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.Performance Issues

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    Optimizing Data Movement in Heterogeneous Computing: A LASSA-based Approach for Efficient Nucleation List Precomputation
    (Institute of Electrical and Electronics Engineers Inc., 2025) Bhowmik, B.; Girish, K.K.; Pandey, H.; Prabhanjans, P.
    In the rapidly evolving landscape of heterogeneous computing, the efficiency of data movement between CPUs and GPUs can make or break system performance. Despite advancements in parallel processing, existing methods for managing data transfers - particularly in GPU offloading scenarios - suffer from significant inefficiencies. These inefficiencies are particularly evident in nucleation list precomputation for non-equilibrium solidification models, where redundant data movements and complex dynamic work-sharing in OpenMP lead to significant performance overhead. To tackle this issue, this paper proposes a novel solution that integrates the Location-Aware Heap Static Single Assignment (LASSA) algorithm into the compilation process. This approach identifies and eliminates redundant memory copy operations, optimizing data transfers and reducing overhead. The findings reveal a dramatic performance boost, with up to a 9.6-fold increase in efficiency. By addressing the specific challenges of nucleation list precomputation, this work provides valuable insights into optimizing data movement in heterogeneous computing environments, paving the way for enhanced performance in parallel programming models. © 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.