EGPGV22: Eurographics Symposium on Parallel Graphics and Visualization

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

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Browsing EGPGV22: Eurographics Symposium on Parallel Graphics and Visualization by Subject "Computing methodologies"

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    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.
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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.
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    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.