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    Area of Science:

    • High-performance computing
    • Parallel program analysis
    • Software engineering

    Background:

    • Modern supercomputers are increasingly complex, making performance optimization of large-scale parallel programs challenging.
    • Inefficiencies in parallel programs can lead to significant waste in compute hours and power consumption.
    • Current trace visualization tools struggle to scale beyond a few hundred processes, hindering performance analysis.

    Purpose of the Study:

    • To develop a new, scalable trace visualization approach for large-scale parallel programs.
    • To improve the understandability and efficiency of performance analysis in high-performance computing.
    • To provide developers with more intuitive insights into code behavior.

    Main Methods:

    • Transforming execution traces into logical time based on happened-before relationships.
    • Encoding original timing data or other metrics using color for intuitive visualization.
    • Clustering processes based on local behavior using discrete logical timelines for scalability.

    Main Results:

    • The proposed visualization method emphasizes the structural behavior of parallel codes, which is more familiar to developers.
    • The approach scales effectively to large process counts, overcoming limitations of traditional time-based visualizations.
    • Demonstrated effectiveness through case studies on large-scale parallel codes.

    Conclusions:

    • The new logical time-based visualization offers a more scalable and intuitive approach to analyzing large-scale parallel program performance.
    • This method aids in identifying inefficiencies and optimizing resource utilization in high-performance computing.
    • The system provides a valuable tool for software developers working with complex parallel applications.