.. highlight:: cython

.. py:module:: cython.parallel

.. _parallel:

**********************************
Using Parallelism
**********************************

Cython supports native parallelism through the :py:mod:`cython.parallel`
module. To use this kind of parallelism, the GIL must be released
(see :ref:`Releasing the GIL <nogil>`).
It currently supports OpenMP, but later on more backends might be supported.

.. NOTE:: Functionality in this module may only be used from the main thread
          or parallel regions due to OpenMP restrictions.


.. function:: prange([start,] stop[, step][, nogil=False][, schedule=None[, chunksize=None]][, num_threads=None])

    This function can be used for parallel loops. OpenMP automatically
    starts a thread pool and distributes the work according to the schedule
    used. ``step`` must not be 0. This function can only be used with the
    GIL released. If ``nogil`` is true, the loop will be wrapped in a nogil
    section.

    Thread-locality and reductions are automatically inferred for variables.

    If you assign to a variable in a prange block, it becomes lastprivate, meaning that the
    variable will contain the value from the last iteration. If you use an
    inplace operator on a variable, it becomes a reduction, meaning that the
    values from the thread-local copies of the variable will be reduced with
    the operator and assigned to the original variable after the loop. The
    index variable is always lastprivate.
    Variables assigned to in a parallel with block will be private and unusable
    after the block, as there is no concept of a sequentially last value.

    The ``schedule`` is passed to OpenMP and can be one of the following:

    static:
       If a chunksize is provided, iterations are distributed to all
       threads ahead of time in blocks of the given chunksize.  If no
       chunksize is given, the iteration space is divided into chunks that
       are approximately equal in size, and at most one chunk is assigned
       to each thread in advance.

       This is most appropriate when the scheduling overhead matters and
       the problem can be cut down into equally sized chunks that are
       known to have approximately the same runtime.

    dynamic:
       The iterations are distributed to threads as they request them,
       with a default chunk size of 1.

       This is suitable when the runtime of each chunk differs and is not
       known in advance and therefore a larger number of smaller chunks
       is used in order to keep all threads busy.

    guided:
       As with dynamic scheduling, the iterations are distributed to
       threads as they request them, but with decreasing chunk size.  The
       size of each chunk is proportional to the number of unassigned
       iterations divided by the number of participating threads,
       decreasing to 1 (or the chunksize if provided).

       This has an advantage over pure dynamic scheduling when it turns
       out that the last chunks take more time than expected or are
       otherwise being badly scheduled, so that most threads start running
       idle while the last chunks are being worked on by only a smaller
       number of threads.

    runtime:
       The schedule and chunk size are taken from the runtime scheduling
       variable, which can be set through the ``openmp.omp_set_schedule()``
       function call, or the OMP_SCHEDULE environment variable.  Note that
       this essentially disables any static compile time optimisations of
       the scheduling code itself and may therefore show a slightly worse
       performance than when the same scheduling policy is statically
       configured at compile time.

    ..  auto             The decision regarding scheduling is delegated to the
    ..                   compiler and/or runtime system. The programmer gives
    ..                   the implementation the freedom to choose any possible
    ..                   mapping of iterations to threads in the team.

    The default schedule is implementation defined. For more information consult
    the OpenMP specification [#]_.

    The ``num_threads`` argument indicates how many threads the team should consist of. If not given,
    OpenMP will decide how many threads to use. Typically this is the number of cores available on
    the machine. However, this may be controlled through the ``omp_set_num_threads()`` function, or
    through the ``OMP_NUM_THREADS`` environment variable.

    The ``chunksize`` argument indicates the chunksize to be used for dividing the iterations among threads.
    This is only valid for ``static``, ``dynamic`` and ``guided`` scheduling, and is optional. Different chunksizes
    may give substantially different performance results, depending on the schedule, the load balance it provides,
    the scheduling overhead and the amount of false sharing (if any).

    Example with a reduction:

    .. literalinclude:: ../../examples/userguide/parallelism/simple_sum.pyx

    Example with a typed memoryview (e.g. a NumPy array)::

        from cython.parallel import prange

        def func(double[:] x, double alpha):
            cdef Py_ssize_t i

            for i in prange(x.shape[0]):
                x[i] = alpha * x[i]

.. function:: parallel(num_threads=None)

    This directive can be used as part of a ``with`` statement to execute code
    sequences in parallel. This is currently useful to setup thread-local
    buffers used by a prange. A contained prange will be a worksharing loop
    that is not parallel, so any variable assigned to in the parallel section
    is also private to the prange. Variables that are private in the parallel
    block are unavailable after the parallel block.

    Example with thread-local buffers::

       from cython.parallel import parallel, prange
       from libc.stdlib cimport abort, malloc, free

       cdef Py_ssize_t idx, i, n = 100
       cdef int * local_buf
       cdef size_t size = 10

       with nogil, parallel():
           local_buf = <int *> malloc(sizeof(int) * size)
           if local_buf == NULL:
               abort()

           # populate our local buffer in a sequential loop
           for i in xrange(size):
               local_buf[i] = i * 2

           # share the work using the thread-local buffer(s)
           for i in prange(n, schedule='guided'):
               func(local_buf)

           free(local_buf)

    Later on sections might be supported in parallel blocks, to distribute
    code sections of work among threads.

.. function:: threadid()

    Returns the id of the thread. For n threads, the ids will range from 0 to
    n-1.


Compiling
=========

To actually use the OpenMP support, you need to tell the C or C++ compiler to
enable OpenMP.  For gcc this can be done as follows in a setup.py::

    from distutils.core import setup
    from distutils.extension import Extension
    from Cython.Build import cythonize

    ext_modules = [
        Extension(
            "hello",
            ["hello.pyx"],
            extra_compile_args=['-fopenmp'],
            extra_link_args=['-fopenmp'],
        )
    ]

    setup(
        name='hello-parallel-world',
        ext_modules=cythonize(ext_modules),
    )

For Microsoft Visual C++ compiler, use ``'/openmp'`` instead of ``'-fopenmp'``.


Breaking out of loops
=====================

The parallel with and prange blocks support the statements break, continue and
return in nogil mode. Additionally, it is valid to use a ``with gil`` block
inside these blocks, and have exceptions propagate from them.
However, because the blocks use OpenMP, they can not just be left, so the
exiting procedure is best-effort. For prange() this means that the loop
body is skipped after the first break, return or exception for any subsequent
iteration in any thread. It is undefined which value shall be returned if
multiple different values may be returned, as the iterations are in no
particular order::

    from cython.parallel import prange

    cdef int func(Py_ssize_t n):
        cdef Py_ssize_t i

        for i in prange(n, nogil=True):
            if i == 8:
                with gil:
                    raise Exception()
            elif i == 4:
                break
            elif i == 2:
                return i

In the example above it is undefined whether an exception shall be raised,
whether it will simply break or whether it will return 2.

Using OpenMP Functions
======================
OpenMP functions can be used by cimporting ``openmp``:

.. literalinclude:: ../../examples/userguide/parallelism/cimport_openmp.pyx

.. rubric:: References

.. [#] http://www.openmp.org/mp-documents/spec30.pdf
