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

.. only:: html

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

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

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

.. _sphx_glr_auto_examples_plot_benchmark_dot.py:


.. _l-example-dot:

Compares dot implementations (numpy, python, blas)
==================================================

:epkg:`numpy` has a very fast implementation of
the dot product. It is difficult to be better and very easy
to be slower. This example looks into a couple of slower
implementations.

Compared implementations:

* :func:`pydot <td3a_cpp.tutorial.dotpy.pydot>`
* `cblas_ddot <https://github.com/sdpython/td3a_cpp/
  blob/master/td3a_cpp/tutorial/dot_blas_lapack.pyx#L39>`_

.. contents::
    :local:

.. GENERATED FROM PYTHON SOURCE LINES 22-30

.. code-block:: default


    import pprint
    import numpy
    import matplotlib.pyplot as plt
    from pandas import DataFrame, concat
    from td3a_cpp.tutorial import pydot, cblas_ddot
    from td3a_cpp.tools import measure_time_dim








.. GENERATED FROM PYTHON SOURCE LINES 31-37

python dot: pydot
+++++++++++++++++

The first function :func:`pydot
<td3a_cpp.tutorial.pydot>` uses
python to implement the dot product.

.. GENERATED FROM PYTHON SOURCE LINES 37-48

.. code-block:: default


    ctxs = [dict(va=numpy.random.randn(n).astype(numpy.float64),
                 vb=numpy.random.randn(n).astype(numpy.float64),
                 pydot=pydot,
                 x_name=n)
            for n in range(10, 1000, 100)]

    res_pydot = list(measure_time_dim('pydot(va, vb)', ctxs, verbose=1))

    pprint.pprint(res_pydot[:2])





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

 .. code-block:: none


      0%|          | 0/10 [00:00<?, ?it/s]
     30%|###       | 3/10 [00:00<00:00, 18.30it/s]
     50%|#####     | 5/10 [00:00<00:00,  9.46it/s]
     70%|#######   | 7/10 [00:00<00:00,  6.16it/s]
     80%|########  | 8/10 [00:01<00:00,  5.11it/s]
     90%|######### | 9/10 [00:01<00:00,  4.32it/s]
    100%|##########| 10/10 [00:02<00:00,  3.68it/s]
    100%|##########| 10/10 [00:02<00:00,  4.98it/s]
    [{'average': 2.4982207454741003e-05,
      'context_size': 232,
      'deviation': 4.265833773511768e-07,
      'max_exec': 2.5938916951417924e-05,
      'min_exec': 2.463754266500473e-05,
      'number': 50,
      'repeat': 10,
      'x_name': 10},
     {'average': 0.00010771532170474529,
      'context_size': 232,
      'deviation': 4.2879408576822083e-07,
      'max_exec': 0.00010854371823370456,
      'min_exec': 0.000107117323204875,
      'number': 50,
      'repeat': 10,
      'x_name': 110}]




.. GENERATED FROM PYTHON SOURCE LINES 49-52

numpy dot
+++++++++


.. GENERATED FROM PYTHON SOURCE LINES 52-63

.. code-block:: default


    ctxs = [dict(va=numpy.random.randn(n).astype(numpy.float64),
                 vb=numpy.random.randn(n).astype(numpy.float64),
                 dot=numpy.dot,
                 x_name=n)
            for n in range(10, 50000, 100)]

    res_dot = list(measure_time_dim('dot(va, vb)', ctxs, verbose=1))

    pprint.pprint(res_dot[:2])





