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@INPROCEEDINGS{Gtz:841394,
      author       = {Götz, Markus and Book, Matthias and Bodenstein, Christian
                      and Riedel, Morris},
      title        = {{S}upporting {S}oftware {E}ngineering {P}ractices in the
                      {D}evelopment of {D}ata-{I}ntensive {HPC} {A}pplications
                      with the {J}u{ML} {F}ramework},
      publisher    = {ACM Press},
      reportid     = {FZJ-2017-08469},
      pages        = {1-8},
      year         = {2017},
      comment      = {Proceedings of the 1st International Workshop on Software
                      Engineering for High Performance Computing in Computational
                      and Data-enabled Science $\&$ Engineering},
      booktitle     = {Proceedings of the 1st International
                       Workshop on Software Engineering for
                       High Performance Computing in
                       Computational and Data-enabled Science
                       $\&$ Engineering},
      abstract     = {The development of high performance computing applications
                      is considerably different from traditional software
                      development. This distinction is due to the complex hardware
                      systems, inherent parallelism, different software lifecycle
                      and workflow, as well as (especially for scientific
                      computing applications) partially unknown requirements at
                      design time. This makes the use of software engineering
                      practices challenging, so only a small subset of them are
                      actually applied. In this paper, we discuss the potential
                      for applying software engineering techniques to an emerging
                      field in high performance computing, namely large-scale data
                      analysis and machine learning. We argue for the employment
                      of software engineering techniques in the development of
                      such applications from the start, and the design of generic,
                      reusable components. Using the example of the Juelich
                      Machine Learning Library (JuML), we demonstrate how such a
                      framework can not only simplify the design of new parallel
                      algorithms, but also increase the productivity of the actual
                      data analysis workflow. We place particular focus on the
                      abstraction from heterogeneous hardware, the architectural
                      design as well as aspects of parallel and distributed unit
                      testing.},
      month         = {Nov},
      date          = {2017-11-12},
      organization  = {Workshop on Software Engineering for
                       High Performance Computing in
                       Computational and Data-enabled Science
                       $\&$ Engineering, Denver (USA), 12 Nov
                       2017 - 17 Nov 2017},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {512 - Data-Intensive Science and Federated Computing
                      (POF3-512) / PhD no Grant - Doktorand ohne besondere
                      Förderung (PHD-NO-GRANT-20170405) / DEEP-EST - DEEP -
                      Extreme Scale Technologies (754304)},
      pid          = {G:(DE-HGF)POF3-512 / G:(DE-Juel1)PHD-NO-GRANT-20170405 /
                      G:(EU-Grant)754304},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.1145/3144763.3144765},
      url          = {https://juser.fz-juelich.de/record/841394},
}