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"""

List sort performance test.

To install `pyperf` you would need to:

python3 -m pip install pyperf

To run:

python3 Tools/scripts/sortperf

Options:

* `benchmark` name to run

* `--rnd-seed` to set random seed

* `--size` to set the sorted list size

Based on https://github.com/python/cpython/blob/963904335e579bfe39101adf3fd6a0cf705975ff/Lib/test/sortperf.py

"""

from __future__ import annotations

import argparse

import time

import random

# ===============

# Data generation

# ===============

def _random_data(size: int, rand: random.Random) -> list[float]:

result = [rand.random() for _ in range(size)]

# Shuffle it a bit...

for i in range(10):

i = rand.randrange(size)

temp = result[:i]

del result[:i]

temp.reverse()

result.extend(temp)

del temp

assert len(result) == size

return result

def list_sort(size: int, rand: random.Random) -> list[float]:

return _random_data(size, rand)

def list_sort_descending(size: int, rand: random.Random) -> list[float]:

return list(reversed(list_sort_ascending(size, rand)))

def list_sort_ascending(size: int, rand: random.Random) -> list[float]:

return sorted(_random_data(size, rand))

def list_sort_ascending_exchanged(size: int, rand: random.Random) -> list[float]:

result = list_sort_ascending(size, rand)

# Do 3 random exchanges.

for _ in range(3):

i1 = rand.randrange(size)

i2 = rand.randrange(size)

result[i1], result[i2] = result[i2], result[i1]

return result

def list_sort_ascending_random(size: int, rand: random.Random) -> list[float]:

assert size >= 10, "This benchmark requires size to be >= 10"

result = list_sort_ascending(size, rand)

# Replace the last 10 with random floats.

result[-10:] = [rand.random() for _ in range(10)]

return result

def list_sort_ascending_one_percent(size: int, rand: random.Random) -> list[float]:

result = list_sort_ascending(size, rand)

# Replace 1% of the elements at random.

for _ in range(size // 100):

result[rand.randrange(size)] = rand.random()

return result

def list_sort_duplicates(size: int, rand: random.Random) -> list[float]:

assert size >= 4

result = list_sort_ascending(4, rand)

# Arrange for lots of duplicates.

result = result * (size // 4)

# Force the elements to be distinct objects, else timings can be

# artificially low.

return list(map(abs, result))

def list_sort_equal(size: int, rand: random.Random) -> list[float]:

# All equal. Again, force the elements to be distinct objects.

return list(map(abs, [-0.519012] * size))

def list_sort_worst_case(size: int, rand: random.Random) -> list[float]:

# This one looks like [3, 2, 1, 0, 0, 1, 2, 3]. It was a bad case

# for an older implementation of quicksort, which used the median

# of the first, last and middle elements as the pivot.

half = size // 2

result = list(range(half - 1, -1, -1))

result.extend(range(half))

# Force to float, so that the timings are comparable. This is

# significantly faster if we leave them as ints.

return list(map(float, result))

# =========

# Benchmark

# =========

class Benchmark:

def __init__(self, name: str, size: int, seed: int) -> None:

self._name = name

self._size = size

self._seed = seed

self._random = random.Random(self._seed)

def run(self, loops: int) -> float:

all_data = self._prepare_data(loops)

start = time.perf_counter()

for data in all_data:

data.sort() # Benching this method!

return time.perf_counter() - start

def _prepare_data(self, loops: int) -> list[float]:

bench = BENCHMARKS[self._name]

data = bench(self._size, self._random)

return [data.copy() for _ in range(loops)]

def add_cmdline_args(cmd: list[str], args) -> None:

if args.benchmark:

cmd.append(args.benchmark)

cmd.append(f"--size={args.size}")

cmd.append(f"--rng-seed={args.rng_seed}")

def add_parser_args(parser: argparse.ArgumentParser) -> None:

parser.add_argument(

"benchmark",

choices=BENCHMARKS,

nargs="?",

help="Can be any of: {0}".format(", ".join(BENCHMARKS)),

)

parser.add_argument(

"--size",

type=int,

default=DEFAULT_SIZE,

help=f"Size of the lists to sort (default: {DEFAULT_SIZE})",

)

parser.add_argument(

"--rng-seed",

type=int,

default=DEFAULT_RANDOM_SEED,

help=f"Random number generator seed (default: {DEFAULT_RANDOM_SEED})",

)

DEFAULT_SIZE = 1 << 14

DEFAULT_RANDOM_SEED = 0

BENCHMARKS = {

"list_sort": list_sort,

"list_sort_descending": list_sort_descending,

"list_sort_ascending": list_sort_ascending,

"list_sort_ascending_exchanged": list_sort_ascending_exchanged,

"list_sort_ascending_random": list_sort_ascending_random,

"list_sort_ascending_one_percent": list_sort_ascending_one_percent,

"list_sort_duplicates": list_sort_duplicates,

"list_sort_equal": list_sort_equal,

"list_sort_worst_case": list_sort_worst_case,

}

if __name__ == "__main__":

# This needs `pyperf` 3rd party library:

import pyperf

runner = pyperf.Runner(add_cmdline_args=add_cmdline_args)

add_parser_args(runner.argparser)

args = runner.parse_args()

runner.metadata["description"] = "Test `list.sort()` with different data"

runner.metadata["list_sort_size"] = args.size

runner.metadata["list_sort_random_seed"] = args.rng_seed

if args.benchmark:

benchmarks = (args.benchmark,)

else:

benchmarks = sorted(BENCHMARKS)

for bench in benchmarks:

benchmark = Benchmark(bench, args.size, args.rng_seed)

runner.bench_time_func(bench, benchmark.run)

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