13 Zentrale Universitätseinrichtungen

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/14

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Author: search.filters.author.Frey, Steffen

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    Visually enriching and comparing runtime performance of visualization pipelines
    (2026) Tarner, Hagen; Gralka, Patrick; Reina, Guido; Beck, Fabian; Frey, Steffen
    In many graphics and visualization frameworks, directed acyclic graphs are a popular way to model visualization pipelines. Pipeline editors provide a visual interface to modify the underlying graph and use, e.g., a node-link diagram to visualize the data and program flow. This paper proposes an interactive tool for post-mortem performance analysis and comparison of pipeline variants. We extend the node-link representation of a visualization pipeline and enrich it with fine-grained runtime performance metrics. Annotating this static structure with dynamic performance information lets the developer evaluate performance characteristics in depth. Our approach further supports a visual comparison of two user-selected states of the graph. The comparison allows us to identify the impact of (topological) changes on the graph’s performance. We demonstrate the utility of our approach with different scientific visualization use cases and report expert feedback.
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    Visual ensemble analysis of fluid flow in porous media across simulation codes and experiment
    (2023) Bauer, Ruben; Ngo, Quynh Quang; Reina, Guido; Frey, Steffen; Flemisch, Bernd; Hauser, Helwig; Ertl, Thomas; Sedlmair, Michael
    We study the question of how visual analysis can support the comparison of spatio-temporal ensemble data of liquid and gas flow in porous media. To this end, we focus on a case study, in which nine different research groups concurrently simulated the process of injecting CO 2into the subsurface. We explore different data aggregation and interactive visualization approaches to compare and analyze these nine simulations. In terms of data aggregation, one key component is the choice of similarity metrics that define the relationship between different simulations. We test different metrics and find that using the machine-learning model “S4” (tailored to the present study) as metric provides the best visualization results. Based on that, we propose different visualization methods. For overviewing the data, we use dimensionality reduction methods that allow us to plot and compare the different simulations in a scatterplot. To show details about the spatio-temporal data of each individual simulation, we employ a space-time cube volume rendering. All views support linking and brushing interaction to allow users to select and highlight subsets of the data simultaneously across multiple views. We use the resulting interactive, multi-view visual analysis tool to explore the nine simulations and also to compare them to data from experimental setups. Our main findings include new insights into ranking of simulation results with respect to experimental data, and the development of gravity fingers in simulations.
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    Visual analysis of droplet dynamics in large-scale multiphase spray simulations
    (2021) Heinemann, Moritz; Frey, Steffen; Tkachev, Gleb; Straub, Alexander; Sadlo, Filip; Ertl, Thomas
    We present a data-driven visual analysis approach for the in-depth exploration of large numbers of droplets. Understanding droplet dynamics in sprays is of interest across many scientific fields for both simulation scientists and engineers. In this paper, we analyze large-scale direct numerical simulation datasets of the two-phase flow of non-Newtonian jets. Our interactive visual analysis approach comprises various dedicated exploration modalities that are supplemented by directly linking to ParaView. This hybrid setup supports a detailed investigation of droplets, both in the spatial domain and in terms of physical quantities. Considering a large variety of extracted physical quantities for each droplet enables investigating different aspects of interest in our data. To get an overview of different types of characteristic behaviors, we cluster massive numbers of droplets to analyze different types of occurring behaviors via domain-specific pre-aggregation, as well as different methods and parameters. Extraordinary temporal patterns are of high interest, especially to investigate edge cases and detect potential simulation issues. For this, we use a neural network-based approach to predict the development of these physical quantities and identify irregularly advected droplets.
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    Strategies for efficient parallel visualization
    (2014) Frey, Steffen; Ertl, Thomas (Prof. Dr.)
    Visualization is a crucial tool for analyzing data and gaining a deeper understanding of underlying features. In particular, interactive exploration has shown to be indispensable, as it can provide new insights beyond the original focus of analysis. However, efficient interaction requires almost immediate feedback to user input, and achieving this poses a big challenge for the visualization of data that is ever-growing in size and complexity. This motivates the increasing effort in recent years towards high-performance visualization using powerful parallel hardware architectures. The analysis and rendering of large volumetric grids and time-dependent data is particularly challenging. Despite many years of active research, significant improvements are still required to enable the efficient explorative analysis for many use cases and scenarios. In addition, while many diverse kinds of approaches have been introduced to tackle different angles of the issue, no consistent scheme exists to classify previous efforts and to guide further development. This thesis presents research that enables or improves the interactive analysis in various areas of scientific visualization. To begin with, new techniques for the interactive analysis of time-dependent field and particle data are introduced, focusing both on the expressiveness of the visualization and on a structure allowing for efficient parallel computing. Volume rendering is a core technique in scientific visualization, that induces significant costs. In this work, approaches are presented that decrease this cost by means of a new acceleration data structure, and handle it dynamically by adapting the progressive visualization process on-the-fly based on the estimation of spatio-temporal errors. In addition, view-dependent representations are presented that both reduce the size and render cost of volume data with only minor quality impact for a range of camera configurations. Remote and in-situ rendering approaches are discussed for enabling the interactive volume visualization without having to move the actual volume data. In detail, an approach for the integrated adaptive sampling and compression is introduced, as well as a technique allowing for user prioritization of critical results. Computations are further dynamically redistributed to reduce load imbalance. In detail, this encompasses the tackling of divergence issues on GPUs, the adaptation of volume data assigned to each node for rendering in distributed GPU clusters, and the detailed consideration of the different performance characteristics of the components in a heterogeneous system. From these research projects, a variety of generic strategies towards high-performance visualization is extracted, ranging from the parallelization of the program structure and algorithmic optimization, to the efficient execution on parallel hardware architectures. The introduced strategy tree further provides a consistent and comprehensive hierarchical classification of these strategies. It can provide guidance during development to identify and exploit potentials for improving the performance of visualization applications, and it can be used as expressive taxonomy for research on high-performance visualization and computer graphics.
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    Voronoi cell interface‐based parameter sensitivity analysis for labeled samples
    (2025) Bauer, Ruben; Evers, Marina; Ngo, Quynh Quang; Reina, Guido; Frey, Steffen; Sedlmair, Michael
    Varying the input parameters of simulations or experiments often leads to different classes of results. Parameter sensitivity analysis in this context includes estimating the sensitivity to the individual parameters, that is, to understand which parameters contribute most to changes in output classifications and for which parameter ranges these occur. We propose a novel visual parameter sensitivity analysis approach based on Voronoi cell interfaces between the sample points in the parameter space to tackle the problem. The Voronoi diagram of the sample points in the parameter space is first calculated. We then extract Voronoi cell interfaces which we use to quantify the sensitivity to parameters, considering the class label information of each sample's corresponding output. Multiple visual encodings are then utilized to represent the cell interface transitions and class label distribution, including stacked graphs for local parameter sensitivity. We evaluate the approach's expressiveness and usefulness with case studies for synthetic and real‐world datasets.