Preprints
https://doi.org/10.5194/gmd-2021-382
https://doi.org/10.5194/gmd-2021-382

Submitted as: model description paper 01 Dec 2021

Submitted as: model description paper | 01 Dec 2021

Review status: this preprint is currently under review for the journal GMD.

Massive-Parallel Trajectory Calculations version 2.2 (MPTRAC-2.2): Lagrangian transport simulations on Graphics Processing Units (GPUs)

Lars Hoffmann1, Paul F. Baumeister1, Zhongyin Cai1, Jan Clemens1,2, Sabine Griessbach1, Gebhard Günther2, Yi Heng3, Mingzhao Liu1, Kaveh Haghighi Mood1, Olaf Stein1, Nicole Thomas2, Bärbel Vogel2, Xue Wu4,5, and Ling Zou1 Lars Hoffmann et al.
  • 1Jülich Supercomputing Centre, Forschungszentrum Jülich, Jülich, Germany
  • 2Institut für Energie- und Klimaforschung (IEK-7), Forschungszentrum Jülich, Jülich, Germany
  • 3School of Computer Science and Engineering, Sun Yat-Sen University, Guangzhou, China
  • 4Key Laboratory of Middle Atmosphere and Global Environment Observation, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China
  • 5University of Chinese Academy of Sciences, Beijing, China

Abstract. Lagrangian models are fundamental tools to study atmospheric transport processes and for practical applications such as dispersion modeling for anthropogenic and natural emission sources. However, conducting large-scale Lagrangian transport simulations with millions of air parcels or more can become numerically rather costly. In this study, we assessed the potential of exploiting graphics processing units (GPUs) to accelerate Lagrangian transport simulations. We ported the Massive-Parallel Trajectory Calculations (MPTRAC) model to GPUs using the open accelerator (OpenACC) programming model. The trajectory calculations conducted within the MPTRAC model were fully ported to GPUs, i.e., except for feeding in the meteorological input data and for extracting the particle output data, the code operates entirely on the GPU devices without frequent data transfers between CPU and GPU memory. Model verification, performance analyses, and scaling tests of the MPI/OpenMP/OpenACC hybrid parallelization of MPTRAC were conducted on the JUWELS Booster supercomputer operated by the Jülich Supercomputing Centre, Germany. The JUWELS Booster comprises 3744 NVIDIA A100 Tensor Core GPUs, providing a peak performance of 71.0 PFlop/s. As of June 2021, it is the most powerful supercomputer in Europe and listed among the most energy-efficient systems internationally. For large-scale simulations comprising 108 particles driven by the European Centre for Medium-Range Weather Forecasts' ERA5 reanalysis, the performance evaluation showed a maximum speedup of a factor of 16 due to the utilization of GPUs compared to CPU-only runs on the JUWELS Booster. In the large-scale GPU run, about 67 % of the runtime is spent on the physics calculations, conducted on the GPUs. Another 15 % of the runtime is required for file-I/O, mostly to read the large ERA5 data set from disk. Meteorological data preprocessing on the CPUs also requires about 15 % of the runtime. Although this study identified potential for further improvements of the GPU code, we consider the MPTRAC model ready for production runs on the JUWELS Booster in its present form. The GPU code provides a much faster time to solution than the CPU code, which is particularly relevant for near-real-time applications of a Lagrangian transport model.

Lars Hoffmann et al.

Status: open (extended)

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Lars Hoffmann et al.

Lars Hoffmann et al.

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Short summary
We describe the new Version 2.0 of the Lagrangian transport model MPTRAC, which has been ported for application on GPUs. The model was verified by comparing kinematic trajectories and synthetic tracer simulations for the troposphere and stratosphere from GPUs and CPUs. Benchmarking showed a speed-up of a factor of 16x of GPU-enabled simulations compared to CPU-only runs, indicating the great potential of applying GPUs for Lagrangian transport simulations on upcoming HPC systems.