When using NumPy arrays, the np.multiply() function and the * operator behave the same way and perform element-wise multiplication on the arrays. Similarly, the np.add() function and the + operator perform element-wise addition.

However, when using Dask arrays, there is a difference between using the * and + operators and using the dask.array.multiply() and dask.array.add() functions. This is because Dask arrays are lazy and do not compute the result of an operation until it is explicitly requested. When you use the * or + operators, Dask constructs a task graph that describes the computation, but does not actually execute it until you explicitly call a computation method like dask.compute() or dask.persist().

On the other hand, when you use the dask.array.multiply() or dask.array.add() functions, Dask immediately constructs a task graph and adds it to the computation graph, triggering the computation to begin.

Here's an example to illustrate the difference:

import dask.array as da

x = da.ones((1000, 1000), chunks=(100, 100)) y = da.ones((1000, 1000), chunks=(100, 100))

using the * operator

z = x * y

no computation is triggered yet

using dask.array.multiply()

z = da.multiply(x, y)

computation is immediately triggered

In this example, the * operator creates a task graph for the multiplication but does not execute it, whereas the dask.array.multiply() function immediately adds the task graph to the computation graph and triggers the computation to begin.

It's worth noting that using the * and + operators can be more convenient and can lead to cleaner code, especially for simple operations. However, if you need more control over when computations are executed or want to avoid unnecessary computations, you should use the dask.array.multiply() and dask.array.add() functions.