A Comprehensive I/O Knowledge Cycle for Modular and Automated HPC Workload Analysis
Zhao-Bin Zhu, Sarah Neuwirth, Thomas Lippert · 2022
On the way to the exascale era, millions of parallel processing elements are required. Accordingly, one major chal-lenge is the ever-widening gap between computational power and underlying I/O systems. To bridge this gap, I/O resources must be used efficiently, thus a profound I/O knowledge is required. In this work, we analyze state-of-the-art approaches that can be applied to improve the general I/O understanding and performance. Based on our analysis, we present an automated, modular, tool-agnostic I/Oanalysis workflow and a prototype implementation that can be used to generate, extract, store, analyze, and use I/O knowledge in a structured and reproducible way.