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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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