a novel technique inspired by discrete Morse theory to the Delaunay triangulation of the simulated geometry of a thermally annealed carbon nanosphere. We utilize our computed structures to drive further geometric analysis to extract the interstitial diffusion structure as a single mesh. Our results provide a new approach to analyze the geometry of the simulated carbon nanosphere, and new insights into the role of carbon defect size and distribution in determining the charge capacity and charge dynamics of these carbon based battery materials.



Workshop on Quantification, Communication, and Interpretation of Uncertainty in Simulation and Data Science
R. Whitaker, W. Thompson, J. Berger, B. Fischhof, M. Goodchild, M. Hegarty, C. Jermaine, K. S. McKinley, A. Pang, J. Wendelberger. Note: Computing Community Consortium, 2015.

Modern science, technology, and politics are all permeated by data that comes from people, measurements, or computational processes. While this data is often incomplete, corrupt, or lacking in sufficient accuracy and precision, explicit consideration of uncertainty is rarely part of the computational and decision making pipeline. The CCC Workshop on Quantification, Communication, and Interpretation of Uncertainty in Simulation and Data Science explored this problem, identifying significant shortcomings in the ways we currently process, present, and interpret uncertain data. Specific recommendations on a research agenda for the future were made in four areas: uncertainty quantification in large-scale computational simulations, uncertainty quantification in data science, software support for uncertainty computation, and better integration of uncertainty quantification and communication to stakeholders.



Fiber Surfaces: Generalizing Isosurfaces to Bivariate Data
H. Carr, Z. Geng, J. Tierny, A. Chattophadhyay,, A. Knoll. In Computer Graphics Forum, Vol. 34, No. 3, pp. 241-250. 2015.

Scientific visualization has many effective methods for examining and exploring scalar and vector fields, but rather fewer for multi-variate fields. We report the first general purpose approach for the interactive extraction of geometric separating surfaces in bivariate fields. This method is based on fiber surfaces: surfaces constructed from sets of fibers, the multivariate analogues of isolines. We show simple methods for fiber surface definition and extraction. In particular, we show a simple and efficient fiber surface extraction algorithm based on Marching Cubes. We also show how to construct fiber surfaces interactively with geometric primitives in the range of the function. We then extend this to build user interfaces that generate parameterized families of fiber surfaces with respect to arbitrary polylines and polygons. In the special case of isovalue-gradient plots, fiber surfaces capture features geometrically for quantitative analysis that have previously only been analysed visually and qualitatively using multi-dimensional transfer functions in volume rendering. We also demonstrate fiber surface extraction on a variety of bivariate data



CPU Ray Tracing Large Particle Data with Balanced P-k-d Trees
I. Wald, A. Knoll, G. P. Johnson, W. Usher, V. Pascucci, M. E. Papka. In 2015 IEEE Scientific Visualization Conference, IEEE, Oct, 2015.
DOI: 10.1109/scivis.2015.7429492

We present a novel approach to rendering large particle data sets from molecular dynamics, astrophysics and other sources. We employ a new data structure adapted from the original balanced k-d tree, which allows for representation of data with trivial or no overhead. In the OSPRay visualization framework, we have developed an efficient CPU algorithm for traversing, classifying and ray tracing these data. Our approach is able to render up to billions of particles on a typical workstation, purely on the CPU, without any approximations or level-of-detail techniques, and optionally with attribute-based color mapping, dynamic range query, and advanced lighting models such as ambient occlusion and path tracing.



Data Science: What Is It and How Is It Taught?,
H. De Sterck, C.R. Johnson. In SIAM News, SIAM, July, 2015.



Visualization
C.R. Johnson, K. Potter. In The Princeton Companion to Applied Mathematics, Edited by Nicholas J. Higham, Princeton University Press, pp. 843-846. September, 2015.
ISBN: 9780691150390



Morse-Smale Analysis of Ion Diffusion for DFT Battery Materials Simulations,
A. Gyulassy, A. Knoll, K. C. Lau, Bei Wang, P. T. Bremer, M. E. Papka, L. A. Curtiss, V. Pascucci. In Topology-Based Methods in Visualization (TopoInVis), 2015.

