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Optimization and Augmentation for Data Parallel Contour Trees.

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    We introduce a hyperstructure for faster contour tree computation. This new method significantly speeds up the creation of fully augmented contour trees, crucial for topological data analysis.

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    Area of Science:

    • Scientific Visualization
    • Topological Data Analysis
    • Computer Science

    Background:

    • Contour trees are essential for topological data analysis in scientific visualization.
    • Existing serial algorithms are slow, and recent parallel algorithms struggle with fully augmented contour trees.
    • Fully augmented contour trees are necessary for many practical data analysis tasks.

    Purpose of the Study:

    • To develop a more efficient method for computing fully augmented contour trees.
    • To improve the performance of parallel contour tree algorithms.

    Main Methods:

    • Introduction of a novel data structure called the hyperstructure.
    • Utilizing the hyperstructure to enable efficient searches within the contour tree.
    • Construction of a fully augmented contour tree using a data-parallel approach.

    Main Results:

    • The hyperstructure representation allows for efficient tree traversal and augmentation.
    • The new data-parallel algorithm achieves an average performance improvement of 6x compared to the state-of-the-art.
    • The method significantly outperforms the existing parallel algorithm in the TTK topological toolkit.

    Conclusions:

    • The hyperstructure provides a significant speedup for fully augmented contour tree construction.
    • This advancement enhances the efficiency of topological data analysis in scientific visualization.
    • The developed algorithm offers a practical and faster solution for complex data analysis.