Reduction in GPU

Overview

  • Tutorial: 5 min

  1. Learn how to perform reduction operations on GPUs.

  2. Understand how to use the @reduce decorator in Numba.

Numba offers a @reduce decorator that transforms a simple binary operation into a reduction kernel.

Initial Array:
    [2]   [4]  [6]  [8] [1]  [3]  [5]   [7]

      \   /     \   /     \   /     \   /
       6         14        4         12

          \       /         \       /
              20               16

                  \       /
                      36

 1import numpy
 2from numba import cuda
 3
 4@cuda.reduce
 5def sum_reduce(a, b):
 6    return a + b
 7
 8A = (numpy.arange(1234, dtype=numpy.float64)) + 1
 9normal_sum = A.sum()      # NumPy sum reduction
10gpu_sum = sum_reduce(A)   # cuda sum reduction
11assert normal_sum == gpu_sum

Key Points

  1. @reduce can convert a simple binary operation into a reduction kernel.

  2. Numba’s reduction operations can be performed on GPUs, providing efficient parallel computation.