1. What was the GIL?

• GIL stands for Global Interpreter Lock.
• It’s a mutex (mutual exclusion lock) that allowed only one thread to execute Python bytecode at a time, even on multi-core processors.
• Purpose: Protects Python’s memory management (reference counting) from race conditions.
• Problem: It limited true parallelism, especially for CPU-bound tasks.

Analogy:
Think of the Python interpreter as a single-lane bridge (GIL) over a river. Cars (threads) from both sides want to cross. The bridge allows only one car at a time, so even if you have 4 lanes (CPU cores), only one car moves at a time.

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2. Why removing the GIL is a big deal

• Python can now run multiple threads truly in parallel, utilizing all CPU cores.
• CPU-bound tasks (like image processing, simulations, mathematical computations) can scale much better.
• Previously, Python developers often used multiprocessing to bypass GIL, which involves extra memory overhead because each process has its own memory space.

Before GIL (CPU-bound):

import threading
import time

def cpu_task():
    count = 0
    for i in range(10**7):
        count += i

threads = [threading.Thread(target=cpu_task) for _ in range(4)]
start = time.time()
for t in threads: t.start()
for t in threads: t.join()
print("Time taken:", time.time() - start)

• On GIL Python, adding more threads doesn’t reduce execution time significantly for CPU-heavy tasks.

After GIL removal (Python 3.14):

• Threads can now run truly in parallel on multiple cores.
• Execution time for CPU-bound tasks decreases proportionally to the number of cores.

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3. How Python achieves free-threaded execution

• The interpreter has been refactored to allow atomic operations on reference counts and other shared data.
• Internal locks are applied only where absolutely necessary, instead of globally.
• Threads now run independently while still ensuring memory safety.

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4. Implications for developers

1. CPU-bound code scales better: No need for complex multiprocessing workarounds.
2. Existing multithreaded code might behave differently:
   • Some race conditions may appear if the code assumes the GIL prevents concurrent execution.
   • Libraries that rely on GIL assumptions may need updates.
3. I/O-bound tasks:
   • For I/O-heavy programs, Python already performs well with threads and async code.
   • GIL removal mostly improves CPU-intensive applications.

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5. Example – CPU-bound multithreading before vs after

Benchmark (conceptual):

import threading
import time

def cpu_task(n):
    count = 0
    for i in range(n):
        count += i
    return count

N = 10**7
threads = [threading.Thread(target=cpu_task, args=(N,)) for _ in range(4)]

start = time.time()
for t in threads: t.start()
for t in threads: t.join()
print("Time taken on Python 3.14:", time.time() - start)

• On older Python: threads run one at a time → slower
• On Python 3.14: threads run in parallel → faster

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6. Real-world use cases

• Scientific computing: NumPy, SciPy can utilize multiple cores without workarounds.
• Image/video processing: Apply filters on multiple frames concurrently.
• Simulations & gaming engines: Physics, AI calculations parallelized.
• Web servers: CPU-heavy request processing can benefit.

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Summary

• The GIL removal is a game-changer for Python, finally enabling true parallel execution for CPU-bound workloads.
• Threads are now free to run independently, utilizing all cores efficiently.
• Some older assumptions in multithreaded code may break, so careful testing is required.

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Article written by Harshil Bansal, Team edSlash.