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__