oelbert / pace

Re-write of FV3GFS weather/climate model in Python

Pace

Pace is an implementation of the FV3GFS / SHiELD atmospheric model developed by NOAA/GFDL using the NDSL middleware in Python, itself based on GT4Py and DaCe. The model can be run on a laptop using Python-based backend or on thousands of heterogeneous compute nodes of a large supercomputer.

🚧 WARNING This repo is under active development - supported features and procedures can change rapidly and without notice. 🚧

The repository model code is split between pyFV3 for the dynamical core and pySHiELD for the physics parametrization. A full depencies looks like the following:

flowchart TD
GT4Py.cartesian --> |Stencil DSL|NDSL
DaCe  --> |Full program opt|NDSL
NDSL --> pyFV3
NDSL --> pySHiELD
pyFV3 --> |Dynamics|Pace
pySHiELD --> |Physics|Pace

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Quickstart - bare metal

Build

Pace requires:

• GCC > 9.2
• MPI
• Python 3.8.

For GPU backends CUDA and/or ROCm is required depending on the targeted hardware.

For GT stencils backends, you will also need the headers of the boost libraries in your $PATH. This could be down like this.

cd BOOST/ROOT
wget https://boostorg.jfrog.io/artifactory/main/release/1.79.0/source/boost_1_79_0.tar.gz
tar -xzf boost_1_79_0.tar.gz
mkdir -p boost_1_79_0/include
mv boost_1_79_0/boost boost_1_79_0/include/
export BOOST_ROOT=BOOST/ROOT/boost_1_79_0

When cloning Pace you will need to update the repository's submodules as well:

git clone --recursive https://github.com/NOAA-GFDL/pace.git

or if you have already cloned the repository:

git submodule update --init --recursive

We recommend creating a python venv or conda environment specifically for Pace.

python3 -m venv venv_name
source venv_name/bin/activate

Inside of your pace venv or conda environment pip install the Python requirements, GT4Py, and Pace:

pip3 install -r requirements_dev.txt -c constraints.txt

Shell scripts to install Pace on specific machines such as Gaea can be found in examples/build_scripts/.

Run

With the environment activated, you can run an example baroclinic test case with the following command:

mpirun -n 6 python3 -m pace.driver.run driver/examples/configs/baroclinic_c12.yaml

# or with oversubscribe if you do not have at least 6 cores
mpirun -n 6 --oversubscribe python3 -m pace.driver.run driver/examples/configs/baroclinic_c12.yaml

After the run completes, you will see an output direcotry output.zarr. An example to visualize the output is provided in driver/examples/plot_output.py. See the driver example section for more details.

Environment variable configuration

• PACE_CONSTANTS: Pace is bundled with various constants.
  • GFDL NOAA's FV3 dynamical core constants (original port)
  • GFS Constant as defined in NOAA GFS
  • GEOS Constant as defined in GEOS v13
• PACE_FLOAT_PRECISION: default precision of the field & scalars in the numerics. Default to 64.
• PACE_LOGLEVEL: logging level to display (DEBUG, INFO, WARNING, ERROR, CRITICAL). Default to INFO.

Quickstart - Docker

Build

While it is possible to install and build pace bare-metal, we can ensure all system libraries are installed with the correct versions by using a Docker container to test and develop pace.

First, you will need to update the git submodules so that any dependencies are cloned and at the correct version:

git submodule update --init --recursive

Then build the pace docker image at the top level.

make build

Run

make dev
mpirun --mca btl_vader_single_copy_mechanism none -n 6 python3 -m pace.driver.run /pace/driver/examples/configs/baroclinic_c12.yaml

History

This repository was first developed at AI2 and the institute conserves an archived copy with the latest state before the NOAA took over.