Multithreading vs Multiprocessing | Python

Multithreading and multiprocessing are both techniques to achieve parallelism in Python, but they have key differences in terms of execution, memory usage, and use cases.

Multithreading in Python

Multithreading involves running multiple threads within the same process. Threads share the same memory space, which means they can communicate more easily. However, Python's Global Interpreter Lock (GIL) allows only one thread to execute Python bytecode at a time, limiting the true parallel execution of CPU-bound tasks.

import threading def print_numbers(): for i in range(5): print(i) def print_letters(): for letter in 'abcde': print(letter) t1 = threading.Thread(target=print_numbers) t2 = threading.Thread(target=print_letters) t1.start() t2.start() t1.join() t2.join()

Multiprocessing in Python

Multiprocessing involves running multiple processes, each with its own memory space. This allows true parallel execution of CPU-bound tasks since the GIL doesn't apply to separate processes. However, inter-process communication can be more complex.

from multiprocessing import Process def print_numbers(): for i in range(5): print(i) def print_letters(): for letter in 'abcde': print(letter) if __name__ == '__main__': p1 = Process(target=print_numbers) p2 = Process(target=print_letters) p1.start() p2.start() p1.join() p2.join()

Conclusion

Multithreading and multiprocessing are parallel execution techniques in Python with distinct characteristics. Multithreading involves running multiple threads within the same process, suitable for I/O-bound tasks, while multiprocessing runs separate processes with their own memory, ideal for CPU-bound tasks due to true parallelism and avoiding the GIL.






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