The multiprocessing module indeed has some overhead:

- the processes are spawned when needed. Before you perform performance
timings, you should "warm up" the Pool with a line like
    pool.map(f, range(mul.cpu_count()))
(starting a process is a slowish operation specially on Windows)
This reduces timings by a factor of two.

- the dispatch overhead of multiprocessing is certainly greater than a
single multiplication. multiprocessing is for CPU-bound functions!
And do not forget that you have *tree* processes here: two from the
Pool, and your main program.

As Antoine said, try with this function instead:

def f(x):
    for i in range(10):
        x = x * x
    return x

And the timings are much better...