Implementation of GIL lock in python

What is a GIL lock for Python?

GILThe full name ofGlobal Interpreter Lock(Global Interpreter Lock), it is a mechanism in CPython (the mainstream implementation of Python). GIL is a thread-level lock that ensures that only one native thread is executing Python bytecode at any time. This means that even if your Python program has multiple threads, only one thread can get GIL and execute Python code, while other threads must wait for the lock to be released before it can run.

CPythonIt is an official implementation of the Python programming language and is developed and maintained by Guido van Rossum, the creator of Python. It is written in C and is the most commonly used and widely deployed Python interpreter.

Features

Interpreter: CPython parses the Python source code into bytecodes, and then uses the interpreter to execute these bytecodes at runtime. Because it runs code using interpreted ways, CPython may not be faster than some other compiled languages in execution, but it is easy to use and debug.
C Extension: CPython supports writing extension modules in C language, which can easily integrate and optimize performance-critical code snippets and call existing C libraries.
compatibility: It is a standard reference implementation, so all specifications related to Python language features are defined and implemented based on CPython.

use

When you download and install Python, you usually install the CPython implementation. usepythonCommand line tools or run.pyWhen using files, you are using the CPython interpreter.

Example

In CPython, you can execute tasks through common Python code:

print("Hello, World!")

This code is parsed by CPython, compiled into bytecode and executed in its virtual machine.

In short, CPython is currently the most mature and stable Python interpreter and is widely used by the Python community.

Why does Python need a GIL lock?

GIL locks exist because Python's memory management is not thread-safe, especially in CPython implementations, reference counting is used to manage memory. GIL ensures that there is no data race or other memory management issues when managing object reference counts.

Quote counting issues: Python uses reference counting to manage the memory of an object. If multiple threads simultaneously increase or decrease the reference count of an object, it may result in inaccurate counting, resulting in memory leaks or unexpected memory recovery. GIL ensures memory management is safe by having only one thread execute Python code at the same time.

The impact of GIL

The existence of GIL has a great impact on Python's multi-threaded programming, especially on multi-core CPUs:

For I/O intensive tasks(e.g. network requests, file read and write): Multithreaded programming performs better in this case because I/O operations release GIL, and other threads can use this time to run.
For CPU-intensive tasks(such as numerical calculations, data processing): GIL will become a performance bottleneck. Even if you have multiple threads, they cannot use multiple CPU cores to compute in parallel. To make more efficient use of multi-core CPUs, usuallyMulti-processInstead ofMulti-threaded, because multi-processes are not restricted by GIL.

How to bypass GIL restrictions?

Because GIL locks limit the ability to execute Python bytecode in parallel with multiple threads, developers usually use the following methods to bypass GIL:

Multi-process：

usemultiprocessingModules to create multiple processes, each with its own Python interpreter and GIL, so that it can run in parallel.
SuitableCPU intensive tasks, because different processes can really take advantage of the computing power of multi-core CPUs.

from multiprocessing import Process

def cpu_intensive_task():
    # Execute some intensive computing tasks    pass

if __name__ == '__main__':
    processes = [Process(target=cpu_intensive_task) for _ in range(4)]
    for p in processes:
        ()
    for p in processes:
        ()

Use C extensions or third-party libraries：

Some compute-intensive tasks can be optimized by using C extensions, because when running C code, GIL can be temporarily released and other threads can be executed in parallel.
NumPy、SciPyThe library uses the underlying C implementation and releases GIL if necessary, thereby improving performance.
CythonIt is also a common tool that can convert Python code into C code and release GIL in key parts.

Using asynchronous I/O：

For I/O-intensive tasks, Python'sasyncioFramework, improves performance through asynchronous programming, rather than relying on multi-threading or multi-processing.
Asynchronous programming allows other tasks to be performed while waiting for I/O operations, avoiding wasting CPU resources.

The Future of GIL

Remove GIL: For years, discussions about removing GIL have been around, but removing GIL completely will have a big impact on the existing CPython ecosystem, which is a very complex issue because of the need for thread-safe operations.
Other implementations: Some other implementations of Python, such asJython(Java-based Python implementation) andIronPython(Python implementation based on .NET), does not use GIL, so it can better support multi-threaded parallel execution.

Summarize

GILIt is a global lock in CPython, ensuring that only one thread is executing Python code at the same time.
It's rightI/O intensive tasksSmall impact, butCPU intensive tasksThe limitation is large and the multi-core CPU cannot be fully utilized.
To bypass GIL, multi-process, C extensions, third-party optimization libraries, or asynchronous I/O programming can be used.

The existence of GIL complicates Python's multi-threaded programming, but in many applications, rational design and selection of appropriate parallel mechanisms can still achieve good performance.

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