How Can I Efficiently Share Large In-Memory Arrays in Multiprocessing?

Shared-Memory Objects in Multiprocessing

When utilizing large in-memory arrays within multiprocessing architectures, it's common to face the concern of memory consumption. Copying these arrays into multiple processes can be highly inefficient.

Read-Only Shared Arrays

For read-only in-memory arrays, such as NumPy arrays, the copy-on-write fork() semantics present in operating systems like Unix offers a solution. If the array remains unmodified throughout its lifespan, it can be shared among child processes without additional memory allocation. No specific modifications are required in your code to achieve this.

Efficient Approach for Large Arrays

For large arrays, an efficient approach is to pack them into a structured array (such as NumPy arrays), store them in shared memory, wrap them with multiprocessing.Array, and pass them to the required functions. This approach minimizes memory overhead.

Writeable Shared Objects

In cases where writeable shared objects are essential, synchronization or locking mechanisms become necessary. Multiprocessing offers two options: shared memory (suitable for simple values, arrays, or ctypes) or a Manager proxy. The Manager proxy grants one process ownership of the memory, while others access it through arbitration.

Shared Memory Considerations

The Manager proxy approach accommodates arbitrary Python objects but may experience performance degradation due to serialization and deserialization overheads. It's crucial to acknowledge that even with copy-on-write fork(), there may still be some overhead associated with shared memory operations.

Alternative Libraries

Beyond multiprocessing, numerous parallel processing libraries and approaches are available in Python. If your requirements extend beyond the capabilities of multiprocessing, exploring alternative libraries may prove beneficial.

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