Python Multiprocessing Pool in a class смотреть онлайн

In this tutorial, we'll explore the multiprocessing.Pool class in Python, which allows you to easily parallelize tasks across multiple CPU cores, improving the performance of CPU-bound operations. The multiprocessing module is a part of the Python standard library, making it a convenient choice for parallel processing.
The multiprocessing.Pool class provides a high-level interface to create a pool of worker processes that can execute tasks concurrently. It abstracts many of the complexities involved in starting, managing, and synchronizing multiple processes. Here's how to use it:
Let's create a simple example to demonstrate the use of multiprocessing.Pool for parallel processing. In this example, we will calculate the squares of a list of numbers concurrently.
In this example, we import the multiprocessing module and define a function square() to calculate the square of a number. We create a Pool with four worker processes and submit the square function to the pool using the map() method. The map() method divides the input data (list of numbers) into chunks and assigns each chunk to a worker process. After all tasks are complete, the results are returned in the same order as the input.
When you run the code, you'll see that the square function is applied to each number concurrently, and the squared numbers are printed.
The multiprocessing.Pool class is a valuable tool for parallelizing CPU-bound tasks and taking advantage of multiple CPU cores in Python. It simplifies the process of managing worker processes and makes parallel programming more accessible.
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