bpo-35094: Improved algorithms for random.sample

[ciphergoth]

Current algorithms for random.sample allocate considerable auxiliary memory to track what's been used so far; with this pull request we sample in a maximally memory efficient way. Peformance is similar or in some cases faster. See also https://github.com/ciphergoth/sansreplace

https://bugs.python.org/issue35094

[the-knights-who-say-ni]

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[pablogsal]

You can reduce one indentation level with an early continue:

if r >= k:
   continue
if i < k:
    result[i] = result[r]
result[r] = item

[ciphergoth]

Fixed - thanks!

[ciphergoth]

BTW CLA signed at 10:52 Pacific time this morning, just waiting for the systems to catch up!

[rhettinger]

For future reference, here are my notes from evaluating the PR:

Call count summary

k	Baseline	Patched
10	10	20
20	20	40
100	100	200
200	200	400
1,000	1,000	2,000
2,000	2,000	4,000
5,001	5,001	10,000
7,500	5,001	10,000
8,500	8,500	10,000
9,500	9,500	10,000

Test Code

from unittest.mock import Mock
from random import Random
import sys

prng = Random()
prng._randbelow = Mock(wraps=prng._randbelow)

n, k = map(int, sys.argv[1:3])
prng.sample(range(n), k)
rbc = prng._randbelow.call_count
print(f'randbelow_calls={rbc} <-- n={n} k={k}')

Results with the PR Applied

$ python3.8 randombelow_counter.py 10000 10
randbelow_calls=20 <-- n=10000 k=10
$ python3.8 randombelow_counter.py 10000 20
randbelow_calls=40 <-- n=10000 k=20
$ python3.8 randombelow_counter.py 10000 100
randbelow_calls=200 <-- n=10000 k=100
$ python3.8 randombelow_counter.py 10000 200
randbelow_calls=400 <-- n=10000 k=200
$ python3.8 randombelow_counter.py 10000 1000
randbelow_calls=2000 <-- n=10000 k=1000
$ python3.8 randombelow_counter.py 10000 2000
randbelow_calls=4000 <-- n=10000 k=2000
$ python3.8 randombelow_counter.py 10000 5001
randbelow_calls=10000 <-- n=10000 k=5001
$ python3.8 randombelow_counter.py 10000 7500
randbelow_calls=10000 <-- n=10000 k=7500
$ python3.8 randombelow_counter.py 10000 8500
randbelow_calls=10000 <-- n=10000 k=8500
$ python3.8 randombelow_counter.py 10000 9500
randbelow_calls=10000 <-- n=10000 k=9500

Results for the Baseline Version

$ python3.8 randombelow_counter.py 10000 10
randbelow_calls=10 <-- n=10000 k=10
$ python3.8 randombelow_counter.py 10000 20
randbelow_calls=20 <-- n=10000 k=20
$ python3.8 randombelow_counter.py 10000 100
randbelow_calls=100 <-- n=10000 k=100
$ python3.8 randombelow_counter.py 10000 200
randbelow_calls=201 <-- n=10000 k=200
$ python3.8 randombelow_counter.py 10000 1000
randbelow_calls=1055 <-- n=10000 k=1000
$ python3.8 randombelow_counter.py 10000 2000
randbelow_calls=2000 <-- n=10000 k=2000
$ python3.8 randombelow_counter.py 10000 5001
randbelow_calls=5001 <-- n=10000 k=5001
$ python3.8 randombelow_counter.py 10000 7500
randbelow_calls=7500 <-- n=10000 k=7500
$ python3.8 randombelow_counter.py 10000 8500
randbelow_calls=8500 <-- n=10000 k=8500
$ python3.8 randombelow_counter.py 10000 9500
randbelow_calls=9500 <-- n=10000 k=9500

[rhettinger]

Also, here at the comparative timings with and without the patch:

With the PR Applied

$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=10)'
20000 loops, best of 11: 14.9 usec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=20)'
10000 loops, best of 11: 26.7 usec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=100)'
2000 loops, best of 11: 122 usec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=200)'
1000 loops, best of 11: 243 usec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=1_000)'
200 loops, best of 11: 1.27 msec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=2_000)'
100 loops, best of 11: 2.58 msec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=5_001)'
50 loops, best of 11: 5.56 msec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=7500)'
50 loops, best of 11: 5.7 msec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=8500)'
50 loops, best of 11: 5.78 msec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=9500)'
50 loops, best of 11: 5.83 msec per loop

Baseline

$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=10)'
20000 loops, best of 11: 10.6 usec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=20)'
20000 loops, best of 11: 17.9 usec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=100)'
5000 loops, best of 11: 74.9 usec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=200)'
2000 loops, best of 11: 144 usec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=1_000)'
500 loops, best of 11: 738 usec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=2_000)'
200 loops, best of 11: 1.63 msec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=5_001)'
100 loops, best of 11: 3.38 msec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=7500)'
50 loops, best of 11: 4.9 msec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=8500)'
50 loops, best of 11: 5.52 msec per loop
$ ./python.exe -m timeit -r11 -s 'from random import sample' 'sample(range(10_000), k=9500)'

[ciphergoth]

Thanks for following up on this! Surprised by the numbers you're seeing - I'll try reproducing your tests and investigate further. Thanks again!