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@INPROCEEDINGS{Comito:1034687,
      author       = {Comito, C. and Gutiérrez Hermosillo Muriedas, J. P. and
                      Götz, M. and Hagemeier, B. and Hoppe, F. and Knechtges, P.
                      and Krajsek, K. and Rüttgers, Alexander and Tarnawa, M.},
      title        = {{S}caling data-intensive analytics with {H}eat: a {P}ython
                      library for massively-parallel array computing and machine
                      learning},
      reportid     = {FZJ-2024-07444},
      year         = {2024},
      abstract     = {Handling and analyzing massive data sets is highly
                      important for the vast majority of research communities, but
                      it is also challenging, especially for those communities
                      without a background in HPC. The Helmholtz Analytics Toolkit
                      (Heat) library offers a solution to this problem by
                      providing memory-distributed and hardware-accelerated array
                      manipulation, data analytics, and machine learning
                      algorithms in Python, targeting the usage by non-experts in
                      HPC. In short: Heats objective is to make array computing
                      and machine learning as easy on a CPU/GPU-cluster as it is
                      on a workstation.Our poster provides an overview of Heats
                      design principles, its current features and capabilities,
                      and discusses its role in the ecosystem of distributed array
                      computing and machine learning in Python.},
      month         = {Jun},
      date          = {2024-06-12},
      organization  = {Helmholtz AI Conference, Düsseldorf
                       (Germany), 12 Jun 2024 - 14 Jun 2024},
      subtyp        = {After Call},
      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) / SLNS - SimLab Neuroscience (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF4-5111 / G:(DE-HGF)POF4-5112 /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)24},
      url          = {https://juser.fz-juelich.de/record/1034687},
}