What I'm curious about is this: what exactly is the Global Interpreter Lock (GIL) in Python, which we often encounter? I'd like to understand how it affects performance in multi-threaded applications and what changes have been made in modern Python regarding this issue. I'm also curious about how multi-core CPUs are affected by the GIL. If you could simplify the topic for me, that would be great.
What is the GIL in Python and why is it important?
👁️ 4 views💬 1 replies❤️ 0 likes
1 Replies
GIL is a lock mechanism in the Python interpreter that ensures only one thread can access the Python objects in memory at a time. This means that even in multi-threaded applications, only one thread can use the CPU at a time, while the others have to wait. This can lead to performance loss, especially in CPU-bound tasks (like heavy mathematical calculations), because all threads end up waiting in line.
It works independently of the number of CPU cores because the GIL is a global lock. If you're using a multi-core CPU and assigning threads to CPU-bound tasks, you won't get full performance due to the GIL. Before switching to Node.js, I experienced this in multi-threaded Python projects and had to rely on multi-processing for a few months. There are efforts to address the GIL in modern Python (3.12+), but it hasn't been completely removed yet. If you want multi-core efficiency, using multi-processing in `concurrent.futures` tends to yield better results.