scikit-learn
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diff --git a/‎doc/glossary.rst
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diff --git a/‎doc/modules/computing.rst
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diff --git a/‎doc/modules/ensemble.rst
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@@ -299,23 +299,20 @@ documentation <https://docs.python.org/3/library/multiprocessing.html#contexts-a
 
 .. _faq_mkl_threading:
 
-Why does my job use more cores than specified with n_jobs under OSX or Linux?
------------------------------------------------------------------------------
+Why does my job use more cores than specified with n_jobs?
+----------------------------------------------------------
 
-This happens when vectorized numpy operations are handled by libraries such
-as MKL or OpenBLAS.
+This is because ``n_jobs`` only controls the number of jobs for
+routines that are parallelized with ``joblib``, but parallel code can come
+from other sources:
 
-While scikit-learn adheres to the limit set by ``n_jobs``,
-numpy operations vectorized using MKL (or OpenBLAS) will make use of multiple
-threads within each scikit-learn job (thread or process).
+- some routines may be parallelized with OpenMP (for code written in C or
+  Cython).
+- scikit-learn relies a lot on numpy, which in turn may rely on numerical
+  libraries like MKL, OpenBLAS or BLIS which can provide parallel
+  implementations.
 
-The number of threads used by the BLAS library can be set via an environment
-variable. For example, to set the maximum number of threads to some integer
-value ``N``, the following environment variables should be set:
-
-* For MKL: ``export MKL_NUM_THREADS=N``
-
-* For OpenBLAS: ``export OPENBLAS_NUM_THREADS=N``
+For more details, please refer to our :ref:`Parallelism notes <parallelism>`.
 
 
 Why is there no support for deep or reinforcement learning / Will there be support for deep or reinforcement learning in scikit-learn?
 
@@ -1508,45 +1508,23 @@ functions or non-estimator constructors.
         early.
 
     ``n_jobs``
-        This is used to specify how many concurrent processes/threads should be
-        used for parallelized routines.  Scikit-learn uses one processor for
-        its processing by default, although it also makes use of NumPy, which
-        may be configured to use a threaded numerical processor library (like
-        MKL; see :ref:`FAQ <faq_mkl_threading>`).
-
-        ``n_jobs`` is an int, specifying the maximum number of concurrently
-        running jobs.  If set to -1, all CPUs are used. If 1 is given, no
-        joblib level parallelism is used at all, which is useful for
-        debugging. Even with ``n_jobs = 1``, parallelism may occur due to
-        numerical processing libraries (see :ref:`FAQ <faq_mkl_threading>`).
-        For n_jobs below -1, (n_cpus + 1 + n_jobs) are used. Thus for
-        ``n_jobs = -2``, all CPUs but one are used.
-
-        ``n_jobs=None`` means *unset*; it will generally be interpreted as
-        ``n_jobs=1``, unless the current :class:`joblib.Parallel` backend
-        context specifies otherwise.
-
-        The use of ``n_jobs``-based parallelism in estimators varies:
-
-        * Most often parallelism happens in :term:`fitting <fit>`, but
-          sometimes parallelism happens in prediction (e.g. in random forests).
-        * Some parallelism uses a multi-threading backend by default, some
-          a multi-processing backend.  It is possible to override the default
-          backend by using :func:`sklearn.utils.parallel_backend`.
-        * Whether parallel processing is helpful at improving runtime depends
-          on many factors, and it's usually a good idea to experiment rather
-          than assuming that increasing the number of jobs is always a good
-          thing.  *It can be highly detrimental to performance to run multiple
-          copies of some estimators or functions in parallel.*
-
-        Nested uses of ``n_jobs``-based parallelism with the same backend will
-        result in an exception.
-        So ``GridSearchCV(OneVsRestClassifier(SVC(), n_jobs=2), n_jobs=2)``
-        won't work.
-
-        When ``n_jobs`` is not 1, the estimator being parallelized must be
-        picklable.  This means, for instance, that lambdas cannot be used
-        as estimator parameters.
+        This parameter is used to specify how many concurrent processes or
+        threads should be used for routines that are parallelized with
+        :term:`joblib`.
+
+        ``n_jobs`` is an integer, specifying the maximum number of concurrently
+        running workers. If 1 is given, no joblib parallelism is used at all,
+        which is useful for debugging. If set to -1, all CPUs are used. For
+        ``n_jobs`` below -1, (n_cpus + 1 + n_jobs) are used. For example with
+        ``n_jobs=-2``, all CPUs but one are used.
+
+        ``n_jobs`` is ``None`` by default, which means *unset*; it will
+        generally be interpreted as ``n_jobs=1``, unless the current
+        :class:`joblib.Parallel` backend context specifies otherwise.
+
+        For more details on the use of ``joblib`` and its interactions with
+        scikit-learn, please refer to our :ref:`parallelism notes
+        <parallelism>`.
 
