Parallel visualization of large-scale multifield scientific data (original) (raw)

Abstract

Following the recent rapid growth in supercomputer performance, many real-world problems in fields such as nuclear fusion energy and electromagnetic environments can be solved via multiphysics simulation, which outputs multifield datasets. However, current multifield visualization has difficulty handling multiphysics parallel simulation data. First, it is difficult to correctly visualize overlapping multifield data with semitransparent properties because of the complex distribution of partitioned data domains across multicore processors. Second, the interactive visualization performance of large-scale multifield data in serial processing mode on a personal computer is often slow because multiphysics simulations can produce large-scale datasets, i.e., of the order of gigabytes to terabytes. Considering the fidelity and efficiency of large-scale data visualization on supercomputer, a new parallel visualization method is required for multifield scientific data that do not change the original distribution of the mesh data generated by the multiphysics applications. This paper introduces a hybrid scheduling framework for the parallel visualization of large-scale multifield scientific data. This framework is used to overcome problems both in correct visual representation and in efficient visualization of large-scale multiphysics applications. We discuss the results of several typical multiphysics applications to verify the feasibility and reliability of our proposed framework. This framework currently supports scalable in situ visualization of up to 8.5 billion mesh cells on the 10 k cores of China’s Tianhe-2 supercomputer, which could help domain scientists understand multiphysics phenomena more clearly and accurately.

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Acknowledgements

This work was supported by the National Key R&D Program of China under Grant No. 2017YFB0202203 and the Defense Industrial Technology Development Program of China (Grant No. C1520110002).

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Authors and Affiliations

1. High Performance Computing Center, Institute of Applied Physics and Computational Mathematics, Beijing, China
   Yi Cao, Zeyao Mo, Zhiwei Ai, Huawei Wang, Li Xiao & Zhe Zhang

Authors

1. Yi Cao
2. Zeyao Mo
3. Zhiwei Ai
4. Huawei Wang
5. Li Xiao
6. Zhe Zhang

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Correspondence toYi Cao.

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Cao, Y., Mo, Z., Ai, Z. et al. Parallel visualization of large-scale multifield scientific data.J Vis 22, 1107–1123 (2019). https://doi.org/10.1007/s12650-019-00591-4

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• Received: 06 July 2019
• Accepted: 31 July 2019
• Published: 24 August 2019
• Version of record: 24 August 2019
• Issue date: December 2019
• DOI: https://doi.org/10.1007/s12650-019-00591-4

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