LRT_OpenMC/README_config.md

Configurations options

About MPI parallelism (corrected for depletion)

For OpenMC there are two layers of MPI, but the rules are a bit different when you use the Python depletion module.

1. How depletion actually uses MPI

When you use openmc.deplete:

• Python imports the OpenMC shared library (libopenmc.so) directly via openmc.lib.

• The depletion integrator (e.g. Integrator, CECMIntegrator, etc.) calls the transport solver inside that shared library, not via openmc.run() and not via subprocesses.

• If you want parallel depletion, you run your script like:

  mpiexec -n 4 python depletion_mpi.py
  

• Inside the script, mpi4py is used to:

  • Get MPI.COMM_WORLD or a sub-communicator
  • Distribute depletion work across ranks
  • Coordinate calls into the OpenMC C++ side

Because the OpenMC library itself also uses MPI for transport in this mode, two things must match:

1. OpenMC must be compiled with MPI

   • CMake:

     cmake .. -DOPENMC_USE_MPI=on ...
     

   • openmc --version should show:

     MPI enabled:  yes
     

2. mpi4py must be built against the same MPI implementation that was used to build OpenMC. When installing the Python API from the openmc dir, put the depletion-mpi option.

   python -m pip install .[depletion-mpi,test,docs,vtk]
   

You do not call openmc.run() yourself in depletion; the integrator does that via the in-memory C++ API.

2. Outer vs inner MPI

Conceptually you can still think of:

• Outer MPI = mpi4py distributing work between Python ranks
• Inner MPI = OpenMC C++ solver running on multiple ranks for one calculation

For OpenMC’s depletion module, the integrator assumes there is a valid MPI world communicator shared between mpi4py and the OpenMC C++ library.That only works cleanly if OpenMC was compiled with MPI and linked to the same MPI libraries as mpi4py.