Python · Theory

Multiprocessing in Python

Theory

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multiprocessing starts separate processes — real parallel CPU. threading shares memory, good for I/O waits. GIL limits CPU threads in CPython.

Fresher line: I/O bound → threads. CPU bound → processes. Don’t claim threads make matrix multiply 8× faster on CPython.

Multiprocessing in Python — output — CPUs and your real core count. Processes use separate memory; threads share memory.

Exam tip

I/O vs CPU + GIL in one sentence.

# Multiprocessing in Python
from multiprocessing import cpu_count
print("CPUs", cpu_count())

Multiprocessing in Python — output: CPUs and your real core count. Processes use separate memory; threads share memory.