11. Global Interpreter Lock (GIL) in Python

11. What do you know about the global interpreter lock?

🧠 What is the Global Interpreter Lock (GIL) in Python?

The Global Interpreter Lock (GIL) is a mutex (mutual exclusion lock) that allows only one thread to execute Python bytecode at a time, even on multi-core processors.

🧱 Why Does the GIL Exist?

• To simplify memory management, especially in CPython (the standard Python interpreter).
• Python uses reference counting for memory management, and the GIL ensures thread-safe updates to reference counts.

🔄 What It Means Practically:

✅ Single-threaded code:

• No impact. The GIL is invisible and everything runs normally.

❌ Multi-threaded CPU-bound code:

• Only one thread can execute Python code at a time, so multi-threading does not speed up CPU-bound tasks (e.g., computation-heavy code).

✅ Multi-threaded I/O-bound code:

• The GIL releases during I/O operations (like file, network access), so multithreading can improve performance for I/O-heavy programs.

💡 Example Problem:

import threading

def compute():
    for _ in range(10**7):
        pass

threads = [threading.Thread(target=compute) for _ in range(4)]

for t in threads:
    t.start()

for t in threads:
    t.join()

This code won’t run faster on multiple cores because of the GIL.

🚀 Workarounds to the GIL:

1. Use multiprocessing instead of threading:

   • Separate processes don’t share a GIL.
   • Leverages multiple CPU cores.

   from multiprocessing import Pool
   

2. Use C extensions or Jython, IronPython, or PyPy:

   • Some interpreters don’t have a GIL.
   • Some C libraries release the GIL internally (e.g., NumPy with C backend).

🔍 Summary: