Concurrency and parallel programming in Python

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    Concurrency and parallelism are important concepts in modern computing, and Python provides a number of tools for writing concurrent and parallel programs. Here's an overview of how to write concurrent and parallel programs in Python:

    Threads

    Threads are a lightweight way to achieve concurrency in Python. Python provides a built-in threading module that can be used to create and manage threads. Here's an example of how to create a thread:

    import threading
def print_numbers():
for i in range(1, 11):
print(i)
t = threading.Thread(target=print_numbers)
t.start()

    Processes

    Processes are a way to achieve true parallelism in Python, since each process runs in a separate memory space. Python provides a built-in multiprocessing module that can be used to create and manage processes. Here's an example of how to create a process:

    import multiprocessing
def print_numbers():
for i in range(1, 11):
print(i)

p = multiprocessing.Process(target=print_numbers)
p.start()

    Asyncio

    Asyncio is a way to achieve concurrency in Python through cooperative multitasking. Asyncio provides a built-in asyncio module that can be used to create and manage coroutines. In contrast to threads and processes, where the operating system schedules and switches between tasks, coroutines are cooperatively scheduled, meaning that they must explicitly give up control of the event loop to other coroutines. The core component of asyncio is the event loop, which is responsible for scheduling and executing coroutines. Coroutines are executed within an event loop and are managed by it.

    Here's an example of how to create a coroutine:

    import asyncio

async def print_numbers():
    for i in range(1, 11):
        print(i)
        await asyncio.sleep(1)

asyncio.run(print_numbers())

    In this example, the coroutine print_numbers is created with the async keyword and defined with the async def syntax. The await keyword is used to explicitly give up control of the event loop to other coroutines. The asyncio.sleep() function is used to simulate a blocking operation, during which other coroutines can execute.

    The event loop is responsible for scheduling and executing coroutines. When a coroutine is scheduled to run, it continues executing until it reaches an await statement. At that point, the coroutine gives up control of the event loop, allowing other coroutines to run. When the blocking operation is complete, the event loop resumes the original coroutine from where it left off.

    Finally, the asyncio.run() function is used to run the coroutine to completion. This function creates a new event loop, runs the coroutine until completion, and then closes the event loop.

    Conclusion

    Python provides a number of tools for writing concurrent and parallel programs, including threads, processes, and asyncio. By understanding these tools and when to use them, you can write efficient and scalable programs that take full advantage of modern computing architectures.