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@ARTICLE{Baumeister:906560,
      author       = {Baumeister, Paul F. and Hoffmann, Lars},
      title        = {{F}ast infrared radiative transfer calculations using
                      graphics processing units: {JURASSIC}-{GPU} v2.0},
      journal      = {Geoscientific model development},
      volume       = {15},
      number       = {5},
      issn         = {1991-959X},
      address      = {Katlenburg-Lindau},
      publisher    = {Copernicus},
      reportid     = {FZJ-2022-01520},
      pages        = {1855 - 1874},
      year         = {2022},
      abstract     = {Remote sensing observations in the mid-infrared spectral
                      region (4–15 μm) play a key role in monitoring the
                      composition of the Earth’s atmosphere. Mid-infrared
                      spectral measurements from satellite, aircraft, balloons,
                      and ground-based instruments provide information on
                      pressure, temperature, trace gases, aerosols, and clouds. As
                      state-of-the-art instruments deliver a vast amount of data
                      on a global scale, their analysis may require advanced
                      methods and high-performance computing capacities for data
                      processing. A large amount of computing time is usually
                      spent on evaluating the radiative transfer equation.
                      Line-by-line calculations of infrared radiative transfer are
                      considered to be the most accurate, but they are also the
                      most time-consuming. Here, we discuss the emissivity growth
                      approximation (EGA), which can accelerate infrared radiative
                      transfer calculations by several orders of magnitude
                      compared with line-by-line calculations. As future satellite
                      missions will likely depend on exascale computing systems to
                      process their observational data in due time, we think that
                      the utilization of graphical processing units (GPUs) for the
                      radiative transfer calculations and satellite retrievals is
                      a logical next step in further accelerating and improving
                      the efficiency of data processing. Focusing on the EGA
                      method, we first discuss the implementation of infrared
                      radiative transfer calculations on GPU-based computing
                      systems in detail. Second, we discuss distinct features of
                      our implementation of the EGA method, in particular
                      regarding the memory needs, performance, and scalability, on
                      state-of-the-art GPU systems. As we found our implementation
                      to perform about an order of magnitude more energy-efficient
                      on GPU-accelerated architectures compared to CPU, we
                      conclude that our approach provides various future
                      opportunities for this high-throughput problem.},
      cin          = {JSC},
      ddc          = {550},
      cid          = {I:(DE-Juel1)JSC-20090406},
      pnm          = {5111 - Domain-Specific Simulation $\&$ Data Life Cycle Labs
                      (SDLs) and Research Groups (POF4-511)},
      pid          = {G:(DE-HGF)POF4-5111},
      typ          = {PUB:(DE-HGF)16},
      UT           = {WOS:000766895400001},
      doi          = {10.5194/gmd-15-1855-2022},
      url          = {https://juser.fz-juelich.de/record/906560},
}