Parallelism in Python

Parallelism in Python#

The GIL Challenge#

🔒 Global Interpreter Lock

Only one thread executes Python bytecode at a time

Threading: Concurrency (task switching)
Multiprocessing: True parallelism

🎯 Python’s Role

Orchestration layer for compiled code

numpy, pandas, scipy → C/C++/Fortran
Python releases GIL → multi-core execution

Performance Patterns#

Scenario

Approach

Speedup

CPU-bound

multiprocessing

~N cores

I/O-bound

threading/asyncio

Significant

Vectorized (NumPy)

Already parallel

None needed

Warning

Anti-pattern: Iterating over NumPy arrays with Python loops

Tip

Golden Rule: Optimize sequential code (vectorization) before parallelizing

Content