Ab initio molecular dynamics (AIMD) simulations are increasingly useful in modeling, optimizing and synthesizing materials in energy sciences. In solving Schrodinger's equation, they generate the electronic structure of the simulated atoms as a scalar field. However, methods for analyzing these volume data are not yet common in molecular visualization. The Morse-Smale complex is a proven, versatile tool for topological analysis of scalar fields. In this paper, we apply the discrete Morse-Smale complex to analysis of first-principles battery materials simulations. We consider a carbon nanosphere structure used in battery materials research, and employ Morse-Smale decomposition to determine the possible lithium ion diffusion paths within that structure. Our approach is novel in that it uses the wavefunction itself as opposed distance fields, and that we analyze the 1-skeleton of the Morse-Smale complex to reconstruct our diffusion paths. Furthermore, it is the first application where specific motifs in the graph structure of the complete 1-skeleton define features, namely carbon rings with specific valence. We compare our analysis of DFT data with that of a distance field approximation, and discuss implications on larger classical molecular dynamics simulations.



ND2AV: N-Dimensional Data Analysis and Visualization -- Analysis for the National Ignition Campaign
P. T. Bremer, D. Maljovec, A. Saha, Bei Wang, J. Gaffney, B. K. Spears, V. Pascucci. In Computing and Visualization in Science, 2015.

One of the biggest challenges in high-energy physics is to analyze a complex mix of experimental and simulation data to gain new insights into the underlying physics. Currently, this analysis relies primarily on the intuition of trained experts often using nothing more sophisticated than default scatter plots. Many advanced analysis techniques are not easily accessible to scientists and not flexible enough to explore the potentially interesting hypotheses in an intuitive manner. Furthermore, results from individual techniques are often difficult to integrate, leading to a confusing patchwork of analysis snippets too cumbersome for data exploration. This paper presents a case study on how a combination of techniques from statistics, machine learning, topology, and visualization can have a significant impact in the field of inertial confinement fusion. We present the ND2AV: N-Dimensional Data Analysis and Visualization framework, a user-friendly tool aimed at exploiting the intuition and current work flow of the target users. The system integrates traditional analysis approaches such as dimension reduction and clustering with state-of-the-art techniques such as neighborhood graphs and topological analysis, and custom capabilities such as defining combined metrics on the fly. All components are linked into an interactive environment that enables an intuitive exploration of a wide variety of hypotheses while relating the results to concepts familiar to the users, such as scatter plots. ND2AV uses a modular design providing easy extensibility and customization for different applications. ND2AV is being actively used in the National Ignition Campaign and has already led to a number of unexpected discoveries.



Local, Smooth, and Consistent Jacobi Set Simplification
H. Bhatia, Bei Wang, G. Norgard, V. Pascucci, P. T. Bremer. In Computational Geometry, Vol. 48, No. 4, Elsevier, pp. 311-332. May, 2015.
DOI: 10.1016/j.comgeo.2014.10.009

The relation between two Morse functions defined on a smooth, compact, and orientable 2-manifold can be studied in terms of their Jacobi set. The Jacobi set contains points in the domain where the gradients of the two functions are aligned. Both the Jacobi set itself as well as the segmentation of the domain it induces, have shown to be useful in various applications. In practice, unfortunately, functions often contain noise and discretization artifacts, causing their Jacobi set to become unmanageably large and complex. Although there exist techniques to simplify Jacobi sets, they are unsuitable for most applications as they lack fine-grained control over the process, and heavily restrict the type of simplifications possible.

This paper introduces the theoretical foundations of a new simplification framework for Jacobi sets. We present a new interpretation of Jacobi set simplification based on the perspective of domain segmentation. Generalizing the cancellation of critical points from scalar functions to Jacobi sets, we focus on simplifications that can be realized by smooth approximations of the corresponding functions, and show how these cancellations imply simultaneous simplification of contiguous subsets of the Jacobi set. Using these extended cancellations as atomic operations, we introduce an algorithm to successively cancel subsets of the Jacobi set with minimal modifications to some userdefined metric. We show that for simply connected domains, our algorithm reduces a given Jacobi set to its minimal configuration, that is, one with no birth-death points (a birth-death point is a specific type of singularity within the Jacobi set where the level sets of the two functions and the Jacobi set have a common normal direction).