BRIEF RESEARCH REPORT article
Front. High Perform. Comput.
Sec. Big Data and AI
This article is part of the Research TopicAdvancements in Extreme-Scale I/O, Storage Systems, and Data AnalyticsView all articles
Improving I/O Phase Predictions in FTIO Using Hybrid Wavelet-Fourier Analysis
Provisionally accepted
- Technische Universitat Darmstadt, Darmstadt, Germany
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With the growing complexity of I/O software stacks and the rise of data-intensive workloads, optimizing I/O performance is essential for enhancing overall system performance on HPC clusters. While many sophisticated I/O management approaches exist that try to alleviate I/O contention, they often rely on models that predict the future I/O behavior of applications. Yet, these models are often created from past execution runs and can be error-prone due to I/O variability. In this work, we propose an enhancement to an existing tool that leverages frequency-based techniques to characterize I/O phase. We explore methods to improve prediction accuracy by incorporating multiple frequency components. Furthermore, by coupling the wavelet transformation with the Fourier transformation, we enhance the precision of our predictions while maintaining a compact and efficient behavioral characterization. We demonstrate our approach using a deep learning benchmark executed on a production cluster.
Keywords: Behavioral characterization, HPC, I/O, Performance modeling, Wavelet Transform
Received: 31 May 2025; Accepted: 18 Dec 2025.
Copyright: © 2025 Tarraf and Wolf. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Ahmad Tarraf
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