3.6. Supported Atomic Operations

Numba provides access to some of the atomic operations supported in CUDA, in the numba.cuda.atomic class.

Those that are presently implemented are as follows:

class numba.cuda.atomic

Namespace for atomic operations

class add(ary, idx, val)

Perform atomic ary[idx] += val. Supported on int32, float32, and float64 operands only.

class atomic.max(ary, idx, val)

Perform atomic ary[idx] = max(ary[idx], val). NaN is treated as a missing value, so max(NaN, n) == max(n, NaN) == n. Note that this differs from Python and Numpy behaviour, where max(a, b) is always a when either a or b is a NaN.

Supported on float64 operands only.

3.6.1. Example

The following code demonstrates the use of numba.cuda.atomic.max to find the maximum value in an array. Note that this is not the most efficient way of finding a maximum in this case, but that it serves as an example:

from numba import cuda
import numpy as np

def max_example(result, values):
    """Find the maximum value in values and store in result[0]"""
    tid = cuda.threadIdx.x
    bid = cuda.blockIdx.x
    bdim = cuda.blockDim.x
    i = (bid * bdim) + tid
    cuda.atomic.max(result, 0, values[i])

arr = np.random.rand(16384)
result = np.zeros(1, dtype=np.float64)

max_example[256,64](result, arr)
print(result[0]) # Found using cuda.atomic.max
print(max(arr))  # Print max(arr) for comparision (should be equal!)

Multiple dimension arrays are supported by using a tuple of ints for the index:

def max_example_3d(result, values):
    Find the maximum value in values and store in result[0].
    Both result and values are 3d arrays.
    i, j, k = cuda.grid(3)
    # Atomically store to result[0,1,2] from values[i, j, k]
    cuda.atomic.max(result, (0, 1, 2), values[i, j, k])

arr = np.random.rand(1000).reshape(10,10,10)
result = np.zeros((3, 3, 3), dtype=np.float64)
max_example_3d[(2, 2, 2), (5, 5, 5)](result, arr)
print(result[0, 1, 2], '==', np.max(arr))