Building from Source

monoprop has one supported from-source build workflow:

• the Python bindings — the nanobind extension behind import monoprop, built with scikit-build-core and driven by uv (or pip);
• the C++ unit tests — built within the same uv (or pip) invocation.

MPI is off by default in every build path; you enable it explicitly. The mechanism differs by build:

The prebuilt wheels published to PyPI (pip install monoprop) already include an MPI backend for the MPICH family and Open MPI, so a from-source build is only needed for another MPI implementation or for the C++ library; see Parallelism.

PrerequisitesCopy Anchor Link

• a C++23-compliant compiler; on Linux the minimum supported versions are GCC 14 and Clang 18
• CMake and Ninja
• Python 3.11 to 3.15 and the uv package manager (for the bindings)
• an MPI implementation such as Open MPI (only for MPI builds)
• hwloc (version 2.9+) and pkg-config (required so CMake can locate hwloc)

The repository ships a DevContainer with all of the above pre-configured; opening the folder in VS Code and rebuilding the container is the quickest route to a working environment.

Building with NixCopy Anchor Link

The repository is also a Nix flake:

Flake outputs support x86_64 Linux, aarch64 Linux, and aarch64 macOS. The nixos-unstable input no longer supports x86_64 macOS.

The unsuffixed packages, nix run and nix develop use Python 3.14, the interpreter nixpkgs builds and caches; older interpreters are compiled locally.

Inside nix develop the uv sync and just workflows below apply unchanged. The shell sets two variables that only matter on NixOS: UV_PYTHON_PREFERENCE=only-system, because uv's managed interpreters expect a loader NixOS does not provide, and LD_LIBRARY_PATH, so that manylinux wheels can resolve libstdc++.

The packaged build deviates from the uv build in three places, all in nix/monoprop.nix:

• the C++ unit tests are disabled, because they resolve msgpack-cxx through a git fetch that the build sandbox denies — build them from the dev shell instead;
• monoprop_ENABLE_ARCH_FLAGS is off, since a store path may be substituted onto a machine other than the one that built it; pass .override { enableArchFlags = true; } for a native build;
• the version comes from the tracked root VERSION file, because setuptools-scm cannot read git metadata inside the sandbox; update it to the stable version when preparing a release. The release workflow rejects tags that do not match it.

Using monoprop downstream with NixCopy Anchor Link

Use the exported overlay when monoprop must share a downstream project's Python and native dependency package set. Following the downstream nixpkgs input avoids introducing a second Python, Boost, hwloc, or MPI closure:

{
  inputs = {
    nixpkgs.url = "github:NixOS/nixpkgs/nixos-unstable";
    monoprop = {
      url = "github:Algorithmiq/monoprop";
      inputs.nixpkgs.follows = "nixpkgs";
    };
  };

  outputs = { nixpkgs, monoprop, ... }:
    let
      system = "x86_64-linux";
      pkgs = import nixpkgs {
        inherit system;
        overlays = [ monoprop.overlays.default ];
      };
      python = pkgs.python313.withPackages (ps: [
        ps.monoprop
        ps.numpy
      ]);
    in {
      devShells.${system}.default = pkgs.mkShell {
        packages = [ python ];
      };
    };
}

The overlay adds monoprop, monoprop-mpi and nanobind-backend to every Python package set through pythonPackagesExtensions, so ps.monoprop works for any interpreter in withPackages. On Python 3.12 it also skips mpi4py's test suite, because the binary cache has no mpi4py for that interpreter and its tests add minutes to every build. CI evaluates the packages for Python 3.12, 3.13 and 3.14 but builds only Python 3.12, the interpreter the binary cache covers least. The top-level pkgs.monoprop and pkgs.monoprop-mpi are the Python 3.14 builds. For consumers that follow their own nixpkgs input, its nanobind_3 version must match the nanobind pin in pyproject.toml (currently 3.1.0). For consumers that do not need to compose package sets, the pre-built flake outputs remain available as monoprop.packages.${system}.monoprop, monoprop.packages.${system}.monoprop-mpi and the per-interpreter monoprop-py3XX[-mpi] variants. Consume these entrypoints from the Git repository flake; flake.nix, flake.lock, and nix/ are deliberately excluded from the Python source distribution.

Building the Python bindingsCopy Anchor Link

uv creates a virtual environment, installs the Python dependencies, and compiles the nanobind extension in editable mode. Re-run the sync command whenever the dependency graph or the C++ sources change.

