How to Sync Shared Variable in Multiprocessing with Python

Learn how to effectively sync shared variables in Python's multiprocessing to ensure proper communication and process handling.
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Syncing Shared Variables in Python Multiprocessing

When working with Python's multiprocessing library, one of the common challenges is sharing variables between processes. Consider that you are trying to calculate the total number of perfect square integers from two separate lists, but the sum of these numbers isn't computing correctly. In this guide, we'll explore the reason behind this issue and how to fix it efficiently.

The Problem

You want to run two parallel processes that calculate the total number of perfect squares from two lists. While each individual process can count perfect squares successfully, they can’t communicate their results effectively because each process ends up creating its own instance of the shared variable. This leads to incorrect totals being displayed.

Here’s a quick overview of your initial approach:

Define a Shared class with a shared variable to store the total count.

Run two processes for each list of numbers.

Each process updates the shared variable to record the count of perfect squares.

While this seems like it should work, under Windows, the process creation model utilizes spawn. This means new processes re-execute the whole program, leading to separate instances of the shared variable—resulting in incorrect summations.

Solution Overview

To resolve this issue, we need to ensure that the shared variable used for counting the perfect squares exists in a way that is accessible by both processes. Here's how to do it:

Step 1: Modify the perfectSquares Function

Instead of creating the shared variable outside the function and relying on global scope, we will create it within the perfectSquares function itself. This ensures that both processes work with the same shared variable.

Step 2: Pass the Shared Variable

Adjust the find_perfect function to accept the shared variable as an additional argument. This way, both processes can increment the same shared total.

Updated Code Snippet

Here’s the enhanced version of your original code with the corrections detailed above:

[[See Video to Reveal this Text or Code Snippet]]

Expected Output

Running the corrected code should yield output similar to the following:

[[See Video to Reveal this Text or Code Snippet]]

Conclusion

By moving the creation of the shared variable into the perfectSquares function and passing it as an argument to the counting function, we ensured that both processes share and update the same variable. This simple adjustment resolves the initial issue and allows your multiprocessing program to function correctly without encountering variable conflicts.

If you’re interested in diving deeper into the world of Python multiprocessing, remember to always consider the implications of process creation models on your shared data structures. Happy coding!