cpython/Tools/scripts/sortperf.py at 3.15 · python/cpython · GitHub

Repository navigation

History

100

101

102

103

104

105

106

107

108

109

110

111

112

113

114

115

116

117

118

119

120

121

122

123

124

125

126

127

128

129

130

131

132

133

134

135

136

137

138

139

140

141

142

143

144

145

146

147

148

149

150

151

152

153

154

155

156

157

158

159

160

161

162

163

164

165

166

167

168

169

170

171

172

173

174

175

176

177

178

179

180

181

182

183

184

185

186

187

188

189

190

191

192

193

194

195

196

197

"""

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)

cpython/Tools/scripts/sortperf.py at 3.15 · python/cpython · GitHub

Repository navigation

Expand file tree

Breadcrumbs

sortperf.py

Latest commit

History

Breadcrumbs

sortperf.py

File metadata and controls