Build optionsCopy Anchor Link

The build is configured through a few package options, declared in [tool.scikit-build.config-setting] in pyproject.toml. Each one can be set as a PEP 517 config-setting or through an environment variable:

Booleans accept ON/OFF, true/false, 1/0 and similar. These two commands are equivalent:

monoprop_ENABLE_MPI=ON uv sync --all-extras -v
uv sync --all-extras -v --config-settings-package="monoprop:monoprop.mpi=ON"

In the uv workspace, prefer --config-settings-package="monoprop:..." over --config-settings, so the setting does not reach the sibling distributions in packages/. With pip, pass -C monoprop.mpi=ON. A misspelled option is rejected with a suggestion.

uv rebuilds monoprop whenever an option changes, by either route: config-settings are part of its build cache key, and [tool.uv] cache-keys in pyproject.toml adds the environment variables above, along with SKBUILD_BUILD_DIR, SKBUILD_CMAKE_BUILD_TYPE and SKBUILD_CMAKE_DEFINE. Switching variants therefore needs no --reinstall-package or --no-cache. Any other CMake cache variable can still be passed as cmake.define.<NAME>=... or through SKBUILD_CMAKE_DEFINE.

Without MPI (default)Copy Anchor Link

uv sync --all-extras -v

This produces a single-process build with no MPI dependency.

With MPICopy Anchor Link

Turn on the monoprop.mpi build option, for example through the environment:

monoprop_ENABLE_MPI=ON uv sync --all-extras -v

It works the same with pip when installing from a checkout:

pip install . -C monoprop.mpi=ON

That one option does three things. It sets the monoprop_ENABLE_MPI CMake option, and, through the [[tool.scikit-build.overrides]] block in pyproject.toml, it adds mpi4py to the build requirements and moves the build to its own tree, build/editable/Release-mpi.

An MPI build compiles the core twice: the serial _core, and _core_mpi.<abi> for the MPI ABI family of the MPI CMake finds (openmpi or mpich; see Parallelism and distribution). Expect about twice the build time of a serial build.

mpi4py is a build-time input — the bindings need its headers to hand an MPI communicator across the nanobind boundary — but only for MPI builds, so it is injected per-build through PEP 517's get_requires_for_build_wheel rather than sitting in build-system.requires where every build would pay for it.

The separate tree means switching between the serial and MPI variants rebuilds incrementally instead of reconfiguring a shared tree from scratch. It is a directory suffix rather than a build type, so the build keeps CMake's Release flags.

Setting the CMake option directly (cmake.define.monoprop_ENABLE_MPI=ON or SKBUILD_CMAKE_DEFINE) bypasses that override, so mpi4py is not provisioned and the build fails at configure time with a message telling you as much. Use monoprop.mpi instead.

Release wheelsCopy Anchor Link

The published wheels contain one MPI variant per ABI family, built against MPI installations under fixed prefixes rather than the MPI CMake would find. To reproduce one locally, install mpich and openmpi from PyPI with pip install --target, then:

CIBUILDWHEEL=1 monoprop_ENABLE_MPI=ON \
  monoprop_MPI_ABIS="mpich=$PWD/mpi/mpich;openmpi=$PWD/mpi/openmpi" \
  uv build --wheel

CIBUILDWHEEL=1 is what cibuildwheel exports; it keeps the build machine's library directories out of the RPATHs.

Verify the installCopy Anchor Link

uv run python -c "import monoprop as mp; print(mp.__version__)"

Running the bindingsCopy Anchor Link

A serial run is just a normal Python invocation:

uv run python your_script.py

For a multi-rank run, launch the same script under mpiexec (requires an MPI build) and pass comm=MPI.COMM_WORLD to the simulator. Without comm, every rank runs its own serial propagation:

mpiexec -n 8 uv run python your_script.py

See Parallelism and distribution for the communicator options and the operator-partitioning controls.

Building the C++ unit testsCopy Anchor Link

The supported C++ workflow reuses the build tree produced by uv sync. Do not run cmake --preset ... to configure this project directly: the top-level CMake configuration expects scikit-build-core to provide Python, nanobind, and related cache variables. Instead, first create the tree with uv sync, then invoke ctest directly to run the C++ unit tests.

