How to Share Large, Read-Only Arrays and Python Objects in Multiprocessing without Memory Overhead?

Shared-Memory Objects in Multiprocessing

Question:

In multiprocessing, how can you share a large, read-only array or any arbitrary Python object across multiple processes without incurring memory overhead?

Answer:

In operating systems that use copy-on-write fork() semantics, unaltered data structures remain available to all child processes without additional memory consumption. Simply ensure that the shared object remains unmodified.

For Arrays:

Efficient Approach:

1. Pack the array into an efficient array structure (e.g., numpy array).
2. Place the array in shared memory.
3. Wrap the shared array with multiprocessing.Array.
4. Pass the shared array to your functions.

Writeable Shared Objects:

• Requires synchronization or locking.

• multiprocessing provides two methods:

  • Shared memory: Suitable for simple values, arrays, or ctypes (fast).
  • Manager proxy: Process holds the memory, and a manager arbitrates access from others (slower due to serialization/deserialization).

Arbitrary Python Objects:

• Use the Manager proxy approach.
• Slower than shared memory due to communication overhead.

Optimization Concerns:

The overhead observed in the provided code snippet is not caused by memory copying. Instead, it stems from the serialization/deserialization of the function's arguments (the arr array), which incurs a performance penalty when using the Manager proxy.

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