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.

Highlights

  • Lagrangian transport models are frequently applied to study chemical and dynamical processes of the Earth’s atmosphere

  • Performance analyses, and scaling tests of the Message Passing Interface (MPI)/Open Multi-Processing (OpenMP)/open accelerator (OpenACC) hybrid parallelization of Massive-Parallel Trajectory Calculations (MPTRAC) were conducted on the JUWELS Booster supercomputer 10 operated by the Jülich Supercomputing Centre, Germany

  • The aim of the present study is to investigate the potential of using graphics processing units (GPUs) for accelerating Lagrangian transport simulations, a topic first explored by Molnár et al (2010)

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Summary

Introduction

Lagrangian transport models are frequently applied to study chemical and dynamical processes of the Earth’s atmosphere They have important practical applications in modeling and assessing the dispersion of anthropogenic and natural emissions from local to global scale, for instance, for air pollution (Hirdman et al, 2010; Lee et al, 2014), nuclear accidents (Becker et al, 2007; 25 Draxler et al, 2015), volcanic eruptions (Prata et al, 2007; D’Amours et al, 2010; Stohl et al, 2011), or wildfires (Forster et al, 2001; Damoah et al, 2004). The Massive-Parallel Trajectory Calculations (MPTRAC) model is applied to exploit the potential of conducting Lagrangian transport simulations on graphics processing units (GPUs). MPTRAC was first described by Hoffmann et al (2016), discussing Lagrangian transport simulations for volcanic eruptions with different meteorological data sets.

Overview on model features and code structure
Initial air parcel positions
Requirements and preprocessing of meteorological input data
Boundary conditions for meteorological data
Interpolation and sampling of meteorological data
Advection
Turbulent diffusion
Subgrid-scale wind fluctuations
Convection
Sedimentation
Dry deposition
Hydroxyl chemistry
Exponential decay
2.3.10 Boundary conditions
Model output data
Output from Lagrangian transport simulations
Parallelization strategy
Description of the GPU test system and software environment
Comparison of kinematic trajectory calculations
OpenMP scaling analysis
GPU scaling analysis
Multi-GPU usage and MPI scaling test
Findings
Conclusions

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