Conference Papers

Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506

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Author: search.filters.author.Girish, K.K.Subject: search.filters.subject.High Performance Computing (HPC)

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    Performance Analysis and Predictive Modeling of MPI Collective Algorithms in Multi-Core Clusters: A Comparative Study
    (Institute of Electrical and Electronics Engineers Inc., 2025) Reddy, M.R.V.S.R.S.; Raju, S.R.; Girish, K.K.; Bhowmik, B.
    Efficient communication is the foundation of parallel computing systems, enabling seamless coordination across multiple processors for optimal performance. At the core of this communication lies the Message Passing Interface, a crucial framework designed to facilitate data exchange between processors through collective operations. However, these MPI operations often face challenges, including fluctuating process counts, varying message sizes, and increased communication overhead. These issues can significantly impact execution times and scalability, leading to potential bottlenecks in large-scale systems. To address these concerns, this paper provides an in-depth evaluation of key MPI collective algorithms - Flat Tree, Chain, and Binary Tree - by examining their performance under varying configurations. By analyzing execution times and communication overhead, the study reveals the trade-offs inherent in each algorithm, offering insights into strategies for reducing communication costs. Through this analysis, we aim to provide valuable guidance to improve the efficiency and scalability of parallel computing, particularly in high-performance systems where communication efficiency is critical. © 2025 IEEE.
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    Exploring Hidden Behaviors in OpenMP Multi-threaded Applications for Anomaly Detection in HPC Environments
    (Springer Science and Business Media Deutschland GmbH, 2025) Bhowmik, B.; Girish, K.K.; Mishra, P.; Mishra, R.
    In 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.