👨🚀Parallel programming allows you to utilize all CPU cores in Python
🤔Asyncio is ideal for I/O-bound operations
⚙️Threading in Python is limited by the Global Interpreter Lock (GIL)
💯Multiprocessing is the best option for compute-bound tasks
🤜Optimizing chunk size can improve multiprocessing performance
What is parallel programming?
—Parallel programming is the execution of multiple tasks simultaneously using multiple CPU cores.
What is the Global Interpreter Lock (GIL)?
—The Global Interpreter Lock is a mechanism in CPython that allows only one thread to execute Python bytecode at a time.
When should I use asyncio?
—Asyncio is ideal for I/O-bound operations, such as reading or writing to disk or waiting on a network connection.
Why is threading limited in Python?
—Thread-based parallelism is limited by the Global Interpreter Lock, which prevents multiple threads from executing Python bytecode simultaneously.
When should I use multiprocessing?
—Multiprocessing is the best option for compute-bound tasks where the majority of time is spent on raw computation.
00:00The video introduces parallel programming in Python and how to utilize all CPU cores.
05:30Asyncio is recommended for I/O-bound operations that involve reading or writing to disk or waiting on a network connection.
11:56Optimizing the chunk size parameter in multiprocessing can improve performance.
