There are reasons why the same program in Julia can be 60x faster than in Python, see e.g. slide 5 in https://www.cl.cam.ac.uk/teaching/2526/TeX+Julia/julia-slide... for an example.
Julia is compiled and for simple code like that example code will have performance on par with C, Rust etc.
xxxx@xxxx:~
$ python3 -VV
Python 3.11.2 (main, Apr 28 2025, 14:11:48) [GCC 12.2.0]
xxxx@xxxx:~
$ pypy3 -VV
Python 3.9.16 (7.3.11+dfsg-2+deb12u3, Dec 30 2024, 22:36:23)
[PyPy 7.3.11 with GCC 12.2.0]
xxxx@xxxx:~
$ cat original_benchmark.py
#-------------------------------------------
import random
import time
def monte_carlo_pi(n):
inside = 0
for i in range(n):
x = random.random()
y = random.random()
if x**2 + y**2 <= 1.0:
inside += 1
return 4.0 * inside / n
# Benchmark
start = time.time()
result = monte_carlo_pi(100_000_000)
elapsed = time.time() - start
print(f"Time: {elapsed:.3f} seconds")
print(f"Estimated pi: {result}")
#-------------------------------------------
xxxx@xxxx:~
$ python3 original_benchmark.py
Time: 16.487 seconds
Estimated pi: 3.14177012
xxxx@xxxx:~
$ pypy3 original_benchmark.py
Time: 3.357 seconds
Estimated pi: 3.14166756
xxxx@xxxx:~
$ python3 -c "print(round(16.487/3.357, 1))"
4.9
xxxx@xxxx:~
$ cat mod_benchmark.py
#-------------------------------------------
from itertools import repeat
from math import hypot
from random import random
import time
def monte_carlo_pi(n):
inside = sum(hypot(random(), random()) <= 1.0 for i in repeat(None, n))
return 4.0 * inside / n
# Benchmark
start = time.time()
result = monte_carlo_pi(100_000_000)
elapsed = time.time() - start
print(f"Time: {elapsed:.3f} seconds")
print(f"Estimated pi: {result}")
#-------------------------------------------
xxxx@xxxx:~
$ python3 mod_benchmark.py
Time: 12.998 seconds
Estimated pi: 3.14149268
xxxx@xxxx:~
$ pypy3 mod_benchmark.py
Time: 12.684 seconds
Estimated pi: 3.14160844
xxxx@xxxx:~
$ python3 -c "print(round(16.487/12.684, 1))"
1.3 import random
import time
from numba import jit, int32, float64
@jit(float64(int32), nopython=True)
def monte_carlo_pi(n):
inside = 0
for i in range(n):
x = random.random()
y = random.random()
if x**2 + y**2 <= 1.0:
inside += 1
return 4.0 * inside / n
# Warm up (compile)
monte_carlo_pi(100)
# Benchmark
start = time.time()
result = monte_carlo_pi(100_000_000)
elapsed = time.time() - start
print(f"Time: {elapsed:.3f} seconds")
print(f"Estimated pi: {result}")
$ python -m monte
Time: 13.758 seconds
Estimated pi: 3.14159524
$ python -m monte-numba
Time: 1.212 seconds
Estimated pi: 3.14143924