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@INPROCEEDINGS{Fabian:1044789,
      author       = {Fabian, Hoppe and Gutiérrez Hermosillo Muriedas, J. P. and
                      Tarnawa, Michael and Philipp, Knechtges and Hagemeier,
                      Björn and Krajsek, Kai and Alexander, Rüttgers and Markus,
                      Götz and Comito, Claudia},
      title        = {{E}ngineering a large-scale data analytics and array
                      computing library for research: {H}eat},
      publisher    = {Electronic Communications of the EASST},
      reportid     = {FZJ-2025-03345},
      pages        = {1-26},
      year         = {2025},
      comment      = {Electronic Communications of the EASST},
      booktitle     = {Electronic Communications of the
                       EASST},
      abstract     = {Heat is a Python library for massively-parallel and
                      GPU-accelerated arraycomputing and machine learning. It is
                      developed by researchers for researchers,with the ultimate
                      goal to make multi-dimensional array processing and
                      machinelearning for scientists (almost) as easy on a
                      supercomputer as it is on a workstationwith NumPy or
                      scikit-learn. This paper highlights the relevance of this
                      project to theresearch software engineering community by
                      giving a short, but illustrative overviewof Heat and
                      discusses its role in the context of related libraries with
                      a specific focuson its research software aspects.},
      month         = {Mar},
      date          = {2024-03-05},
      organization  = {Fourth Conference on Research Software
                       Engineering in Germany, deRSE24,
                       Würzburg (Germany), 5 Mar 2024 - 7 Mar
                       2024},
      keywords     = {Multi-dimensional Arrays (Other) / Machine learning (Other)
                      / Data Science (Other) / Data analytics (Other) /
                      High-Performance Computing (Other) / Parallel Computing
                      (Other) / GPUs (Other) / Big Data (Other) / Research
                      Software (Other)},
      cin          = {JSC},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5112 - Cross-Domain Algorithms, Tools, Methods Labs (ATMLs)
                      and Research Groups (POF4-511) / 5111 - Domain-Specific
                      Simulation $\&$ Data Life Cycle Labs (SDLs) and Research
                      Groups (POF4-511) / SLNS - SimLab Neuroscience
                      (Helmholtz-SLNS)},
      pid          = {G:(DE-HGF)POF4-5112 / G:(DE-HGF)POF4-5111 /
                      G:(DE-Juel1)Helmholtz-SLNS},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.14279/eceasst.v83.2626},
      url          = {https://juser.fz-juelich.de/record/1044789},
}