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    This study introduces a new method for detecting anomalous runtime behavior in High Performance Computing (HPC) by analyzing call stack trees. This approach captures execution context for more accurate performance diagnosis.

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    Area of Science:

    • Computer Science
    • Software Engineering

    Background:

    • Anomalous runtime behavior detection is crucial for High Performance Computing (HPC) performance diagnosis.
    • Existing methods often lack contextual information, focusing only on individual function properties like execution time.

    Purpose of the Study:

    • To improve anomaly detection in HPC by incorporating execution context.
    • To develop a novel approach using call stack structures for more comprehensive analysis.

    Main Methods:

    • Representing program executions as call stack trees (CSTrees).
    • Formulating anomaly detection as identifying anomalous tree structures within a call stack forest.
    • Utilizing a proposed stack2vec embedding to convert CSTrees into vector representations.
    • Providing structural and temporal visualizations for anomaly identification and verification.

    Main Results:

    • Demonstrated the effectiveness of the CSTree representation in capturing rich temporal and contextual information.
    • Successfully converted CSTrees into vector embeddings for analysis.
    • Enabled user-supported anomaly identification through visualizations.

    Conclusions:

    • The proposed CSTree-based approach enhances anomaly detection in HPC by considering execution context.
    • This method offers a more robust solution for performance diagnosis compared to traditional function-property-based methods.