5 ms·

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-sli

• jakobnissen 11mo ago
  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.
  • Qem 10mo ago
    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
    • dragonwriter 10mo ago
      I tested staying in CPython but jitting the main function with numba (no code changes but adding the jit decorator and expected type signature, and adding the same jit warmup call before the benchmark that the Julia version uses), and its about an 11× speedup. Code: 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}") Base version (using the unmodified Python code from the slide): $ python -m monte Time: 13.758 seconds Estimated pi: 3.14159524 Numba version: $ python -m monte-numba Time: 1.212 seconds Estimated pi: 3.14143924