EGPGV25: Eurographics Symposium on Parallel Graphics and Visualization

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https://diglib.eg.org/handle/10.2312/3607184

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Browsing EGPGV25: Eurographics Symposium on Parallel Graphics and Visualization by Title

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    From Cluster to Desktop: A Cache-Accelerated INR framework for Interactive Visualization of Tera-Scale Data
    (The Eurographics Association, 2025) Zavorotny, Daniel; Wu, Qi; Bauer, David; Ma, Kwan-Liu; Reina, Guido; Rizzi, Silvio; Gueunet, Charles
    Machine learning has enabled the use of implicit neural representations (INRs) to efficiently compress and reconstruct massive scientific datasets. However, despite advances in fast INR rendering algorithms, INR-based rendering remains computationally expensive, as computing data values from an INR is significantly slower than reading them from GPU memory. This bottleneck currently restricts interactive INR visualization to professional workstations. To address this challenge, we introduce an INR rendering framework accelerated by a scalable, multi-resolution GPU cache capable of efficiently representing tera-scale datasets. By minimizing redundant data queries and prioritizing novel volume regions, our method reduces the number of INR computations per frame, achieving an average 5× speedup over the state-of-the-art INR rendering method while still maintaining high visualization quality. Coupled with existing hardware-accelerated INR compressors, our framework enables scientists to generate and compress massive datasets in situ on high-performance computing platforms and then interactively explore them on consumer-grade hardware post hoc.
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    GPU Volume Rendering with Hierarchical Compression Using VDB
    (The Eurographics Association, 2025) Zellmann, Stefan; Jaroš, Milan; Amstutz, Jefferson; Wald, Ingo; Reina, Guido; Rizzi, Silvio; Gueunet, Charles
    We propose a compression-based approach to GPU rendering of large volumetric data using OpenVDB and NanoVDB. We use OpenVDB to create a lossy, fixed-rate compressed representation of the volume on the host, and use NanoVDB to perform fast, low-overhead, and on-the-fly decompression during rendering. We show that this approach is fast, works well even in a (incoherent) Monte Carlo path tracing context, can significantly reduce the memory requirements of volume rendering, and can be used as an almost drop-in replacement into existing 3D texture-based renderers.
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    Multi-Density Woodcock Tracking: Efficient & High-Quality Rendering for Multi-Channel Volumes
    (The Eurographics Association, 2025) Sahistan, Alper; Zellmann, Stefan; Morrical, Nate; Pascucci, Valerio; Wald, Ingo; Reina, Guido; Rizzi, Silvio; Gueunet, Charles
    Volume rendering techniques for scientific visualization have increasingly transitioned toward Monte Carlo (MC) methods in recent years due to their flexibility and robustness. However, their application in multi-channel visualization remains underexplored. Traditional compositing-based approaches often employ arbitrary color blending functions, which lack a physical basis and can obscure data interpretation. We introduce multi-density Woodcock tracking, a simple and flexible extension of Woodcock tracking for multi-channel volume rendering that leverages the strengths of Monte Carlo methods to generate high-fidelity visuals. Our method offers a physically grounded solution for inter-channel color blending and eliminates the need for arbitrary blending functions. We also propose a unified blending modality by generalizing Woodcock's distance tracking method, facilitating seamless integration of alternative blending functions from prior works. Through evaluation across diverse datasets, we demonstrate that our approach maintains real-time interactivity while achieving high-quality visuals by accumulating frames over time.
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    PGV 2025: Frontmatter
    (The Eurographics Association, 2025) Reina, Guido; Rizzi, Silvio; Gueunet, Charles; Reina, Guido; Rizzi, Silvio; Gueunet, Charles
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    A Transparent and Efficient Extension of IceT for Parallel Compositing on Non-Convex Volume Domain Decompositions
    (The Eurographics Association, 2025) Hempel, Paul; Gupta, Aryaman; Sbalzarini, Ivo F.; Gumhold, Stefan; Reina, Guido; Rizzi, Silvio; Gueunet, Charles
    The IceT library is widely used for parallel compositing but does not support non-convex volume domain decompositions. We provide a backward-compatible extension of IceT to handle non-convex domain decompositions of volume data. These are frequently produced in numerical simulations, but it is challenging to render them in parallel due to the non-commutativity of alpha compositing. We enable parallel volume rendering of non-convex domains in IceT by extending its parallel compositing to layered images. Our code follows an embedded design, extending and generalizing IceT's internal functions for image compression, splitting, compositing, and decompression to efficiently handle layered images, while maintaining the existing functionality and API. We perform scalability tests and provide our implementation open-source in a public repository, with in-line documentation and integration tests.
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    XEventNet: Extreme Weather Event Prediction using Convolutional Neural Networks and In Situ Visualization
    (The Eurographics Association, 2025) Wani, Muzafar Ahmad; Malakar, Preeti; Reina, Guido; Rizzi, Silvio; Gueunet, Charles
    Extreme weather phenomena such as cyclones, torrential rainfall, snow storms, flash floods and landslides pose serious threat to living beings and property all over the world. An accurate and early prediction system for these extreme events may minimize the loss of life and property. However, this requires an online prediction system integrated with the weather simulation model for faster prediction such that low I/O bandwidth does not hinder performance. We present an in situ framework, XEventNet, that integrates weather simulation, deep learning-based prediction, and visualization. XEventNet predicts extreme events at real-time while the simulation is running using a Convolutional Neural Network (CNN). XEventNet is trained and tested on 400 events (extreme and non-extreme). Data is streamed online from XEventNet simulation processes to prediction processes for parallel inference. XEventNet uses the prediction values with high confidence to selectively transfer sub-domains of the large parent simulation domain. We use ADIOS2 for parallel data transfers via memory between groups of processes. This helps in timely prediction and visualization of critical weather events despite large volume of simulation data. We performed weather simulations at 9 km resolutions, thereby producing gigabytes of data per time step. XEventNet is able to classify four extreme events at real-time and visualize the same. We achieved an average prediction accuracy of 90.25% for all extreme events using a single CNN model. We ran weather simulations on up to 512 processes and parallel predictions on up to 64 processes, thereby streaming gigabytes of data in parallel within seconds. This was possible due to efficient data transfer and process mapping. Furthermore, our selective data transfer for visualization resulted in more than 70% reduction in data size, thereby improving the end-to-end simulation-prediction-visualization times.