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+========================
+How to parallelize loops
+========================
+
+In image processing, we frequently apply the same algorithm
+on a large batch of images. Here is an example:
+
+.. code-block:: python
+
+    from skimage import data, color, util
+    from skimage.restoration import denoise_tv_chambolle
+    from skimage.feature import hog
+
+    def task(image):
+        """
+        Apply some functions and return an image.
+        """
+        image = denoise_tv_chambolle(image[0][0], weight=0.1, multichannel=True)
+        fd, hog_image = hog(color.rgb2gray(image), orientations=8,
+                            pixels_per_cell=(16, 16), cells_per_block=(1, 1),
+                            visualise=True)
+        return hog_image
+
+
+    # Prepare images
+    hubble = data.hubble_deep_field()
+    width = 10
+    pics = util.view_as_windows(hubble, (width, hubble.shape[1], hubble.shape[2]), step=width)
+
+To call the function ``task`` on each element of the list ``pics``, it is
+usual to write a for loop. To measure the execution time of this loop, you can
+use ipython and measure the execution time with ``%timeit``.
+
+.. code-block:: python
+
+    def classic_loop():
+        for image in pics:
+            task(image)
+
+
+    %timeit classic_loop()
+
+Another equivalent way to code this loop is to use a comprehension list which has the same efficiency.
+
+.. code-block:: python
+
+    def comprehension_loop():
+        [task(image) for image in pics]
+
+    %timeit comprehension_loop()
+
+``joblib`` is a library providing an easy way to parallelize for loops once we have a comprehension list.
+The number of jobs can be specified.
+
+.. code-block:: python
+
+    from joblib import Parallel, delayed
+    def joblib_loop():
+        Parallel(n_jobs=4)(delayed(task)(i) for i in pics)
+
+    %timeit joblib_loop()