Configurations options

About MPI parallelism

When talking about MPI with OpenMC + Python, it helps to separate two different layers:

This is the outer parallelism:

You run your script with:
```
mpiexec -n 4 python depletion_mpi.py
```
mpi4py handles communication between ranks (e.g. splitting burnup steps, distributing materials, etc.).
Each rank calls openmc.deplete or openmc.run() from Python.
For this you only need:
```
python -m pip install mpi4py
```
in the same virtual environment where openmc is installed.

This works even if OpenMC itself was built without MPI — each rank just runs its own independent OpenMC process.

This is the inner parallelism, in the C++ code:

Enabled at build time with:
```
cmake .. -DOPENMC_USE_MPI=on ...
```
Then each OpenMC run can use multiple MPI ranks for particle transport.
Typically you’d start OpenMC with mpiexec (directly or via the Python API / executor).

This is useful if you want a single calculation to run faster using multiple ranks.

For a typical depletion-MPI pattern using only mpi4py (one Python rank ↔ one OpenMC process, no inner MPI):
- ❌ You do not strictly need OpenMC compiled with MPI.
- mpiexec -n N python depletion_mpi.py with a serial OpenMC build is perfectly fine.
If you want both:
- MPI between depletion tasks (mpi4py), and
- MPI inside each OpenMC transport solve,
then ✅ you do need OPENMC_USE_MPI=on in your OpenMC build and a careful layout of ranks/cores.

You already have an OpenMC build with:

So you’re fully covered:

To use depletion-MPI, just install mpi4py in your venv:
```
python -m pip install mpi4py
```

Run your script with:

mpiexec -n 4 python your_depletion_script.py

You can choose to: