VisGap2020 - The Gap between Visualization Research and Visualization Software

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Browsing VisGap2020 - The Gap between Visualization Research and Visualization Software by Subject "Computing methodologies"

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    Lessons Learned from Large Data Visualization Software Development for the K computer
    (The Eurographics Association, 2020) Nonaka, Jorji; Sakamoto, Naohisa; Gillmann, Christina and Krone, Michael and Reina, Guido and Wischgoll, Thomas
    High Performance Computing (HPC) always had a close relationship with visualization as we can remember the landmark report on ''Visualization in Scientific Computing'', which was credited to have coined the term Scientific Visualization (SciVis). K computer, a Japanese flagship HPC system, appeared in 2011 as the most powerful supercomputer in the Top500 list, and as other similar HPC systems in that ranking, it was designed to enable ''Grand Challenge'' scientific computing with unprecedented scale and size. RIKEN Center for Computational Science (RIKEN R-CCS) operated and provided the K computer's computational resources to the HPC community for almost 8 years until it was decommissioned in 2019. Considering that most of the scientific computing results were publicly presented in the form of visual images and movies, we can infer that the SciVis was widely applied for assisting the domain scientists with their end-to-end scientific computing workflows. In addition to the traditional visualization applications, various others large data visualization software development were conducted in order to tackle the increased size and amount of the simulation outputs. RIKEN R-CCS participated in some of these development and deployment dealing with several environmental and human factors. Although we have no precise statistics regarding the visualization software usage, in this paper, we would like to present some findings and lessons learned from the large data visualization software development in the K computer environment.
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    Selecting and Sharing Multidimensional Projection Algorithms: A Practical View
    (The Eurographics Association, 2020) Espadoto, Mateus; Vernier, Eduardo Faccin; Telea, Alexandru C.; Gillmann, Christina and Krone, Michael and Reina, Guido and Wischgoll, Thomas
    Multidimensional Projection techniques are often used by data analysts for exploring multivariate datasets, but the task of selecting the best technique for the job is not trivial, as there are many candidates and the reasons for picking one over another are usually unclear. On the other hand, researchers developing new techniques can have a hard time comparing their new technique to existing ones and sharing their code in a way that makes it readily available for the public. In this paper, we try to address those issues systematically by analyzing recent surveys in the area, identifying the methods and tools used, and discussing challenges, limitations, and ideas for further work.
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    Tales from the Trenches: Developing sciview, a new 3D viewer for the ImageJ community
    (The Eurographics Association, 2020) Günther, Ulrik; Harrington, Kyle I. S.; Gillmann, Christina and Krone, Michael and Reina, Guido and Wischgoll, Thomas
    ImageJ/Fiji is a widely-used tool in the biomedical community for performing everyday image analysis tasks. However, its 3D viewer component (aptly named 3D Viewer) has become dated and is no longer actively maintained. We set out to create an alternative tool that not only brings modern concepts and APIs from computer graphics to ImageJ, but is designed to be robust to long-term, open-source development. To achieve this we divided the visualization logic into two parts: the rendering framework, scenery, and the user-facing application, sciview. In this paper we describe the development process and design decisions made, putting an emphasis on sustainable development, community building, and software engineering best practises. We highlight the motivation for the Java Virtual Machine (JVM) as a target platform for visualisation applications. We conclude by discussing the remaining milestones and strategy for long-term sustainability.