.. DO NOT EDIT.
.. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY.
.. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE:
.. "examples/mda/plot_mdachain_parallel_tasks.py"
.. LINE NUMBERS ARE GIVEN BELOW.

.. only:: html

    .. note::
        :class: sphx-glr-download-link-note

        :ref:`Go to the end <sphx_glr_download_examples_mda_plot_mdachain_parallel_tasks.py>`
        to download the full example code

.. rst-class:: sphx-glr-example-title

.. _sphx_glr_examples_mda_plot_mdachain_parallel_tasks.py:


MDAChain with independent parallel MDAs
=======================================

This example illustrates the possibility to parallelize MDAs in an MDAChain,
given that these MDA are independent and can be run in parallel.

.. GENERATED FROM PYTHON SOURCE LINES 27-36

.. code-block:: Python


    from __future__ import annotations

    from gemseo import configure_logger
    from gemseo import create_discipline
    from gemseo.mda.mda_chain import MDAChain

    configure_logger()





.. rst-class:: sphx-glr-script-out

 .. code-block:: none


    <RootLogger root (INFO)>



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Introduction
----------------------------------------------

In an :class:`.MDAChain`,
there may be an opportunity to parallelize the execution of :class:`.BaseMDA`
that can be executed independently.
As an example,
let us consider the following expressions,
which will be used to instantiate analytic disciplines:

.. GENERATED FROM PYTHON SOURCE LINES 46-58

.. code-block:: Python


    disciplines_expressions = [
        {"a": "x"},
        {"y1": "x1", "b": "a+1"},
        {"x1": "1.-0.3*y1"},
        {"y2": "x2", "c": "a+2"},
        {"x2": "1.-0.3*y2"},
        {"obj1": "x1+x2"},
        {"obj2": "b+c"},
        {"obj": "obj1+obj2"},
    ]








.. GENERATED FROM PYTHON SOURCE LINES 59-89

We can easily observe in these disciplines,
that the :math:`x_1` and :math:`y_1` variables are strongly coupled.
It follows that the second and third disciplines are strongly coupled and
constitute a :class:`.BaseMDA`.


The same statement can be done for the disciplines that provide the output variables
:math:`x_2` and :math:`y_2`,
and the fourth and fifth disciplines which are also strongly coupled.
These two MDAs are independent and only depend on the variable :math:`a` given by
the first discipline.


Thus,
they can be run in parallel,
hence reducing the overall :class:`.MDAChain` execution provided that enough
resources are available on the computing node (in our case, at least two CPUs).
By default, the parallel execution of the independent :class:`.BaseMDA` are deactivated,
meaning that the execution of the two independent :class:`.BaseMDA` will remain sequential.
Yet, a parallel execution of the two :class:`.BaseMDA` can be  activated using the
`mdachain_parallelize_task` boolean option.


If activated,
the user has also the possibility to provide parallelization options,
such as using either threads or processes to perform the parallelization,
or the number of processes or threads to use.
By default, as more lightweight, threading is used but on some specific case,
where for instance race conditions may occur,
multiprocessing can be employed.

.. GENERATED FROM PYTHON SOURCE LINES 91-97

Example of :class:`.MDAChain` with parallelization
--------------------------------------------------

We are here using the disciplines previously defined by their analytical expressions,
and we are going to explicitly ask for a parallelization of the execution of the
two independent :class:`.BaseMDA`.

.. GENERATED FROM PYTHON SOURCE LINES 97-110

.. code-block:: Python


    disciplines = []
    for expr in disciplines_expressions:
        disciplines.append(create_discipline("AnalyticDiscipline", expressions=expr))

    mdo_parallel_chain_options = {"use_threading": True, "n_processes": 2}
    mdachain = MDAChain(
        disciplines,
        name="mdachain_lower",
        mdachain_parallelize_tasks=True,
        mdachain_parallel_options=mdo_parallel_chain_options,
    )
    res = mdachain.execute()








.. rst-class:: sphx-glr-timing

   **Total running time of the script:** (0 minutes 0.098 seconds)


.. _sphx_glr_download_examples_mda_plot_mdachain_parallel_tasks.py:

.. only:: html

  .. container:: sphx-glr-footer sphx-glr-footer-example

    .. container:: sphx-glr-download sphx-glr-download-jupyter

      :download:`Download Jupyter notebook: plot_mdachain_parallel_tasks.ipynb <plot_mdachain_parallel_tasks.ipynb>`

    .. container:: sphx-glr-download sphx-glr-download-python

      :download:`Download Python source code: plot_mdachain_parallel_tasks.py <plot_mdachain_parallel_tasks.py>`


.. only:: html

 .. rst-class:: sphx-glr-signature

    `Gallery generated by Sphinx-Gallery <https://sphinx-gallery.github.io>`_