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

.. only:: html

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

        Click :ref:`here <sphx_glr_download_auto_features_plot_parallelization.py>`
        to download the full example code

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

.. _sphx_glr_auto_features_plot_parallelization.py:


Parallel processing capabilities of pygpc
=========================================

pygpc is capable of to evaluate multiple sampling points, i.e. multiple model instances, in parallel.
Depending on your model and its hardware requirements there exist three ways to evaluate your model
controlled by the algorithm options "n_cpu":

- :code:`n_cpu = 0` : Use this option if your model is capable of to evaluate sampling points in parallel. In this way,
  arrays are passed to your model for each parameter
- :code:`n_cpu = 1` : The model is called in serial for every sampling point. A single floating point number is passed for
  each parameter.
- :code:`n_cpu > 1` : A multiprocessing.Pool will be opened and n_cpu sampling points are calculated in parallel.
  In each thread, a single floating point number is passed for each parameter.

.. GENERATED FROM PYTHON SOURCE LINES 17-19

Example
^^^^^^^

.. GENERATED FROM PYTHON SOURCE LINES 19-72

.. code-block:: default


    # Windows users have to encapsulate the code into a main function to avoid multiprocessing errors.
    # def main():

    import time
    import pygpc
    import numpy as np
    import multiprocessing
    import seaborn as sns
    from matplotlib import pyplot as plt
    from collections import OrderedDict

    SurfaceCoverageSpecies = pygpc.SurfaceCoverageSpecies()

    # generate grid with 1000 sampling points
    grid = pygpc.Random(parameters_random=SurfaceCoverageSpecies.problem.parameters_random, n_grid=100)

    # define different values for n_cpu
    n_cpu_list = [0, 1, multiprocessing.cpu_count()]

    t_eval = dict()

    # evaluate model with different values for n_cpu
    for n_cpu in n_cpu_list:
        # initialize computation class; this is done in the algorithm with options["n_cpu"]
        com = pygpc.Computation(n_cpu=n_cpu)

        # run model and determine computation time
        t_n_cpu = []

        start = time.time()
        res = com.run(model=SurfaceCoverageSpecies.model, problem=SurfaceCoverageSpecies.problem, coords=grid.coords)
        stop = time.time()

        t_eval[str(n_cpu)] = stop - start

    # plot results
    plt.figure(figsize=[4, 4])
    for ind, t in enumerate(t_eval):
        plt.bar(ind, t_eval[t], color=sns.color_palette("pastel", len(t_eval))[ind])

    plt.xlabel("n_cpu", fontsize=11)
    plt.ylabel("Computation time in s", fontsize=11)
    plt.xticks(range(len(t_eval)), t_eval.keys())
    plt.title("Parallel model evaluation", fontsize=12)
    plt.tight_layout()

    # On Windows subprocesses will import (i.e. execute) the main module at start.
    # You need to insert an if __name__ == '__main__': guard in the main module to avoid
    # creating subprocesses recursively.
    #
    # if __name__ == '__main__':
    #     main()



.. image-sg:: /auto_features/images/sphx_glr_plot_parallelization_001.png
   :alt: Parallel model evaluation
   :srcset: /auto_features/images/sphx_glr_plot_parallelization_001.png
   :class: sphx-glr-single-img






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

   **Total running time of the script:** ( 0 minutes  1.577 seconds)


.. _sphx_glr_download_auto_features_plot_parallelization.py:

.. only:: html

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


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

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

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

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


.. only:: html

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

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