     ``pos_label``
         Value with which positive labels must be encoded in binary
 
@@ -504,20 +504,152 @@ Links
   - `Scipy sparse matrix formats documentation <https://docs.scipy.org/doc/scipy/reference/sparse.html>`_
 
 Parallelism, resource management, and configuration
-=====================================================
+===================================================
 
 .. _parallelism:
 
-Parallel and distributed computing
------------------------------------
+Parallelism
+-----------
 
-Scikit-learn uses the `joblib <https://joblib.readthedocs.io/en/latest/>`__
-library to enable parallel computing inside its estimators. See the
-joblib documentation for the switches to control parallel computing.
+Some scikit-learn estimators and utilities can parallelize costly operations
+using multiple CPU cores, thanks to the following components:
+
+- via the `joblib <https://joblib.readthedocs.io/en/latest/>`_ library. In
+  this case the number of threads or processes can be controlled with the
+  ``n_jobs`` parameter.
+- via OpenMP, used in C or Cython code.
+
+In addition, some of the numpy routines that are used internally by
+scikit-learn may also be parallelized if numpy is installed with specific
+numerical libraries such as MKL, OpenBLAS, or BLIS.
+
+We describe these 3 scenarios in the following subsections.
+
+Joblib-based parallelism
+........................
+
+When the underlying implementation uses joblib, the number of workers
+(threads or processes) that are spawned in parallel can be controled via the
+``n_jobs`` parameter.
+
+.. note::
+
+    Where (and how) parallelization happens in the estimators is currently
+    poorly documented. Please help us by improving our docs and tackle `issue
+    14228 <https://github.com/scikit-learn/scikit-learn/issues/14228>`_!
+
+Joblib is able to support both multi-processing and multi-threading. Whether
+joblib chooses to spawn a thread or a process depends on the **backend**
+that it's using.
+
+Scikit-learn generally relies on the ``loky`` backend, which is joblib's
+default backend. Loky is a multi-processing backend. When doing
+multi-processing, in order to avoid duplicating the memory in each process
+(which isn't reasonable with big datasets), joblib will create a `memmap
+<https://docs.scipy.org/doc/numpy/reference/generated/numpy.memmap.html>`_
+that all processes can share, when the data is bigger than 1MB.
+
+In some specific cases (when the code that is run in parallel releases the
+GIL), scikit-learn will indicate to ``joblib`` that a multi-threading
+backend is preferable.
+
+As a user, you may control the backend that joblib will use (regardless of
+what scikit-learn recommends) by using a con
10000
text manager::
+
+    from joblib import parallel_backend
+
+    with parallel_backend('threading', n_jobs=2):
+        # Your scikit-learn code here
+
+Please refer to the `joblib's docs
+<https://joblib.readthedocs.io/en/latest/parallel.html#thread-based-parallelism-vs-process-based-parallelism>`_
+for more details.
+
+In practice, whether parallelism is helpful at improving runtime depends on
+many factors. It is usually a good idea to experiment rather than assuming
+that increasing the number of workers is always a good thing. In some cases
+it can be highly detrimental to performance to run multiple copies of some
+estimators or functions in parallel (see oversubscription below).
+
+OpenMP-based parallelism
+........................
+
+OpenMP is used to parallelize code written in Cython or C, relying on
+multi-threading exclusively. By default (and unless joblib is trying to
+avoid oversubscription), the implementation will use as many threads as
+possible.
+
+You can control the exact number of threads that are used via the
+``OMP_NUM_THREADS`` environment variable::
+
+    OMP_NUM_THREADS=4 python my_script.py
+
+Parallel Numpy routines from numerical libraries
+................................................
+
