Read Python GIL in one article: making multi-threaded programming easier

python The GIL (Global Interpreter Lock) is a mechanism that allows only one thread to execute at the same time Python Bytecode. This helps ensure that the Python interpreter does not have problems in a multithreaded environment, but it also means that multithreaded Python programs cannot truly execute in parallel.

GIL is a very important concept because it has a great impact on Python's multi-threaded performance. If a Python program uses multiple threads, the GIL prevents these threads from truly executing in parallel. This means that even if a Python program has multiple threads, it can only execute one thread at a time.

GIL exists for several reasons. First, it prevents multiple threads from accessing the same Python object simultaneously, causing data corruption. Second, it simplifies the implementation of the Python interpreter. If the Python interpreter didn't have to deal with multi-threading

concurrency, then its implementation would be simpler.

Although the GIL will prevent multi-threaded Python programs from truly being executed in parallel, this does not mean that multi-threaded Python programs are useless. In some cases, using multi-threaded Python programs can still improve the performance of the program. For example, if a Python program needs to perform a lot of I/O operations, using multiple threads can improve the performance of the program. This is because I/O operations are usually blocking, which means that while one thread is performing an I/O operation, other threads can continue executing.

The following is an example of a Python program using multi-threading:

import threading

def worker():
# Do some work

threads = []
for i in range(10):
thread = threading.Thread(target=worker)
threads.append(thread)
thread.start()

for thread in threads:
thread.join()

This program creates 10 threads and then starts these threads. Each thread will execute the same function

worker(). Function worker() performs some work and returns. The main thread waits for all threads to finish executing before continuing.

This program can execute 10 threads in parallel, but due to the existence of GIL, these threads cannot truly execute in parallel. This means that even if this program has multiple threads, it can only execute one thread at a time.

If you want to solve the GIL problem, you can use the following methods:

Use multi-process instead of multi-thread. Multiple processes are not affected by the GIL and can therefore be executed in true parallelism.
Use GIL-less Python implementation such as Cython or PyPy. These implementations do not use the GIL and therefore can execute truly in parallel.
Use the GIL with care. For example, avoid performing long-running operations while the GIL is held.

In general, GIL is a very important concept because it has a great impact on Python's multi-threaded performance. If a Python program uses multiple threads, the GIL prevents these threads from truly executing in parallel. This means that even if a Python program has multiple threads, it can only execute one thread at a time.

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