multiprocessing - Python - Multiprocess shared memory with python numpy array?

Question

Here i have a 2 numpy arrays, and a function that will take those arrays as an input, and then do some numpy calculation and return the result. It works as it is but it's slow and i think we can use multiprocessing to make it a bit faster.

Anyway, here's my code :

A = #4 dimensions big numpy array
B = #1 dimension numpy array

def function(A, B):
    P = np.einsum("ijkl,ij->kl", A, B)
    return P.astype(np.uint8)

result = function(A,B)

I'm still quite new into this Multiprocessing stuff, but think that we're able to make array A and B as a shared memory (maybe using multiprocessing.Array() ??) , and then make multiple processes to compute the function(A, B). But i still can't quite understand how to put all of that in the code.

EDIT:

Alright, so it seems like the approach above doesn't work, but let's try another case, but now, lets say the length of array A is 120, and now i want to use only 3/4 parts of array A from index number 0 to 89 and use all of array B in the Process No.1

And then, i also want to use 3/4 parts of array A but from index number 30 to 119 and use all parts of array B in the Process No.2, will that help? Of course i can make the A array even larger to get it's part computed with even more process where, but the thing is, will this concept works?

question from:https://stackoverflow.com/questions/65917489/python-multiprocess-shared-memory-with-python-numpy-array

Answer 1

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