+Scikit-learn relies heavily on NumPy and SciPy, which internally call
+multi-threaded linear algebra routines implemented in libraries such as MKL,
+OpenBLAS or BLIS.
+
+The number of threads used by the OpenBLAS, MKL or BLIS libraries can be set
+via the ``MKL_NUM_THREADS``, ``OPENBLAS_NUM_THREADS``, and
+``BLIS_NUM_THREADS`` environment variables.
+
+Please note that scikit-learn has no direct control over these
+implementations. Scikit-learn solely relies on Numpy and Scipy.
+
+.. note::
+    At the time of writing (2019), NumPy and SciPy packages distributed on
+    pypi.org (used by ``pip``) and on the conda-forge channel are linked
+    with OpenBLAS, while conda packages shipped on the "defaults" channel
+    from anaconda.org are linked by default with MKL.
+
+
+Oversubscription: spawning too many threads
+...........................................
+
+It is generally recommended to avoid using significantly more processes or
+threads than the number of CPUs on a machine. Over-subscription happens when
+a program is running too many threads at the same time.
+
+Suppose you have a machine with 8 CPUs. Consider a case where you're running
+a :class:`~GridSearchCV` (parallelized with joblib) with ``n_jobs=8`` over
+a :class:`~HistGradientBoostingClassifier` (parallelized with OpenMP). Each
+instance of :class:`~HistGradientBoostingClassifier` will spawn 8 threads
+(since you have 8 CPUs). That's a total of ``8 * 8 = 64`` threads, which
+leads to oversubscription of physical CPU resources and to scheduling
+overhead.
+
+Oversubscription can arise in the exact same fashion with parallelized
+routines from MKL, OpenBLAS or BLIS that are nested in joblib calls.
+
+Starting from ``joblib >= 0.14``, when the ``loky`` backend is used (which
+is the default), joblib will tell its child **processes** to limit the
+number of threads they can use, so as to avoid oversubscription. In practice
+the heuristic that joblib uses is to tell the processes to use ``max_threads
+= n_cpus // n_jobs``, via their corresponding environment variable. Back to
+our example from above, since the joblib backend of :class:`~GridSearchCV`
+is ``loky``, each process will only be able to use 1 thread instead of 8,
+thus mitigating the oversubscription issue.
+
+Note that:
+
+ - Manually setting one of the environment variables (``OMP_NUM_THREADS``,
+   ``MKL_NUM_THREADS``, ``OPENBLAS_NUM_THREADS``, or ``BLIS_NUM_THREADS``)
+   will take precedence over what joblib tries to do. The total number of
+   threads will be ``n_jobs * <LIB>_NUM_THREADS``. Note that setting this
+   limit will also impact your computations in the main process, which will
+   only use ``<LIB>_NUM_THREADS``. Joblib exposes a context manager for
+   finer control over the number of threads in its workers (see joblib docs
+   linked below).
+- Joblib is currently unable to avoid oversubscription in a
+  multi-threading context. It can only do so with the ``loky`` backend
+  (which spawns processes).
+
+You will find additional details about joblib mitigation of oversubscription
+in `joblib documentation
+<https://joblib.readthedocs.io/en/latest/parallel.html#avoiding-over-subscription-of-cpu-ressources>`_.
 
-Note that, by default, scikit-learn uses its embedded (vendored) version
-of joblib. A configuration switch (documented below) controls this
-behavior.
 
 Configuration switches
 -----------------------
 
@@ -946,10 +946,8 @@ Low-level parallelism
 
 :class:`HistGradientBoostingClassifier` and
 :class:`HistGradientBoostingRegressor` have implementations that use OpenMP
-for parallelization through Cython. The number of threads that is used can
-be changed using the ``OMP_NUM_THREADS`` environment variable. By default,
-all available cores are used. Please refer to the OpenMP documentation for
-details.
+for parallelization through Cython. For more details on how to control the
+number of threads, please refer to our :ref:`parallelism` notes.
 
 The following parts are parallelized: