Py Multiprocessing — Free Python Tutorial
Learn Py Multiprocessing in Python with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Py Multiprocessing in Python with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated
Py Multiprocessing in Python
Python's threading module allows concurrent execution for I/O-bound tasks (network, file, database operations). Multiple threads share memory but Python's GIL limits true parallelism for CPU-bound tasks.
For CPU-heavy tasks (calculations, image processing), use multiprocessing — each process has its own Python interpreter and memory, bypassing the GIL.
concurrent.futures provides a simpler API: ThreadPoolExecutor for I/O-bound, ProcessPoolExecutor for CPU-bound tasks.
A common real-world pattern: use threads to download files concurrently, use processes to process/analyse the downloaded files in parallel.
Py Multiprocessing — Syntax
from concurrent.futures import ThreadPoolExecutor, as_completed
# Run tasks concurrently
with ThreadPoolExecutor(max_workers=4) as executor:
futures = [executor.submit(task_function, arg) for arg in args]
for future in as_completed(futures):
result = future.result()
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