Concurrent and Parallel Programming in Python - Combining Async and Multiprocessing

The video tutorial explores combining multiprocessing and asynchronous programming in Python. It discusses the limitations of Python's Global Interpreter Lock (GIL) and how multiprocessing can help utilize full hardware capabilities. The tutorial defines a class that inherits from multiprocessing.Process to create separate processes, each running an asynchronous event loop. It demonstrates scheduling tasks using async sleep and run methods, and highlights the benefits of running multiple processes to improve concurrency and performance. The video also provides tips on optimizing task distribution and handling potential timeouts.

What is the significance of the Global Interpreter Lock (GIL) in Python when it comes to multi-processing?

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Describe the role of the 'asyncio' library in the context of multi-processing.

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What is the purpose of the 'consecutive sleeps' coroutine in the provided code?

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What is the significance of the 'pending' set in the context of the provided code?

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How does the 'asyncio.run()' function work in the context of the multi-processing class?

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In what way can multiple event loops improve the performance of a program?

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What are the steps involved in initializing and starting a multi-processing class in the provided code?

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How can the use of multi-processing and asynchronous programming together enhance the efficiency of a program?

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Explain how multi-processing can help overcome the limitations of Python's concurrency.

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What are the potential issues that can arise from scheduling too many tasks at once?

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