The function on that slide is dominated by the call to rand, which uses quite different implementations in Julia and Python, so may not be the best example.
Julia is compiled and for simple code like that example code will have performance on par with C, Rust etc.
I tested how PyPy performs on that. Just changing the implementation of Python drops the runtime from ~16.5s to ~3.5s in my computer, approximately a 5x speedup:
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
I changed the code to take advantage of some basic performance tips that are commonly given for CPython (taking advantage of stardard library - itertools, math; prefer comprehensions/generator expressions to loose for loops), and was able to get CPython numbers improve by ~1.3x. But then PyPy numbers took a hit:
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
jakobnissen|3 months ago
Julia is compiled and for simple code like that example code will have performance on par with C, Rust etc.
Qem|3 months ago