Why Closing Multiprocessing Threads Stops Entire Programs in Python

In Python, the multiprocessing module leverages multiple processes to perform computations. Despite the name, it works across processes, making it a powerful tool for parallel computing. However, there can be some unexpected behavior when you close or terminate one of these multiprocessing threads. In this article, we will explore why closing a multiprocessing thread can stop an entire program and discuss the reasons behind it.

Understanding Multiprocessing and Sub-Processes

The multiprocessing module in Python operates across separate processes, unlike conventional threading which operates within the same process. This approach allows multiprocessing to create truly independent programs that can run in parallel. The syntax and mental model of multiprocessing can be similar to threading, but under the hood, these processes are governed by their own lifecycles and processes.

Each process in a multiprocessing job can behave independently, similar to a separate, daemon thread. Typically, when the main instance of Python (which initiated the job) is terminated, it will also terminate all subordinate processes. However, there are nuanced cases where closing a process or thread can lead to unpredictable outcomes, as discussed below.

Terminating Processes and Threads

When you decide to terminate a process in a multiprocessing job, several outcomes can occur. Most frequently, the main Python interpreter that initiated the job will kill all subordinate processes. This behavior is similar to how daemon threads behave and die when their creator (the main process) does.

However, the behavior can differ if you terminate one of the subordinate processes. This outcome depends on how the code is structured. For example, if you are using a Pool object, which executes tasks in a pool of worker processes, terminating one process may or may not stop the entire program. Similarly, if you use shared memory objects for inter-process communication, the way your program handles this can affect its behavior.

Alternatives to Multiprocessing

If you find that multiprocessing is causing unexpected behavior in your program, consider the following alternatives:

Use Threads: Python's threading module provides a simpler way to achieve parallelism within the same process. Although threads share the same memory space as the main process, they may still offer a more predictable behavior in terms of thread management. Use multiprocessing.Dummy Module: The multiprocessing.Dummy module is designed to use threads but still retains the multiprocessing API. This can provide a balance between the simplicity of threading and the power of multiprocessing.

Key Takeaways

Multiprocessing in Python operates across processes, providing independent subprocesses. Terminating a main process typically kills all subordinate processes, but behavior can vary for subordinate processes. Consider using threads or the multiprocessing.Dummy module if you encounter issues with multiprocessing.

By understanding these nuances, you can better manage and control your multiprocessing jobs in Python, ensuring that your programs behave as expected in diverse scenarios.