mpi4py

============== MPI for Python

This package provides Python bindings for the Message Passing Interface (MPI_) standard. It is implemented on top of the MPI specification and exposes an API which grounds on the standard MPI-2 C++ bindings.

.. _MPI: https://www.mpi-forum.org

Features

This package supports:

• Convenient communication of any picklable Python object

  • point-to-point (send & receive)
  • collective (broadcast, scatter & gather, reductions)

• Fast communication of Python object exposing the Python buffer interface (NumPy arrays, builtin bytes/string/array objects)

  • point-to-point (blocking/nonblocking/persistent send & receive)
  • collective (broadcast, block/vector scatter & gather, reductions)

• Process groups and communication domains

  • creation of new intra/inter communicators
  • creation/query of Cartesian & graph topologies

• Parallel input/output:

  • read & write
  • blocking/nonblocking & collective/noncollective
  • individual/shared file pointers & explicit offset

• Dynamic process management

  • spawn & spawn multiple
  • accept/connect
  • name publishing & lookup

• One-sided operations

  • remote memory access (put, get, accumulate)
  • passive target synchronization (start/complete & post/wait)
  • active target synchronization (lock & unlock)

Install

Wheel packages

The mpi4py project builds and publishes binary wheels able to run in a variety of:

• operating systems: Linux, macOS, Windows;
• processor architectures: AMD64, ARM64;
• MPI implementations: MPICH, Open MPI, MVAPICH, Intel MPI, HPE Cray MPICH, Microsoft MPI;
• Python implementations: CPython, PyPy.

.. _MPICH: https://mpich.org .. _Open MPI: https://open-mpi.org .. _MVAPICH: https://mvapich.cse.ohio-state.edu .. _HPE Cray MPICH: https://cpe.ext.hpe.com/docs/latest/mpt/mpich/ .. _NVIDIA HPC-X: https://developer.nvidia.com/networking/hpc-x .. _Intel MPI: https://software.intel.com/intel-mpi-library .. _Microsoft MPI: https://learn.microsoft.com/message-passing-interface/microsoft-mpi

These mpi4py wheels are distributed via the Python Package Index (PyPI <https://pypi.org/project/mpi4py/>) and can be installed with Python package managers like pip:

.. code:: sh

python -m pip install mpi4py

.. _pip: https://pip.pypa.io

The mpi4py wheels can be installed in standard Python virtual environments. The MPI runtime can be provided by other wheels installed in the same virtual environment.

.. tip::

Intel publishes production-grade Intel MPI wheels <impi-rt-wheels_>_ for Linux (x86_64) and Windows (AMD64). mpi4py and MPI wheels can be installed side by side to get a ready-to-use Python+MPI environment:

.. code:: sh

  python -m pip install mpi4py impi-rt

.. _impi-rt-wheels: https://pypi.org/project/impi-rt/#files

.. tip::

The mpi4py project publishes MPICH wheels <mpich-wheels_>_ and Open MPI wheels <openmpi-wheels_>_ for Linux (x86_64/aarch64) and macOS (arm64/x86_64). mpi4py and MPI wheels can be installed side by side to get a ready-to-use Python+MPI environment:

.. code:: sh

  python -m pip install mpi4py mpich    # for MPICH
  python -m pip install mpi4py openmpi  # for Open MPI

.. _mpich-wheels: https://pypi.org/project/mpich/#files .. _openmpi-wheels: https://pypi.org/project/openmpi/#files

.. warning::

  The MPI wheels are distributed with special focus on ease of
  use, convenience, compatibility, and interoperability. The Linux
  wheels are built in somewhat constrained environments with
  relatively dated Linux distributions (`manylinux`_ container
  images). Therefore, they may lack support for features like GPU
  awareness (CUDA/ROCm) and C++/Fortran bindings. In production
  scenarios, it is recommended to use external (either
  custom-built or system-provided) MPI installations.

  .. _manylinux: https://github.com/pypa/manylinux

The mpi4py wheels can also be installed (with pip) in conda environments and they should work out of the box, without any special tweak to environment variables, for any of the MPI packages provided by conda-forge_.

Externally-provided MPI implementations may come from a system package manager, sysadmin-maintained builds accessible via module files, or customized user builds. Such usage is supported and encouraged. However, there are a few platform-specific considerations to take into account.

Linux ^^^^^

The Linux (x86_64/aarch64) wheels require one of

• MPICH_ or any other ABI-compatible derivative, like MVAPICH, Intel MPI, HPE Cray MPICH_

• Open MPI_ or any other ABI-compatible derivative, like NVIDIA HPC-X_

Users may need to set the LD_LIBRARY_PATH environment variable such that the dynamic linker is able to find at runtime the MPI shared library file (libmpi.so.*).

Fedora/RHEL

On Fedora/RHEL systems, both MPICH and Open MPI are available for
installation. There is no default or preferred MPI implementation.
Instead, users must select their favorite MPI implementation by
loading the proper MPI module.

