Ahmad Tarraf, Felix Wolf
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.