Understanding I/O Behavior in Scientific and Data-Intensive Computing (Dagstuhl Seminar 21332)

Authors Philip Carns, Julian Kunkel, Kathryn Mohror, Martin Schulz and all authors of the abstracts in this report

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Author Details

Philip Carns
  • Argonne National Laboratory, USA
Julian Kunkel
  • Universität Göttingen / GWDG, DE
Kathryn Mohror
  • Lawrence Livermore National Laboratory, USA
Martin Schulz
  • TU München, DE
and all authors of the abstracts in this report

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Philip Carns, Julian Kunkel, Kathryn Mohror, and Martin Schulz. Understanding I/O Behavior in Scientific and Data-Intensive Computing (Dagstuhl Seminar 21332). In Dagstuhl Reports, Volume 11, Issue 7, pp. 16-75, Schloss Dagstuhl – Leibniz-Zentrum für Informatik (2021)


Two key changes are driving an immediate need for deeper understanding of I/O workloads in high-performance computing (HPC): applications are evolving beyond the traditional bulk-synchronous models to include integrated multistep workflows, in situ analysis, artificial intelligence, and data analytics methods; and storage systems designs are evolving beyond a two-tiered file system and archive model to complex hierarchies containing temporary, fast tiers of storage close to compute resources with markedly different performance properties. Both of these changes represent a significant departure from the decades-long status quo and require investigation from storage researchers and practitioners to understand their impacts on overall I/O performance. Without an in-depth understanding of I/O workload behavior, storage system designers, I/O middleware developers, facility operators, and application developers will not know how best to design or utilize the additional tiers for optimal performance of a given I/O workload. The goal of this Dagstuhl Seminar was to bring together experts in I/O performance analysis and storage system architecture to collectively evaluate how our community is capturing and analyzing I/O workloads on HPC systems, identify any gaps in our methodologies, and determine how to develop a better in-depth understanding of their impact on HPC systems. Our discussions were lively and resulted in identifying critical needs for research in the area of understanding I/O behavior. We document those discussions in this report.

Subject Classification

ACM Subject Classification
  • Software and its engineering → Software design engineering
  • Networks → Network performance analysis
  • I/O performance measurement
  • Understanding user I/O patterns
  • HPC I/O
  • I/O characterization


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