Next-Gen Techniques for Bottleneck Detection in High-Performance Computing

Year : 2025 | Volume : 12 | Issue : 02 | Page : 09 14
    By

    Vasudevan Senathi Ramdoss,

  1. Senior Performance Engineer, Financial Investment Sector, McKinney, Texas, USA

Abstract

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Modern computing systems face new challenges in bottleneck detection and mitigation due to their increasing complexity which stems from multi-core architectures alongside distributed platforms and real-time processing needs. Traditional methods like hardware profiling and static analysis which used to work well now struggle to keep up with the changing conditions of dynamic system behaviors and diverse computing environments along with variable workload patterns. The current limitations restrict their capability to deliver precise and prompt optimization insights when systems need ongoing performance checks and swift modifications. This study introduces new intelligent methods that overcome identified limitations through combined real-time profiling and machine learning with complete system-level analysis. Our framework combines trace-based profiling methods with minimal overhead and hardware performance counters to use machine learning algorithms which detect anomalous patterns and match them to the underlying causes of performance slowdowns. The primary advancement emerges from combining system-wide data analysis with artificial intelligence to reveal bottlenecks which traditional diagnostic tools usually overlook. The approach achieves minimal performance overhead which allows it to be deployed in production environments where system availability and responsiveness are essential. The framework proves its effectiveness by showing significant gains in accuracy and detection speed along with improved system tuning across multiple platforms like cloud systems and edge devices through rigorous experimental validation. Our approach demonstrates superior scalability capabilities and real-time system state adaptability while providing guidance for developers to execute successful optimization techniques compared to traditional methods. The study contributes to performance engineering by presenting an automated data-driven solution that meets the operational requirements of current computing environments. This solution enables organizations to evolve their system architectures while sustaining superior performance levels and system dependability which drives better user experience together with operational efficiency and system sustainability.

Keywords: Distributed computing, cloud environments, edge computing, bottleneck detection

[This article belongs to Recent Trends in Parallel Computing ]

How to cite this article:
Vasudevan Senathi Ramdoss. Next-Gen Techniques for Bottleneck Detection in High-Performance Computing. Recent Trends in Parallel Computing. 2025; 12(02):09-14.
How to cite this URL:
Vasudevan Senathi Ramdoss. Next-Gen Techniques for Bottleneck Detection in High-Performance Computing. Recent Trends in Parallel Computing. 2025; 12(02):09-14. Available from: https://journals.stmjournals.com/rtpc/article=2025/view=0


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Regular Issue Subscription Original Research
Volume 12
Issue 02
Received 07/02/2025
Accepted 30/04/2025
Published 04/06/2025
Publication Time 117 Days
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