ParMGMC is a C library implementation of the Multigrid Monte Carlo method in PETSc to sample from high-dimensional Gaussian distributions on distributed memory machines.

ParMGMC has the following dependencies:

• An MPI installation (e.g., OpenMPI or MPICH)
• PETSc (tested with version 3.21, anything >= 3.19 should work) built with C/Pardiso enabled
• Intel MKL

After Intel MKL has been installed can be configured by running

./configure --with-mkl_cpardiso   \
            --with-mkl_pardiso    \
            --with-blas-lapack-dir=/opt/intel/oneapi/mkl/latest/lib

in the PETSc directory (the path to the Intel MKL might differ depending on the platform). For details, see PETSc documentation.

If the Python bindings should be enabled, pybind11 and petsc4py are also required. petsc4py can be built by passing --with-petsc4py during configure.

To build the ParMGMC library CMake is required. Run

$ git clone https://github.com/nilsfriess/ParMGMC.git
$ cd ParMGMC
$ mkdir build && cd build
$ cmake .. -DCMAKE_PREFIX_PATH=/path/to/petsc
$ make

To specify a custom compiler (e.g., a MPI compiler wrapper) add -DCMAKE_C_COMPILER=mpicc.

To install the library to some directory, add -DCMAKE_INSTALL_PREFIX=/path/to/install during CMake configuration. Then run make install to copy the compiled library and headers to the specified directory. This also generates a pkg-config file that can be used to simplify using this library in other projects. If the environment variable PKG_CONFIG_PATH contains both the path to parmgmc.pc (located in /path/to/install/lib/pkgconfig) and the path to PETSc's pkg-config file (located in /path/to/petsc/lib/pkgconfig), then a program using ParMGMC can be compiled with

gcc main.c -o main $(pkg-config --cflags --libs petsc parmgmc)

The library has experimental support for usage from Python. Pass -DPARMGMC_ENABLE_PYTHON_BINDINGS=True during the CMake config to enable Python bindings.