Multiprocessing in Python — Python Technical Interview Questions & Answers
Multiprocessing in Python
Multiprocessing in Python
- 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.
Technical Interview Tip: I/O vs CPU + GIL in one sentence.
Code Examples for Multiprocessing in Python
Example
# 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.
How to Explain Multiprocessing in Python to an Interviewer
Explain Multiprocessing as if you are teaching a junior — definition, then one tiny script.
Process : separate memory, CPU. Thread : shared memory, I/O.
What does the example print, and why?
Multiprocessing in Python — output: CPUs and your real core count
What mistake do freshers make with Multiprocessing?
GIL on CPU threads.