Will Large Lists be Copied in Shared Memory Multiprocessing in Python?

Shared Memory in Multiprocessing: Data Copying Implications

Multiprocessing in Python allows multiple processes to work concurrently on shared data. When using this feature with large data structures, it is crucial to understand the behavior of shared memory to optimize resource utilization.

In the provided scenario, three large lists (l1, l2, and l3) are created, each containing bitarrays or arrays of integers, totaling 16GB of RAM. The question arises: when 12 subprocesses are initiated using multiprocessing.Process(), will these lists be copied for each sub-process, or will they be shared?

Copy-on-Write vs. Reference Counting

Linux's copy-on-write approach typically prevents data copying until a modification is made. However, reference counting in Python can alter this behavior. When a child process references an object, the reference count of that object increases.

In your example function someFunction(), each sub-process accesses values from the lists l1, l2, and l3, triggering an increase in reference count. This leads the system to believe that the objects in these lists need to be preserved independently. As a result, they are entirely copied for each sub-process.

Disabling Reference Counting for Lists

To avoid unnecessary copying, one potential solution is to disable reference counting for the large lists and their constituent objects. This ensures that the child processes do not increment the reference count, preventing the system from considering them as objects that require preservation.

However, it is important to note that reference counting serves the purpose of deallocating memory when objects are no longer needed. By disabling reference counting, you may introduce memory leaks or other memory management issues in your program. Consult the Python documentation for more information on modifying reference counting behavior.

Other Considerations

In your specific scenario, the sub-processes do not modify the lists, only access their values. You could explore alternative approaches that do not involve shared lists. For instance, you could serialize each list into a unique file, and have the subprocesses read and process them separately.

Conclusion

The behavior of shared memory in multiprocessing can have significant implications on resource utilization and program efficiency. Careful consideration of data sharing requirements and the potential impact of reference counting is essential for optimizing your code.

The above is the detailed content of Will Large Lists be Copied in Shared Memory Multiprocessing in Python?. For more information, please follow other related articles on the PHP Chinese website!