Sebastian Grottel

Coherent culling and shading for large molecular dynamics visualization

Molecular dynamics simulations are a principal tool for studying molecular systems. Such simulations are used to investigate molecular structure, dynamics, and thermodynamical properties, as well as a replacement for, or complement to, costly and dangerous experiments. With the increasing availability of computational power the resulting data sets are becoming increasingly larger, and benchmarks indicate that the interactive visualization on desktop computers poses a challenge when rendering substantially more than millions of glyphs. Trading visual quality for rendering performance is a common approach when interactivity has to be guaranteed. In this paper we address both problems and present a method for high‐quality visualization of massive molecular dynamics data sets. We employ several optimization strategies on different levels of granularity, such as data quantization, data caching in video memory, and a two‐level occlusion culling strategy: coarse culling via hardware occlusion queries and a vertex‐level culling using maximum depth mipmaps. To ensure optimal image quality we employ GPU raycasting and deferred shading with smooth normal vector generation. We demonstrate that our method allows us to interactively render data sets containing tens of millions of high‐quality glyphs.

MegaMol—A Prototyping Framework for Particle-Based Visualization

Visualization applications nowadays not only face increasingly larger datasets, but have to solve increasingly complex research questions. They often require more than a single algorithm and consequently a software solution will exceed the possibilities of simple research prototypes. Well-established systems intended for such complex visual analysis purposes have usually been designed for classical, mesh-based graphics approaches. For particle-based data, however, existing visualization frameworks are too generic - e.g. lacking possibilities for consistent low-level GPU optimization for high-performance graphics - and at the same time are too limited - e.g. by enforcing the use of structures suboptimal for some computations. Thus, we developed the system softwareMegaMol for visualization research on particle-based data. On the one hand, flexible data structures and functional module design allow for easy adaption to changing research questions, e.g. studying vapors in thermodynamics, solid material in physics, or complex functional macromolecules like proteins in biochemistry. Therefore, MegaMol is designed as a development framework. On the other hand, common functionality for data handling and advanced rendering implementations are available and beneficial for all applications. We present several case studies of work implemented using our system as well as a comparison to other freely available or open source systems.

Object-space ambient occlusion for molecular dynamics

In many different application fields particle-based simulation, like molecular dynamics, are used to study material properties and behavior. Nowadays, simulation data sets consist of millions of particles and thousands of time steps challenging interactive visualization. Direct glyph-based representations of the particle data are important for the visual analysis process and these rendering methods can be optimized to be able to work sufficiently fast with huge data sets. However, the perception of the implicit spatial structures formed by such data is often hindered by aliasing and visual clutter. Especially the depth of these structures can be grasped better if visual cues are applied, even in interactive representations. We hence present a method to apply object-space ambient occlusion, based on local neighborhood information, to large timedependent particle-based data sets without the need for any precomputations. Based on density information collected in real-time, glyph-based representations of the data sets can be visually enhanced without significant impact on the rendering performance allowing to visualize multi-million particle data sets interactively on commodity workstations.

Parallel Contour-Buildup algorithm for the molecular surface

Molecular Dynamics simulations are an essential tool for many applications. The simulation of large molecules — like proteins — over long trajectories is of high importance e. g. for pharmaceutical, biochemical and medical research. For analyzing these data sets interactive visualization plays a crucial role as details of the interactions of molecules are often affected by the spatial relations between these molecules. From the large range of visual representations for such data, molecule surface representations are of high importance as they clearly depict geometric interactions, such as docking or substrate channel accessibility. However, these surface visualizations are computationally demanding and thus pose a challenge for interactive visualization of time-dependent data sets. We propose an optimization of the Contour-Buildup algorithm for the Solvent Excluded Surface (SES) to remedy this issue. An optimized subdivision of calculation tasks of the original algorithm allows for full utilization of massive parallel processing hardware. Our approach is especially well suited for modern graphics hardware employing the CUDA programming language. As we do not rely on any pre-computations our method is intrinsically applicable to time-dependent data with arbitrarily long trajectories. We are able to visualize the SES for molecules with up to ten thousand atoms interactively on standard consumer graphics cards.

Optimized data transfer for time-dependent, GPU-based glyphs

Particle-based simulations are a popular tool for researchers in various sciences. In combination with the availability of ever larger COTS clusters and the consequently increasing number of simulated particles the resulting datasets pose a challenge for real-time visualization. Additionally the semantic density of the particles exceeds the possibilities of basic glyphs, like splats or spheres and results in dataset sizes larger by at least an order of magnitude. Interactive visualization on common workstations requires a careful optimization of the data management, especially of the transfer between CPU and GPU. We propose a flexible benchmarking tool along with a series of tests to allow the evaluation of the performance of different CPU/GPU combinations in relation to a particular implementation. We evaluate different uploading strategies and rendering methods for point-based compound glyphs suitable for representing the aforementioned datasets. CPU and GPU-based approaches are compared with respect to their rendering and storage efficiency to point out the optimal solution when dealing with time-dependent datasets. The results of our research are of general interest since they can be transferred to other applications where CPU-GPU bandwidth and a high number of graphical primitives per dataset pose a problem. The employed tool set for streamlining the measurement process is made publicly available.

