Performance regression for loops in 3.12 vs 3.11

Description

I believe I've found a performance regression for large loops in Python 3.12 vs. 3.11. This effect is more pronounced when the value being stored is non-constant – with the list comprehension changed to [0 for x in range(chunk_size)], the relative difference dropped to 1.03x (still in favor of 3.11). Additionally, if the number of rows being generated is small, e.g. 1000, the difference disappears, and both versions are dead even. The results shown below were with 5,000,000 rows.

Interestingly, the array.array() performance difference was massive, but only on MacOS. On Linux, it was approximately the same as with a list. I'm not sure if this is due to the Linux installations being compiled with optimizations, hardware differences, OS differences, etc.

Environment

• macOS Sonoma 14.6.1 on Apple Air M1
• Debian Bullseye 12 5.10.0-32-amd64 on Xeon E5-2650 v2
• Python 3.11.9, 3.12.5
  • Installed via Homebrew on Mac
  • Built from source on Linux with --enable-optimizations --with-lto=full --with-pkg-config=yes

Results

Mac

List

❯ hyperfine -w 20 -r 100 "python3.11 test_loops.py --num-rows 5000000" "python3.12 test_loops.py --num-rows 5000000"
Benchmark 1: python3.11 test_loops.py --num-rows 5000000
  Time (mean ± σ):     136.3 ms ±   1.3 ms    [User: 109.2 ms, System: 25.8 ms]
  Range (min … max):   133.7 ms … 142.3 ms    100 runs

Benchmark 2: python3.12 test_loops.py --num-rows 5000000
  Time (mean ± σ):     144.3 ms ±   4.2 ms    [User: 119.1 ms, System: 23.7 ms]
  Range (min … max):   138.6 ms … 170.5 ms    100 runs

Summary
  python3.11 test_loops.py --num-rows 5000000 ran
    1.06 ± 0.03 times faster than python3.12 test_loops.py --num-rows 5000000

Array

❯ hyperfine -w 20 -r 100 "python3.11 test_loops.py --num-rows 5000000" "python3.12 test_loops.py --num-rows 5000000"
Benchmark 1: python3.11 test_loops.py --num-rows 5000000
  Time (mean ± σ):     176.5 ms ±   1.3 ms    [User: 169.8 ms, System: 5.4 ms]
  Range (min … max):   174.7 ms … 185.0 ms    100 runs

Benchmark 2: python3.12 test_loops.py --num-rows 5000000
  Time (mean ± σ):     277.4 ms ±   1.6 ms    [User: 270.7 ms, System: 5.4 ms]
  Range (min … max):   274.2 ms … 283.9 ms    100 runs

Summary
  python3.11 test_loops.py --num-rows 5000000 ran
    1.57 ± 0.01 times faster than python3.12 test_loops.py --num-rows 5000000

Linux

List

❯ hyperfine -w 20 -r 100 "python3.11 test_loops.py --num-rows 5000000" "python3.12 test_loops.py --num-rows 5000000"
Benchmark 1: python3.11 test_loops.py --num-rows 5000000
  Time (mean ± σ):     378.5 ms ±  22.1 ms    [User: 260.1 ms, System: 118.3 ms]
  Range (min … max):   356.1 ms … 484.0 ms    100 runs

Benchmark 2: python3.12 test_loops.py --num-rows 5000000
  Time (mean ± σ):     405.0 ms ±  27.1 ms    [User: 283.1 ms, System: 121.9 ms]
  Range (min … max):   379.2 ms … 527.1 ms    100 runs

Summary
  python3.11 test_loops.py --num-rows 5000000 ran
    1.07 ± 0.10 times faster than python3.12 test_loops.py --num-rows 5000000

Array

❯ hyperfine -w 20 -r 100 "python3.11 test_loops.py --num-rows 5000000" "python3.12 test_loops.py --num-rows 5000000"

Benchmark 1: python3.11 test_loops.py --num-rows 5000000
  Time (mean ± σ):     387.4 ms ±  26.1 ms    [User: 263.8 ms, System: 123.5 ms]
  Range (min … max):   356.0 ms … 478.5 ms    100 runs

Benchmark 2: python3.12 test_loops.py --num-rows 5000000
  Time (mean ± σ):     408.3 ms ±  27.9 ms    [User: 284.6 ms, System: 123.6 ms]
  Range (min … max):   371.0 ms … 540.5 ms    100 runs

Summary
  python3.11 test_loops.py --num-rows 5000000 ran
    1.05 ± 0.10 times faster than python3.12 test_loops.py --num-rows 5000000

Code

from array import array
import argparse
from typing import Iterable, List


def get_args() -> argparse.Namespace:
    parser = argparse.ArgumentParser()
    parser.add_argument("--chunk-size", type=int, default=250_000)
    parser.add_argument("--num-rows", type=int, default=1_000_000)

    return parser.parse_args()


def generate(num_rows: int, chunk_size: int) -> Iterable[List]:
    for i in range(0, num_rows, chunk_size):
        chunk_size = min(chunk_size, num_rows - i)

        yield generate_chunk(chunk_size)


def generate_chunk(chunk_size: int):
    return [x for x in range(chunk_size)]
    # alternate return type for testing
    # return array("I", (x for x in range(chunk_size)))


if __name__ == "__main__":
    args = get_args()

    # optionally remove the list creation and discard the results
    lst: List = []

    for chunks in generate(
        num_rows=args.num_rows,
        chunk_size=args.chunk_size,
    ):
        # optional if discarding results of generator
        # pass
        lst.append(chunks)

Linked Issues:

• https://github.com/faster-cpython/ideas/discussions/319

Inizia con lo script di benchmark Python incorporato e riproduci i tempi di Python 3.11 rispetto a 3.12 negli ambienti macOS e Linux indicati, includendo sia i casi con liste sia quelli con array. Leggi la discussione faster-cpython collegata per il contesto; il lavoro è completo quando la regressione è isolata e la sua causa o risoluzione è documentata.