30 research outputs found

    Visual debugging for particle-based simulations of fluids

    Get PDF
    Visualizations represent an important tool that we have at our disposal when it comes to analyzing large data sets. A significant amount of data comes from simulations such as fluid, weather, biology and chemistry simulations. Due to increases in computation power the simulations have become more comprehensive, resulting in a larger amount of data. Increased volumes of the simulations require more specialized tools that can offer an insight so we can better understand the phenomena that is reproduced. The present thesis presents a visual debugging plug-in for Particle-based simulations of fluids that can help the researchers to better explain the simulation scenario and to identify possible errors. Moreover, the tool can be used to comprehend modeling and development of new techniques. The environment in which I have implemented the plug-in is MegaMol, a system software focus on visualizing particle-based simulations. There are four modules that I have implemented to enhance MegaMol functionality. In order to import a specific multidimensional data set I have created the BGEODataSource module which converts Houdini geometry formats into MegaMol Particle List Data (MMPLD). By doing this, the simulation data is available for other modules that are already implemented. To explore different particles that have certain properties I have created the ScatterPlot module that offers a way to select and visualize interesting regions of the attribute space. The user can select two attributes that will generate a scatter plot and interact with it by brushing. In order to get another perspective on the data I have implemented the ParallelCoordPlot module which allow the user to identify different patterns and trends between various attributes. By choosing distinct attributes we can see the correlation between different properties and clusters within a specific value range. The modules mentioned above work in the 2D space for observing the feature space. In SimpleSpherePickingRenderer module we can select particles in the 3D space that will serve as input data for the ScatterPlot and ParallelCoordPlot. This is done by a simple selection of the region of interest

    Interactive GPU-based generation of solvent-excluded surfaces

    Get PDF
    The solvent-excluded surface (SES) is a popular molecular representation that gives the boundary of the molecular volume with respect to a specific solvent. SESs depict which areas of a molecule are accessible by a specific solvent, which is represented as a spherical probe. Despite the popularity of SESs, their generation is still a compute-intensive process, which is often performed in a preprocessing stage prior to the actual rendering (except for small models). For dynamic data or varying probe radii, however, such a preprocessing is not feasible as it prevents interactive visual analysis. Thus, we present a novel approach for the on-the-fly generation of SESs, a highly parallelizable, grid-based algorithm where the SES is rendered using ray-marching. By exploiting modern GPUs, we are able to rapidly generate SESs directly within the mapping stage of the visualization pipeline. Our algorithm can be applied to large time-varying molecules and is scalable, as it can progressively refine the SES if GPU capabilities are insufficient. In this paper, we show how our algorithm is realized and how smooth transitions are achieved during progressive refinement. We further show visual results obtained from real-world data and discuss the performance obtained, which improves upon previous techniques in both the size of the molecules that can be handled and the resulting frame rate.Peer ReviewedPostprint (author's final draft

    scenery: Flexible Virtual Reality Visualization on the Java VM

    Full text link
    Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data from analysis of such data or simulations. Visualization is often the first step in making sense of data, and a crucial part of building and debugging analysis pipelines. It is therefore important that visualizations can be quickly prototyped, as well as developed or embedded into full applications. In order to better judge spatiotemporal relationships, immersive hardware, such as Virtual or Augmented Reality (VR/AR) headsets and associated controllers are becoming invaluable tools. In this work we introduce scenery, a flexible VR/AR visualization framework for the Java VM that can handle mesh and large volumetric data, containing multiple views, timepoints, and color channels. scenery is free and open-source software, works on all major platforms, and uses the Vulkan or OpenGL rendering APIs. We introduce scenery's main features and example applications, such as its use in VR for microscopy, in the biomedical image analysis software Fiji, or for visualizing agent-based simulations.Comment: Added IEEE DOI, version published at VIS 201

    Visual Analysis of Polarization Domains in Barium Titanate during Phase Transitions

    Get PDF
    In recent years, the characteristics of ferroelectric barium titanate (BaTiO3) have been studied extensively in materials science. Barium titanate has been widely used for building transducers, capacitors and, as of late, for memory devices. In this context, a precise understanding of the formation of polarization domains during phase transitions within the material is especially important. Therefore, we propose an application that uses a combination of proven visualization techniques in order to aid physicists in the visual analysis of molecular dynamic simulations of BaTiO3. A set of linked 2D and 3D views conveys an overview of the evolution of dipole moments over time by visualizing single time steps as well as combining multiple time steps in one single static image using flow radar glyphs. In addition, our system semi-automatically detects polarization domains, whose spatial relation can be interactively analyzed at different levels of detail on commodity hardware. The evolution of selected polarization domains over their lifetime can be observed by a combination of animated spatial and quantitative views

