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Mastering Python’s Multiprocessing Pool: Simplify Parallel Programming
Learn how to harness the power of Python’s multiprocessing.Pool to speed up your tasks without breaking a sweat.
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Why Multiprocessing is Like Running a Restaurant
Consider this: You are the sole chef in your little restaurant. Consumers submit orders, such as one for a biryani, another for a dosa, and so forth. It will take a long time to prepare each item individually, and patrons may become impatient.
Consider adding three more chefs to your staff. Each chef receives one order and cooks it concurrently. Everyone is suddenly pleased, and orders are fulfilled considerably faster. This is exactly how multiprocessing works: jobs are assigned to multiple “chefs” (or processes) in order to speed up operations.
What is multiprocessing.Pool?
In Python, a process is analogous to an independent chef. When you have a large assignment, divide it into smaller projects and assign each chef one to handle.
However, manually controlling these chefs (or processes) might be challenging. Who is doing what? Are they finished? Enter multiprocessing.Pool.
