Скачать или смотреть Why is my Python parallel process running too many times?

Discover why your Python `multiprocessing` is executing more simulations than expected and learn how to fix it effectively!
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Understanding the Issue: Why is My Python Parallel Process Running Too Many Times?

If you've recently ventured into using Python for complex simulations, such as stochastic simulations in chemical reactions, you may have found yourself puzzled by the behavior of your parallel processing code. Specifically, you might be wondering why it seems to be running more times than you've commanded it to do. In this guide, we will unpack the issue and provide you with a clear solution to ensure your simulation runs exactly as intended.

The Initial Setup

In the code you’re working on, you defined a function named gillespie_tau_leaping which is designed to conduct your simulation. You then attempted to execute this function multiple times using Python's multiprocessing module, specifically the Pool class. Here’s a key excerpt from your original setup:

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

This setup is meant to trigger the gillespie_tau_leaping function four times. However, you noticed that it runs nine times instead, leading to confusion.

Why Is This Happening?

The unexpected behavior boils down to how Python processes functions in multiprocesses. Here’s a breakdown of what your code does:

Initial Function Call: Your code calls gillespie_tau_leaping once before entering the if _name_ == '__main__' block to make certain variables accessible for plotting. This accounts for the initial execution.

Multiprocessing Pool Behavior: When the multiprocessing pool is initiated:

The main process spawns multiple subprocesses. Each subprocess runs the same code as the main process, including the part that defines your function.

Each subprocess evaluates _name_ == '__main__' as false. Thus, they won't start a new pool but will still call gillespie_tau_leaping as instructed by the starmap command.

What this means is that you end up with four calls of gillespie_tau_leaping from the main process and additional calls spawning from each of those subprocesses, resulting in an unintentional total of nine executions.

Streamlining the Process: What You Need to Do

To fix this and run your simulation only four times, you’ll want to adjust how your code is structured. Follow these steps:

1. Adjust the gillespie_plot Function

You should modify your gillespie_plot function to ensure it doesn't block execution by displaying the plot too early. Here’s an example:

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

2. Run the Multiprocessing Call Efficiently

Here’s an updated version of how to utilize the Pool without unnecessary calls:

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

This revised structure prevents any redundant executions of gillespie_tau_leaping and allows you to generate your plots with the correct data per simulation in a streamlined manner.

Conclusion

By understanding how Python's multiprocessing works and restructuring your code to match that logic, you can achieve the desired outcome of executing your simulations only as many times as you intend. This not only saves computation time but also simplifies your workflow considerably.

In summary, take care to control where and how your functions are called within multiprocessing. Happy coding!