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@INPROCEEDINGS{Comito:1019995,
      author       = {Comito, Claudia and Götz, Markus and Gutiérrez Hermosillo
                      Muriedas, Juan Pedro and Hagemeier, Björn and Hoppe, Fabian
                      and Knechtges, Philipp and Krajsek, Kai and Rüttgers,
                      Alexander and Streit, Achim and Tarnawa, Michael},
      title        = {{A}ccelerating massive data processing in {P}ython with
                      {H}eat},
      reportid     = {FZJ-2023-05810},
      year         = {2023},
      abstract     = {Heat [1, 2] is an open-source Python library designed to
                      address the challenges of working with massive data sets and
                      harnessing the power of machine learning across disciplines.
                      Developed collaboratively by within the Helmholtz
                      Association (FZJ, KIT, and DLR), Heat offers cutting-edge
                      capabilities for high-performance data analytics, machine
                      learning, and deep learning.Heat provides a Numpy-like API
                      that simplifies the development of scalable, GPU-accelerated
                      applications. What sets Heat apart is its underlying
                      data-parallelism, implemented on top of MPI, which
                      significantly enhances efficiency and performance of data
                      processing compared to traditional task-parallel
                      frameworks.By exploring practical use cases in space science
                      (materials engineering, atmospheric modeling, anomaly
                      detection) and its potential as a backend for diverse data
                      processing pipelines, we will illustrate how Heat can
                      accelerate AI research and applications.[1] Götz, M.,
                      Debus, C., Coquelin, et al.: "HeAT - a Distributed and
                      GPU-accelerated Tensor Framework for Data Analytics" [2]
                      https://github.com/helmholtz-analytics/heat},
      month         = {Sep},
      date          = {2023-09-27},
      organization  = {Artificial Intelligence Symposium on
                       Theory, Application and Research 2023,
                       ESOC, Darmstadt (Germany), 27 Sep 2023
                       - 28 Sep 2023},
      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/1019995},
}