mirror of
https://github.com/RustPython/RustPython.git
synced 2026-10-29 03:04:38 +08:00
Lee Dogeon
99a63f92aa
Add dependency-free CPU-bound workloads adapted from pyperformance 1.14.0 to cover interpreter, standard-library serialization, regex, numeric, and garbage-collection paths under the existing Criterion and CodSpeed harnesses. Keep inputs deterministic and scale expensive cases for simulation while checking that benchmark execution does not leak interpreter state. Assisted-by: Codex:gpt-5.6-sol
159 lines
4.6 KiB
Python
159 lines
4.6 KiB
Python
# Adapted from python/pyperformance 1.14.0 (regex_effbot).
|
|
|
|
"""Benchmarks for Python's regex engine.
|
|
|
|
These are some of the original benchmarks used to tune Python's regex engine
|
|
in 2000 written by Fredrik Lundh. Retreived from
|
|
http://mail.python.org/pipermail/python-dev/2000-August/007797.html and
|
|
integrated into Unladen Swallow's pyperf.py in 2009 by David Laing.
|
|
|
|
These benchmarks are of interest since they helped to guide the original
|
|
optimization of the sre engine, and we shouldn't necessarily ignore them just
|
|
because they're "old".
|
|
"""
|
|
|
|
# Python imports
|
|
import re
|
|
|
|
# Local imports
|
|
USE_BYTES = False
|
|
|
|
|
|
def re_compile(s):
|
|
if USE_BYTES:
|
|
return re.compile(s.encode('latin1'))
|
|
else:
|
|
return re.compile(s)
|
|
|
|
# These are the regular expressions to be tested. These sync up,
|
|
# index-for-index with the list of strings generated by gen_string_table()
|
|
# below.
|
|
|
|
|
|
def gen_regex_table():
|
|
return [
|
|
re_compile('Python|Perl'),
|
|
re_compile('Python|Perl'),
|
|
re_compile('(Python|Perl)'),
|
|
re_compile('(?:Python|Perl)'),
|
|
re_compile('Python'),
|
|
re_compile('Python'),
|
|
re_compile('.*Python'),
|
|
re_compile('.*Python.*'),
|
|
re_compile('.*(Python)'),
|
|
re_compile('.*(?:Python)'),
|
|
re_compile('Python|Perl|Tcl'),
|
|
re_compile('Python|Perl|Tcl'),
|
|
re_compile('(Python|Perl|Tcl)'),
|
|
re_compile('(?:Python|Perl|Tcl)'),
|
|
re_compile('(Python)\\1'),
|
|
re_compile('(Python)\\1'),
|
|
re_compile('([0a-z][a-z0-9]*,)+'),
|
|
re_compile('(?:[0a-z][a-z0-9]*,)+'),
|
|
re_compile('([a-z][a-z0-9]*,)+'),
|
|
re_compile('(?:[a-z][a-z0-9]*,)+'),
|
|
re_compile('.*P.*y.*t.*h.*o.*n.*')]
|
|
|
|
|
|
def gen_string_table(n):
|
|
"""Generates the list of strings that will be used in the benchmarks.
|
|
|
|
All strings have repeated prefixes and suffices, and n specifies the
|
|
number of repetitions.
|
|
"""
|
|
strings = []
|
|
|
|
def append(s):
|
|
if USE_BYTES:
|
|
strings.append(s.encode('latin1'))
|
|
else:
|
|
strings.append(s)
|
|
append('-' * n + 'Perl' + '-' * n)
|
|
append('P' * n + 'Perl' + 'P' * n)
|
|
append('-' * n + 'Perl' + '-' * n)
|
|
append('-' * n + 'Perl' + '-' * n)
|
|
append('-' * n + 'Python' + '-' * n)
|
|
append('P' * n + 'Python' + 'P' * n)
|
|
append('-' * n + 'Python' + '-' * n)
|
|
append('-' * n + 'Python' + '-' * n)
|
|
append('-' * n + 'Python' + '-' * n)
|
|
append('-' * n + 'Python' + '-' * n)
|
|
append('-' * n + 'Perl' + '-' * n)
|
|
append('P' * n + 'Perl' + 'P' * n)
|
|
append('-' * n + 'Perl' + '-' * n)
|
|
append('-' * n + 'Perl' + '-' * n)
|
|
append('-' * n + 'PythonPython' + '-' * n)
|
|
append('P' * n + 'PythonPython' + 'P' * n)
|
|
append('-' * n + 'a5,b7,c9,' + '-' * n)
|
|
append('-' * n + 'a5,b7,c9,' + '-' * n)
|
|
append('-' * n + 'a5,b7,c9,' + '-' * n)
|
|
append('-' * n + 'a5,b7,c9,' + '-' * n)
|
|
append('-' * n + 'Python' + '-' * n)
|
|
return strings
|
|
|
|
|
|
def init_benchmarks(n_values=None):
|
|
"""Initialize the strings we'll run the regexes against.
|
|
|
|
The strings used in the benchmark are prefixed and suffixed by
|
|
strings that are repeated n times.
|
|
|
|
The sequence n_values contains the values for n.
|
|
If n_values is None the values of n from the original benchmark
|
|
are used.
|
|
|
|
The generated list of strings is cached in the string_tables
|
|
variable, which is indexed by n.
|
|
|
|
Returns:
|
|
A list of string prefix/suffix lengths.
|
|
"""
|
|
|
|
if n_values is None:
|
|
n_values = (0, 5, 50, 250, 1000, 5000, 10000)
|
|
|
|
string_tables = {n: gen_string_table(n) for n in n_values}
|
|
regexs = gen_regex_table()
|
|
|
|
data = []
|
|
for n in n_values:
|
|
for id in range(len(regexs)):
|
|
regex = regexs[id]
|
|
string = string_tables[n][id]
|
|
data.append((regex, string))
|
|
return data
|
|
|
|
|
|
def bench_regex_effbot(loops):
|
|
if bench_regex_effbot.data is None:
|
|
bench_regex_effbot.data = init_benchmarks()
|
|
data = bench_regex_effbot.data
|
|
|
|
range_it = range(loops)
|
|
search = re.search
|
|
|
|
for _ in range_it:
|
|
# Runs all of the benchmarks for a given value of n.
|
|
for regex, string in data:
|
|
# search 10 times
|
|
search(regex, string)
|
|
search(regex, string)
|
|
search(regex, string)
|
|
search(regex, string)
|
|
search(regex, string)
|
|
search(regex, string)
|
|
search(regex, string)
|
|
search(regex, string)
|
|
search(regex, string)
|
|
search(regex, string)
|
|
|
|
# cached data, generated at the first call
|
|
bench_regex_effbot.data = None
|
|
|
|
|
|
def add_cmdline_args(cmd, args):
|
|
if args.force_bytes:
|
|
cmd.append("--force_bytes")
|
|
|
|
bench_regex_effbot(1)
|