I/O

• 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.

• MOSAIC: Detection and Categorization of I/O Patterns in HPC Applications

  Théo Jolivel, François Tessier, Julien Monniot, Guillaume Pallez November 2024 - PDSW 2024 - Workshop Paper Abstract: With the gap between computing power and I/O performance growing ever wider on HPC systems, it is becoming crucial to optimize how applications perform I/O on storage resources. To achieve this, a good understanding of application I/O behavior is an essential preliminary step. In this paper, we introduce MOSAIC, a method for categorizing applications according to their I/O behavior. We first propose an abstraction for characterizing I/O operations in terms of periodicity, temporality and metadata access. We then present a set of segmentation-based techniques for quickly and automatically detecting meaningful data access patterns. In the end, MOSAIC is able to characterize a full set of real-world I/O traces from the Blue Waters supercomputer with 92% accuracy.