Release treeCopy Anchor Link

uv sync --all-extras -v
ctest --test-dir build/editable/Release

This uses the scikit-build-core Release tree at build/editable/Release and runs bin/monoprop_unit_tests.x there, along with bin/monoprop_link_export_probe.x — a link-time check that the installed shared monoprop library exports every detail/** free function reachable from the public template chain. In an MPI build the same probe also runs against every monoprop_mpi_<abi> library.

monoprop::monoprop is always serial: a serial target never acquires MPI compile definitions or link dependencies, even if the consuming project has an MPI::MPI_CXX target. MPI consumers request the MPI component, which picks the libmonoprop variant for the MPI ABI family of the MPI CMake finds for the consuming project:

find_package(monoprop CONFIG REQUIRED COMPONENTS MPI)
target_link_libraries(my_target PRIVATE monoprop::monoprop_mpi)

Configuration fails, naming both, when the installation was built without that family; monoprop_MPI_ABIS lists the families it ships. Projects that linked monoprop::monoprop from an MPI build before must switch to the MPI component.

just test-find-package goes one step further out: it builds cpp/tests/find_package_smoke — a standalone project that consumes the installed package through find_package(monoprop CONFIG) — and runs it. That is the only leg that exercises monopropConfig.cmake, so it is what catches a missing find_dependency or a usage requirement the package fails to export. It compiles the same source as the in-tree probe, so the two cannot drift. With an MPI installation it also builds the consumer through the MPI component, runs it under mpiexec -n 2, and checks that a package lacking the consumer's MPI ABI fails at configure time.

Debug treeCopy Anchor Link

uv sync --all-extras -v --config-settings=cmake.build-type=Debug
ctest --test-dir build/editable/Debug

Sanitizer treesCopy Anchor Link

The QA workflow builds two sanitizer profiles, selected with the monoprop.sanitizer build option (none, asan-ubsan, or tsan). Each profile has its own build type, AsanUbsan or Tsan, so it builds into its own tree next to build/editable/Release (with an -mpi suffix for an MPI build). Both require Linux and either GCC or Clang; CI uses the default system GCC, which is what builds the released wheels.

Export monoprop_SANITIZER rather than setting it for the build command alone: the just test recipes read it too, to find the tree.

Sanitizer trees intentionally skip the _core.pyi typing stubs — generating them means importing the instrumented extension, which is not worth arranging for a tree that is never packaged.

ASan + LSan + UBSanCopy Anchor Link

export monoprop_SANITIZER=asan-ubsan
just build --group workspace-test

The C++ test binary is fully instrumented, so every check applies to it, including leak detection:

just test-cpp-asan

The Python tests are a different situation: an instrumented _core is loaded into an ordinary CPython, so the ASan runtime must be preloaded, and the checks that assume the whole process is instrumented have to be switched off.

just test-py-asan

Both preloads are required. Without libasan.so, the first import monoprop aborts with ASan runtime does not come first in initial library list. Without libstdc++.so.6, ASan initialises before libstdc++ is loaded — python is not linked against it — so ASan's __cxa_throw interceptor never resolves the real symbol, and the first C++ exception thrown out of the engine dies with CHECK failed: real___cxa_throw != 0. monoprop reports validation errors as C++ exceptions, so that affects a large part of the suite.

Leak detection is off here because LSan cannot get a clean baseline from an uninstrumented interpreter — the C++ leg above is what covers leaks.

just sanitizer-reports

ThreadSanitizerCopy Anchor Link

export monoprop_SANITIZER=tsan
just build --group workspace-test

just test-cpp-tsan

TSan is scoped to the concurrency surface — the partition and ShmComm tests — which is where its reports are worth the runtime cost.

There is no ThreadSanitizer equivalent of the ASan Python leg. TSan does not support dlopen of instrumented libraries, so loading an instrumented _core into a stock interpreter crashes during TSan initialisation; exercising the Python API under TSan would require a TSan-instrumented CPython.

Related workflowsCopy Anchor Link

• Use just code-coverage for the coverage build.
• Use just test-cpp and just test-cpp-mpi for the two C++ legs, or ctest --test-dir build/editable/Release-mpi -L mpi-2 (or -L mpi-4) to filter further.
• Use just build [uv sync args…] when you want the build CI performs. It adds --all-extras and, for a serial build, drops the mpi extra — importing mpi4py.MPI without an MPI runtime raises. monoprop_ENABLE_MPI drives all of it, so monoprop_ENABLE_MPI=ON just build --group workspace-test is the MPI build and a bare just build is the serial one. The recipes read the environment variables, not config-settings, so select variants that way here.
• The platform packages the build needs are listed in tools/packages/, one per line: apt.txt / brew.txt, plus apt-mpi.txt / brew-mpi.txt for an MPI build. .github/actions/setup and .devcontainer/Dockerfile both install from those files, so there is one list per platform to keep current.

See alsoCopy Anchor Link

• Getting Started — installing a prebuilt release from PyPI.
• Parallelism and distribution — running across MPI ranks and shared-memory threads.
• Testing — the full Python and C++ test workflow.
• How to Contribute — contributor workflow and documentation checks.