Submitted as: model description paper 23 Aug 2021

Submitted as: model description paper | 23 Aug 2021

Review status: a revised version of this preprint is currently under review for the journal GMD.

Fast Infrared Radiative Transfer Calculations Using Graphics Processing Units: JURASSIC-GPU v2.0

Paul F. Baumeister and Lars Hoffmann Paul F. Baumeister and Lars Hoffmann
  • Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany

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, balloon and ground-based instruments provide information on pressure and temperature, trace gases as well as aerosols and clouds. As state-of-the-art instruments deliver a vast amount of data on a global scale, their analysis, however, 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 most accurate, but they are also 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.

Paul F. Baumeister and Lars Hoffmann

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2021-203', Anonymous Referee #1, 15 Oct 2021
    • AC1: 'Reply on RC1', Paul Baumeister, 03 Jan 2022
  • RC2: 'Comment on gmd-2021-203', Anonymous Referee #2, 09 Nov 2021
    • AC2: 'Reply on RC2', Paul Baumeister, 03 Jan 2022

Paul F. Baumeister and Lars Hoffmann

Model code and software

JURASSIC-GPU v2.0 Paul Baumeister; Lars Hoffmann

Paul F. Baumeister and Lars Hoffmann


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Short summary
The efficiency of the numerical simulation of radiative transport is shown on modern server-class graphics cards (GPUs). The low cost prefactor on GPUs compared to general purpose processor (CPUs) enables future large retrieval campaigns on multi-channel data from infrared sounders aboard low orbit satellites. The validated research software JURASSIC is available in the public domain.