Bypassing the GIL for Parallel Processing in Python

Unlocking Python’s true potential in terms of speed through shared-memory parallelism has traditionally been limited and challenging to achieve. That’s because the global interpreter lock (GIL) doesn’t allow for thread-based parallel processing in Python. Fortunately, there are several work-arounds for this notorious limitation, which you’re about to explore now!

To get the most out of this advanced tutorial, you should understand the difference between concurrency and parallelism. You’ll benefit from having previous experience with multithreading in programming languages other than Python. Finally, it’s best if you’re eager to explore uncharted territory, such as calling foreign Python bindings or writing bits of C code.

Don’t worry if your knowledge of parallel processing is a bit rusty, as




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