Python GIL (Global Interpreter Lock): Uncovering the principles and performance impact behind it

python GIL (Global Interpreter Lock) is an important mechanism in Python, which limits the same There can only be one thread executing Python bytecode at a time. This is mainly to ensure the stability of the Python interpreter, because Python's memory management and garbage collection mechanisms are single-threaded. If multiple threads are allowed to execute Python bytecode at the same time, it is possible to cause memory corruption or other unpredictable errors.

The principle of GIL is relatively simple. It is a lock maintained by the Python interpreter, and when a thread executes Python bytecode, it acquires the GIL. If other threads want to execute Python bytecode, they must wait for the GIL to be released. When the GIL is released, other threads can obtain the GIL and execute Python bytecode.

The existence of GIL has a great impact on Python's

multithreading performance. Due to GIL limitations, only one thread can execute Python bytecode at the same time, so the advantages of multi-core CPUs cannot be fully utilized. Especially when there are a large number of I/O operations in Python code, since I/O operations usually block the process, causing the GIL to be released, other threads can execute Python bytecode, so the performance improvement of multi-threading will be obvious.

In order to overcome the limitations of GIL, the following methods can be used:

• Use multiple processes. Multi-process is a more lightweight concurrency mechanism that allows multiple processes to perform different tasks at the same time. Since processes are independent of each other, there are no GIL restrictions. However, the creation and destruction overhead of multi-process is greater than that of multi-thread, so it is only suitable for processing some relatively independent tasks.
• Use coroutines. Coroutines are a lightweight concurrency mechanism that allow multiple tasks to execute alternately. The switching overhead of coroutines is much smaller than that of threads, so it can better take advantage of multi-core CPUs. However, the programming model of coroutines is different from the traditional thread programming model, so a certain learning cost is required.
• Use Cython. Cython is a tool that can compile Python code into C code. C code can be executed in multiple threads, so using Cython can bypass GIL restrictions. However, the use of Cython requires a certain foundation in C language programming and a certain understanding of the underlying principles of Python Virtual Machine.

To sum up, Python GIL is an important mechanism in Python, which has a great impact on Python's multi-threaded performance. You can improve Python's multi-threaded performance by overcoming the limitations of the GIL by using methods such as multi-processing, coroutines, or Cython.

The above is the detailed content of Python GIL (Global Interpreter Lock): Uncovering the principles and performance impact behind it. For more information, please follow other related articles on the PHP Chinese website!