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GPU-based adaptive data reconstruction for large-scale statistical visualization

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Abstract

Statistical data summarization can significantly reduce the data storage footprint for large-scale scientific simulations while maintaining data accuracy. However, the high-resolution reconstructed data causes a memory bottleneck in graphics processing unit (GPU)-based post-hoc visualization using limited graphics memory. In this paper, we propose a statistical summarization model-driven adaptive data reconstruction method for large-scale statistical visualization on GPUs. It uses the spatial Gaussian mixture model to iteratively compute the Shannon entropy on multi-level grids, driving an adaptive mesh refinement that retains complex physical features. A graphics shader-based data reconstruction algorithm is used to efficiently generate the scalar field on the adaptive grid while seamlessly integrating with GPU-accelerated rendering algorithms. The experimental tests used data generated by five real-world scientific simulations with a maximum grid resolution of 134 million. Qualitative and quantitative analysis results show that our method can achieve efficient and high-quality reconstruction of the statistical summary data on a GPU, and the maximum data compression ratio is close to two orders of magnitude.

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Authors and Affiliations

  1. Institute of Applied Physics and Computational Mathematics, Beijing, 100094, China

    Yu Wu, Yang Yang & Yi Cao

  2. CAEP Software Center for High Performance Numerical Simulation, Beijing, 100088, China

    Yi Cao

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  1. Yu Wu
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  2. Yang Yang
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  3. Yi Cao
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Correspondence to Yi Cao.

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Wu, Y., Yang, Y. & Cao, Y. GPU-based adaptive data reconstruction for large-scale statistical visualization. J Vis 26, 899–915 (2023). https://doi.org/10.1007/s12650-022-00892-1

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