Exploring Hidden Behaviors in OpenMP Multi-threaded Applications for Anomaly Detection in HPC Environments

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dc.contributor.authorBhowmik, B.
dc.contributor.authorGirish, K.K.
dc.contributor.authorMishra, P.
dc.contributor.authorMishra, R.
dc.date.accessioned2026-02-06T06:33:14Z
dc.date.issued2025
dc.description.abstractIn high-performance computing (HPC), multi-threaded applications using OpenMP face complex challenges in identifying hidden performance issues, often due to resource conflicts, software inefficiencies, and hardware anomalies. These subtle issues can significantly degrade performance and reduce system reliability. This paper introduces an innovative approach designed to address these concealed issues in OpenMP multi-threaded applications. The proposed method integrates a Random Forest classifier with anthropomorphic diagnosis to effectively identify and diagnose performance-affecting problems. The approach has demonstrated a remarkable ability to detect 90% of performance-affecting issues that are often obscured within complex HPC environments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.
dc.identifier.citationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2025, Vol.15507 LNCS, , p. 61-67
dc.identifier.issn3029743
dc.identifier.urihttps://doi.org/10.1007/978-3-031-81404-4_5
dc.identifier.urihttps://idr.nitk.ac.in/handle/123456789/28531
dc.publisherSpringer Science and Business Media Deutschland GmbH
dc.subjectAnthropomorphic Diagnosis
dc.subjectHigh Performance Computing (HPC)
dc.subjectOpenMP
dc.subjectPerformance Issues
dc.subjectRandom Forest
dc.subjectSystem Efficiency
dc.titleExploring Hidden Behaviors in OpenMP Multi-threaded Applications for Anomaly Detection in HPC Environments

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