Optimising Python3 Multiprocessing for Multi-threaded Applications

Improve the efficiency of your Python3 applications by properly optimising multiprocessing with multi-threaded functions. Learn how to run multiple samples with efficient thread management.
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Optimising Python3 Multiprocessing for Multi-threaded Applications: A Guide

When working with Python3, you may encounter situations where you need to run functions that execute multiple threads simultaneously, especially when performing tasks like data analysis or processing large datasets. In this article, we will explore an approach to effectively utilize multiprocessing in Python 3 when each process requires multiple threads.

The Problem at Hand

Consider a scenario where you have a list of 12 items, each representing a sample. The goal is to analyze each sample using a function that requires four threads of execution. Running these samples in a serial loop can be inefficient, especially since the CPU has multiple cores available for parallel processing.

Example of Initial Approach

Here's a simplified version of what the original code looks like:

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

This code snippet effectively processes all samples, but it does so one at a time, thereby underutilizing the available CPU cores.

The Need for Multiprocessing

To enhance efficiency, the solution involves leveraging the multiprocessing library. The starmap function is particularly useful for distributing tasks across multiple cores. However, the challenge arises when each multiprocessing task itself requires multiple threads to function efficiently.

Why the Current Approach Fails

Utilizing starmap alone may not suffice when the function under scrutiny requires more threads than the available cores. In this specific case, while you might have 20 available threads, each sample requires 4 threads, leading to a total necessity of 48 threads to operate all samples in parallel efficiently. Trying to run all 12 samples simultaneously would push the CPU threads to their maximum, inevitably leading to inefficiency and potential slowdowns.

Proposed Solution: Core Limitation in Multiprocessing

To tackle this issue, it’s pivotal to limit the number of cores utilized by the multiprocessing pool, allowing some cores to remain free for the wrapper to utilize. The following adjustment can ensure that only a specified number of samples are processed simultaneously.

Adjusted Code Implementation:

Here’s how to adjust the code accordingly:

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

Breakdown of the Code:

Parallel Execution: The starmap function allows for parallel execution of my_wrapper, processing samples in tuples, creating efficient task distribution with proper handling of available threads.

Error Handling: While not shown in this code snippet, it’s advisable to include error handling for subprocess execution to ensure robust functionality.

Conclusion

In summary, optimizing Python3's multiprocessing for functions requiring multiple threads involves careful management of how many processes run in parallel to avoid exceeding the thread limits of your CPU. By adjusting the pool size to account for the threads each function needs, you can achieve a more efficient execution of your sample processing tasks.

If you face similar challenges while dealing with multi-threaded applications in Python, consider applying this approach. Not only will it lead to performance improvements, but it will also ensure that your CPU is used effectively.