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@INPROCEEDINGS{Knobloch:893077,
      author       = {Knobloch, Michael and Saviankou, Pavel and Schlütter, Marc
                      and Visser, Anke and Mohr, Bernd},
      title        = {{A} {P}icture {I}s {W}orth a {T}housand
                      {N}umbers—{E}nhancing {C}ube’s {A}nalysis {C}apabilities
                      with {P}lugins},
      address      = {Cham},
      publisher    = {Springer International Publishing},
      reportid     = {FZJ-2021-02545},
      isbn         = {978-3-030-66057-4},
      pages        = {237 - 259},
      year         = {2021},
      comment      = {Tools for High Performance Computing 2018 / 2019 / Mix,
                      Hartmut (Editor) ; Cham : Springer International Publishing,
                      2021, Chapter 13 ; ISBN: 978-3-030-66056-7 ;
                      doi:10.1007/978-3-030-66057-4},
      booktitle     = {Tools for High Performance Computing
                       2018 / 2019 / Mix, Hartmut (Editor) ;
                       Cham : Springer International
                       Publishing, 2021, Chapter 13 ; ISBN:
                       978-3-030-66056-7 ;
                       doi:10.1007/978-3-030-66057-4},
      abstract     = {In the last couple of years, supercomputers became
                      increasingly large and more and more complex. Performance
                      analysis tools need to adapt to the system complexity in
                      order to be used effectively at large scale. Thus, we
                      introduced a plugin infrastructure in Cube 4, the
                      performance report explorer for Score-P and Scalasca, which
                      allows to extend Cube’s analysis features without
                      modifying the source code of the GUI. In this paper we
                      describe the Cube plugin infrastructure and show how it
                      makes Cube a more versatile and powerful tool. We present
                      different plugins provided by JSC that extend and enhance
                      the CubeGUI’s analysis capabilities. These add new types
                      of system-tree visualizations, help create reasonable filter
                      files for Score-P and visualize simple OTF2 trace files. We
                      also present a plugin which provides a high-level overview
                      of the efficiency of the application and its kernels. We
                      further discuss context-free plugins, which are used to
                      integrate command-line Cube algebra utilities, like
                      $cube_diff$ and similar commands, in the GUI.},
      month         = {Sep},
      date          = {2019-09-02},
      organization  = {Tools for High Performance Computing
                       2019, Dresden (Fed Rep Germany), 2 Sep
                       2019 - 3 Sep 2019},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / ATMLPP - ATML Parallel
                      Performance (ATMLPP)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-Juel-1)ATMLPP},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1007/978-3-030-66057-4_13},
      url          = {https://juser.fz-juelich.de/record/893077},
}