GPU Reduction
==============

Writing a reduction algorithm for CUDA GPU can be tricky.
NumbaPro provides a ``@reduce`` decorator for converting simple binary operation into a reduction kernel.

``@reduce``
------------

Example::

    import numpy
    from numbapro import cuda

    @cuda.reduce
    def sum_reduce(a, b):
        return a + b

    A = (numpy.arange(1234, dtype=numpy.float64)) + 1
    expect = A.sum()      # numpy sum reduction
    got = sum_reduce(A)   # cuda sum reduction
    assert expect == got

User can also use a lambda function::

    sum_reduce = cuda.reduce(lambda a, b: a + b)

The decorated function **must not use CUDA specific features** because it is also used for host-side execution for the final round of reduction.

class Reduce
-------------

The ``reduce`` decorator creates an instance of the ``Reduce`` class.  (Currently, ``reduce`` is an alias to ``Reduce``, but this behavior is not guaranteed.)

.. autoclass:: numbapro.cuda.Reduce
   :members:
