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@INPROCEEDINGS{Riedel:1007353,
      author       = {Riedel, M. and Book, M. and Neukirchen, H. and Cavallaro,
                      G. and Lintermann, A.},
      title        = {{P}ractice and {E}xperience using {H}igh {P}erformance
                      {C}omputing and {Q}uantum {C}omputing to {S}peed-up {D}ata
                      {S}cience {M}ethods in {S}cientific {A}pplications},
      publisher    = {IEEE},
      reportid     = {FZJ-2023-02022},
      pages        = {281-286},
      year         = {2022},
      abstract     = {High-Performance Computing (HPC) can quickly process
                      scientific data and perform complex calculations at
                      extremely high speeds. A vast increase in HPC use across
                      scientific communities is observed, especially in using
                      parallel data science methods to speed-up scientific
                      applications. HPC enables scaling up machine and deep
                      learning algorithms that inherently solve optimization
                      problems. More recently, the field of quantum machine
                      learning evolved as another HPC related approach to speed-up
                      data science methods. This paper will address primarily
                      traditional HPC and partly the new quantum machine learning
                      aspects, whereby the latter specifically focus on our
                      experiences on using quantum annealing at the Juelich
                      Supercomputing Centre (JSC). Quantum annealing is
                      particularly effective for solving optimization problems
                      like those that are inherent in machine learning methods. We
                      contrast these new experiences with our lessons learned of
                      using many parallel data science methods with a high number
                      of Graphical Processing Units (GPUs). That includes modular
                      supercomputers such as JUWELS, the fastest European
                      supercomputer at the time of writing. Apart from practice
                      and experience with HPC co-design applications, technical
                      challenges and solutions are discussed, such as using
                      interactive access via JupyterLab on typical batch-oriented
                      HPC systems or enabling distributed training tools for deep
                      learning on our HPC systems.},
      month         = {May},
      date          = {2022-05-23},
      organization  = {45th Jubilee International Convention
                       on Information, Communication and
                       Electronic Technology (MIPRO), Opatija
                       (Croatia), 23 May 2022 - 27 May 2022},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511) / RAISE - Research on
                      AI- and Simulation-Based Engineering at Exascale (951733)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)951733},
      typ          = {PUB:(DE-HGF)8},
      doi          = {10.23919/MIPRO55190.2022.9803802},
      url          = {https://juser.fz-juelich.de/record/1007353},
}