GPU Reduction

Writing a reduction algorithm for CUDA GPU can be tricky. Numba provides a @reduce decorator for converting a simple binary operation into a reduction kernel. An example follows:

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

Lambda functions can also be used here:

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

The Reduce class

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

class numba.cuda.Reduce(functor)

Create a reduction object that reduces values using a given binary function. The binary function is compiled once and cached inside this object. Keeping this object alive will prevent re-compilation.

__init__(functor)
Parameters:

functor – A function implementing a binary operation for reduction. It will be compiled as a CUDA device function using cuda.jit(device=True).

__call__(arr, size=None, res=None, init=0, stream=0)

Performs a full reduction.

Parameters:

  • arr – A host or device array.

  • size – Optional integer specifying the number of elements in arr to reduce. If this parameter is not specified, the entire array is reduced.

  • res – Optional device array into which to write the reduction result to. The result is written into the first element of this array. If this parameter is specified, then no communication of the reduction output takes place from the device to the host.

  • init – Optional initial value for the reduction, the type of which must match arr.dtype.

  • stream – Optional CUDA stream in which to perform the reduction. If no stream is specified, the default stream of 0 is used.

Returns:

If res is specified, None is returned. Otherwise, the result of the reduction is returned.