What should be done if performance limits are reached in the system?

If the system has reached performance limits, consider the following steps:

1. Profiling and bottleneck analysis — identify which parts of the code or infrastructure are causing slowdowns.
2. Algorithm and data structure optimization — replace inefficient solutions with faster ones.
3. Caching — use caching of computation results or queries to reduce load.
4. Parallelism and asynchronous processing — parallelize tasks, use multithreading or asynchronous calls.
5. Scaling — horizontal (adding servers) or vertical (increasing resources).
6. Using specialized tools — for example, databases with indexes, message queues.

In Python, you can, for example, use the multiprocessing module to parallelize tasks:

from multiprocessing import Pool

def f(x):
    return x*x

if __name__ == '__main__':
    with Pool(5) as p:
        print(p.map(f, [1, 2, 3]))

It is important to approach this comprehensively, combining code and infrastructure optimization.