.. code:: sh

   module load mpi/mpich-$(arch)    # for MPICH
   module load mpi/openmpi-$(arch)  # for Open MPI

After loading the requested MPI module, the ``LD_LIBRARY_PATH``
environment variable should be properly setup.

Debian/Ubuntu

On Debian/Ubuntu systems, Open MPI is the default MPI implementation and most of the MPI-based applications and libraries provided by the distribution depend on Open MPI. Nonetheless, MPICH is also available to users for installation.

In Ubuntu 22.04 and older, due to legacy reasons, the MPICH ABI is slightly broken: the MPI shared library file is named libmpich.so.12 instead of libmpi.so.12 as required by the MPICH ABI Compatibility Initiative <https://www.mpich.org/abi/>_.

Users without sudo access can workaround this issue creating a symbolic link anywhere in their home directory and appending to LD_LIBRARY_PATH.

.. code:: sh

mkdir -p ~/.local/lib libdir=/usr/lib/$(arch)-linux-gnu ln -s $libdir/libmpich.so.12 /.local/lib/libmpi.so.12 export LD_LIBRARY_PATH=$LD_LIBRARY_PATH:/.local/lib

A system-wide fix for all users requires sudo access:

.. code:: sh

libdir=/usr/lib/$(arch)-linux-gnu sudo ln -sr $libdir/libmpi{ch,}.so.12

HPE Cray OS

On HPE Cray systems, users must load the ``cray-mpich-abi`` module.
For further details, refer to `man intro_mpi <cray-mpt-mpichabi_>`_.

.. _cray-mpt-mpichabi: https://cpe.ext.hpe.com/docs/latest/mpt/mpich/intro_mpi.html#using-mpich-abi-compatibility


macOS
^^^^^

The macOS (arm64/x86_64) wheels require

* `MPICH`_ or `Open MPI`_ installed (either manually or via a package
  manager) in the standard system prefix ``/usr/local``

* `MPICH`_ or `Open MPI`_ installed via `Homebrew`_ in the default
  prefix ``/opt/homebrew``

* `MPICH`_ or `Open MPI`_ installed via `MacPorts`_ in the default
  prefix ``/opt/local``

.. _Homebrew: https://brew.sh/
.. _MacPorts: https://www.macports.org/


Windows
^^^^^^^

The Windows (AMD64) wheels require one of

* `Intel MPI`_

* `Microsoft MPI`_

User may need to set the ``I_MPI_ROOT`` or ``MSMPI_BIN`` environment
variables such that the MPI dynamic link library (DLL) (``impi.dll``
or ``msmpi.dll``) can be found at runtime.

Intel MPI is under active development and supports recent versions of
the MPI standard. Intel MPI can be installed with ``pip`` (see the
`impi-rt`_ package on PyPI), being therefore straightforward to get it
up and running within a Python environment. Intel MPI can also be
installed system-wide as part of the Intel oneAPI HPC Toolkit for
Windows or via standalone online/offline installers.

.. _impi-rt: https://pypi.org/project/impi-rt/


**Conda** packages
------------------

The `conda-forge`_ community provides ready-to-use binary packages
from an ever growing collection of software libraries built around the
multi-platform *conda* package manager. Four MPI implementations are
available on conda-forge: Open MPI (Linux and macOS), MPICH (Linux and
macOS), Intel MPI (Linux and Windows), and Microsoft MPI (Windows).
You can install mpi4py and your preferred MPI implementation using the
`conda`_ package manager:

* to use MPICH do:

  .. code:: sh

     conda install -c conda-forge mpi4py mpich

* to use Open MPI do:

  .. code:: sh

     conda install -c conda-forge mpi4py openmpi

* to use Intel MPI do:

  .. code:: sh

     conda install -c conda-forge mpi4py impi_rt

* to use Microsoft MPI do:

  .. code:: sh

     conda install -c conda-forge mpi4py msmpi

MPICH and many of its derivatives are ABI-compatible. You can provide
the package specification ``mpich=X.Y.*=external_*`` (where ``X`` and
``Y`` are the major and minor version numbers) to request the conda
package manager to use system-provided MPICH (or derivative)
libraries. Similarly, you can provide the package specification
``openmpi=X.Y.*=external_*`` to use system-provided Open MPI
libraries.

The ``openmpi`` package on conda-forge has built-in CUDA support, but
it is disabled by default. To enable it, follow the instruction
outlined during ``conda install``. Additionally, UCX support is also
available once the ``ucx`` package is installed.

.. warning::

   The MPI conda-forge packages are built with special focus on
   compatibility. The MPICH and Open MPI packages are built in a
   constrained environment with relatively dated OS images. Therefore,
   they may lack support for high-performance features like
   cross-memory attach (XPMEM/CMA). In production scenarios, it is
   recommended to use external (either custom-built or system-provided)
   MPI installations. See the relevant conda-forge documentation about
   `using external MPI libraries <cf-mpi-docs_>`_ .

