How Can I Efficiently Use NumPy Arrays in Shared Memory for Multiprocessing?

Using Numpy Arrays in Shared Memory for Multiprocessing

Utilizing shared memory for numpy arrays is common in multiprocessing scenarios. However, exploiting their full potential as numpy arrays, rather than merely ctypes arrays, can be a challenge.

The solution lies in leveraging mp.Array() from the multiprocessing module. This function allows the creation of shared arrays that can be accessed by multiple processes simultaneously. To access these arrays as numpy arrays, you can employ numpy.frombuffer(), without incurring any data copying overhead.

Example:

import multiprocessing as mp
import numpy as np

def f(shared_arr):
    arr = np.frombuffer(shared_arr.get_obj()) 
    arr[:] = -arr[:] 

if __name__ == '__main__':
    N = 10
    shared_arr = mp.Array(ctypes.c_double, N) 
    arr = np.frombuffer(shared_arr.get_obj()) 

    # Create, start, and finish child processes
    p = mp.Process(target=f, args=(shared_arr,)) 
    p.start() 
    p.join()

In this example, the f() function uses a shared array to perform element-wise negation. By accessing the array as a numpy array, you gain access to all its powerful operations and methods.

Synchronization:

When multiple processes access the same shared array, synchronization is crucial to prevent conflicts. mp.Array() provides a get_lock() method that allows you to synchronize access as needed.

# ...
def f(i): 
    with shared_arr.get_lock(): # synchronize access
        arr = np.frombuffer(shared_arr.get_obj()) 
        arr[i] = -arr[i] 

Utilizing this approach, you can share numpy arrays in multiprocessing environments while maintaining their full functionality as numpy arrays.

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