Universität Stuttgart
Permanent URI for this communityhttps://elib.uni-stuttgart.de/handle/11682/1
Browse
35 results
Search Results
Item Open Access Visualization challenges in distributed heterogeneous computing environments(2015) Panagiotidis, Alexandros; Ertl, Thomas (Prof. Dr.)Large-scale computing environments are important for many aspects of modern life. They drive scientific research in biology and physics, facilitate industrial rapid prototyping, and provide information relevant to everyday life such as weather forecasts. Their computational power grows steadily to provide faster response times and to satisfy the demand for higher complexity in simulation models as well as more details and higher resolutions in visualizations. For some years now, the prevailing trend for these large systems is the utilization of additional processors, like graphics processing units. These heterogeneous systems, that employ more than one kind of processor, are becoming increasingly widespread since they provide many benefits, like higher performance or increased energy efficiency. At the same time, they are more challenging and complex to use because the various processing units differ in their architecture and programming model. This heterogeneity is often addressed by abstraction but existing approaches often entail restrictions or are not universally applicable. As these systems also grow in size and complexity, they become more prone to errors and failures. Therefore, developers and users become more interested in resilience besides traditional aspects, like performance and usability. While fault tolerance is well researched in general, it is mostly dismissed in distributed visualization or not adapted to its special requirements. Finally, analysis and tuning of these systems and their software is required to assess their status and to improve their performance. The available tools and methods to capture and evaluate the necessary information are often isolated from the context or not designed for interactive use cases. These problems are amplified in heterogeneous computing environments, since more data is available and required for the analysis. Additionally, real-time feedback is required in distributed visualization to correlate user interactions to performance characteristics and to decide on the validity and correctness of the data and its visualization. This thesis presents contributions to all of these aspects. Two approaches to abstraction are explored for general purpose computing on graphics processing units and visualization in heterogeneous computing environments. The first approach hides details of different processing units and allows using them in a unified manner. The second approach employs per-pixel linked lists as a generic framework for compositing and simplifying order-independent transparency for distributed visualization. Traditional methods for fault tolerance in high performance computing systems are discussed in the context of distributed visualization. On this basis, strategies for fault-tolerant distributed visualization are derived and organized in a taxonomy. Example implementations of these strategies, their trade-offs, and resulting implications are discussed. For analysis, local graph exploration and tuning of volume visualization are evaluated. Challenges in dense graphs like visual clutter, ambiguity, and inclusion of additional attributes are tackled in node-link diagrams using a lens metaphor as well as supplementary views. An exploratory approach for performance analysis and tuning of parallel volume visualization on a large, high-resolution display is evaluated. This thesis takes a broader look at the issues of distributed visualization on large displays and heterogeneous computing environments for the first time. While the presented approaches all solve individual challenges and are successfully employed in this context, their joint utility form a solid basis for future research in this young field. In its entirety, this thesis presents building blocks for robust distributed visualization on current and future heterogeneous visualization environments.Item Open Access 3D visualization of multivariate data(2012) Sanftmann, Harald; Weiskopf, Daniel (Prof. Dr.)Nowadays large amounts of data are organized in tables, especially in relational databases where the rows store the data items to which multiple attributes are stored in the columns. Information stored this way, having multiple (more than two or three) attributes, can be treated as multivariate data. Therefore, visualization methods for multivariate data have a large application area and high potential utility. This thesis focuses on the application of 3D scatter plots for the visualization of multivariate data. When dealing with 3D, spatial perception needs to be exploited, by effectively using depth cues to convey spatial information to the user. To improve the presentation of individual 3D scatter plots, a technique is presented that applies illumination to them, thus using the shape-from-shading depth cue. To enable the analysis not only of 3D but of multivariate data, a novel technique is introduced that allows the navigation between 3D scatter plots. Inspecting the large number of 3D scatter plots that can be projected from a multivariate data set is very time consuming. The analysis of multivariate data can benefit from automatic machine learning approaches. A presented method uses decision trees to increase the speed a user can gain an understanding of the multivariate data at no extra cost. Stereopsis can also support the display of 3D scatter plots. Here an improved anaglyph rendering technique is presented, significantly reducing ghosting artifacts. The technique is not only applicable for information visualization, but for general rendering or to present stereoscopic image data. Some information visualization algorithms require high computation time. Many of these algorithms can be parallelized to run interactively. A framework that supports the parallelization on shared and distributed memory systems is presented.Item Open Access Visualization and mesoscopic simulation in systems biology(2013) Falk, Martin