Publications

• 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.

• A Deep Look into the Temporal I/O Behavior of HPC Applications

  Francieli Boito, Luan Teylo, Mihail Popov, Théo Jolivel, François Tessier, Jakob Luettgau, Julien Monniot, Ahmad Tarraf, André Carneiro, Carla Osthoff June 2025 - IPDPS 2025 - Conference Paper Abstract: The increasing gap between compute and I/O speeds in high-performance computing (HPC) systems imposes the need for techniques to improve applications' I/O performance. Such techniques must rely on assumptions about I/O behavior in order to efficiently allocate I/O resources such as burst buffers, to schedule accesses to the shared parallel file system or to delay certain applications at the batch scheduler level to prevent contention, for instance. In this paper, we verify these common assumptions about I/O behavior, specifically about temporal behavior, using over 440,000 traces from real HPC systems. By combining traces from diverse systems, we characterize the behaviors observed in real HPC workloads. Among other findings, we show that I/O activity tends to last for a few seconds, and that periodic jobs are the minority, but responsible for a large portion of the I/O time. Furthermore, we make projections for the expected improvement yielded by popular approaches for I/O performance improvement. Our work provides valuable insights to everyone working to alleviate the I/O bottleneck in HPC.

• 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.