cunumeric.cumprod#
- cunumeric.cumprod(a: ndarray, axis: int | None = None, dtype: np.dtype[Any] | None = None, out: ndarray | None = None) ndarray#
Return the cumulative product of the elements along a given axis.
- Parameters:
a (array_like) – Input array.
axis (int, optional) – Axis along which the cumulative product is computed. The default (None) is to compute the cumprod over the flattened array.
dtype (dtype, optional) – Type of the returned array and of the accumulator in which the elements are multiplied. If dtype is not specified, it defaults to the dtype of a, unless a has an integer dtype with a precision less than that of the default platform integer. In that case, the default platform integer is used.
out (ndarray, optional) – Alternative output array in which to place the result. It must have the same shape and buffer length as the expected output but the type will be cast if necessary. See Output type determination for more details.
- Returns:
cumprod – A new array holding the result is returned unless out is specified, in which case a reference to out is returned. The result has the same size as a, and the same shape as a if axis is not None or a is a 1-d array.
- Return type:
See also
Notes
CuNumeric’s parallel implementation may yield different results from NumPy with floating point and complex types. For example, when boundary values such as inf occur they may not propagate as expected. Consider the float32 array
[3e+37, 1, 100, 0.01]. NumPy’s cumprod will return a result of[3e+37, 3e+37, inf, inf]. However, cuNumeric might internally partition the array such that partition 0 has[3e+37, 1]and partition 1 has[100, 0.01], returning the result[3e+37, 3e+37, inf, 3e+37].- Availability:
Multiple GPUs, Multiple CPUs