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arXiv · 2304.10516

Distributed Neural Representation for Reactive in situ Visualization

Qi Wu·Joseph A. Insley·Victor A. Mateevitsi·Silvio Rizzi·Michael E. Papka·Kwan-Liu Ma
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Abstract

Implicit neural representations (INRs) have emerged as a powerful tool for compressing large-scale volume data. This opens up new possibilities for in situ visualization. However, the efficient application of INRs to distributed data remains an underexplored area. In this work, we develop a distributed volumetric neural representation and optimize it for in situ visualization. Our technique eliminates data exchanges between processes, achieving state-of-the-art compression speed, quality and ratios. Our technique also enables the implementation of an efficient strategy for caching large-scale simulation data in high temporal frequencies, further facilitating the use of reactive in situ visualization in a wider range of scientific problems. We integrate this system with the Ascent infrastructure and evaluate its performance and usability using real-world simulations.

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Qi Wu, Joseph A. Insley, Victor A. Mateevitsi, Silvio Rizzi, Michael E. Papka, Kwan-Liu Ma. 2023-03-28. Distributed Neural Representation for Reactive in situ Visualization. https://arxiv.org/abs/2304.10516

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