Browsing by Author "Reina, Guido"

Now showing 1 - 5 of 5

Open Access
Accelerated 2D visualization using adaptive resolution scaling and temporal reconstruction
(2023) Becher, Michael; Heinemann, Moritz; Marmann, Thomas; Reina, Guido; Weiskopf, Daniel; Ertl, Thomas
Data visualization relies on efficient rendering to allow users to interactively explore and understand their data. However, achieving interactive frame rates is often challenging, especially for high-resolution displays or large datasets. In computer graphics, several methods temporally reconstruct full-resolution images from multiple consecutive lower-resolution frames. Besides providing temporal image stability, they amortize the rendering costs over multiple frames and thus improve the minimum frame rate. We present a method that adopts this idea to accelerate 2D information visualization, without requiring any changes to the rendering itself. By exploiting properties of orthographic projection, our method significantly improves rendering performance while minimizing the loss of image quality during camera manipulation. For static scenes, it quickly converges to the full-resolution image. We discuss the characteristics and different modes of our method concerning rendering performance and image quality and the corresponding trade-offs. To improve ease of use, we provide automatic resolution scaling in our method to adapt to user-defined target frame rate. Finally, we present extensive rendering benchmarks to examine real-world performance for examples of parallel coordinates and scatterplot matrix visualizations, and discuss appropriate application scenarios and contraindications for usage.
Open Access
Power overwhelming : the one with the oscilloscopes
(2024) Gralka, Patrick; Müller, Christoph; Heinemann, Moritz; Reina, Guido; Weiskopf, Daniel; Ertl, Thomas
Visualization as a discipline has to investigate its practical implications in a world steadily moving toward greener computing methods. Quantifying the power consumption of visualization algorithms is thus essential, given the ever-increasing energy needs of GPUs. Previous approaches rely on integrated sensors or invasive methods that require modifications and special test setups. However, they still suffer from imprecision from low sampling rates and integration over time. Using a high-precision, high-frequency setup via steerable oscilloscopes, we can objectively measure the resulting quality of previous approaches. This is essential to establish a ground truth, pave the way for improved modeling of power consumption in general, and enable better estimates based on the output of lower-quality sensors. We finally discuss benefits that can be drawn from the additional insight of the higher-precision setup and which additional use cases can justify the incurred costs.
Open Access
Umgang mit Forschungssoftware an der Universität Stuttgart
(2020) Flemisch, Bernd; Hermann, Sibylle; Holm, Christian; Mehl, Miriam; Reina, Guido; Uekermann, Benjamin; Boehringer, David; Ertl, Thomas; Grad, Jean-Noël; Iglezakis, Dorothea; Jaust, Alexander; Koch, Timo; Seeland, Anett; Weeber, Rudolf; Weik, Florian; Weishaupt, Kilian
Wir empfehlen die Einrichtung einer Organisationseinheit Forschungssoftware-Entwicklung an der Universität Stuttgart und eines daran angegliederten Stellenpools von Research Software Engineers (RSEs). Dazu schlagen wir Maßnahmen zur Schaffung und Finanzierung entsprechender neuer RSE-Stellen, zur Integration bestehender Stellen sowie zur Gewinnung und Förderung geeigneter Personen vor. RSEs sind Personen, die sich um Konzeption, Organisation, Implementierung, Testen, Dokumentation und Wartung von Forschungssoftware kümmern. Die institutionelle Förderung von Forschungssoftware-Entwicklung ist notwendig, da die Bedeutung von Software für die Forschung und Anforderungen an die entsprechende Software, u.a. durch die DFG, stetig zunimmt.
Open Access
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.
Open Access
Visualization of uncorrelated point data
(2008) Reina, Guido; Ertl, Thomas (Prof. Dr.)
Sciences are the most common application context for computer-generated visualization. Researchers in these areas have to work with large datasets of many different types, but the one trait that is common to all is that in their raw form they exceed the cognitive abilities of human beings. Visualization not only aims at enabling users to quickly extract as much information as possible from datasets, but also at allowing the user to work at all with those that are too large and complex to be directly grasped by human cognition. In this work, the focus is on uncorrelated point data, or point clouds, which is sampled from real-world measurements or generated by computer simulations. Such datasets are gridless and exhibit no connectivity, and each point represents an entity of its own. To effectively work with such datasets, two main problems must be solved: on the one hand, a large number of complex primitives with potentially many attributes must be visualized, and on the other hand the interaction with the datasets must be designed in an intuitive way. This dissertation will present novel methods which allow the handling of large, point-based data sets of high dimensionality. The contribution for the rendering of hundreds of thousands of application-specific glyphs is a Graphics-Processing-Unit(GPU)-based solution that allows the exploration of datasets that exhibit a moderate number of dimensions, but an extremely large number of points. These approaches are proven to be working for molecular dynamics(MD) datasets as well as for 3D tensor fields. Factors critical for the performance of these algorithms are thoroughly analyzed, the main focus being on the fast rendering of these complex glyphs in high quality. To improve the visualization of datasets with many attributes and only a moderate number of points, methods for the interactive reduction of dimensionality and analysis of the influences of different dimensions as well as of different metrics will be presented. The rendering of the resulting data in 3D similarity space is also addressed. A GPU-based reduction of dimensions has been implemented that allows interactive tweaking of the reduction parameters while observing the results in real time. With the availability of a fast and responsive visualization, the missing component for a complete system is the human-computer interaction. The user must be able to navigate the information space and interact with a dataset, selecting or filtering the items that are of interest to him, inspecting the attributes of particular data points. Today, one must distinguish between the application context and the modality of different interaction approaches. Current research ranges from keyboard-and-mouse desktop interaction over different haptic interfaces (also including feedback) up to tracked interaction for virtual reality(VR) installations. In the context of this work, the problem of interacting with point-based datasets is tackled for two different situations. The first is the workstation-based analysis of clustering mechanics in thermodynamics simulations, the second a VR immersive navigation and interaction with point cloud datasets.