Understanding the GIL in Python

Posted by Afsal on 23-Jun-2023

Hi Pythonistas!

Today we will learn about controversial topic in Python which is GIL.The Global Interpreter Lock (GIL) is a concept that affects the way Python handles multithreading. Although Python allows for multiple threads, the GIL restricts the execution of multiple threads to occur only one at a time. Understanding the GIL is essential for Python developers to effectively utilize multithreading in their programs.


  1. Thread Creation:
    • When a new thread is created in a Python program, it starts with a request to acquire the GIL.
    • If the GIL is currently held by another thread, the new thread waits until the GIL is released by the current holder.
  2. GIL Acquisition:
    • When a thread is scheduled to execute Python bytecodes, it needs to acquire the GIL to gain exclusive access to Python objects.
    • The thread requests the GIL and checks if it is available.
    • If the GIL is available, the thread acquires the GIL and proceeds to execute the critical section of Python bytecode.
  3. Execution of Critical Section:
    • Once a thread acquires the GIL, it can execute the critical section of code, which typically consists of a small chunk of Python bytecode.
    • The thread performs the necessary operations on Python objects, accessing and modifying them within the critical section.
  4. GIL Release Points:
    • During certain operations, the GIL is released to allow other threads to acquire it and execute their critical sections. These are known as GIL release points.
    • Common GIL release points include I/O operations, system calls, and certain blocking operations where the current thread is not actively executing Python bytecodes.
    • When the GIL is released, the thread enters a waiting state and other threads have an opportunity to acquire the GIL.
  5. GIL Handoff:
    • When a GIL release point is reached, the thread releases the GIL and enters a state where it is no longer executing Python bytecodes.
    • Other waiting threads have a chance to acquire the GIL in a process known as GIL handoff.
    • The specific mechanism of thread selection for acquiring the GIL can vary and may involve factors like thread priority or scheduling algorithms.
  6. GIL Reacquisition:
    • Once the waiting threads are scheduled and acquire the GIL, they can execute their critical sections of code.
    • The threads perform their operations on Python objects within their respective critical sections.

The GIL operation repeats as threads acquire, release, and reacquire the GIL throughout the execution of the Python program. The GIL ensures that only one thread executes Python bytecodes at a time, providing memory safety and simplifying memory management.

Advantages of the GIL

  1. Simplicity: The GIL simplifies memory management and thread synchronization by ensuring that only one thread can execute Python bytecodes at a time. This eliminates the need for explicit locking and complex synchronization mechanisms, making the interpreter implementation more straightforward.
  2. Memory Safety: The GIL ensures memory safety by preventing multiple threads from simultaneously accessing and modifying Python objects. This avoids potential race conditions and memory inconsistencies that can occur when multiple threads operate on shared data.
  3. Compatibility: The GIL allows seamless compatibility with C extension modules. Many popular libraries and modules in Python's ecosystem are implemented in C and are not thread-safe. The GIL provides a simple mechanism to protect against concurrent access to these modules.

Disadvantages of the GIL

  1. Limited Parallelism for CPU-Bound Tasks: The GIL can limit the performance of CPU-bound tasks that primarily involve computational work within Python bytecodes. Since only one thread can execute Python bytecodes at a time, the GIL restricts true parallelism, resulting in potential performance bottlenecks.
  2. Inefficiency for Multi-Core Systems: On multi-core systems, the GIL prevents Python programs from efficiently utilizing multiple CPU cores for CPU-bound tasks. This can lead to underutilization of available computing resources and hinder performance improvements that could be achieved through parallel execution.
  3. Perception of Limited Multithreading: The GIL has led to a perception that Python's multithreading capabilities are limited compared to other languages. Developers coming from backgrounds where multithreading achieves true parallelism may find the GIL's restrictions counterintuitive and consider it a drawback of Python.

How to overcome GIL

Use multiprocessing: Instead of relying on threads, you can leverage the multiprocessing module, which allows you to spawn multiple processes. Each process will have its own interpreter and, therefore, its own GIL.

Utilize asynchronous programming: Asynchronous programming, with libraries like asyncio or Trio, enables you to write concurrent code without relying on threads.

Use alternative Python implementations: Python has multiple interpreter implementations, such as Jython, IronPython, and PyPy, which do not have a Global Interpreter Lock or have different threading models

Why does the GIL still  exist?

The GIL has historical reasons and design trade-offs. It simplifies memory management and allows for efficient garbage collection, making Python memory-safe and reducing the risk of memory leaks. Additionally, the GIL avoids complex thread synchronization issues that can arise in other languages.


The Global Interpreter Lock (GIL) is an important aspect of Python's threading model. While it limits the parallel execution of Python bytecodes within a single process, it ensures memory safety and simplifies memory management. Understanding the GIL's behavior helps developers make informed decisions when designing multithreaded applications and choose appropriate strategies for achieving concurrency and performance in Python programs.