How can you ensure data integrity when sharing large lists of objects across multiple subprocesses using multiprocessing in Python?

Shared Memory in Multiprocessing

Multiprocessing in Python allows you to create multiple processes that run concurrently, enabling you to leverage multiple cores and improve performance. However, sharing large amounts of data between processes can be a concern. Here, we discuss the behavior of shared memory when using multiprocessing to handle large lists of different objects.

Overview of Shared Memory

In general, Python uses copy-on-write (COW) semantics when creating new processes. This means that when a new process is created, it shares the same memory with the parent process. Any modifications made by either process will create a new copy of the affected memory region. However, accessing a shared object will increment its reference count, raising concerns about the possibility of memory being copied due to reference counting.

Impact of Reference Counting on Data Transfer

In the example provided, where three large lists containing bitarrays and integer arrays are shared among multiple subprocesses, the reference counting mechanism can indeed lead to the entire objects being copied. This is because the function someFunction accesses each list, incrementing its reference count. Since the lists are large, the memory usage will increase significantly with each subprocess.

Ensuring Data Integrity with Shared Memory

To prevent unnecessary duplication of shared data, such as the large lists in this case, you need to devise a mechanism to disable reference counting for these lists and their constituent objects. However, the Python documentation advises against modifying reference counting, as it is a fundamental part of Python's memory management system.

Alternative Solution for Shared Memory

A possible solution to ensure data integrity while sharing it between subprocesses is to use True Shared Memory. Introduced in Python version 3.8, True Shared Memory allows you to create shared memory objects that are directly accessible from all subprocesses without duplicating the data.

Example of True Shared Memory

The provided code sample demonstrates the use of True Shared Memory with NumPy arrays, a common use case. The add_one function utilizes an existing NumPy array backed by shared memory (created in the create_shared_block function) to perform calculations without copying the entire array. The Final array printout shows the updated array, verifying that changes made in the subprocesses are reflected in the shared memory.

Conclusion

Sharing large amounts of data between multiple subprocesses using multiprocessing can be challenging due to the inherent reference counting mechanism. However, with the advent of True Shared Memory, you can overcome this limitation and ensure data integrity while leveraging the benefits of parallelization.

The above is the detailed content of How can you ensure data integrity when sharing large lists of objects across multiple subprocesses using multiprocessing in Python?. For more information, please follow other related articles on the PHP Chinese website!