What is the Global Interpreter Lock (GIL) in CPython and how do you bypass it for concurrency?
The GIL is a mutex in CPython that prevents multiple native OS threads from executing Python bytecodes simultaneously. It was designed to protect CPython's reference-counting memory management from race conditions.
• For I/O-bound tasks (network, disk): Standard `threading` or `asyncio` works great because threads release the GIL while waiting on I/O. • For CPU-bound tasks (math, data science): Standard threads cannot achieve parallelism. Use `multiprocessing` or `concurrent.futures.ProcessPoolExecutor` to spawn separate processes with independent Python interpreters and memory spaces.
from concurrent.futures import ProcessPoolExecutor
def heavy_computation(number):
return sum(i * i for i in range(number))
if __name__ == "__main__":
numbers = [5_000_000, 6_000_000, 7_000_000]
# Bypasses the GIL by running across multiple CPU cores
with ProcessPoolExecutor() as executor:
results = list(executor.map(heavy_computation, numbers))
print("Computed parallel results:", results)`ProcessPoolExecutor` launches separate worker processes, allowing true parallel CPU utilization across all computer cores.
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