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FALL3D

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Code name FALL3D
Developer(s)

Arnau Folch, Leonardo Mingari, Natalia Gutierrez, Antonio Costa and Giovanni Macedonio and Eduardo Cabrera

Contact person: Arnau Folch

Link

https://gitlab.com/fall3d-distribution

Short description

Open-source off-line Eulerian model for atmospheric passive transport and deposition based on the Advection-Diffusion-Sedimentation (ADS) equation. In FALL3D-8.0 (Folch et al., 2020; Prata et al., 2020), the ADS equation has been extended to handle passive transport of other substances different from tephra (aerosols, particles and radionuclides)

Original code level

3

Current code level

2

Pilot(s) involved

PD3, PD6, PD12

Co-design

Indirectly. The mini-app “MiniAero” is an explicit (using RK4) unstructured finite volume code that solves the compressible Navier-Stokes equations, applicable to FALL3D. Both inviscid and viscous terms are included. The viscous terms can be optionally included or excluded.

A FALL3D mini-app was developed for both CPU & GPU using OpenACC directives in the latter case. This mini-app only solves the FALL3D task assuming a fixed problem, i.e., helicoidal velocity field. This FALL3D task involves fully solving the main computational kernel, i.e., solves a set of advection-diffusion-sedimentation (ADS) equations on a structured grid using a second order finite volume explicit scheme (Euler or RK 4th in time). As stated, the ADS kernel is fully computed through GPU cards, i.e., 100% 

Main results and References

MAIN RESULTS:

  • New code release (8.0) and ensemble-based forecasts with parallel data assimilation implemented (8.1 release).

  • Better communication/computation ratio by refactoring the original (7.3) code version. The speedup increases with the number of cores up to a factor of 4.3x.

  • GPU porting: migrated solver kernels to GPUs using OpenACC directives; the improvement is almost 60% comparing GPU version with CPU version.

  • Vectorization: some directives implemented to enforce vectorization for loops that the compiler does not auto-vectorize. Speedup up to 1.2 achieved.

  • Parallel I/O: A parallel I/O strategy for NetCDF files implemented. The performance, up to a factor 2x, is also improved.

  • GPU porting is almost complete. Approximately 90% of the main FALL3D task is ported to the accelerators.

 

REFERENCES:

 

  • Mingari, L., Folch, A., Prata, A. T., Pardini, F., Macedonio, G., and Costa, A.: Data assimilation of volcanic aerosol observations using FALL3D+PDAF, Atmos. Chem. Phys., 22, 1773–1792, https://doi.org/10.5194/acp-22-1773-2022, 2022.

  • Folch A, Mingari L and Prata AT (2022) Ensemble-Based Forecast of Volcanic Clouds Using FALL3D-8.1. Front. Earth Sci. 9:741841. doi: 10.3389/feart.2021.741841

  • Prata, A. T., Mingari, L., Folch, A., Macedonio, G., and Costa, A.: FALL3D-8.0: a computational model for atmospheric transport and deposition of particles, aerosols and radionuclides – Part 2: Model validation, Geosci. Model Dev., 14, 409–436, https://doi.org/10.5194/gmd-14-409-2021, 2021.

  • Folch, A., Mingari, L., Gutierrez, N., Hanzich, M., Costa, A., Macedonio, G., FALL3D-8.0: a computational model for atmospheric transport and deposition of particles and aerosols. Part I: model physics and numerics, geoscientific model development, https://doi.org/10.5194/gmd-13-1431-2020, 2020.

 

Performance results

Strong scalability tests up to 10000 cores (irene-rome and MN-4) with parallel efficiency above 80%.

FALL3D Mini-app
The Mini-fall3d GPU version (MPI + OpenACC) is almost 7x faster than the previous version (MPI + OpenACC + OpenMP) on a medium size problem (400x400x240 grid cells)
A speedup of circa 29x has been achieved compared to the plain MPI version 

Full  FALL3D
The full FALL3D GPU version (MPI + OpenACC) is almost 6x faster than the original version (MPI) on a large  size problem (1000x760x64 grid cells)

Ensemble probability of exceedance considering a threshold of 0.2 g/m2 for ash column mass for the 2010 Eyjafjallajökull event. Results before and after the assimilation step (16 April 2010 at 00:00 UTC). Simulation at 4km grid resolution (50M grid points) and 30 ensemble members.
The ChEESE project has received funding from the European Union’s Horizon 2020
research and innovation programme under the grant agreement Nº 823844. All rights reserved. Legal Notice.
© CHEESE-COE.EU COPYRIGHT 2018 - 2019
  • About
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