How is CPU load managed when using asynchronous operations?

When using asynchronous operations in Python, the CPU load is distributed differently than during synchronous execution:

• Asynchronous code typically uses an event loop that manages task execution without creating new threads or processes.
• During I/O operations (such as network requests or file reads), the CPU is not actively busy but switches to other tasks.
• This allows for efficient use of CPU time, minimizing idle periods.

However, if asynchronous tasks perform intensive computations, they can block the event loop and increase CPU load. In such cases, it is better to use separate threads or processes.

Example using asyncio:

import asyncio

async def fetch_data():
    await asyncio.sleep(1)  # simulate an asynchronous operation
    return 'data'

async def main():
    result = await fetch_data()
    print(result)

asyncio.run(main())

Here, the CPU is only busy during the execution of the code, and during await asyncio.sleep(1), it is free for other tasks.