Scalable Volume Visualization for Big Scientific Data Modeled by Functional Approximation

Sun, Jianxin; Lenz, David; Yu, Hongfeng; Peterka, Tom

doi:10.1109/BigData59044.2023.10386434

Scalable Volume Visualization for Big Scientific Data Modeled by Functional Approximation

Conference · Sun Jan 01 04:00:00 EST 2023

DOI:https://doi.org/10.1109/BigData59044.2023.10386434· OSTI ID:2467465

Sun, Jianxin; Lenz, David; Yu, Hongfeng; Peterka, Tom

Considering the challenges posed by the space and time complexities in handling extensive scientific volumetric data, various data representations have been developed for the analysis of large-scale scientific data. Multivariate functional approximation (MFA) is an innovative data model designed to tackle substantial challenges in scientific data analysis. It computes values and derivatives with high-order accuracy throughout the spatial domain, mitigating artifacts associated with zero- or first-order interpolation. However, the slow query time through MFA makes it less suitable for interactively visualizing a large MFA model. In this work, we develop the first scalable interactive volume visualization pipeline, MFA-DVV, for the MFA model encoded from large-scale datasets. Our method achieves low input latency through distributed architecture, and its performance can be further enhanced by utilizing a compressed MFA model while still maintaining a high-quality rendering result for scientific datasets. We conduct comprehensive experiments to show that MFA-DVV can decrease the input latency and achieve superior visualization results for big scientific data compared with existing approaches.

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Research Organization:: Argonne National Laboratory (ANL)

Sponsoring Organization:: USDOE Office of Science - Office of Advanced Scientific Computing Research (ASCR)

DOE Contract Number:: AC02-06CH11357

OSTI ID:: 2467465

Country of Publication:: United States

Language:: English

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References (26)

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