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        <datestamp>2024-03-06T10:27:21Z</datestamp>
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          <dc:title>Understanding I/O Behavior in Scientific and Data-Intensive Computing (Dagstuhl Seminar 21332)</dc:title>
          <dc:creator>Carns, Philip</dc:creator>
          <dc:creator>Kunkel, Julian</dc:creator>
          <dc:creator>Mohror, Kathryn</dc:creator>
          <dc:creator>Schulz, Martin</dc:creator>
          <dc:subject>I/O performance measurement</dc:subject>
          <dc:subject>Understanding user I/O patterns</dc:subject>
          <dc:subject>HPC I/O</dc:subject>
          <dc:subject>I/O characterization</dc:subject>
          <dc:description>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.</dc:description>
          <dc:publisher>Schloss Dagstuhl – Leibniz-Zentrum für Informatik</dc:publisher>
          <dc:contributor>Philip Carns and Julian Kunkel and Kathryn Mohror and Martin Schulz</dc:contributor>
          <dc:date>2021</dc:date>
          <dc:relation>Is Part Of Dagstuhl Reports, Volume 11, Issue 7 (2021)</dc:relation>
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          <dc:language>eng</dc:language>
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