Distributed Neural Representation for Reactive in situ Visualization

• URL: http://arxiv.org/abs/2304.10516v2
• Date: Sat, 20 Jul 2024 22:14:42 GMT
• Title: Distributed Neural Representation for Reactive in situ Visualization
• Authors: Qi Wu, Joseph A. Insley, Victor A. Mateevitsi, Silvio Rizzi, Michael E. Papka, Kwan-Liu Ma,
• Abstract summary: Implicit neural representations (INRs) have emerged as a powerful tool for compressing large-scale volume data. We develop a distributed 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.
• Score: 23.80657290203846
• License: http://creativecommons.org/licenses/by-sa/4.0/
• 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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