EGPGV22: Eurographics Symposium on Parallel Graphics and Visualization

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https://diglib.eg.org/handle/10.2312/2633195

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Massively Parallel Large Scale Inundation Modelling
(The Eurographics Association, 2022) Rak, Arne; Guthe, Stefan; Mewis, Peter; Bujack, Roxana; Tierny, Julien; Sadlo, Filip
Over the last 20 years, flooding has been the most common natural disaster, accounting for 44.7% of all disasters, affecting about 1.65 billion people worldwide and causing roughly 105 thousand deaths†. In contrast to other natural disasters, the impact of floods is preventable through affordable structures such as dams, dykes and drainage systems. To be most effective, however, these structures have to be planned and evaluated using the highest precision data of the underlying terrain and current weather conditions. Modern laser scanning techniques provide very detailed and reliable terrain information that may be used for flood inundation modelling in planning and hazard warning systems. These warning systems become more important since flood hazards increase in recent years due to ongoing climate change. In contrast to simulations in planning, simulations in hazard warning systems are time critical due to potentially fast changing weather conditions and limited accuracy in forecasts. In this paper we present a highly optimized CUDA implementation of a numerical solver for the hydraulic equations. Our implementation maximizes the GPU's memory throughput, achieving up to 80% utilization. A speedup of a factor of three is observed in comparison to previous work. Furthermore, we present a low-overhead, in-situ visualization of the simulated data running entirely on the GPU. With this, an area of 15 km2 with a resolution of 1 m can be visualized hundreds of times faster than real time on consumer grade hardware. Furthermore, the flow settings can be changed interactively during computation.
Iterative Discrete Element Solver for Efficient Snow Simulation
(The Eurographics Association, 2022) Goswami, Prashant; Nordin, Adrian; Nylén, Simon; Bujack, Roxana; Tierny, Julien; Sadlo, Filip
This paper presents a novel Discrete Element Method (DEM) on the GPU for efficient snow simulation. To this end, our approach employs an iterative scheme on particles that easily allows the snow density to vary vastly for simulation while still maintaining a relatively large time step. We provide computationally inexpensive ways to capture cohesion and compression in the snow that enables us to generalize the behavior of various kinds of snow (like dry, wet, etc.) by varying physical parameters within the same simulator. We achieve a speed-up of nearly eight times with one million snow particles over the existing realtime method, even while dealing with scenes containing complex object boundaries. Furthermore, our simulator not only retains stability at these large time steps but also improves upon the physical behavior of the existing method. We have also conducted a user evaluation of our approach, where a majority of the participants voted in favor of its realism value for computer games.
Profiling and Visualizing GPU Memory Access and Cache Behavior of Ray Tracers
(The Eurographics Association, 2022) Buelow, Max von; Riemann, Kai; Guthe, Stefan; Fellner, Dieter W.; Bujack, Roxana; Tierny, Julien; Sadlo, Filip
Graphical processing units (GPUs) have gained popularity in recent years due to their efficiency in running massively parallel applications. Recent developments have also adapted ray-tracing algorithms to the GPU, where the bottleneck in the overall performance is usually given by the memory bandwidth. In this paper, we present an interactive, web-based visualization tool for GPU memory traces that provides visual insight into the memory and cache behavior of our reference ray tracer, by mapping internal GPU state back onto 3D objects. In order to visualize cache behavior, we use reuse distances on both GPU cache layers that are calculated on the basis of memory traces extracted from a real GPU using binary instrumentation. An advantage of our system is that it runs independently of the ray-tracing program. We further show visualizations of our GPU ray tracer and compare the visualizations of several ray-tracing approaches. We find our work to act as a convenient toolset to gather insights on which data structures and mesh regions can be cached efficiently, and how ray-tracing acceleration structures behave on various input meshes, bounding volume hierarchies, memory layouts, frame buffer resolutions, and work distribution techniques.
A Flexible Data Streaming Design for Interactive Visualization of Large-Scale Volume Data
(The Eurographics Association, 2022) Wu, Qi; Doyle, Michael J.; Ma, Kwan-Liu; Bujack, Roxana; Tierny, Julien; Sadlo, Filip
Modern simulations and experiments can produce massive amounts of high-fidelity data that are challenging to transport and visualize interactively. We have designed a data streaming system to support interactive visualization of large volume data. Our streaming system design is unique in its flexibility to support diverse data organizations and its coupling with a highly efficient CPU-based ray-tracing renderer. In this paper, we present our streaming and rendering design and demonstrate the efficacy of our system with progressive rendering of streaming tree-based AMR (TAMR) volume data and radial basis function (RBF) particle volume data. With our system, interactive visualization can be achieved using only a mid-range workstation with a single CPU and a modest quantity of RAM.
