How Can I Share Large Read-Only Arrays Between Multiple Processes in Python\'s Multiprocessing?

Shared Memory Objects in Multiprocessing

In Python's multiprocessing library, you face the challenge of sharing large read-only arrays between multiple processes simultaneously.

Using Fork() Semantics

If your operating system uses copy-on-write fork() semantics (e.g., Unix), your read-only data structure will be accessible to all child processes without additional memory consumption. This is because fork() creates a copy-on-write operation, so changes to the data structure by one process will only be written to its own memory space, leaving the original data structure intact for other processes.

Packing Array into Shared Memory

For greater efficiency, convert your array into a NumPy or array structure and store it in shared memory. Create a multiprocessing.Array wrapper around it and pass it to your functions.

Writeable Shared Objects

If you need writeable shared objects, use synchronization or locking mechanisms. multiprocessing offers two methods:

• Shared memory for simple values, arrays, or ctypes
• Manager proxy, where one process stores the memory and a manager manages access from others

The Manager proxy approach can handle arbitrary Python objects but is slower due to object serialization and deserialization involved in inter-process communication.

Alternative Approaches

Beyond multiprocessing, there are various parallel processing libraries in Python. Consider these options if you have specific requirements that multiprocessing may not adequately address.

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