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    Area of Science:

    • Computer Science
    • Scientific Computing
    • Data Visualization

    Background:

    • Large-scale data processing is common in science and engineering.
    • Conventional MapReduce is challenging for interactive visualization due to its batch-processing nature.
    • Existing systems struggle with efficient big-data visualization.

    Purpose of the Study:

    • To demonstrate the effectiveness of the MapReduce computing model for interactive visualization.
    • To develop a novel system for GPU-accelerated big-data processing and visualization.
    • To overcome limitations of disk-based MapReduce for scientific visualization.

    Main Methods:

    • Extended Spark, an open-source MapReduce framework, for GPU-accelerated computing.
    • Implemented GPU in-memory caching and MPI-based direct communication.
    • Utilized CUDA-OpenGL interoperability for GPU-accelerated in-situ visualization.
    • Leveraged raster graphics for visualization within the Spark framework.

    Main Results:

    • Achieved interactive visualization using the MapReduce computing model, contrary to common belief.
    • Demonstrated significant speedups, several orders of magnitude faster than conventional MapReduce systems.
    • Successfully performed volume processing and visualization tasks including direct volume rendering and iso-surface extraction.

    Conclusions:

    • The proposed Spark extension effectively enables interactive visualization via MapReduce.
    • GPU acceleration and optimized communication minimize performance bottlenecks in big-data visualization.
    • This approach significantly enhances processing speeds for scientific computing and visualization tasks.