Think Different

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 du...