A simple Python benchmark

bench.py

from __future__ import print_function
from math import sin, cos, radians
import timeit

'''
A simple Python benchmark.

Results on an overclocked AMD FX-8150 Eight-Core CPU @ 3.0 GHz, and
an Intel Core i5-2410M CPU @ 2.30GHz.

$ python -OO bench.py
1.99843406677 2.00139904022 2.0145778656
2.38226699829 2.38675498962 2.38853287697

$ python3 -OO bench.py
2.2073315899979207 2.2098999509980786 2.222747125000751
2.273064840992447  2.274112678001984 2.2759074380010134

$ pypy -OO bench.py
0.245079994202 0.24707698822  0.247714996338
0.241708040237 0.242873907089 0.245008945465

$ pypy3 -OO bench.py
1.1291401386260986 1.1360960006713867 1.1375579833984375
1.2108190059661865 1.2172389030456543 1.2178328037261963

'''


def bench():
    product = 1.0
    for counter in range(1, 1000, 1):
        for dex in list(range(1, 360, 1)):
            angle = radians(dex)
            product *= sin(angle)**2 + cos(angle)**2
    return product

if __name__ == '__main__':
    result = timeit.repeat('bench.bench()', setup='import bench', number=10, repeat=10)
    result = list(sorted(result))
    print(*result[:3])

1.0594426930183545 1.062258291989565 1.0630121920257807
On Gitpod
CPU: AMD EPYC 7B13
Arch: x86_64
OS: Linux

Maybe the list() on line 33 produces variations that depend on the memory manager or system load?

The benchmark could use just the range(), or precompute the list.

...I did the optimization, and it had no effect on my numbers.

def bench():
    product = 1.0
    dexes = list(range(1, 360, 1))
    for counter in range(1, 1000, 1):
        for dex in dexes:
            angle = radians(dex)
            product *= sin(angle)**2 + cos(angle)**2
    return product

$ python3.10 -OO bench.py
1.2302762420004 1.2326733869995223 1.23436711600516

$ pypy3 -OO bench.py
0.1486122600035742 0.15070434800873045 0.15452972600178327

Comparing my old Intel MacBook to new Apple Silicon MacBook:

Both running on Python 3.9.12 in a fresh conda environment

Apple MacBook Pro 15" (2015) 2.5 GHz Quad-Core Intel Core i7 16GB Ram

$ python -OO bench.py
1.3066859839999996 1.315500633000001 1.3444327110000005

$ pypy -OO bench.py
0.13471456800016313 0.13493599199955497 0.1350344459997359

Jupyter %timeit function:
129 ms ± 615 µs per loop (mean ± std. dev. of 10 runs, 10 loops each)

Apple MacBook Pro 16" (2021) M1 Max 10-core 64GB RAM

$ python -OO bench.py
0.60054525 0.6010592090000002 0.6021369170000002

$ pypy -OO bench.py (pypy nightly build for Apple Silicon)
0.03519466705620289 0.035204792162403464 0.03520554187707603

Jupyter %timeit function:
60.7 ms ± 1.22 ms per loop (mean ± std. dev. of 10 runs, 10 loops each)

Acer Aspire A315, AMD A6 dual-core 2.9GHz, 8GB ddr4 ram, AMD R5 GPU 2GB gddr5, SSD SATA, Zorin os 16.1 pro (Linux kernel 5.15):
1.9391337739998562 1.939522442999987 1.9427987970002505

M1 Pro (2021)

Intel binaries through Rosetta 2

Python 3.10.8, osx-64 through Rosetta 2 (conda-forge)
1.580 1.589 1.591

Python 3.11.0, osx-64 through Rosetta 2 (conda-forge)
1.517 1.517 1.521

Python 3.11.2, osx-64 (binary from https://www.python.org/ftp/python/3.11.2/python-3.11.2-macos11.pkg)
1.302 1.386 1.299

Python 3.12.0a5, osx-64 through Rosetta 2 (binary from https://www.python.org/downloads/release/python-3120a5/)
1.326 1.295 1.270

Native binaries

Python 3.10.9, osx-arm64 (conda-forge)
0.762 0.762 0.769

Python 3.11.0, osx-arm64 (conda-forge)
0.637 0.637 0.637

Python 3.11.2, osx-arm64 (binary from https://www.python.org/ftp/python/3.11.2/python-3.11.2-macos11.pkg)
0.599 0.598 0.599

Python 3.12.0a5, osx-arm64 (binary from https://www.python.org/downloads/release/python-3120a5/)
0.602 0.602 0.602

Intel Pypy through Rosetta 2

pypy 3.9 (7.3.11), osx-64 through Rosetta 2 (conda-forge)
0.159 0.160 0.160

@corneliusroemer, Why not a Python 3.12.x test too?

Raspberry Pi 4B (4GB Ram): Python 3.9.2
pi17@rpi17:~ $ python3 bench.py
3.983417603000021 4.000531667000018 4.001489117999995
pi17@rpi17:~ $ pypy bench.py
0.395509004593 0.395509958267 0.395529985428

MacBook Pro 2015

$ python3.12 bench.py
1.1071506180014694 1.1081515140103875 1.1114070440089563

$ pypy3
Python 3.10.12 (af44d0b8114cb82c40a07bb9ee9c1ca8a1b3688c, Jun 25 2023, 12:41:21)
[PyPy 7.3.12 with GCC Apple LLVM 14.0.0 (clang-1400.0.29.202)] on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>>>

$ pypy3 -O bench.py
0.1369127530051628 0.13725044101011008 0.13727515301434323

Macbook Air M2 2023

$ python3.11 -O bench.py
0.47316745800344506 0.47342624999873806 0.4737870000026305

$ python3.12 -O bench.py
0.5029823330041836 0.5032062500031316 0.5033741669976735

$ pypy3 -O bench.py
0.03534275000129128 0.03537720799795352 0.0354161660070531

Raspberry Pi 5 Model B Rev 1.0

Official python docker image 3.13.0:

$ python
Python 3.13.0 (main, Oct  8 2024, 00:55:30) [GCC 13.2.1 20240309] on linux

$ docker run --rm -it -v $(pwd):/bench python:3.13-alpine python /bench/bench.py
1.379287590039894 1.3798810039879754 1.3804634480038658

Custom python 3.13.0 compiled with --enable-experimental-jit:

$ python
Python 3.13.0 (main, Oct  9 2024, 01:04:00) [GCC 14.2.0] on linux

$ docker run --rm -it -v $(pwd):/bench my_custom_python /root/.pyenv/versions/3.13.0/bin/python /bench/bench.py
1.1263249380281195 1.1263620959362015 1.1266266249585897

Not bad.

MacBook Air M2 2023

$ python3.13 --version
Python 3.13.1

$ python3.13 bench.py
0.5406068330048583 0.5409433339955285 0.5412480410013814

$ python3.13 -OO bench.py
0.5364589579985477 0.5369448330020532 0.5370732499868609