What methods can be used to prevent race conditions in multithreaded programming?

To prevent data races in multithreaded programming, the following methods are used:

• Synchronization (locks, mutexes) — locking access to shared resources so that only one thread can modify them at a time.

• Using atomic operations — operations that are executed entirely without interruption, for example, using the threading or multiprocessing modules in Python.

• Using thread-safe data structures — for example, queues from the queue module.

• Avoiding shared state — designing the program so that threads do not share mutable data.

• Using high-level synchronization primitives — events, semaphores, barriers.

Example using locking in Python:

import threading

lock = threading.Lock()
shared_resource = 0

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

threads = [threading.Thread(target=increment) for _ in range(100)]
for t in threads:
    t.start()
for t in threads:
    t.join()

print(shared_resource)  # Expected 100

Thus, proper management of access to shared data prevents races and ensures correct operation.