    Cinema Darkroom: A Deferred Rendering Framework for Large-Scale Datasets

    Full text link
    This paper presents a framework that fully leverages the advantages of a deferred rendering approach for the interactive visualization of large-scale datasets. Geometry buffers (G-Buffers) are generated and stored in situ, and shading is performed post hoc in an interactive image-based rendering front end. This decoupled framework has two major advantages. First, the G-Buffers only need to be computed and stored once---which corresponds to the most expensive part of the rendering pipeline. Second, the stored G-Buffers can later be consumed in an image-based rendering front end that enables users to interactively adjust various visualization parameters---such as the applied color map or the strength of ambient occlusion---where suitable choices are often not known a priori. This paper demonstrates the use of Cinema Darkroom on several real-world datasets, highlighting CD's ability to effectively decouple the complexity and size of the dataset from its visualization

    Enabling Detailed, Biophysics-Based Skeletal Muscle Models on HPC Systems

    Get PDF
    Realistic simulations of detailed, biophysics-based, multi-scale models often require very high resolution and, thus, large-scale compute facilities. Existing simulation environments, especially for biomedical applications, are typically designed to allow for high flexibility and generality in model development. Flexibility and model development, however, are often a limiting factor for large-scale simulations. Therefore, new models are typically tested and run on small-scale compute facilities. By using a detailed biophysics-based, chemo-electromechanical skeletal muscle model and the international open-source software library OpenCMISS as an example, we present an approach to upgrade an existing muscle simulation framework from a moderately parallel version toward a massively parallel one that scales both in terms of problem size and in terms of the number of parallel processes. For this purpose, we investigate different modeling, algorithmic and implementational aspects. We present improvements addressing both numerical and parallel scalability. In addition, our approach includes a novel visualization environment which is based on the MegaMol framework and is capable of handling large amounts of simulated data. We present the results of a number of scaling studies at the Tier-1 supercomputer HazelHen at the High Performance Computing Center Stuttgart (HLRS). We improve the overall runtime by a factor of up to 2.6 and achieve good scalability on up to 768 cores

    A morphological visualization analysis of porous structures in phase inversion processes

    Get PDF
    Porous membranes are an important technology that continually become more established as steps in research lead to its better understanding. Pore formation within the porous membranes is an emerging field, and current research is focused on understanding their development in phase inversion processes. This thesis attempts to aid in the analyis of these types of simulations by providing a visualization tool to extract information about the material and its pores. A morphological analysis was implemented on the structure of three simulated data sets. The analysis consisted of extracting the pore network from the data and creating sets of visualizations to interpret the structure and properties of the material. A special focus with regard to shape of the pores was applied when creating these visual tools. The results showed a good agreement with an intuitive visual analysis of the input data set and the resulting output, and they resulted in new visual tools to better understand the structure of porous membrane development and point to promising work for the future

    Visualization of large molecular trajectories

    Get PDF
    The analysis of protein-ligand interactions is a time-intensive task. Researchers have to analyze multiple physico-chemical properties of the protein at once and combine them to derive conclusions about the protein-ligand interplay. Typically, several charts are inspected, and 3D animations can be played side-by-side to obtain a deeper understanding of the data. With the advances in simulation techniques, larger and larger datasets are available, with up to hundreds of thousands of steps. Unfortunately, such large trajectories are very difficult to investigate with traditional approaches. Therefore, the need for special tools that facilitate inspection of these large trajectories becomes substantial. In this paper, we present a novel system for visual exploration of very large trajectories in an interactive and user-friendly way. Several visualization motifs are automatically derived from the data to give the user the information about interactions between protein and ligand. Our system offers specialized widgets to ease and accelerate data inspection and navigation to interesting parts of the simulation. The system is suitable also for simulations where multiple ligands are involved. We have tested the usefulness of our tool on a set of datasets obtained from protein engineers, and we describe the expert feedback.Peer ReviewedPostprint (author's final draft

    PetaFLOP Molecular Dynamics for Engineering Applications

    Get PDF
    Molecular dynamics (MD) simulations enable the investigation of multicomponent and multiphase processes relevant to engineering applications, such as droplet coalescence or bubble formation. These scenarios require the simulation of ensembles containing a large number of molecules. We present recent advances within the MD framework ls1 mardyn which is being developed with particular regard to this class of problems. We discuss several OpenMP schemes that deliver optimal performance at node-level. We have further introduced nonblocking communication and communication hiding for global collective operations. Together with revised data structures and vectorization, these improvements unleash PetaFLOP performance and enable multi-trillion atom simulations on the HLRS supercomputer Hazel Hen. We further present preliminary results achieved for droplet coalescence scenarios at a smaller scale.BMBF, 01IH16008, Verbundprojekt: TaLPas - Task-basierte Lastverteilung und Auto-Tuning in der Partikelsimulatio
    corecore