Samuel; Ertl, Thomas (Prof. Dr.)A better understanding of the internal mechanisms and interplays within a single cell is key to the understanding of life. The focus of this thesis lies on the mechanism of cellular signal transduction, i.e. relaying a signal from outside the cell by different means of transport toward its target inside the cell. Besides experiments, understanding can also be achieved by numerical simulations of cellular behavior which require theoretical models to be designed and evaluated. This is where systems biology closely relates and depends on recent research results in computer science in order to deal with the modeling, the simulation, and the analysis of the computational results. Since a single cell can consist of billions of atoms, the simulation of intracellular processes requires a simplified, mesoscopic model. The simulation domain has to be three dimensional to consider the spatial, possibly asymmetric, intracellular architecture filled with individual particles representing signaling molecules. In contrast to continuous models defined by systems of partial differential equations, a particle-based model allows tracking individual molecules moving through the cell. The overall process of signal propagation usually requires between minutes and hours to complete, but the movement of molecules and the interactions between them have to be determined in the microsecond range. Hence, the computation of thousands of consecutive time steps is necessary, requiring several hours or even days of computational time for a non-parallel simulation. To speed up the simulation, the parallel hardware of current central processing units (CPUs) and graphics processing units (GPUs) can be employed. Finally, the resulting data has to be analyzed by domain experts and, therefore, has to be represented in meaningful ways. Typical prevalent analysis methods include the aggregation of the data in tables or simple 2D graph plots, sometimes 3D plots for continuous data. Despite the fact that techniques for interactive visualization of data in 3D are well-known, so far none of the methods have been applied to the biological context of single cell models and specialized visualizations fitted to the experts’ need are missing. Another issue is the hardware available to the domain experts that can be used for the task of visualizing the increasing amount of time-dependent data resulting from simulations. It is important that the visualization keeps up with the simulations to ensure that domain experts can still analyze their data sets. To deal with the massive amount of data to come, compute clusters will be necessary with specialized hardware dedicated to data visualization. It is, thus, important, to develop visualization algorithms for this dedicated hardware, which is currently available as GPU. In this thesis, the computational power of recent many-core architectures (CPUs and GPUs) is harnessed for both the simulation and the visualizations. Novel parallel algorithms are introduced to parallelize the spatio-temporal, mesoscopic particle simulation to fit the architectures of CPU and GPU in a similar way. Besides molecular diffusion, the simulation considers extracellular effects on the signal propagation as well as the import of molecules into the nucleus and a dynamic cytoskeleton. An extensive comparison between different configurations is performed leading to the conclusion that the usage of GPUs is not always beneficial. For the visual data analysis, novel interactive visualization techniques were developed to visualize the 3D simulation results. Existing glyph-based approaches are combined in a new way facilitating the visualization of the individual molecules in the interior of the cell as well as their trajectories. A novel implementation of the depth of field effect combined with additional depth cues and coloring aid the visual perception and reduce visual clutter. To obtain a continuous signal distribution from the discrete particles, techniques known from volume rendering are employed. The visualization of the underlying atomic structures provides new detailed insights and can be used for educational purposes besides showing the original data. A microscope-like visualization allows for the first time to generate images of synthetic data similar to images obtained in wet lab experiments. The simulation and the visualizations are merged into a prototypical framework, thereby supporting the domain expert during the different stages of model development, i.e. design, parallel simulation, and analysis. Although the proposed methods for both simulation and visualization were developed with the study of single-cell signal transduction processes in mind, they are also applicable to models consisting of several cells and other particle-based scenarios. Examples in this thesis include the diffusion of drugs into a tumor, the detection of protein cavities, and molecular dynamics data from laser ablation simulations, among others.Item Open Access Prediction and similarity models for visual analysis of spatiotemporal data(2022) Tkachev, Gleb; Ertl, Thomas (Prof. Dr.)Ever since the early days of computers, their usage have become essential in natural sciences. Whether through simulation, computer-aided observation or data processing, the progress in computer technology have been mirrored by the constant growth in the size of scientific data. Unfortunately, as the data sizes grow, and human capabilities remains constant, it becomes increasingly difficult to analyze and understand the data. Over the last decades, visualization experts have proposed many approaches to address the challenge, but even these methods have their limitations. Luckily, recent advances in the field of Machine Learning can provide the tools needed to overcome the obstacle. Machine learning models are a particularly good fit as they can both benefit from the large amount of data present in the scientific context and allow the visualization system to adapt to the problem at hand. This thesis presents research into how machine learning techniques can be adapted and extended to enable visualization of scientific data. It introduces a