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.)