Titlebook: Interactive GPU-based Visualization of Large Dynamic Particle Data; Martin Falk,Sebastian Grottel,Guido Reina Book 2017 Springer Nature Sw

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History,, 1983]. As such, the widely accepted solutions presented in this lecture shall be briefly put into historical context. Very well- known and early examples for particle visualization were all applied to molecular dynamics, for example VMD [Humphrey et al., 1996], PyMOL [Schrödinger, LLC, 2015], Chim

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GPU-based Glyph Ray Casting,s it is true to the original simulation results. The basic idea is that each element in the data set, in the case of data sets from MD simulations atoms or rigid molecules, is depicted by a compact graphical element—a .—representing the principal attributes of the data element. There are several dif

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Data Structures, techniques are only profitable for dense data featuring inherent occlusion and, thus, potential for culling. When dealing with even more particles or sparse data, we need to resort to explicit data structures for both storing the data on the GPU as well as efficient data access during rendering.

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Improved Visual Quality,s possible, i.e., we can interactively view data sets with billions of particles on a single desktop machine. However, when viewing such large data sets problems with the visual quality occur, which are mainly related to aliasing. Figure 7.1 shows such an example. Obviously, these aliasing effects a

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Application-driven Abstractions,re given and discussed. The general idea is to create visualizations that show a special feature or property of the data. This can make the analysis of the data much easier. Often, the abstract visualization is based on a feature extraction, i.e., additional information is derived from the raw parti

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Summary and Outlook, historical overview of evolving hardware and the corresponding improvements in technique. First, the basic hybrid image/object-order GPU-based glyph ray casting was detailed, also showing approaches for optimizing data handling as well as improving visual quality for easier interpretation of data.