benchmarks python's build-in multiprocessing. uses all the threads available in the system.

benchmark_python_multiprocessing.py

import multiprocessing as mp
import timeit

threads=mp.cpu_count()
print(f"Number of threads: {threads}")
vlist=range(15000) # takes about 5 seconds to run serially
def f(v):
    return sum([v_ for v_ in range(v)])
start_pool = timeit.default_timer()
with mp.Pool(threads) as p:
    vals_pool = p.map(f, vlist)
stop_pool = timeit.default_timer()
start_serial = timeit.default_timer()
vals_serial = [f(v) for v in vlist]
stop_serial = timeit.default_timer()

assert all([v1==v2 for v1,v2 in zip(vals_serial, vals_pool)])

print(f"Pool  : {stop_pool-start_pool:.5f} s (time taken)")
print(f"Serial: {stop_serial-start_serial:.5f} s (time taken)")
print(f"Speed up from multiprocessing: {(stop_serial-start_serial)/(stop_pool-start_pool):.5f} X")

possible result:

#Number of threads: 56
#Pool  : 0.55056 s (time taken)
#Serial: 4.74138 s (time taken)
#Speed up from multiprocessing: 8.61188 X

note that the 56 threads does not mean 56X speed up, this is due to message passing overhead.
also, the variation of the speed up may be large (than expected), so multiple runs may be needed.