Because of Python's Global Interpreter Lock (GIL), CPU-heavy calculations cannot run in parallel across CPU cores using threads. The multiprocessing module spawns separate OS processes to utilize all CPU cores.
Python Multiprocessing
Bypass the Global Interpreter Lock (GIL) using multiple CPU process pools.
Key Takeaways: Python Multiprocessing
Mastering Python Multiprocessing helps you write clean and reliable Python code. Follow standard conventions, test edge cases, and keep logic simple. Try running the code example below and complete the exercises to build confidence.
Python Multiprocessing Code Example
Pythonfrom multiprocessing import Pool
def square(n):
return n * n
if __name__ == "__main__":
with Pool(4) as p:
results = p.map(square, [1, 2, 3, 4])
print("Parallel squares:", results)Output
Parallel squares: [1, 4, 9, 16]
Free sign-in required · Google login available
Finished this lesson?
Mark it complete to track your progress.
Stuck on something?
Ask the community in the CodesInTech forum.
Need a developer?
Learning on your own is great — and when you need professional help, our team builds custom software, websites and mobile apps for clients worldwide.
Explore Our Services