.. _conda: https://docs.conda.io
.. _conda-forge: https://conda-forge.org/
.. _cf-mpi-docs: https://conda-forge.org/docs/user/tipsandtricks/#using-external-message-passing-interface-mpi-libraries


System packages
---------------

mpi4py is readily available through system package managers of most
Linux distributions and the most popular community package managers
for macOS.


.. _sys-pkg-linux:

Linux
^^^^^

On **Fedora Linux** systems (as well as **RHEL** and their derivatives
using the EPEL software repository), you can install binary packages
with the system package manager:

* using ``dnf`` and the ``mpich`` package:

  .. code:: sh

     sudo dnf install python3-mpi4py-mpich

* using ``dnf`` and the ``openmpi`` package:

  .. code:: sh

     sudo dnf install python3-mpi4py-openmpi

Please remember to load the correct MPI module for your chosen MPI
implementation:

* for the ``mpich`` package do:

  .. code:: sh

     module load mpi/mpich-$(arch)
     python -c "from mpi4py import MPI"

* for the ``openmpi`` package do:

  .. code:: sh

     module load mpi/openmpi-$(arch)
     python -c "from mpi4py import MPI"

On **Ubuntu Linux** and **Debian Linux** systems, binary packages are
available for installation using the system package manager:

.. code:: sh

   sudo apt install python3-mpi4py

On **Arch Linux** systems, binary packages are available for
installation using the system package manager:

.. code:: sh

   sudo pacman -S python-mpi4py


.. _sys-pkg-macos:

macOS
^^^^^

macOS users can install mpi4py using the `Homebrew`_ package
manager:

.. code:: sh

   brew install mpi4py

Note that the Homebrew mpi4py package uses Open MPI. Alternatively,
install the ``mpich`` package and next install mpi4py from sources
using ``pip``.

Alternatively, mpi4py can be installed from `MacPorts`_:

.. code:: sh

   sudo port install py-mpi4py


Building from sources
---------------------

Installing mpi4py from pre-built binary wheels, conda packages, or
system packages is not always desired or appropriate. For example, the
mpi4py wheels published on PyPI may not be interoperable with
non-mainstream, vendor-specific MPI implementations; or a system
mpi4py package may be built with a alternative, non-default MPI
implementation. In such scenarios, mpi4py can still be installed from
its source distribution (sdist) using ``pip``:

.. code:: sh

   python -m pip install --no-binary=mpi4py mpi4py

You can also install the in-development version with:

.. code:: sh

   python -m pip install git+https://github.com/mpi4py/mpi4py

or:

.. code:: sh

   python -m pip install https://github.com/mpi4py/mpi4py/tarball/master

.. note::

   Installing mpi4py from its source distribution (available on PyPI)
   or Git source code repository (available on GitHub) requires a C
   compiler and a working MPI implementation with development headers
   and libraries.

.. warning::

   ``pip`` keeps previously built wheel files in its cache for future
   reuse. If you want to reinstall the ``mpi4py`` package from its source
   distribution using a different or updated MPI implementation, you have
   to either first remove the cached wheel file:

   .. code:: sh

      python -m pip cache remove mpi4py
      python -m pip install --no-binary=mpi4py mpi4py

   or ask ``pip`` to disable the cache:

   .. code:: sh

      python -m pip install --no-cache-dir --no-binary=mpi4py mpi4py


Citation
========

If MPI for Python been significant to a project that leads to an
academic publication, please acknowledge that fact by citing the
project.

* M. Rogowski, S. Aseeri, D. Keyes, and L. Dalcin,
  *mpi4py.futures: MPI-Based Asynchronous Task Execution for Python*,
  IEEE Transactions on Parallel and Distributed Systems, 34(2):611-622, 2023.
  https://doi.org/10.1109/TPDS.2022.3225481

* L. Dalcin and Y.-L. L. Fang,
  *mpi4py: Status Update After 12 Years of Development*,
  Computing in Science & Engineering, 23(4):47-54, 2021.
  https://doi.org/10.1109/MCSE.2021.3083216

* L. Dalcin, P. Kler, R. Paz, and A. Cosimo,
  *Parallel Distributed Computing using Python*,
  Advances in Water Resources, 34(9):1124-1139, 2011.
  https://doi.org/10.1016/j.advwatres.2011.04.013

* L. Dalcin, R. Paz, M. Storti, and J. D'Elia,
  *MPI for Python: performance improvements and MPI-2 extensions*,
  Journal of Parallel and Distributed Computing, 68(5):655-662, 2008.
  https://doi.org/10.1016/j.jpdc.2007.09.005

* L. Dalcin, R. Paz, and M. Storti,
  *MPI for Python*,
  Journal of Parallel and Distributed Computing, 65(9):1108-1115, 2005.
  https://doi.org/10.1016/j.jpdc.2005.03.010