What limitations exist in asynchronous programming when performing CPU-intensive tasks?

Asynchronous programming in Python is well-suited for tasks with a large number of input/output operations, but has limitations when executing tasks that heavily utilize processor resources:

• GIL (Global Interpreter Lock): In the standard implementation of Python (CPython), there is a global interpreter lock that prevents multiple threads from executing Python bytecode simultaneously. This limits parallelism in compute-intensive tasks.

• Asynchrony does not speed up CPU-bound tasks: Asynchronous code is effective for I/O-bound tasks, but for CPU-bound tasks, it does not provide a performance boost, as computations block the execution thread.

• Need for multiprocessing: For intensive computations, it is better to use multiprocessing or external libraries that bypass GIL.

Thus, asynchronous programming does not replace multithreading or multiprocessing for tasks with high CPU load.