diverse set of techniques for analysis of spatiotemporal data, including detection of irregular behavior, self-supervised similarity metrics, automatic selection of visual representations and more. It also discusses the general challenges of applying Machine Learning to Scientific Visualization and how to address them.Item Open Access Advanced visualization techniques for flow simulations : from higher-order polynomial data to time-dependent topology(2013) Üffinger, Markus; Ertl, Thomas (Prof. Dr.)Computational Wuid dynamics (CFD) has become an important tool for predicting Fluid behavior in research and industry. Today, in the era of tera- and petascale computing, the complexity and the size of simulations have reached a state where an extremely large amount of data is generated that has to be stored and analyzed. An indispensable instrument for such analysis is provided by computational Wow visualization. It helps in gaining insight and understanding of the Wow and its underlying physics, which are subject to a complex spectrum of characteristic behavior, ranging from laminar to turbulent or even chaotic characteristics, all of these taking place on a wide range of length and time scales. The simulation side tries to address and control this vast complexity by developing new sophisticated models and adaptive discretization schemes, resulting in new types of data. Examples of such emerging simulations are generalized Vnite element methods or hp-adaptive discontinuous Galerkin schemes of high-order. This work addresses the direct visualization of the resulting higher-order Veld data, avoiding the traditional resampling approach to enable a more accurate visual analysis. The second major contribution of this thesis deals with the inherent complexity of Wuid dynamics. New feature-based and topology-based visualization algorithms for unsteady Wow are proposed to reduce the vast amounts of raw data to their essential structure. For the direct visualization pixel-accurate techniques are presented for 2D Veld data from generalized Vnite element simulations, which consist of a piecewise polynomial part of high order enriched with problem-dependent ansatz functions. Secondly, a direct volume rendering system for hp-adaptive Vnite elements, which combine an adaptive grid discretization with piecewise polynomial higher-order approximations, is presented. The parallel GPU implementation runs on single workstations, as well as on clusters, enabling a real-time generation of high quality images, and interactive exploration of the volumetric polynomial solution. Methods for visual debugging of these complex simulations are also important and presented. Direct Wow visualization is complemented by new feature and topology-based methods. A promising approach for analyzing the structure of time-dependent vector Velds is provided by Vnite-time Lyapunov exponent (FTLE) Velds. In this work, interactive methods are presented that help in understanding the cause of FTLE structures, and novel approaches to FTLE computation are developed to account for the linearization error made by traditional methods. Building on this, it is investigated under which circumstances FTLE ridges represent Lagrangian coherent structures (LCS)—the timedependent counterpart to separatrices of traditional “steady” vector Veld topology. As a major result, a novel time-dependent 3D vector Veld topology concept based on streak surfaces is proposed. Streak LCS oUer a higher quality than corresponding FTLE ridges, and animations of streak LCS can be computed at comparably low cost, alleviating the topological analysis of complex time-dependent Velds.Item Open Access Uncertainty-aware visualization techniques(2021) Schulz, Christoph; Weiskopf, Daniel (Prof. Dr.)Nearly all information is uncertainty-afflicted. Whether and how we present this uncertainty can have a major impact on how our audience perceives such information. Still, uncertainty is rarely visualized and communicated. One difficulty is that we tend to interpret illustrations as truthful. For example, it is difficult to understand that a drawn point’s presence, absence, and location may not convey its full information. Similarly, it may be challenging to classify a point within a probability distribution. One must learn how to interpret uncertainty-afflicted information. Accordingly, this thesis addresses three research questions: How can we identify and reason about uncertainty? What are approaches to modeling flow of uncertainty through the visualization pipeline? Which methods are suitable for harnessing uncertainty? The first chapter is concerned with sources of uncertainty. Then, approaches to model uncertainty using descriptive statistics and unsupervised learning are discussed. Also, a model for validation and evaluation of visualization methods is proposed. Further, methods for visualizing uncertainty-afflicted networks, trees, point data, sequences, and time series are presented. The focus lies on modeling, propagation, and visualization of uncertainty. As encodings of uncertainty, we propose wave-like splines and sampling-based transparency. As an overarching approach to adapt existing visualization methods for uncertain information, we identify the layout process (the placement of objects). The main difficulty is that these objects are not simple points but distribution functions or convex hulls. We also develop two stippling-based methods for rendering that utilize the ability of the human visual system to cope with uncertainty. Finally, I provide insight into possible directions for future research.Item Open Access Visualization techniques for parallel coordinates(2013) Heinrich, Julian; Weiskopf, Daniel (Prof. Dr.)Visualization plays a key role in knowledge discovery, visual data exploration, and visual analytics. Static images are an effective tool for visual communication, summarization, and pattern extraction in large and complex datasets. Only together with human-computer-interaction techniques, visual interfaces enable an analyst to explore large information spaces and to drive the whole analytical reasoning process. Scatterplots and parallel coordinates are well-recognized