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    Area of Science:

    • Scientific Visualization
    • High-Performance Computing (HPC)
    • Computer Graphics

    Background:

    • Visualizing large finite element simulation data on HPC systems presents rendering challenges due to unstructured data and non-convex domains.
    • Data parallel rendering struggles with rays straddling domain boundaries, impacting ray marching and compositing.
    • Existing methods face difficulties with complex geometries and distributed rendering environments.

    Purpose of the Study:

    • To develop an efficient and interactive visualization technique for large-scale finite element simulation data on HPC systems.
    • To address the challenges of rendering unstructured data and handling domain straddling in parallel rendering.
    • To enable real-time or near real-time visual analysis of complex simulation results.

    Main Methods:

    • Employs a GPU-optimized ray marching technique with an XOR-based compaction scheme for high-performance ray traversal.
    • Integrates hardware-accelerated ray tracing to efficiently determine ray entry points into simulation domains.
    • Utilizes a "deep" compositing scheme to accurately merge rendering results from different processing units (ranks).
    • Leverages GPU-to-GPU remote direct memory access (RDMA) for efficient data transfer and synchronization.

    Main Results:

    • Achieved interactive frame rates of 10-15 frames per second for the Fun3D NASA Mars Lander use case.
    • Demonstrated the effectiveness of the combined techniques in overcoming rendering bottlenecks associated with unstructured data.
    • Successfully handled complex spatial domain boundaries and ray straddling issues in a data-parallel context.

    Conclusions:

    • The proposed holistic approach effectively visualizes large finite element simulation data interactively on HPC systems.
    • The combination of optimized ray marching, ray tracing, and deep compositing provides a robust solution for complex visualization tasks.
    • This technique significantly enhances the usability of HPC resources for scientific data exploration and analysis.