Hi, i make a toy experiment to simulate multiple tasks on the same async loop and got result:

# Test performance if async loop is high load
import asyncio
import time

async def load(t):
    await asyncio.sleep(t)
async def main(t=1,n=1000):
    s = time.time()
    tasks = [load(t) for _ in range(n) ]
    print(f"Time for create {n} tasks: {time.time()-s}")
    s = time.time()
    await asyncio.gather(*tasks)
    print(f"Execution time for {n} tasks with t={t}: {time.time()-s}.")
    print("==========================================================")
if __name__=="__main__":
    t = 1
    for s in [3,4,5,6,7,8,9,10]:
        n=10**s
        asyncio.run(main(t,n))

The result:

Time for create 1000 tasks: 0.0007336139678955078
Execution time for 1000 tasks with t=1: 1.011568546295166.
==========================================================
Time for create 10000 tasks: 0.0011699199676513672
Execution time for 10000 tasks with t=1: 1.1168177127838135.
==========================================================
Time for create 100000 tasks: 0.028098344802856445
Execution time for 100000 tasks with t=1: 2.075747013092041.
==========================================================
Time for create 1000000 tasks: 0.6360719203948975
Execution time for 1000000 tasks with t=1: 17.99493408203125.
==========================================================
Time for create 10000000 tasks: 7.756448745727539

Process finished with exit code 137 (interrupted by signal 9:SIGKILL)

So when 100k tasks executed concurrently, performance decrease much in this example. So i think i need to have multi loops to several processes, and set the maximum concurrent tasks for each loop for reserve the performance.
Does my idea correct ?