GIL Gallows Survivor: The Impossible Journey of Concurrent Python

GIL (Global Interpreter Lock) is the core component of the python interpreter, which ensures that there is only one thread# at the same time ##ExecutionPython bytecode. While the GIL provides thread safety, it also limits Python's potential for concurrent programming because threads can only execute serially. To overcome the limitations of the GIL, various techniques have emerged to circumvent its locking and achieve

concurrency

. These technologies include:

Multithreading:

Multi-threading

is a technology that uses multiple CPU threads to execute code in parallel. In Python, threads can be created and managed using the threading module. However, the GIL limits each thread's ability to execute Python code simultaneously. <pre class="brush:python;toolbar:false;">import threading def task(): # 执行耗时的操作 threads = [] for i in range(4): thread = threading.Thread(target=task) threads.append(thread) thread.start() for thread in threads: thread.join()</pre> This code creates 4 threads, but due to the GIL, they cannot execute the

task()

function at the same time.

multi-Progress:

Multiple processes is a technology that uses multiple

operating system

processes to execute code in parallel. In Python, processes can be created and managed using the multiprocessing module. Unlike threads, processes have their own Python interpreter and are therefore not restricted by the GIL. <pre class="brush:python;toolbar:false;">import multiprocessing def task(): # 执行耗时的操作 processes = [] for i in range(4): process = multiprocessing.Process(target=task) processes.append(process) process.start() for process in processes: process.join()</pre> This code creates 4 processes, and they can run the

task()

function on different CPU cores at the same time without being restricted by the GIL.

GIL Lift:

GIL Release

Tools

Allows Python code to temporarily release the GIL, allowing other threads or processes to execute Python code. This can be achieved by using ThreadPoolExecutor or ProcessPoolExecutor from the concurrent.futures module. <pre class="brush:python;toolbar:false;">from concurrent.futures import ThreadPoolExecutor def task(): # 执行耗时的操作 with ThreadPoolExecutor(max_workers=4) as executor: executor.submit(task)# 提交任务到线程池</pre> This code uses the thread pool to execute the

task()

function, while the main thread can continue to perform other tasks.

in conclusion:

Although the GIL limits Python's native concurrency, by leveraging multithreading, multiprocessing, and GIL unwinding techniques,

developers

can circumvent its locks and take advantage of Python's full concurrency potential. These techniques enable Python to perform parallel tasks, thereby improving application performance and scalability.

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