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@INPROCEEDINGS{Baker:1026335,
      author       = {Baker, Dirk and Bauer, Felix and Schnepf, Andrea and
                      Scharr, Hanno and Riedel, Morris and Göbbert, Jens Henrik
                      and Hvannberg, Ebba},
      title        = {{A}dapting {A}gricultural {V}irtual {E}nvironments in
                      {G}ame {E}ngines to {I}mprove {HPC} {A}ccessibility},
      publisher    = {Springer},
      reportid     = {FZJ-2024-03386},
      pages        = {1-15},
      year         = {2024},
      comment      = {Communications in Computer and Information Science},
      booktitle     = {Communications in Computer and
                       Information Science},
      abstract     = {E-infrastructures deliver basic supercomputing and storage
                      capabilities but can benefit from innovative higher-level
                      services that enable use-cases in critical domains, such as
                      environmental and agricultural science.This work describes
                      methods to distribute virtual scenes to the GPU nodes of a
                      modular supercomputer for data generation.High information
                      density virtual scenes, containing >100k geometries,
                      typically cannot be rendered in real-time without techniques
                      that change the information content, such as level-of-detail
                      or culling approaches.Our work enables the concurrent and
                      partitioned coupling to the image analysis in such a way
                      that the data generation is dynamic and can be allocated to
                      GPU nodes on demand, resulting in the possibility of moving
                      through a continuous virtual scene rendered on multiple
                      nodes.Within agricultural data analysis, the approach is
                      especially impactful as virtual fields contain many
                      individual geometries that coexist in one continuous
                      system.Our work facilitates the generation of high-quality
                      image data sets which has the potential to solve the
                      challenge of scarcity of well-annotated data in agricultural
                      science.We use real-time communication standards to couple
                      the data production with the image analysis training.We
                      demonstrate how the use-case rendering impacts effective use
                      of the compute nodes and furthermore develop techniques to
                      distribute the workload to improve the data production.},
      month         = {May},
      date          = {2024-05-27},
      organization  = {nordic e-Infrastructure Collaboration
                       Conference, Tallinn (Estonia), 27 May
                       2024 - 29 May 2024},
      cin          = {JSC / IBG-3 / IAS-8},
      cid          = {I:(DE-Juel1)JSC-20090406 / I:(DE-Juel1)IBG-3-20101118 /
                      I:(DE-Juel1)IAS-8-20210421},
      pnm          = {5121 - Supercomputing $\&$ Big Data Facilities (POF4-512) /
                      2A3 - Remote Sensing (CARF - CCA) (POF4-2A3) / 2173 -
                      Agro-biogeosystems: controls, feedbacks and impact
                      (POF4-217) / EUROCC-2 (DEA02266) / DFG project 390732324 -
                      EXC 2070: PhenoRob - Robotik und Phänotypisierung für
                      Nachhaltige Nutzpflanzenproduktion (390732324) / 5112 -
                      Cross-Domain Algorithms, Tools, Methods Labs (ATMLs) and
                      Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5121 / G:(DE-HGF)POF4-2A3 /
                      G:(DE-HGF)POF4-2173 / G:(DE-Juel-1)DEA02266 /
                      G:(GEPRIS)390732324 / G:(DE-HGF)POF4-5112},
      typ          = {PUB:(DE-HGF)8 / PUB:(DE-HGF)7},
      doi          = {10.34734/FZJ-2024-03386},
      url          = {https://juser.fz-juelich.de/record/1026335},
}