From Cluster to Desktop: A Cache-Accelerated INR framework for Interactive Visualization of Tera-Scale Data
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
The Eurographics Association
Abstract
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.
Description
CCS Concepts: Computing methodologies → 3D imaging; Ray tracing; Volumetric models
@inproceedings{10.2312:pgv.20251153,
booktitle = {Eurographics Symposium on Parallel Graphics and Visualization},
editor = {Reina, Guido and Rizzi, Silvio and Gueunet, Charles},
title = {{From Cluster to Desktop: A Cache-Accelerated INR framework for Interactive Visualization of Tera-Scale Data}},
author = {Zavorotny, Daniel and Wu, Qi and Bauer, David and Ma, Kwan-Liu},
year = {2025},
publisher = {The Eurographics Association},
ISSN = {1727-348X},
ISBN = {978-3-03868-274-5},
DOI = {10.2312/pgv.20251153}
}