GIL hound: hunting down bottlenecks in concurrent Python

GIL: Bottlenecks in Concurrency Python

GIL (Global Interpreter Lock) is a mechanism in python that ensures that only one thread can execute bytecode at the same time. This is crucial in ensuring the thread safety of the Python interpreter, but it also limits the concurrency of a multithreaded program, Especially when it comes to computationally intensive tasks.

How GIL works

GIL works by controlling access to Python objects. When a thread acquires the GIL, it prevents all other threads from accessing any Python objects, including global and local variables, classes, and functions. This ensures that the Python interpreter does not cause race conditions due to simultaneous manipulation of the same object.

Impact of GIL

The GIL has the following effects on concurrent Python programs:

• Poor multi-threading performance: The GIL limits the parallelism of multi-threaded programs because only one thread can execute Python code at the same time. This makes multithreading almost useless for CPU-intensive tasks.
• Deadlock: The GIL may cause deadlock because the thread holding the GIL may wait for another thread to release a lock it holds.
• Increased overhead: The acquisition and release of GIL will increase the overhead of the program, which is especially important for programs that frequently switch threads.

Overcoming GIL limitations

Despite these limitations, there are several strategies that can be used to overcome the limitations of the GIL:

1. Multi-process:

Multiple processes create multiple instances of the Python interpreter, each with its own GIL. This removes the limitations of the GIL between processes, allowing true parallel processing. However, using multiple processes requires careful handling of data sharing and inter-process communication.

Code example:

import multiprocessing

def worker(num):
# 执行密集计算任务
return num * num

if __name__ == "__main__":
pool = multiprocessing.Pool(4)# 创建具有 4 个进程的进程池
results = pool.map(worker, range(1000000))
pool.close()
pool.join()

2. CPython extension:

GIL is implemented by CPython, the standard interpreter for Python. The GIL can be bypassed by writing a

C/C extension to interact directly with the underlying operating system. This requires a higher level of programming skills, but can significantly improve concurrency performance.

Code example:

#include <Python.h>

PyObject *my_function(PyObject *self, PyObject *args) {
// 执行密集计算任务，无需 GIL 保护
// ...

Py_INCREF(Py_None);
return Py_None;
}

static PyMethodDef my_methods[] = {
{"my_function", my_function, METH_VARARGS, "My function"},
{NULL, NULL, 0, NULL}
};

PyMODINIT_FUNC initmymodule(void) {
Py_InitModule("mymodule", my_methods);
}

3. GIL release:

The GIL is optional and can be released under certain circumstances. The GIL can be released temporarily by using the

with statement or by calling the sys.settrace() function. This allows other threads to acquire the GIL and perform tasks during release.

Code example:

import sys

def worker():
# 执行密集计算任务
pass

if __name__ == "__main__":
sys.settrace(None)# 禁用追踪函数，释放 GIL
threads = []
for _ in range(4):
threads.append(threading.Thread(target=worker))
for thread in threads:
thread.start()
for thread in threads:
thread.join()

in conclusion

The GIL is an important consideration for

concurrent programming in Python. By understanding how it works and its impact, and applying appropriate strategies to overcome its limitations, you can improve the concurrency performance of your Python programs and reduce bottlenecks. As computer hardware continues to evolve, the limitations of the GIL are likely to become more apparent, so it is critical to explore and adopt these techniques to maximize the performance of your Python programs.

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