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@INPROCEEDINGS{Zhu:1006595,
      author       = {Zhu, Zhaobin and Neuwirth, Sarah and Lippert, Thomas},
      title        = {{A} {C}omprehensive {I}/{O} {K}nowledge {C}ycle for
                      {M}odular and {A}utomated {HPC} {W}orkload {A}nalysis},
      publisher    = {IEEE},
      reportid     = {FZJ-2023-01736},
      pages        = {581-588},
      year         = {2022},
      abstract     = {On the way to the exascale era, millions of parallel
                      processing elements are required. Accordingly, one major
                      chal-lenge is the ever-widening gap between computational
                      power and underlying I/O systems. To bridge this gap, I/O
                      resources must be used efficiently, thus a profound I/O
                      knowledge is required. In this work, we analyze
                      state-of-the-art approaches that can be applied to improve
                      the general I/O understanding and performance. Based on our
                      analysis, we present an automated, modular, tool-agnostic
                      I/Oanalysis workflow and a prototype implementation that can
                      be used to generate, extract, store, analyze, and use I/O
                      knowledge in a structured and reproducible way.},
      month         = {Sep},
      date          = {2022-09-05},
      organization  = {2022 IEEE International Conference on
                       Cluster Computing (CLUSTER), Heidelberg
                       (Germany), 5 Sep 2022 - 8 Sep 2022},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5121 - Supercomputing $\&$ Big Data Facilities (POF4-512)},
      pid          = {G:(DE-HGF)POF4-5121},
      typ          = {PUB:(DE-HGF)8},
      UT           = {WOS:000920273100061},
      doi          = {10.1109/CLUSTER51413.2022.00076},
      url          = {https://juser.fz-juelich.de/record/1006595},
}