Day 44: Using Threads for CPU-Bound Tasks

 

🐍 Python Mistakes Everyone Makes ❌

Day 44: Using Threads for CPU-Bound Tasks

Threads in Python feel like the obvious way to make programs faster.
But when it comes to CPU-bound work, threads often do the opposite.

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❌ The Mistake

Using threads to speed up heavy computation.

import threading

def work():
    total = 0
    for i in range(10_000_000):
      total += i

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

for t in threads:
    t.start()
for t in threads: 

    t.join()

This looks parallel — but it isn’t.

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❌ Why This Fails

• Python has a Global Interpreter Lock (GIL)

• Only one thread runs Python bytecode at a time

• CPU-bound threads cannot execute in parallel

• Thread context-switching adds overhead

• Performance may be worse than single-threaded code

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🧠 What Threads Are Actually Good For

Threads work well for:

• Network requests

• File I/O

• Waiting on external resources

They are not meant for heavy computation.

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✅ The Correct Way

Use multiprocessing for CPU-bound tasks.

from multiprocessing import Pool

def work(n):
  total = 0 

 for i in range(n):
      total += i
   return total

if __name__ == "__main__":
  with Pool(4) as p: 

     p.map(work, [10_000_000] * 4)

Each process:

• Has its own Python interpreter

• Has its own GIL

• Runs truly in parallel on multiple cores

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🧠 Simple Rule to Remember

🐍 Threads for I/O-bound work
🐍 Processes for CPU-bound work

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🚀 Final Takeaway

If your program is doing heavy computation, threads won’t save you.
Understanding the GIL helps you choose the right tool — and avoid wasted effort.

Write smarter, faster Python 🐍⚡