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Python library for generating high-performance implementations of stencil kernels for weather and climate modeling from a domain-specific language (DSL).

GT4Py requires Python 3.8+ and uses the standard Python packaging method, so can be installed using pip.

It is recommended to install the package in a virtual environment. For example:

python -m venv venv
source venv/bin/activate
# For the CUDA backends add the '[cudaXXX]' optional dependency
pip install gt4py[cuda11x]

GT4Py is a Python library for expressing computational motifs as found in weather and climate applications. These computations are expressed in a domain specific language (GTScript) which is translated to high-performance implementations for CPUs and GPUs.

The DSL expresses computations on a 3-dimensional Cartesian grid. The horizontal axes (I, J) are always computed in parallel, while the vertical (K) can be iterated in sequential, forward or backward, order. Cartesian offsets are expressed relative to a center index.

In addition, GT4Py provides functions to allocate arrays with memory layout suited for a particular backend.

The following backends are supported:

• numpy: Pure-Python backend
• gt:cpu_ifirst: GridTools C++ CPU backend using I-first data ordering
• gt:cpu_kfirst: GridTools C++ CPU backend using K-first data ordering
• gt:gpu: GridTools backend for CUDA
• cuda: CUDA backend minimally using utilities from GridTools
• dace:cpu: Dace code-generated CPU backend
• dace:gpu: Dace code-generated GPU backend

For testing GT4Py with the numpy backend, all dependencies are included in the setup.cfg and are automatically installed. The performance backends also require

1. the Boost library, a dependency of GridTools C++, which needs to be installed by the user.

If GridTools or Boost are not found in the compiler's standard include path, or a custom version is desired, then a couple configuration environment variables will allow the compiler to use them:

• GT_INCLUDE_PATH: Path to the GridTools installation.
• BOOST_ROOT: Path to a boost installation.

Other commonly used environment variables are:

• CUDA_ARCH: Set the compute capability of the NVIDIA GPU if it is not detected automatically by cupy.
• CXX: Set the C++ compiler.
• GT_CACHE_DIR_NAME: Name of the compiler's cache directory (defaults to .gt_cache)
• GT_CACHE_ROOT: Path to the compiler cache (defaults to ./)

More options and details in config.py.

GT4Py uses Sphinx documentation. To build the documentation install the dependencies in requirements-dev.txt

pip install -r ./gt4py/requirements-dev.txt

and then build the docs with

cd gt4py/docs
make html  # run 'make help' for a list of targets

For developing GT4Py we recommend to clone the repository and use an editable installation of GT4Py:

git clone https://github.com/gridtools/gt4py.git
pip install -e ./     # pip install -e ./[cudaXX] for GPU support
pip install -r requirements-dev.txt
pre-commit install-hooks

Dependencies for running tests locally and for linting and formatting source are listed in requirements-dev.txt.

There are notebooks in the examples/ folder that can be run using IPython notebooks on Jupyter.

pip install jupyterlab matplotlib
jupyter-lab

There are two options to run the unit and integration tests in tests/:

1. Use tox: pip install tox && tox -r -e py39-all-cpu.
2. Install the development requirements: pip install -r requirements-dev.txt, then pytest can execute the tests directly.

GT4Py is licensed under the terms of the GPLv3.