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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2304.10516 |
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| _version_ | 1866913438488330240 |
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| author | Wu, Qi Insley, Joseph A. Mateevitsi, Victor A. Rizzi, Silvio Papka, Michael E. Ma, Kwan-Liu |
| author_facet | Wu, Qi Insley, Joseph A. Mateevitsi, Victor A. Rizzi, Silvio Papka, Michael E. Ma, Kwan-Liu |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2304_10516 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Distributed Neural Representation for Reactive in situ Visualization Wu, Qi Insley, Joseph A. Mateevitsi, Victor A. Rizzi, Silvio Papka, Michael E. Ma, Kwan-Liu Distributed, Parallel, and Cluster Computing Artificial Intelligence 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. |
| title | Distributed Neural Representation for Reactive in situ Visualization |
| topic | Distributed, Parallel, and Cluster Computing Artificial Intelligence |
| url | https://arxiv.org/abs/2304.10516 |