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

 .. code-block:: none


      0%|          | 0/500 [00:00<?, ?it/s]
      4%|3         | 18/500 [00:00<00:02, 177.61it/s]
      7%|7         | 36/500 [00:00<00:02, 156.16it/s]
     10%|#         | 52/500 [00:00<00:03, 140.54it/s]
     13%|#3        | 67/500 [00:00<00:03, 127.80it/s]
     16%|#6        | 80/500 [00:00<00:03, 117.82it/s]
     18%|#8        | 92/500 [00:00<00:03, 109.21it/s]
     21%|##        | 104/500 [00:00<00:03, 103.48it/s]
     23%|##3       | 115/500 [00:00<00:03, 102.50it/s]
     25%|##5       | 126/500 [00:01<00:03, 101.31it/s]
     27%|##7       | 137/500 [00:01<00:03, 100.02it/s]
     30%|##9       | 148/500 [00:01<00:03, 98.72it/s] 
     32%|###1      | 158/500 [00:01<00:03, 97.45it/s]
     34%|###3      | 168/500 [00:01<00:03, 96.43it/s]
     36%|###5      | 178/500 [00:01<00:03, 95.29it/s]
     38%|###7      | 188/500 [00:01<00:03, 94.51it/s]
     40%|###9      | 198/500 [00:01<00:03, 93.57it/s]
     42%|####1     | 208/500 [00:01<00:03, 92.69it/s]
     44%|####3     | 218/500 [00:02<00:03, 91.85it/s]
     46%|####5     | 228/500 [00:02<00:02, 91.01it/s]
     48%|####7     | 238/500 [00:02<00:02, 90.24it/s]
     50%|####9     | 248/500 [00:02<00:02, 89.50it/s]
     51%|#####1    | 257/500 [00:02<00:02, 88.67it/s]
     53%|#####3    | 266/500 [00:02<00:02, 87.97it/s]
     55%|#####5    | 275/500 [00:02<00:02, 87.28it/s]
     57%|#####6    | 284/500 [00:02<00:02, 86.58it/s]
     59%|#####8    | 293/500 [00:02<00:02, 85.90it/s]
     60%|######    | 302/500 [00:03<00:02, 85.22it/s]
     62%|######2   | 311/500 [00:03<00:02, 84.66it/s]
     64%|######4   | 320/500 [00:03<00:02, 84.13it/s]
     66%|######5   | 329/500 [00:03<00:02, 83.50it/s]
     68%|######7   | 338/500 [00:03<00:01, 82.86it/s]
     69%|######9   | 347/500 [00:03<00:01, 82.34it/s]
     71%|#######1  | 356/500 [00:03<00:01, 81.73it/s]
     73%|#######3  | 365/500 [00:03<00:01, 81.24it/s]
     75%|#######4  | 374/500 [00:03<00:01, 80.68it/s]
     77%|#######6  | 383/500 [00:04<00:01, 80.10it/s]
     78%|#######8  | 392/500 [00:04<00:01, 79.69it/s]
     80%|########  | 400/500 [00:04<00:01, 79.13it/s]
     82%|########1 | 408/500 [00:04<00:01, 78.72it/s]
     83%|########3 | 416/500 [00:04<00:01, 78.20it/s]
     85%|########4 | 424/500 [00:04<00:00, 77.67it/s]
     86%|########6 | 432/500 [00:04<00:00, 76.87it/s]
     88%|########8 | 440/500 [00:04<00:00, 76.24it/s]
     90%|########9 | 448/500 [00:04<00:00, 75.61it/s]
     91%|#########1| 456/500 [00:05<00:00, 75.30it/s]
     93%|#########2| 464/500 [00:05<00:00, 74.93it/s]
     94%|#########4| 472/500 [00:05<00:00, 74.54it/s]
     96%|#########6| 480/500 [00:05<00:00, 74.21it/s]
     98%|#########7| 488/500 [00:05<00:00, 73.95it/s]
     99%|#########9| 496/500 [00:05<00:00, 73.45it/s]
    100%|##########| 500/500 [00:05<00:00, 88.94it/s]
    [{'average': 8.04197695106268e-06,
      'context_size': 232,
      'deviation': 3.954607054646713e-07,
      'max_exec': 9.134504944086075e-06,
      'min_exec': 7.828520610928535e-06,
      'number': 50,
      'repeat': 10,
      'x_name': 10},
     {'average': 8.221659809350966e-06,
      'context_size': 232,
      'deviation': 1.974098308461028e-07,
      'max_exec': 8.763503283262253e-06,
      'min_exec': 8.071912452578544e-06,
      'number': 50,
      'repeat': 10,
      'x_name': 110}]




.. GENERATED FROM PYTHON SOURCE LINES 64-69

blas dot
++++++++

:epkg:`numpy` implementation uses :epkg:`BLAS`.
Let's make a direct call to it.