Rainbow: A Rendering-Aware Index for High-Quality Spatial Scatterplots with Result-Size Budgets
(The Eurographics Association, 2022) Bai, Qiushi; Alsudais, Sadeem; Li, Chen; Zhao, Shuang; Bujack, Roxana; Tierny, Julien; Sadlo, Filip
We study the problem of computing a spatial scatterplot on a large dataset for arbitrary zooming/panning queries. We introduce a general framework called ''Rainbow'' that generates a high-quality scatterplot for a given result-size budget. Rainbow augments a spatial index with judiciously selected representative points offline. To answer a query, Rainbow traverses the index top-down and selects representative points with a good quality until the result-size budget is reached. We experimentally demonstrate the effectiveness of Rainbow.
PGV 2022: Frontmatter
(The Eurographics Association, 2022) Bujack, Roxana; Tierny, Julien; Sadlo, Filip; Bujack, Roxana; Tierny, Julien; Sadlo, Filip
Automatic In Situ Camera Placement for Isosurfaces of Large-Scale Scientific Simulations
(The Eurographics Association, 2022) Marsaglia, Nicole; Mathai, Manish; Fields, Stefan; Childs, Hank; Bujack, Roxana; Tierny, Julien; Sadlo, Filip
High-performance computing trends are requiring in situ processing increasingly often. This work considers automating camera placement for in situ visualization, specifically of isosurfaces, which is needed when there is no human in the loop and no a priori knowledge of where to place the camera. Our approach utilizes Viewpoint Quality (VQ) metrics, which quantify which camera positions provide the most insight. We have two primary contributions. First, we introduce an approach parallelizing the calculation of VQ metrics, which is necessary for usage in an in situ setting. Second, we introduce an algorithm for searching for a good camera position that balances between maximizing VQ metric score and minimizing execution time. We evaluate our contributions with an in situ performance study on a supercomputer. Our findings confirm that our approach is viable, and in particular that we can find good viewpoints with small execution time.
GraphWaGu: GPU Powered Large Scale Graph Layout Computation and Rendering for the Web
(The Eurographics Association, 2022) Dyken, Landon; Poudel, Pravin; Usher, Will; Petruzza, Steve; Chen, Jake Y.; Kumar, Sidharth; Bujack, Roxana; Tierny, Julien; Sadlo, Filip
Large scale graphs are used to encode data from a variety of application domains such as social networks, the web, biological networks, road maps, and finance. Computing enriching layouts and interactive rendering play an important role in many of these applications. However, producing an efficient and interactive visualization of large graphs remains a major challenge, particularly in the web-browser. Existing state of the art web-based visualization systems such as D3.js, Stardust, and NetV.js struggle to achieve interactive layout and visualization for large scale graphs. In this work, we leverage the latest WebGPU technology to develop GraphWaGu, the first WebGPU-based graph visualization system. WebGPU is a new graphics API that brings the full capabilities of modern GPUs to the web browser. Leveraging the computational capabilities of the GPU using this technology enables GraphWaGu to scale to larger graphs than existing technologies. GraphWaGu embodies both fast parallel rendering and layout creation using modified Frutcherman-Reingold and Barnes-Hut algorithms implemented in WebGPU compute shaders. Experimental results demonstrate that our solution achieves the best performance, scalability, and layout quality when compared to current state of the art web-based graph visualization libraries. All of our source code for the project is available at https://github.com/harp-lab/GraphWaGu.
Design and Evaluation of a GPU Streaming Framework for Visualizing Time-Varying AMR Data
(The Eurographics Association, 2022) Zellmann, Stefan; Wald, Ingo; Sahistan, Alper; Hellmann, Matthias; Usher, Will; Bujack, Roxana; Tierny, Julien; Sadlo, Filip
We describe a systematic approach for rendering time-varying simulation data produced by exa-scale simulations, using GPU workstations. The data sets we focus on use adaptive mesh refinement (AMR) to overcome memory bandwidth limitations by representing interesting regions in space with high detail. Particularly, our focus is on data sets where the AMR hierarchy is fixed and does not change over time. Our study is motivated by the NASA Exajet, a large computational fluid dynamics simulation of a civilian cargo aircraft that consists of 423 simulation time steps, each storing 2.5 GB of data per scalar field, amounting to a total of 4 TB. We present strategies for rendering this time series data set with smooth animation and at interactive rates using current generation GPUs. We start with an unoptimized baseline and step by step extend that to support fast streaming updates. Our approach demonstrates how to push current visualization workstations and modern visualization APIs to their limits to achieve interactive visualization of exa-scale time series data sets.

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