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Freeprocessing: Transparent in situ visualization via data interception.

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    Summary

    This study introduces a new abstraction for in situ visualization, improving ease of use and programmability. This approach enables new applications without compromising performance, making complex data analysis more accessible.

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    Area of Science:

    • Scientific Visualization
    • High-Performance Computing

    Background:

    • In situ visualization is popular for avoiding slow disk I/O in data pipelines.
    • Prior work focused on scalability and data movement at extreme scales.

    Purpose of the Study:

    • To improve the ease of use and programmability of in situ analysis.
    • To explore new applications for in situ visualization.

    Main Methods:

    • Developed a novel abstraction for in situ visualization.
    • Expanded the set of use cases for in situ analysis.

    Main Results:

    • The proposed abstraction enhances usability and programmability.
    • New applications for in situ visualization were realized.
    • No performance cost was incurred.

    Conclusions:

    • The new abstraction effectively expands in situ visualization capabilities.
    • Ease of use and programmability can be achieved without performance degradation.