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@INPROCEEDINGS{vonRden:808937,
      author       = {von Rüden, Laura and Hermanns, Marc-André and Behrisch,
                      Michael and Keim, Daniel and Mohr, Bernd and Wolf, Felix},
      title        = {{S}eparating the wheat from the chaff: {I}dentifying
                      {R}elevant and {S}imilar {P}erformance {D}ata with {V}isual
                      {A}nalytics},
      address      = {New York, New York, USA},
      publisher    = {ACM Press},
      reportid     = {FZJ-2016-02457},
      pages        = {Article No. 4},
      year         = {2015},
      comment      = {Proceedings of the 2nd Workshop on Visual Performance
                      Analysis - VPA '15. - ISBN 9781450340137},
      booktitle     = {Proceedings of the 2nd Workshop on
                       Visual Performance Analysis - VPA '15.
                       - ISBN 9781450340137},
      abstract     = {Performance-analysis tools are indispensable for
                      understanding and optimizing the behavior of parallel
                      programs running on increasingly powerful supercomputers.
                      However, with size and complexity of hardware and software
                      on the rise, performance data sets are becoming so
                      voluminous that their analysis poses serious challenges. In
                      particular, the search space that must be traversed and the
                      number of individual performance views that must be explored
                      to identify phenomena of interest becomes too large. To
                      mitigate this problem, we use visual analytics.
                      Specifically, we accelerate the analysis of performance
                      profiles by automatically identifying (1) relevant and (2)
                      similar data subsets and their performance views. We focus
                      on views of the virtual-process topology, showing that their
                      relevance can be well captured with visual-quality metrics
                      and that they can be further assigned to topical groups
                      according to their visual features. A case study
                      demonstrates that our approach helps reduce the search space
                      by up to $80\%.$},
      date          = {11152015},
      organization  = {2nd Workshop on Visual Performance
                       Analysis, Austin (Texas), 15 Nov 2015 -
                       15 Nov 2015},
      cin          = {JSC / JARA-HPC},
      cid          = {I:(DE-Juel1)JSC-20090406 / $I:(DE-82)080012_20140620$},
      pnm          = {511 - Computational Science and Mathematical Methods
                      (POF3-511) / ATMLPP - ATML Parallel Performance (ATMLPP)},
      pid          = {G:(DE-HGF)POF3-511 / G:(DE-Juel-1)ATMLPP},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.1145/2835238.2835242},
      url          = {https://juser.fz-juelich.de/record/808937},
}