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| 001 | 906560 | ||
| 005 | 20230123110603.0 | ||
| 024 | 7 | _ | |a 10.5194/gmd-15-1855-2022 |2 doi |
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| 100 | 1 | _ | |a Baumeister, Paul F. |0 P:(DE-Juel1)156619 |b 0 |e Corresponding author |
| 245 | _ | _ | |a Fast infrared radiative transfer calculations using graphics processing units: JURASSIC-GPU v2.0 |
| 260 | _ | _ | |a Katlenburg-Lindau |c 2022 |b Copernicus |
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| 520 | _ | _ | |a 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. |
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| 700 | 1 | _ | |a Hoffmann, Lars |0 P:(DE-Juel1)129125 |b 1 |
| 773 | _ | _ | |a 10.5194/gmd-15-1855-2022 |g Vol. 15, no. 5, p. 1855 - 1874 |0 PERI:(DE-600)2456725-5 |n 5 |p 1855 - 1874 |t Geoscientific model development |v 15 |y 2022 |x 1991-959X |
| 856 | 4 | _ | |u https://juser.fz-juelich.de/record/906560/files/GMD-JURASSIC-GPU-v2.0.pdf |y OpenAccess |
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