GPU Reduction
==============
Writing a reduction algorithm for CUDA GPU can be tricky. Numba provides a
``@reduce`` decorator for converting simple binary operation into a reduction
kernel.
``@reduce``
------------
Example::
import numpy
from numba 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)
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:: numba.cuda.Reduce
:members: __init__, __call__