.. GENERATED FROM PYTHON SOURCE LINES 69-77

.. code-block:: default


    for ctx in ctxs:
        ctx['ddot'] = cblas_ddot

    res_ddot = list(measure_time_dim('ddot(va, vb)', ctxs, verbose=1))

    pprint.pprint(res_ddot[:2])





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

 .. code-block:: none


      0%|          | 0/500 [00:00<?, ?it/s]
      3%|3         | 16/500 [00:00<00:03, 154.83it/s]
      6%|6         | 32/500 [00:00<00:03, 139.00it/s]
      9%|9         | 47/500 [00:00<00:03, 126.78it/s]
     12%|#2        | 60/500 [00:00<00:03, 117.33it/s]
     14%|#4        | 72/500 [00:00<00:03, 109.19it/s]
     17%|#6        | 83/500 [00:00<00:04, 102.13it/s]
     19%|#8        | 94/500 [00:00<00:04, 95.61it/s] 
     21%|##        | 104/500 [00:01<00:04, 88.13it/s]
     23%|##2       | 113/500 [00:01<00:04, 84.43it/s]
     24%|##4       | 122/500 [00:01<00:04, 84.38it/s]
     26%|##6       | 131/500 [00:01<00:04, 85.79it/s]
     28%|##8       | 140/500 [00:01<00:04, 86.67it/s]
     30%|##9       | 149/500 [00:01<00:04, 87.09it/s]
     32%|###1      | 158/500 [00:01<00:03, 87.10it/s]
     33%|###3      | 167/500 [00:01<00:03, 87.02it/s]
     35%|###5      | 176/500 [00:01<00:03, 86.59it/s]
     37%|###7      | 185/500 [00:01<00:03, 86.12it/s]
     39%|###8      | 194/500 [00:02<00:03, 85.70it/s]
     41%|####      | 203/500 [00:02<00:03, 85.24it/s]
     42%|####2     | 212/500 [00:02<00:03, 84.48it/s]
     44%|####4     | 221/500 [00:02<00:03, 83.92it/s]
     46%|####6     | 230/500 [00:02<00:03, 83.33it/s]
     48%|####7     | 239/500 [00:02<00:03, 82.76it/s]
     50%|####9     | 248/500 [00:02<00:03, 82.22it/s]
     51%|#####1    | 257/500 [00:02<00:02, 81.59it/s]
     53%|#####3    | 266/500 [00:02<00:02, 81.01it/s]
     55%|#####5    | 275/500 [00:03<00:02, 80.46it/s]
     57%|#####6    | 284/500 [00:03<00:02, 79.89it/s]
     58%|#####8    | 292/500 [00:03<00:02, 79.42it/s]
     60%|######    | 300/500 [00:03<00:02, 78.91it/s]
     62%|######1   | 308/500 [00:03<00:02, 78.36it/s]
     63%|######3   | 316/500 [00:03<00:02, 77.97it/s]
     65%|######4   | 324/500 [00:03<00:02, 77.54it/s]
     66%|######6   | 332/500 [00:03<00:02, 77.03it/s]
     68%|######8   | 340/500 [00:03<00:02, 76.57it/s]
     70%|######9   | 348/500 [00:04<00:01, 76.14it/s]
     71%|#######1  | 356/500 [00:04<00:01, 75.61it/s]
     73%|#######2  | 364/500 [00:04<00:01, 75.27it/s]
     74%|#######4  | 372/500 [00:04<00:01, 74.87it/s]
     76%|#######6  | 380/500 [00:04<00:01, 74.36it/s]
     78%|#######7  | 388/500 [00:04<00:01, 74.02it/s]
     79%|#######9  | 396/500 [00:04<00:01, 73.59it/s]
     81%|########  | 404/500 [00:04<00:01, 73.25it/s]
     82%|########2 | 412/500 [00:04<00:01, 72.77it/s]
     84%|########4 | 420/500 [00:04<00:01, 72.37it/s]
     86%|########5 | 428/500 [00:05<00:01, 71.98it/s]
     87%|########7 | 436/500 [00:05<00:00, 71.63it/s]
     89%|########8 | 444/500 [00:05<00:00, 71.23it/s]
     90%|######### | 452/500 [00:05<00:00, 70.86it/s]
     92%|#########2| 460/500 [00:05<00:00, 70.47it/s]
     94%|#########3| 468/500 [00:05<00:00, 70.15it/s]
     95%|#########5| 476/500 [00:05<00:00, 69.77it/s]
     97%|#########6| 483/500 [00:05<00:00, 69.26it/s]
     98%|#########8| 490/500 [00:05<00:00, 69.00it/s]
     99%|#########9| 497/500 [00:06<00:00, 68.60it/s]
    100%|##########| 500/500 [00:06<00:00, 81.40it/s]
    [{'average': 9.0926056727767e-06,
      'context_size': 360,
      'deviation': 5.179199338513208e-07,
      'max_exec': 1.06026791036129e-05,
      'min_exec': 8.81449319422245e-06,
      'number': 50,
      'repeat': 10,
      'x_name': 10},
     {'average': 9.519362822175027e-06,
      'context_size': 360,
      'deviation': 1.7103723574109565e-07,
      'max_exec': 1.0011699050664901e-05,
      'min_exec': 9.383894503116607e-06,
      'number': 50,
      'repeat': 10,
      'x_name': 110}]




