Pattern

• A Methodology for System-Scale I/O Pattern Taxonomy for HPC Workloads

  Théo Jolivel, François Tessier, Jakob Luettgau, Gabriel Antoniu, Philippe Deniel October 2026 - SBAC-PAD 2026 - Conference Paper Abstract: As High-Performance Computing (HPC) races towards exascale, I/O bottlenecks threaten to throttle the performance of data-intensive applications. Multiple paths have been explored in the literature to optimize I/O, such as I/O-awareness in job scheduling algorithms or data prefetching on a caching layer. However, these techniques require a systematic collection of information about the I/O behavior of applications. In this paper, we propose a methodology to classify data access patterns from I/O traces through three lenses: temporality, periodicity, and metadata load. With the instantiation of our methodology through MOSAIC, a Python library, we are able to detect I/O behaviors that are relevant to the deployment of I/O optimizations at a system scale. We validate MOSAIC on two datasets from top-tier supercomputers and show that our methodology is able to reveal noteworthy correlations, such as near-permanent accesses with larger data writes, that can help better understand how I/O are performed and identify patterns useful for I/O optimizations.