visualization techniques that are commonly employed for statistics (both explorative and descriptive) and data-mining, but are also gaining importance for scientific visualization. While scatterplots are restricted to the display of at most three dimensions due to the orthogonal layout of coordinate axes, a parallel arrangement allows for the visualization of multiple attributes of a dataset. Although both techniques rely on projections of higher-dimensional geometry and are related by a point–line duality, parallel coordinates enjoy great popularity for the visualization and analysis of multivariate data. Despite their popularity, parallel coordinates are subject to a number of limitations that remain to be solved. For large datasets, the potentially high amount of overlapping lines may hinder the observer from visually extracting meaningful patterns. Encoding observations with polylines make it difficult to follow lines over all dimensions, as they lose visual continuation across the axes. Clusters cannot be represented by the geometry of lines, and the order of axes has a high impact on the patterns exhibited by parallel coordinates. This thesis presents visualization techniques for parallel coordinates that address these limitations. As a foundation, an extensive review of the state of the art of parallel coordinates will be given. Based on the point–line duality, the existing model of continuous scatterplots is adapted to parallel coordinates for the visualization of data defined on continuous domains. To speed up computation and obtain interactive frame rates, a scalable and progressive rendering algorithm is introduced that further allows for arbitrary reconstruction and interpolation schemes. A curve-bundling model for parallel coordinates is evaluated with a user study showing that bundling is effective for cluster visualization based on geometric cues while being equally capable of revealing correlations between neighboring axes. To address the axis-order problem, a graph-based approach is presented that allows for the visualization of all pairwise relations in a matrix layout without redundancy. Finally, the use of parallel coordinates is demonstrated for real datasets from computational fluid dynamics, motion capturing, bioinformatics, and systems biology.Item Open Access Interactive web-based visualization(2018) Mwalongo, FinianThe visualization of large amounts of data, which cannot be easily copied for processing on a user’s local machine, is not yet a fully solved problem. Remote visualization represents one possible solution approach to the problem, and has long been an important research topic. Depending on the device used, modern hardware, such as high-performance GPUs, is sometimes not available. This is another reason for the use of remote visualization. Additionally, due to the growing global networking and collaboration among research groups, collaborative remote visualization solutions are becoming more important. The additional use of collaborative visualization solutions is eventually due to the growing global networking and collaboration among research groups. The attractiveness of web-based remote visualization is greatly increased by the wide availability of web browsers on almost all devices; these are available today on all systems - from desktop computers to smartphones. In order to ensure interactivity, network bandwidth and latency are the biggest challenges that web-based visualization algorithms have to solve. Despite the steady improvements in available bandwidth, these improvements are still significantly slower than, for example, processor performance, resulting in increasing the impact of this bottleneck. For example, visualization of large dynamic data in low-bandwidth environments can be challenging because it requires continuous data transfer. However, bandwidth improvement alone cannot improve the latency because it is also affected by factors such as the distance between server and client and network utilization. To overcome these challenges, a combination of techniques is needed to customize the individual processing steps of the visualization pipeline, from efficient data representation to hardware-accelerated rendering on the client side. This thesis first deals with related work in the field of remote visualization with a particular focus on interactive web-based visualization and then presents techniques for interactive visualization in the browser using modern web standards such as WebGL and HTML5. These techniques enable the visualization of dynamic molecular data sets with more than one million atoms at interactive frame rates using GPU-based ray casting. Due to the limitations which exist in a browser-based environment, the concrete implementation of the GPU-based ray casting had to be customized. Evaluation of the resulting performance shows that GPU-based techniques enable the interactive rendering of large data sets and achieve higher image quality compared to polygon-based techniques. In order to reduce data transfer times and network latency, and improve rendering speed, efficient approaches for data representation and transmission are used. Furthermore, this thesis introduces a GPU-based volume-ray marching technique based on WebGL 2.0, which uses progressive brick-wise data transfer, as well as multiple levels of detail in order to achieve interactive volume rendering of datasets stored on a server. The concepts and results presented in this thesis contribute to the further spread of interactive web-based visualization. The algorithmic and technological advances that have been achieved form a basis for further development of interactive browser-based visualization applications. At the same time, this approach has the potential for enabling future collaborative visualization in the cloud.Item Open Access Visualization of two-phase flow dynamics : techniques for droplet interactions, interfaces, and material transport(2017) Karch, Grzegorz Karol; Ertl, Thomas (Prof. Dr.)Computational visualization allows scientists and engineers to better understand simulation data and gain insights into the studied natural processes. Particularly in the field of