How is memory accessed when working with multiple threads simultaneously?

When working with multiple threads in Python, memory access requires synchronization to avoid race conditions and data corruption.

Main mechanisms:

• GIL (Global Interpreter Lock): In the standard CPython interpreter, there is a global lock that allows only one thread to execute bytecode at a time. This simplifies memory management but limits parallelism in CPU-bound tasks.

• Mutexes and Locks: To protect shared resources and data, objects like threading.Lock, RLock, and others are used. They ensure that only one thread modifies data at a time.

• Queues: Thread-safe queues from the queue module are often used for data exchange between threads.

• multiprocessing module: To bypass GIL limitations, processes are used, which have separate memory.

Example of using a lock:

import threading

counter = 0
lock = threading.Lock()

def increment():
    global counter
    with lock:
        temp = counter
        temp += 1
        counter = temp

threads = [threading.Thread(target=increment) for _ in range(1000)]
for t in threads:
    t.start()
for t in threads:
    t.join()

print(counter)  # Expected 1000

Thus, memory access in multithreading in Python requires synchronization to work correctly with shared data.