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@INPROCEEDINGS{Cavallaro:1017826,
      author       = {Cavallaro, Gabriele and Sedona, Rocco and Riedel, Morris
                      and Lintermann, Andreas and Michielsen, Kristel},
      title        = {{C}hallenges and {O}pportunities in the {A}doption of
                      {H}igh {P}erformance {C}omputing for {E}arth {O}bservation
                      in the {E}xascale {E}ra},
      publisher    = {Publications Office of the European Union},
      reportid     = {FZJ-2023-04350},
      pages        = {25-28},
      year         = {2023},
      comment      = {Proceedings of the 2023 Conference on Big Data from Space
                      (BiDS’23) - From foresight to impact},
      booktitle     = {Proceedings of the 2023 Conference on
                       Big Data from Space (BiDS’23) - From
                       foresight to impact},
      abstract     = {High-Performance Computing (HPC) enables precise analysis
                      of large and complex Earth Observation (EO) datasets.
                      However, the adoption of supercomputing in the EO community
                      faces challenges from the increasing heterogeneity of HPC
                      systems, limited expertise, and the need to leverage novel
                      computing technologies. This paper explores the implications
                      of exascale computing advancements and the inherent
                      heterogeneity of HPC architectures. It highlights
                      EU-supported projects optimizing software development and
                      harnessing the capabilities of heterogeneous HPC
                      configurations. Methodologies addressing challenges of
                      modular supercomputing, large-scale Deep Learning (DL)
                      models, and hybrid quantum-classical algorithms are
                      presented, aiming to enhance the utilization of
                      supercomputing in EO for improved research, industrial
                      applications, and SME support.},
      month         = {Nov},
      date          = {2023-11-06},
      organization  = {Conference on Big Data from Space
                       2023, Vienna (Austria), 6 Nov 2023 - 9
                       Nov 2023},
      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) /
                      EUPEX - EUROPEAN PILOT FOR EXASCALE (101033975) / EUROCC -
                      National Competence Centres in the framework of EuroHPC
                      (951732) / AIDAS - Joint Virtual Laboratory for AI, Data
                      Analytics and Scalable Simulation $(aidas_20200731)$},
      pid          = {G:(DE-HGF)POF4-5111 / G:(EU-Grant)951733 /
                      G:(EU-Grant)101033975 / G:(EU-Grant)951732 /
                      $G:(DE-Juel-1)aidas_20200731$},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.2760/46796},
      url          = {https://juser.fz-juelich.de/record/1017826},
}