2.5. Installing pyoomph (with PETSc/SLEPc) on Windows via WSL

PETSc/SLEPc cannot be built natively on Windows (see Section 2.4). If you are on Windows and want a good, stable eigensolver, the recommended route is to install pyoomph inside the Windows Subsystem for Linux (WSL). WSL2 runs a real Linux kernel, so once you are inside it, pyoomph and PETSc/SLEPc are installed exactly as on native Linux – the restrictions that apply to native Windows builds simply do not apply anymore.

2.5.1. Install WSL

From an elevated Windows PowerShell, run

wsl --install -d Ubuntu

Reboot if prompted, then open the “Ubuntu” app it creates and finish the first-run setup (choose a username/password). Make sure you end up with WSL2, not WSL1:

wsl -l -v

If a distribution is listed as version 1, upgrade it with

wsl --set-version Ubuntu 2

2.5.2. Install pyoomph inside WSL

Open the Ubuntu terminal and treat it exactly like a normal Linux installation (cf. Section 2.2.2):

sudo apt update
sudo apt install gcc libopenmpi-dev pybind11-dev flex bison
python3 -m pip install --upgrade pyoomph

Or, for an editable install from source:

git clone https://www.github.com/pyoomph/pyoomph.git
cd pyoomph
bash ./build_for_develop.sh

Check that the installation works:

python -m pyoomph check all

Note

Keep the pyoomph (and later PETSc) source trees on the Linux filesystem, e.g. under ~/..., rather than under /mnt/c/.... Cross-filesystem I/O between Windows and WSL is much slower and can cause issues with some build tooling.

2.5.3. Build PETSc/SLEPc inside WSL

Since WSL is a genuine Linux environment, the normal PETSc/SLEPc build recipe from Section 2.4 applies without any changes – including the option to build both a real and a complex arch side by side if you also need normal mode eigenvalue problems (cf. Section 10.3 and Section 10.4):

cd A_FOLDER_OF_YOUR_CHOICE
git clone -b release https://gitlab.com/petsc/petsc.git petsc
cd petsc
export PETSC_DIR=$(pwd)
export PETSC_ARCH=pyoomph_petsc_arch_real

./configure --with-mpi  --with-petsc4py --with-slepc4py --download-mumps=yes --download-hypre=yes --download-parmetis=yes --download-ptscotch=yes --download-slepc=yes --download-superlu=yes --download-superlu_dist=yes --download-suitesparse=yes --download-metis=yes --download-scalapack --with-scalar-type=real

At the end, configure prints the exact make command to run next; execute it. If you also need normal mode eigenvalue problems, repeat with a complex arch:

export PETSC_ARCH=pyoomph_petsc_arch_complex
./configure --with-mpi  --with-petsc4py --with-slepc4py --download-mumps=yes --download-parmetis=yes --download-ptscotch=yes --download-slepc=yes --download-superlu=yes --download-superlu_dist=yes --download-suitesparse=yes --download-metis=yes --download-scalapack --with-scalar-type=complex

and execute the make command it prints, as before. Note that --download-hypre is dropped for the complex arch, since HYPRE only supports real-valued matrices (see Section 2.4 for details); MUMPS remains the factorization backend used via use_mumps() either way.

Afterwards, add the following to ~/.bashrc inside WSL so the variables are set in every new terminal (use the pyoomph_petsc_arch_complex arch here instead if you mainly need normal mode eigenvalue problems; only one arch can be active per process, see Section 2.4 for how to switch between them):

export PETSC_DIR=A_FOLDER_OF_YOUR_CHOICE/petsc
export PETSC_ARCH=pyoomph_petsc_arch_real
export PYTHONPATH=$PYTHONPATH:$PETSC_DIR/$PETSC_ARCH/lib

You can then select SLEPc with MUMPS as eigensolver, either in your driver script

problem.set_eigensolver("slepc").use_mumps()

or via the command line flag

python my_eigenvalue_simulation.py --slepc_mumps

2.5.4. Notes specific to WSL

Memory: building PETSc with MUMPS, SuperLU_dist and ScaLAPACK is memory-hungry. If make gets killed (out of memory), increase the memory available to WSL2 by creating/editing C:\Users\<you>\.wslconfig on the Windows side:
```
[wsl2]
memory=8GB
```
and then restart WSL (wsl --shutdown from PowerShell, then reopen the Ubuntu terminal).
Editor integration: if you use VS Code, install the “WSL” remote extension and open the project from within WSL (code . from the Ubuntu terminal). This makes sure the Python interpreter and terminal used by the editor are the WSL ones, so the PETSC_DIR/PETSC_ARCH/PYTHONPATH variables set above are actually visible to the process running your script. Running the script from a plain Windows terminal or Windows Python installation will not see them.
Everything else – MKL, gcc, output via ParaView on the Windows side (WSL2 can access the Windows filesystem under /mnt/c/... if you want to write output there directly), etc. – behaves as described for native Linux in Section 2.2.2 and Section 2.1.1.