Homogeneous nucleation in supersaturated vapors of methane, ethane, and carbon dioxide predicted by brute force molecular dynamics

Molecular dynamics (MD) simulation is applied to the condensation process of supersaturated vapors of methane, ethane, and carbon dioxide. Simulations of systems with up to a 10(6) particles were conducted with a massively parallel MD program. This leads to reliable statistics and makes nucleation rates down to the order of 10(30) m(-3) s(-1) accessible to the direct simulation approach. Simulation results are compared to the classical nucleation theory (CNT) as well as the modification of Laaksonen, Ford, and Kulmala (LFK) which introduces a size dependence of the specific surface energy. CNT describes the nucleation of ethane and carbon dioxide excellently over the entire studied temperature range, whereas LFK provides a better approach to methane at low temperatures.

Visual abstractions of solvent pathlines near protein cavities

Water is known to play a crucial role in protein structure, flexibility and activity. The use of molecular dynamics simulations allows detailed studies of complex protein‐solvent interactions. Cluster analysis and density‐based approaches have been successfully used for the identification and analysis of conserved water molecules and hydration patterns of proteins. However, appropriate tools for analysing long‐time molecular dynamics simulations with respect to tracking and visualising the paths of solvent molecules are lacking. Our method focuses on visualising the solvent paths entering and leaving cavities of the protein and allows to study the route and dynamics of the exchange of tightly bound internal water molecules with the bulk solvent. The proposed visualisation also represents dynamic properties such as direction and velocity in the solvent. Especially, by clustering similar path‐lines with respect to designated properties the visualisation can be abstracted to represent the principal paths of solvent molecules through the cavities. Its application in the analysis of long‐time scale molecular dynamics simulations not only confirmed conjectures based on previous manual observations made by chance, but also led to novel insights into the dynamical and structural role of water molecules and its interplay with protein structure.

Visualization of Electrostatic Dipoles in Molecular Dynamics of Metal Oxides

Metal oxides are important for many technical applications. For example alumina (aluminum oxide) is the most commonly-used ceramic in microelectronic devices thanks to its excellent properties. Experimental studies of these materials are increasingly supplemented with computer simulations. Molecular dynamics (MD) simulations can reproduce the material behavior very well and are now reaching time scales relevant for interesting processes like crack propagation. In this work we focus on the visualization of induced electric dipole moments on oxygen atoms in crack propagation simulations. The straightforward visualization using glyphs for the individual atoms, simple shapes like spheres or arrows, is insufficient for providing information about the data set as a whole. As our contribution we show for the first time that fractional anisotropy values computed from the local neighborhood of individual atoms of MD simulation data depict important information about relevant properties of the field of induced electric dipole moments. Iso surfaces in the field of fractional anisotropy as well as adjustments of the glyph representation allow the user to identify regions of correlated orientation. We present novel and relevant findings for the application domain resulting from these visualizations, like the influence of mechanical forces on the electrostatic properties.

Special Relativistic Visualization by Local Ray Tracing

Special relativistic visualization offers the possibility of experiencing the optical effects of traveling near the speed of light, including apparent geometric distortions as well as Doppler and searchlight effects. Early high-quality computer graphics images of relativistic scenes were created using offline, computationally expensive CPU-side 4D ray tracing. Alternate approaches such as image-based rendering and polygon-distortion methods are able to achieve interactivity, but exhibit inferior visual quality due to sampling artifacts. In this paper, we introduce a hybrid rendering technique based on polygon distortion and local ray tracing that facilitates interactive high-quality visualization of multiple objects moving at relativistic speeds in arbitrary directions. The method starts by calculating tight image-space footprints for the apparent triangles of the 3D scene objects. The final image is generated using a single image-space ray tracing step incorporating Doppler and searchlight effects. Our implementation uses GPU shader programming and hardware texture filtering to achieve high rendering speed.

Visual Analysis of Trajectories in Multi-Dimensional State Spaces

Multi‐dimensional data originate from many different sources and are relevant for many applications. One specific sub‐type of such data is continuous trajectory data in multi‐dimensional state spaces of complex systems. We adapt the concept of spatially continuous scatterplots and spatially continuous parallel coordinate plots to such trajectory data, leading to continuous‐time scatterplots and continuous‐time parallel coordinates. Together with a temporal heat map representation, we design coordinated views for visual analysis and interactive exploration. We demonstrate the usefulness of our visualization approach for three case studies that cover examples of complex dynamic systems: cyber‐physical systems consisting of heterogeneous sensors and actuators networks (the collection of time‐dependent sensor network data of an exemplary smart home environment), the dynamics of robot arm movement and motion characteristics of humanoids.