{"id":21910369,"url":"https://github.com/nilsfriess/parmgmc","last_synced_at":"2025-03-22T08:18:27.112Z","repository":{"id":192128028,"uuid":"686054173","full_name":"nilsfriess/ParMGMC","owner":"nilsfriess","description":"MPI-parallel implementation of the Multigrid Monte Carlo method based on PETSc","archived":false,"fork":false,"pushed_at":"2024-11-25T19:19:35.000Z","size":4246,"stargazers_count":0,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-01-27T08:24:27.755Z","etag":null,"topics":["bayesian-inference","mcmc","monte-carlo","petsc"],"latest_commit_sha":null,"homepage":"https://parmgmc.readthedocs.io/","language":"C","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"lgpl-2.1","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/nilsfriess.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null}},"created_at":"2023-09-01T16:18:34.000Z","updated_at":"2024-11-25T12:19:03.000Z","dependencies_parsed_at":"2023-09-02T19:29:18.970Z","dependency_job_id":"4989bb36-a5d2-48e1-8c05-e5185020727c","html_url":"https://github.com/nilsfriess/ParMGMC","commit_stats":null,"previous_names":["nilsfriess/pargibbs","nilsfriess/parmgmc"],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nilsfriess%2FParMGMC","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nilsfriess%2FParMGMC/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nilsfriess%2FParMGMC/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/nilsfriess%2FParMGMC/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/nilsfriess","download_url":"https://codeload.github.com/nilsfriess/ParMGMC/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":244925079,"owners_count":20532908,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["bayesian-inference","mcmc","monte-carlo","petsc"],"created_at":"2024-11-28T17:29:53.502Z","updated_at":"2025-03-22T08:18:27.092Z","avatar_url":"https://github.com/nilsfriess.png","language":"C","funding_links":[],"categories":[],"sub_categories":[],"readme":"# Parallel Multigrid Monte Carlo (MGMC)\nParMGMC is a C library implementation of the Multigrid Monte Carlo method in PETSc to sample from high-dimensional Gaussian distributions on distributed memory machines.\n\n## Dependencies\nParMGMC has the following dependencies:\n- An MPI installation (e.g., [OpenMPI](https://www.open-mpi.org/) or [MPICH](https://www.mpich.org/))\n- [PETSc](https://petsc.org/)  (tested with version 3.21, anything \u003e= 3.19 should work) built with C/Pardiso enabled\n- [Intel MKL](https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl-download.html)\n\nAfter Intel MKL has been installed can be configured by running\n```bash\n./configure --with-mkl_cpardiso   \\\n            --with-mkl_pardiso    \\\n            --with-blas-lapack-dir=/opt/intel/oneapi/mkl/latest/lib\n```\nin the PETSc directory (the path to the Intel MKL might differ depending on the platform). For details, see [PETSc documentation](https://petsc.org/release/install/).\n\nIf the Python bindings should be enabled, pybind11 and `petsc4py` are also required. `petsc4py` can be built by passing `--with-petsc4py` during configure.\n\n## Building the library\nTo build the ParMGMC library CMake is required. Run\n```bash\n$ git clone https://github.com/nilsfriess/ParMGMC.git\n$ cd ParMGMC\n$ mkdir build \u0026\u0026 cd build\n$ cmake .. -DCMAKE_PREFIX_PATH=/path/to/petsc\n$ make\n```\nTo specify a custom compiler (e.g., a MPI compiler wrapper) add `-DCMAKE_C_COMPILER=mpicc`.\n\n### Installing the library\nTo 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\n```bash\ngcc main.c -o main $(pkg-config --cflags --libs petsc parmgmc)\n```\n\n## Python bindings (experimental)\nThe library has experimental support for usage from Python. Pass `-DPARMGMC_ENABLE_PYTHON_BINDINGS=True` during the CMake config to enable Python bindings.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnilsfriess%2Fparmgmc","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fnilsfriess%2Fparmgmc","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fnilsfriess%2Fparmgmc/lists"}