How to Ensure Shared Access to Read-Only Data in Multiprocessing?

Multiprocessing: Ensuring Shared Access to Read-Only Data

In multiprocessing scenarios, the question of data sharing among processes arises often. Specifically, when dealing with large read-only data structures, it's desirable to avoid copying this data to each process, which can be both time-consuming and memory-intensive.

Consider the following code snippet:

<code class="python">glbl_array = # a 3 Gb array

def my_func( args, def_param = glbl_array):
    #do stuff on args and def_param

if __name__ == '__main__':
  pool = Pool(processes=4)
  pool.map(my_func, range(1000))</code>

In this example, a global variable glbl_array is defined as a large (3 Gb) array. The my_func function is designed to operate on glbl_array. When multiple processes are spawned for parallel execution using Pool(processes=4), the question arises: will each process receive a separate copy of the data in glbl_array, or will all processes share the same read-only data?

In Linux, the semantics of the fork system call, which underpins multiprocessing, enables copy-on-write semantics. This means that if glbl_array is read-only, the data will not be physically copied between processes unless necessary.

However, if glbl_array is modified, changes made by one process will be reflected in the data accessible to all other processes. To prevent unwanted data overwrites, consider utilizing immutable objects, such as the tostring() representation of glbl_array, as the default parameter for the function.

Alternatively, one can leverage the shared memory facilities provided by the Python multiprocessing module to explicitly create and manage shared memory segments, thus ensuring the data is not duplicated across processes.

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