# 3.6. Supported Atomic Operations¶

Numba provides access to some of the atomic operations supported in CUDA. 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.

Returns the old value at the index location as if it is loaded atomically.

class `compare_and_swap`(ary, old, val)

Conditionally assign `val` to the first element of an 1D array `ary` if the current value matches `old`.

Returns the current value as if it is loaded atomically.

class `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 int32, int64, uint32, uint64, float32, float64 operands only.

Returns the old value at the index location as if it is loaded atomically.

class `min`(ary, idx, val)

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

Supported on int32, int64, uint32, uint64, float32, 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

@cuda.jit
def max_example(result, values):
"""Find the maximum value in values and store in result[0]"""
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 comparison (should be equal!)
```

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

```@cuda.jit
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))
```