Articles | Volume 17, issue 9
Development and technical paper
17 May 2024
Development and technical paper |  | 17 May 2024

Accelerating Lagrangian transport simulations on graphics processing units: performance optimizations of Massive-Parallel Trajectory Calculations (MPTRAC) v2.6

Lars Hoffmann, Kaveh Haghighi Mood, Andreas Herten, Markus Hrywniak, Jiri Kraus, Jan Clemens, and Mingzhao Liu

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Cited articles

Bauer, P., Dueben, P. D., Hoefler, T., Quintino, T., Schulthess, T. C., and Wedi, N. P.: The digital revolution of Earth-system science, Nat. Comput. Sci., 1, 104–113,, 2021. a
Bell, N. and Hoberock, J.: Chapter 26 - Thrust: A Productivity-Oriented Library for CUDA, in: GPU Computing Gems Jade Edition, edited by Hwu, W.-m. W., Applications of GPU Computing Series, Morgan Kaufmann, Boston, 359–371,, 2012. a
Bowman, K. P., Lin, J. C., Stohl, A., Draxler, R., Konopka, P., Andrews, A., and Brunner, D.: Input Data Requirements for Lagrangian Trajectory Models, B. Am. Meteorol. Soc., 94, 1051–1058,, 2013. a
Cai, Z., Griessbach, S., and Hoffmann, L.: Improved estimation of volcanic SO2 injections from satellite retrievals and Lagrangian transport simulations: the 2019 Raikoke eruption, Atmos. Chem. Phys., 22, 6787–6809,, 2022. a, b
Clemens, J., Hoffmann, L., Vogel, B., Grießbach, S., and Thomas, N.: Implementation and evaluation of diabatic advection in the Lagrangian transport model MPTRAC 2.6, Geosci. Model Dev. Discuss. [preprint],, in review, 2023. a
Short summary
Lagrangian particle dispersion models are key for studying atmospheric transport but can be computationally intensive. To speed up simulations, the MPTRAC model was ported to graphics processing units (GPUs). Performance optimization of data structures and memory alignment resulted in runtime improvements of up to 75 % on NVIDIA A100 GPUs for ERA5-based simulations with 100 million particles. These optimizations make the MPTRAC model well suited for future high-performance computing systems.