What are the best practices for parallelism in Python?

What are the best practices for parallelism in Python?

When using parallelism in Python, consider using the multiprocessing module for CPU-bound tasks and concurrent.futures for easier thread management. Always be mindful of the Global Interpreter Lock (GIL) when using threads.

Can you explain the Global Interpreter Lock and its impact?

The GIL is a mutex that protects access to Python objects, preventing multiple threads from executing Python bytecodes simultaneously. This can be a bottleneck for CPU-bound programs but is less of an issue for I/O-bound tasks.

What are some common pitfalls to avoid?

Common pitfalls include not managing shared resources properly, leading to race conditions, and using too many processes or threads, which can cause overhead and reduce performance.

How can I measure the performance of my parallel code?

You can use the time module to measure execution time or the cProfile module for more detailed profiling of your parallel code.