.. _numba-threading-layer:

The Threading Layers
====================

This section is about the Numba threading layer, this is the library that is
used internally to perform the parallel execution that occurs through the use of
the ``parallel`` targets for CPUs, namely:

* The use of the ``parallel=True`` kwarg in ``@jit`` and ``@njit``.
* The use of the ``target='parallel'`` kwarg in ``@vectorize`` and
  ``@guvectorize``.

.. note::
    If a code base does not use the ``threading`` or ``multiprocessing``
    modules (or any other sort of parallelism) the defaults for the threading
    layer that ship with Numba will work well, no further action is required!


Which threading layers are available?
-------------------------------------
There are three threading layers available and they are named as follows:

* ``tbb`` - A threading layer backed by Intel TBB.
* ``omp`` - A threading layer backed by OpenMP.
* ``workqueue`` -A simple built-in work-sharing task scheduler.

In practice, the only threading layer guaranteed to be present is ``workqueue``.
The ``omp`` layer requires the presence of a suitable OpenMP runtime library.
The ``tbb`` layer requires the presence of Intel's TBB libraries, these can be
obtained via the conda command::

    $ conda install tbb

If you installed Numba with ``pip``, TBB can be enabled by running::

    $ pip install tbb

Due to compatibility issues with manylinux1 and other portability concerns,
the OpenMP threading layer is disabled in the Numba binary wheels on PyPI.

.. note::
    The default manner in which Numba searches for and loads a threading layer
    is tolerant of missing libraries, incompatible runtimes etc.


.. _numba-threading-layer-setting-mech:

Setting the threading layer
---------------------------


The threading layer is set via the environment variable
``NUMBA_THREADING_LAYER`` or through assignment to
``numba.config.THREADING_LAYER``. If the programmatic approach to setting the
threading layer is used it must occur logically before any Numba based
compilation for a parallel target has occurred. There are two approaches to
choosing a threading layer, the first is by selecting a threading layer that is
safe under various forms of parallel execution, the second is through explicit
selection via the threading layer name (e.g. ``tbb``).


Selecting a threading layer for safe parallel execution
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Parallel execution is fundamentally derived from core Python libraries in four
forms (the first three also apply to code using parallel execution via other
means!):

* ``threads`` from the ``threading`` module.
* ``spawn`` ing processes from the ``multiprocessing`` module via ``spawn``
  (default on Windows, only available in Python 3.4+ on Unix)
* ``fork`` ing processes from the ``multiprocessing`` module via ``fork``
  (default on Unix and the only option available for Python 2 on Unix).
* ``fork`` ing processes from the ``multiprocessing`` module through the use of
  a ``forkserver`` (only available in Python 3 on Unix). Essentially a new
  process is spawned and then forks are made from this new process on request.

Any library in use with these forms of parallelism must exhibit safe behaviour
under the given paradigm. As a result, the threading layer selection methods
are designed to provide a way to choose a threading layer library that is safe
for a given paradigm in an easy, cross platform and environment tolerant manner.
The options that can be supplied to the
:ref:`setting mechanisms <numba-threading-layer-setting-mech>` are as
follows:

* ``default`` provides no specific safety guarantee and is the default.
* ``safe`` is both fork and thread safe, this requires the ``tbb`` package
  (Intel TBB libraries) to be installed.
* ``forksafe`` provides a fork safe library.
* ``threadsafe`` provides a thread safe library.

To discover the threading layer that was selected, the function
``numba.threading_layer()`` may be called after parallel execution. For example,
on a Linux machine with no TBB installed::

    from numba import config, njit, threading_layer
    import numpy as np

    # set the threading layer before any parallel target compilation
    config.THREADING_LAYER = 'threadsafe'

    @njit(parallel=True)
    def foo(a, b):
        return a + b

    x = np.arange(10.)
    y = x.copy()

    # this will force the compilation of the function, select a threading layer
    # and then execute in parallel
    foo(x, y)

    # demonstrate the threading layer chosen
    print("Threading layer chosen: %s" % threading_layer())

which produces::

    Threading layer chosen: omp

and this makes sense as GNU OpenMP, as present on Linux, is thread safe.

Selecting a named threading layer
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Advanced users may wish to select a specific threading layer for their use case,
this is done by directly supplying the threading layer name to the
:ref:`setting mechanisms <numba-threading-layer-setting-mech>`. The options
and requirements are as follows:

+----------------------+-----------+-------------------------------------------+
| Threading Layer Name | Platform  | Requirements                              |
+======================+===========+===========================================+
| ``tbb``              | All       | The ``tbb`` package (``$ conda install    |
|                      |           | tbb``)                                    |
+----------------------+-----------+-------------------------------------------+
| ``omp``              | Linux     | GNU OpenMP libraries (very likely this    |
|                      |           | will already exist)                       |
|                      |           |                                           |
|                      | Windows   | MS OpenMP libraries (very likely this will|
|                      |           | already exist)                            |
|                      |           |                                           |
|                      | OSX       | The ``intel-openmp`` package (``$ conda   |
|                      |           | install intel-openmp``)                   |
+----------------------+-----------+-------------------------------------------+
| ``workqueue``        | All       | None                                      |
+----------------------+-----------+-------------------------------------------+

Should the threading layer not load correctly Numba will detect this and provide
a hint about how to resolve the problem. It should also be noted that the Numba
diagnostic command ``numba -s`` has a section
``__Threading Layer Information__`` that reports on the availability of
threading layers in the current environment.


Extra notes
-----------
The threading layers have fairly complex interactions with CPython internals and
system level libraries, some additional things to note:

* The installation of Intel's TBB libraries vastly widens the options available
  in the threading layer selection process.
* On Linux, the ``omp`` threading layer is not fork safe due to the GNU OpenMP
  runtime library (``libgomp``) not being fork safe. If a fork occurs in a
  program that is using the ``omp`` threading layer, a detection mechanism is
  present that will try and gracefully terminate the forked child and print an
  error message to ``STDERR``.
* On OSX, the ``intel-openmp`` package is required to enable the OpenMP based
  threading layer.
* For Windows users running Python 2.7, the ``tbb`` threading layer is not
  available.