.. GENERATED FROM PYTHON SOURCE LINES 78-80

Let's display the results
+++++++++++++++++++++++++

.. GENERATED FROM PYTHON SOURCE LINES 80-101

.. code-block:: default


    df1 = DataFrame(res_pydot)
    df1['fct'] = 'pydot'
    df2 = DataFrame(res_dot)
    df2['fct'] = 'numpy.dot'
    df3 = DataFrame(res_ddot)
    df3['fct'] = 'ddot'

    cc = concat([df1, df2, df3])
    cc['N'] = cc['x_name']

    fig, ax = plt.subplots(1, 2, figsize=(10, 4))
    cc[cc.N <= 1100].pivot(
            index='N', columns='fct', values='average').plot(
        logy=True, logx=True, ax=ax[0])
    cc[cc.fct != 'pydot'].pivot(
            index='N', columns='fct', values='average').plot(
        logy=True, logx=True, ax=ax[1])
    ax[0].set_title("Comparison of dot implementations")
    ax[1].set_title("Comparison of dot implementations\nwithout python")




.. image-sg:: /auto_examples/images/sphx_glr_plot_benchmark_dot_001.png
   :alt: Comparison of dot implementations, Comparison of dot implementations without python
   :srcset: /auto_examples/images/sphx_glr_plot_benchmark_dot_001.png
   :class: sphx-glr-single-img


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

 .. code-block:: none


    Text(0.5, 1.0, 'Comparison of dot implementations\nwithout python')



.. GENERATED FROM PYTHON SOURCE LINES 102-105

The results depends on the machine, its
number of cores, the compilation settings
of :epkg:`numpy` or this module.

.. GENERATED FROM PYTHON SOURCE LINES 105-107

.. code-block:: default


    plt.show()








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

   **Total running time of the script:** ( 0 minutes  19.894 seconds)


.. _sphx_glr_download_auto_examples_plot_benchmark_dot.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_benchmark_dot.py <plot_benchmark_dot.py>`

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

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


.. only:: html

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

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