The problem was that this reference count variable needed protection from race conditions where two threads increase or decrease its value simultaneously. The list object was referenced by a, b and the argument passed to sys.getrefcount(). In the above example, the reference count for the empty list object was 3. Let’s take a look at a brief code example to demonstrate how reference counting works: When this count reaches zero, the memory occupied by the object is released. It means that objects created in Python have a reference count variable that keeps track of the number of references that point to the object. Python uses reference counting for memory management. What Problem Did the GIL Solve for Python? In this article you’ll learn how the GIL affects the performance of your Python programs, and how you can mitigate the impact it might have on your code. Since the GIL allows only one thread to execute at a time even in a multi-threaded architecture with more than one CPU core, the GIL has gained a reputation as an “infamous” feature of Python. The impact of the GIL isn’t visible to developers who execute single-threaded programs, but it can be a performance bottleneck in CPU-bound and multi-threaded code. This means that only one thread can be in a state of execution at any point in time. The Python Global Interpreter Lock or GIL, in simple words, is a mutex (or a lock) that allows only one thread to hold the control of the Python interpreter.
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