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@INPROCEEDINGS{Riedel:1009096,
      author       = {Riedel, Morris and Barakat, C. and Fritsch, S. and Aach, M.
                      and Busch, J. and Lintermann, A. and Schuppert, A. and
                      Brynjólfsson, S. and Neukirchen, H. and Book, M.},
      title        = {{E}nabling {H}yperparameter-{T}uning of {AI} {M}odels for
                      {H}ealthcare using the {C}o{E} {RAISE} {U}nique {AI}
                      {F}ramework for {HPC}},
      publisher    = {IEEE},
      reportid     = {FZJ-2023-02635},
      isbn         = {978-953-233-104-2},
      pages        = {435-440},
      year         = {2023},
      comment      = {2023 46th MIPRO ICT and Electronics Convention (MIPRO)},
      booktitle     = {2023 46th MIPRO ICT and Electronics
                       Convention (MIPRO)},
      abstract     = {The European Center of Excellence in Exascale Computing
                      "Research on AI- and Simulation-Based Engineering at
                      Exascale" (CoE RAISE) is a project funded by the European
                      Commission. One of its central goals is to develop a Unique
                      AI Framework (UAIF) that simplifies the development of AI
                      models on cutting-edge supercomputers. However, those
                      supercomputers’ High-Performance Computing (HPC)
                      environments require the knowledge of many low-level modules
                      that all need to work together in different software
                      versions (e.g., TensorFlow, Python, NCCL, PyTorch) and
                      various concrete supercomputer hardware deployments (e.g.,
                      JUWELS, JURECA, DEEP, JUPITER and other EuroHPC Joint
                      Undertaking HPC resources). This paper will describe our
                      analyzed complex challenges for AI researchers using those
                      environments and explain how to overcome them using the
                      UAIF. In addition, it will show the benefits of using the
                      UAIF hypertuning capability to make AI models better (i.e.,
                      better parameters) and faster by using HPC. Also, to
                      demonstrate that the UAIF approach is indeed simple, we
                      describe the adoption of selected UAIF building blocks by
                      healthcare applications. The examples include AI models for
                      the Acute Respiratory Distress Syndrome (ARDS). Finally, we
                      highlight other AI models of use cases that co-designed the
                      UAIF.},
      month         = {May},
      date          = {2023-05-22},
      organization  = {46th MIPRO ICT and Electronics
                       Convention, Opatija (Croatia), 22 May
                       2023 - 26 May 2023},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / 5112 - Cross-Domain
                      Algorithms, Tools, Methods Labs (ATMLs) and Research Groups
                      (POF4-511) / RAISE - Research on AI- and Simulation-Based
                      Engineering at Exascale (951733) / BMBF 01ZZ1803B - SMITH -
                      Medizininformatik-Konsortium - Beitrag Universitätsklinikum
                      Aachen (01ZZ1803B)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5112 /
                      G:(EU-Grant)951733 / G:(BMBF)01ZZ1803B},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.23919/MIPRO57284.2023.10159755},
      url          = {https://juser.fz-juelich.de/record/1009096},
}