update design-goals passage

open-atmos · Mar 26, 2024 · f0c3589 · f0c3589
1 parent c75a3c1
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Showing 1 changed file with 9 additions and 6 deletions.
diff --git a/README.md b/README.md
@@ -152,12 +152,15 @@ licence: [GPL v3](https://www.gnu.org/licenses/gpl-3.0.html)
 
 ## Design goals
 
-- MPI support for PyMPDATA implemented externally (i.e., not incurring any overhead or additional dependencies for PyMPDATA users)
-- MPI calls within Numba njitted code (hence not using `mpi4py`, but leveraging `numba-mpi`)
-- hybrid domain decomposition parallelisation: threading (internal in PyMPDATA, in the inner dimension) + MPI (either inner or outer dimension)
-- portability across major OSes (currently Linux & macOS; no Windows support due [challenges in getting HDF5/MPI-IO to work there](https://docs.h5py.org/en/stable/build.html#source-installation-on-windows))
-- full test coverage including CI builds asserting on same results with multi-node vs. single-node computations
-- Continuous Integration with different OSes and different MPI implementation
+- MPI support for [PyMPDATA](https://pypi.org/project/PyMPDATA/) implemented externally (i.e., not incurring any overhead or additional dependencies for PyMPDATA users)
+- MPI calls within [Numba njitted code](https://numba.pydata.org/numba-doc/dev/reference/jit-compilation.html) (hence not using [`mpi4py`](https://mpi4py.readthedocs.io/), but rather [`numba-mpi`](https://pypi.org/p/numba-mpi/))
+- hybrid domain-decomposition parallelism: threading (internal in PyMPDATA, in the inner dimension) + MPI (either inner or outer dimension)
+- example simulation scenarios featuring HDF5/MPI-IO output storage (using [h5py](https://www.h5py.org/))
+- [py-modelrunner](https://github.com/zwicker-group/py-modelrunner) simulation orchestration
+- portability across Linux & macOS (no Windows support as of now due to [challenges in getting HDF5/MPI-IO to work there](https://docs.h5py.org/en/stable/build.html#source-installation-on-windows))
+- Continuous Integration (CI) with different OSes and different MPI implementations (leveraging to mpi4py's [setup-mpi Github Action](https://github.com/mpi4py/setup-mpi/))
+- full test coverage including CI builds asserting on same results with multi-node vs. single-node computations (with help of [pytest-mpi](https://pypi.org/p/pytest-mpi/))
+- ships as a [pip-installable package](https://pypi.org/project/PyMPDATA-MPI) - aimed to be a dependency of domain-specific packages 
 
 ## Related resources