computational fluid dynamics, interactive visual presentation is essential in the investigation of physical phenomena related to gases and liquids. To ensure effective analysis, flow visualization techniques must adapt to the advancements in the field of fluid dynamics that benefits substantially from the growing computational power of both commodity desktops and supercomputers on the one hand, and steadily expanding knowledge about fluid physics on the other. A prominent example of these advances can be found in the research of two-phase flow with liquid droplets and jets, where high performance computation and sophisticated algorithms for phase tracking enable well resolved and physically accurate simulations of liquid dynamics. Yet, the field of two-phase flow has remained largely unexplored in visualization research so far, leaving the scientists and engineers with a number of challenges when analyzing the data. These include the difficulty in tracking and investigating topological events in large droplet groups, high complexity of droplet dynamics due to the involved interfaces, and a limited choice of high quality interactive methods for the analysis of related transport phenomena. It is therefore the aim of this thesis to address these challenges by providing a multi-scale approach for the visual investigation of two-phase flow, with the focus on the analysis of droplet interaction, fluid interfaces, and material transport. To address the problem of analyzing highly complex two-phase flow simulations with droplet groups and jets, a linked-view approach with three-dimensional and abstract space-time graph representation of droplet dynamics is proposed. The interactive brushing and linking allows for general exploration of topological events as well as detailed inspection of dynamics in terms of oscillations and rotations of droplets. Another approach further examines the separation of liquid phases by segmenting liquid volumes according to their topological changes in future time. For visualization, boundary surfaces of these volume segments are extracted that reveal intricate details of droplet topology dynamics. Additionally, within this framework, visualization of advected particles corresponding to arbitrarily selected segment provides useful insights into the spatio-temporal evolution of the segment. The analysis of interfaces is necessary to understand the interplay of interface dynamics and the dynamics of droplet interactions. A commonly used technique for interface tracking in the volume of fluid-based simulations is the piecewise linear approximation which, although accurate, can affect the quality of the simulation results. To study the influence of the interface reconstruction on the phase tracking procedure, a visualization method is presented that extracts the interfaces by means of the first-order Taylor approximation, and provides several derived quantities that help assess the simulation results in relation to the interface reconstruction quality. The liquid interface is further investigated from the physical standpoint with an approach based on quantities derived from velocity and surface tension gradients. The developed method supports examination of surface tension forces and their impact on the interface instability, as well as detailed analysis of interface deformation characteristics. A line of research important for engineering applications is the analysis of electric fields on droplet interfaces. It is, however, complicated by higher-order elements used in the simulations to preserve field discontinuities. A visualization method has been developed that correctly visualizes these discontinuities at material boundaries. Additionally, the employed space-time representation of the droplet-insulator contact line reveals characteristics of electric field dynamics. The dynamics of droplets are often examined assuming single-phase flow, for instance when the internal material transport is of interest. From the visualization perspective, this allows for adaption of traditional vector field visualization techniques to the investigation of the studied phenomena. As one such concept, dye based visualization is proposed that extends the transport analysis to advection-diffusion problems, therefore revealing true transport behavior. The employed high quality advection preserves fine details of the dye, while the implementation on graphics processing units ensures interactive visualization. Several streamline-based concepts are applied in space-time representation of 2D unsteady flow. By interpreting time as the third spatial dimension, many 3D streamline-based visualization techniques can be applied to investigate 2D unsteady flow. The introduced vortex core ribbons support the examination of vortical flow behavior by revealing rotation near the core lines. For the study of topological structures, a method has been developed that extracts separatrices implicitly as boundaries of regions with different flow behavior, and therefore avoids potentially complicated explicit extraction of various topological structures. All proposed techniques constitute a novel multi-scale approach for visual analysis of two-phase flow. The analysis of droplet interactions is addressed with visualization of the phenomena leading to breakups and with detailed visual inspection of these breakups. On the interface level, techniques for the interface analysis give insights into the simulation quality, mechanisms behind topology changes, as well as the behavior of electrically charged droplets. Further down the scale, the dye-based visualization, streamline-based concepts for space-time analysis, and the implicit extraction of flow topology allow for the investigation of droplet internal transport as well as general single-phase flow scenarios. The applicability of the proposed methods extends, in a varying degree, beyond the use in two-phase flow. Their usability is demonstrated on data from simulations based on Navier-Stokes equations that exemplify practical problems in the research of fluid dynamics.Item Open Access Vision-based methods for evaluating visualizations(2018) Netzel, Rudolf; Weiskopf, Daniel (Prof. Dr.)