From Cluster to Desktop: A Cache-Accelerated INR framework for Interactive Visualization of Tera-Scale Data

2025-04-25

AI Summary

To address the slow, non-interactive volume rendering of tera-scale scientific data on consumer-grade GPUs using implicit neural representations (INRs), this paper proposes the first scalable multi-resolution GPU caching acceleration framework. The framework bridges HPC-level compression and desktop-scale exploration by integrating dynamic prioritized loading of novel voxel regions, multi-resolution sampling scheduling, and hardware-accelerated compression interfaces—thereby significantly reducing redundant INR evaluations. Experiments demonstrate an average 5× speedup over state-of-the-art INR rendering methods while preserving high-fidelity visualization quality. This work achieves, for the first time, real-time interactive exploration of tera-scale scientific datasets on commodity desktop GPUs, effectively closing the gap between supercomputing-grade data compression and desktop analytics.

Technology Category

• Data Mining & Knowledge Management: Data Compression
• Computer Vision: 3D Computer Vision
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Search and Retrieval-Augmented AI: Efficiency and scalability of Web search engines
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Systems and Infrastructure for Web, Mobile and WoT: Data management and stream processing for Web, mobile and wireless applications

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 5x 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.

Problem

• Accelerate INR rendering for tera-scale datasets
• Reduce computational cost of interactive visualization
• Enable consumer-grade hardware for INR exploration

Innovation

• Scalable multi-resolution GPU cache acceleration
• Minimizes redundant INR computations per frame
• Enables interactive exploration on consumer hardware