Python Parallel Processing and Threading Comparison

If you want to maximize your CPU bound #python processing tasks you can think the following way.

Given that your Python process is CPU-bound and you have almost unlimited CPU capacity, using `concurrent.futures.ProcessPoolExecutor` is likely to provide better performance than `concurrent.futures.ThreadPoolExecutor`. Here's why:

1. Parallelism: `ProcessPoolExecutor` utilizes separate processes, each running in its own Python interpreter, which allows them to run truly concurrently across multiple CPU cores. On the other hand, `ThreadPoolExecutor` uses #threads, which are subject to the Global Interpreter Lock (GIL) in Python, limiting true parallelism when it comes to CPU-bound tasks.

2. GIL Limitation: The GIL restricts the execution of Python bytecode to a single thread at a time, even in multi-threaded applications. While threads can be useful for I/O-bound tasks or tasks that release the GIL, they are less effective for CPU-bound tasks because they cannot run simultaneously due to the GIL.

3. Isolation: Processes have their own memory space, providing better isolation compared to threads. This can be beneficial for tasks that involve shared state or resources, as processes don't share memory by default and thus avoid many concurrency issues.

4. CPU Utilization: Since processes run independently and can utilize multiple CPU cores without contention, `ProcessPoolExecutor` can fully utilize the available CPU capacity, leading to better performance for CPU-bound tasks.

Therefore, if you want to maximize the performance of your CPU-bound Python process with unlimited CPU capacity, using `concurrent.futures.ProcessPoolExecutor` is generally the preferred choice. It allows for true #parallelism across multiple CPU cores and avoids the limitations imposed by the GIL.