EGPGV23: Eurographics Symposium on Parallel Graphics and Visualization

Permanent URI for this collection

https://diglib.eg.org/handle/10.2312/2633345

Browse

Browsing EGPGV23: Eurographics Symposium on Parallel Graphics and Visualization by Issue Date

Filter results by year or month
Now showing 1 - 6 of 6
  • Thumbnail Image
    Item
    Parallel Compositing of Volumetric Depth Images for Interactive Visualization of Distributed Volumes at High Frame Rates
    (The Eurographics Association, 2023) Gupta, Aryaman; Incardona, Pietro; Brock, Anton; Reina, Guido; Frey, Steffen; Gumhold, Stefan; Günther, Ulrik; Sbalzarini, Ivo F.; Bujack, Roxana; Pugmire, David; Reina, Guido
    We present a parallel compositing algorithm for Volumetric Depth Images (VDIs) of large three-dimensional volume data. Large distributed volume data are routinely produced in both numerical simulations and experiments, yet it remains challenging to visualize them at smooth, interactive frame rates. VDIs are view-dependent piecewise constant representations of volume data that offer a potential solution. They are more compact and less expensive to render than the original data. So far, however, there is no method for generating VDIs from distributed data. We propose an algorithm that enables this by sort-last parallel generation and compositing of VDIs with automatically chosen content-adaptive parameters. The resulting composited VDI can then be streamed for remote display, providing responsive visualization of large, distributed volume data.
  • Thumbnail Image
    Item
    Efficient Sphere Rendering Revisited
    (The Eurographics Association, 2023) Gralka, Patrick; Reina, Guido; Ertl, Thomas; Bujack, Roxana; Pugmire, David; Reina, Guido
    Glyphs are an intuitive way of displaying the results of atomistic simulations, usually as spheres. Raycasting of camera-aligned billboards is considered the state-of-the-art technique to render large sets of spheres in a rasterization-based pipeline since the approach was first proposed by Gumhold. Over time various acceleration techniques have been proposed, such as the rendering of point primitives as billboards, which are trivial to rasterize and avoid a high workload in the vertex pipeline. Other techniques attempt to optimize data upload and access patterns in shader programs, both relevant aspects for dynamic data. Recent advances in graphics hardware raise the question of whether these optimizations are still valid. We evaluate several rendering and data access scheme combinations on real-world datasets and derive recommendations for efficient rasterization-based sphere rendering.
  • Thumbnail Image
    Item
    A GPU-based Out-of-core Architecture for Interactive Visualization of AMR Time Series Data
    (The Eurographics Association, 2023) Alexandre-Barff, Welcome; Deleau, Hervé; Sarton, Jonathan; Ledoux, Franck; Lucas, Laurent; Bujack, Roxana; Pugmire, David; Reina, Guido
    This paper presents a scalable approach for large-scale Adaptive Mesh Refinement (AMR) time series interactive visualization. We can define AMR data as a dynamic gridding format of cells hierarchically refined from a computational domain described in this study as a regular Cartesian grid. This adaptive feature is essential for tracking time-dependent evolutionary phenomena and makes the AMR format an essential representation for 3D numerical simulations. However, the visualization of numerical simulation data highlights one critical issue: the significant increases in generated data memory footprint reaching petabytes, thus greatly exceeding the memory capabilities of the most recent graphics hardware. Therefore, the question is how to access this massive data - AMR time series in particular - for interactive visualization on a simple workstation. To overcome this main problem, we present an out-of-core GPU-based architecture. Our proposal is a cache system based on an ad-hoc bricking identified by a Space-Filling Curve (SFC) indexing and managed by a GPU-based page table that loads required AMR data on-the-fly from disk to GPU memory.
  • Thumbnail Image
    Item
    FunMC^2: A Filter for Uncertainty Visualization of Marching Cubes on Multi-Core Devices
    (The Eurographics Association, 2023) Wang, Zhe; Athawale, Tushar M.; Moreland, Kenneth; Chen, Jieyang; Johnson, Chris R.; Pugmire, David; Bujack, Roxana; Pugmire, David; Reina, Guido
    Visualization is an important tool for scientists to extract understanding from complex scientific data. Scientists need to understand the uncertainty inherent in all scientific data in order to interpret the data correctly. Uncertainty visualization has been an active and growing area of research to address this challenge. Algorithms for uncertainty visualization can be expensive, and research efforts have been focused mainly on structured grid types. Further, support for uncertainty visualization in production tools is limited. In this paper, we adapt an algorithm for computing key metrics for visualizing uncertainty in Marching Cubes (MC) to multi-core devices and present the design, implementation, and evaluation for a Filter for uncertainty visualization of Marching Cubes on Multi-Core devices (FunMC2). FunMC2 accelerates the uncertainty visualization of MC significantly, and it is portable across multi-core CPUs and GPUs. Evaluation results show that FunMC2 based on OpenMP runs around 11× to 41× faster on multi-core CPUs than the corresponding serial version using one CPU core. FunMC2 based on a single GPU is around 5× to 9× faster than FunMC2 running by OpenMP. Moreover, FunMC2 is flexible enough to process ensemble data with both structured and unstructured mesh types. Furthermore, we demonstrate that FunMC2 can be seamlessly integrated as a plugin into ParaView, a production visualization tool for post-processing.
  • Thumbnail Image
    Item
    PGV 2023: Frontmatter
    (The Eurographics Association, 2023) Bujack, Roxana; Pugmire, David; Reina, Guido; Bujack, Roxana; Pugmire, David; Reina, Guido
  • Thumbnail Image
    Item
    Extended Visual Programming for Complex Parallel Pipelines in ParaView
    (The Eurographics Association, 2023) Petersen, Marvin; Lukasczyk, Jonas; Gueunet, Charles; Chabat, Timothée; Garth, Christoph; Bujack, Roxana; Pugmire, David; Reina, Guido
    Modern visualization software facilitates the creation of visualization pipelines combining a plethora of algorithms to achieve high-fidelity visualization. When the complexity of the pipelines to be created increases, additional techniques are needed to ensure that reasoning about the pipelines structure and its performance remains feasible. This paper presents three additions to ParaView with the goal of improving presentation of complex, parallel pipelines benefiting pipeline realization. More specifically, we provide a runtime performance annotation visualization integrated in a visual programming node editor, allowing all users to reason about basic performance and intuitively manipulate the structure and configuration of pipelines. Further, we extend the list of available filters with control flow filters, supporting for- and while-loops with a comprehensible representation in the node editor. Our extension is based on graphical manipulation of a node graph that expresses the flow of data and computation in a VTK pipeline, and draws upon a long tradition and positive experience with similar interfaces across a wide range of software systems such as the visualization tools SCIRun and VTK Designer, or the rendering systems Blender and Houdini. The extension we provide integrates seamlessly into the existing ParaView architecture as a plug-in, i.e., it does not require any modifications to ParaView itself or VTK's execution model.