* [asyncio](AsyncIo) is useful for **I/O bound tasks**, not for **[CPU]() bound tasks**.
* [threading](PythonThreading) suffers from the [Global Interpreter Lock](GlobalInterpreterLock) aka [GIL]() preventing more than one thread executing **python** at the same time (multiple threads **can** execute C code in parallel).
* [multiprocessing](MultiprocessingModule) allows using separate processes.

## Multiprocessing Module
See [docs here](https://docs.python.org/3/library/multiprocessing.html):

> The multiprocessing module also introduces APIs which do not have analogs in the threading module. A prime example of this is the Pool object which offers a convenient means of parallelizing the execution of a function across multiple input values, distributing the input data across processes (data parallelism). The following example demonstrates the common practice of defining such functions in a module so that child processes can successfully import that module. This basic example of data parallelism using Pool,

```python
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]))
```
## Examples
```python
# TODO
```