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gh-116477: Improve performance of range for the single argument case#116478

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@eendebakpteendebakpt commentedMar 7, 2024•
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Benchmark

range(1): Mean +- std dev: [main_pgo] 44.4 ns +- 1.0 ns -> [pr_pgo] 34.7 ns +- 4.0 ns: 1.28x fasteriter(range(1)): Mean +- std dev: [main_pgo] 78.7 ns +- 1.8 ns -> [pr_pgo] 61.7 ns +- 3.8 ns: 1.28x fasterlist(iter(range(1))): Mean +- std dev: [main_pgo] 219 ns +- 2 ns -> [pr_pgo] 199 ns +- 2 ns: 1.10x fasterrange(2, 1): Mean +- std dev: [main_pgo] 51.8 ns +- 1.3 ns -> [pr_pgo] 50.1 ns +- 2.1 ns: 1.03x fasteriter(range(2, 1)): Mean +- std dev: [main_pgo] 84.1 ns +- 1.2 ns -> [pr_pgo] 86.2 ns +- 3.7 ns: 1.02x slowerrange(10): Mean +- std dev: [main_pgo] 44.0 ns +- 0.6 ns -> [pr_pgo] 34.5 ns +- 1.6 ns: 1.28x fasteriter(range(10)): Mean +- std dev: [main_pgo] 78.5 ns +- 2.0 ns -> [pr_pgo] 61.6 ns +- 3.4 ns: 1.27x fasterlist(iter(range(10))): Mean +- std dev: [main_pgo] 267 ns +- 2 ns -> [pr_pgo] 244 ns +- 5 ns: 1.09x fasterrange(2, 10): Mean +- std dev: [main_pgo] 52.3 ns +- 3.7 ns -> [pr_pgo] 57.5 ns +- 1.5 ns: 1.10x sloweriter(range(2, 10)): Mean +- std dev: [main_pgo] 82.5 ns +- 3.0 ns -> [pr_pgo] 100 ns +- 1 ns: 1.21x slowerlist(iter(range(2, 10))): Mean +- std dev: [main_pgo] 267 ns +- 4 ns -> [pr_pgo] 277 ns +- 2 ns: 1.04x slowerrange(100): Mean +- std dev: [main_pgo] 44.1 ns +- 1.0 ns -> [pr_pgo] 34.1 ns +- 1.9 ns: 1.30x fasteriter(range(100)): Mean +- std dev: [main_pgo] 78.5 ns +- 1.8 ns -> [pr_pgo] 60.6 ns +- 2.2 ns: 1.29x fasterlist(iter(range(100))): Mean +- std dev: [main_pgo] 707 ns +- 29 ns -> [pr_pgo] 681 ns +- 26 ns: 1.04x fasterrange(2, 100): Mean +- std dev: [main_pgo] 52.6 ns +- 2.3 ns -> [pr_pgo] 57.3 ns +- 1.2 ns: 1.09x sloweriter(range(2, 100)): Mean +- std dev: [main_pgo] 83.3 ns +- 2.6 ns -> [pr_pgo] 101 ns +- 2 ns: 1.21x slowerlist(iter(range(2, 100))): Mean +- std dev: [main_pgo] 705 ns +- 23 ns -> [pr_pgo] 718 ns +- 28 ns: 1.02x slowerrange(400): Mean +- std dev: [main_pgo] 58.4 ns +- 0.8 ns -> [pr_pgo] 36.1 ns +- 1.1 ns: 1.62x fasteriter(range(400)): Mean +- std dev: [main_pgo] 93.9 ns +- 1.6 ns -> [pr_pgo] 62.3 ns +- 1.6 ns: 1.51x fasterlist(iter(range(400))): Mean +- std dev: [main_pgo] 3.92 us +- 0.06 us -> [pr_pgo] 3.87 us +- 0.05 us: 1.01x fasterrange(2, 400): Mean +- std dev: [main_pgo] 66.1 ns +- 1.1 ns -> [pr_pgo] 71.2 ns +- 1.0 ns: 1.08x sloweriter(range(2, 400)): Mean +- std dev: [main_pgo] 103 ns +- 6 ns -> [pr_pgo] 115 ns +- 1 ns: 1.11x slowerfor loop: Mean +- std dev: [main_pgo] 295 ns +- 9 ns -> [pr_pgo] 267 ns +- 5 ns: 1.10x fasterBenchmark hidden because not significant (2): list(iter(range(2, 1))), list(iter(range(2, 400)))Geometric mean: 1.08x faster

The fast paths eliminate several calls toPyLong_AsLongAndOverflow and the construction of a new Python integer withPyLong_FromLong.

The fast path for the range object is incompute_range_length. That implies a small performance penalty (two pointer comparisons) for the cases wherestart != 0 orstep != 1. We could move the fast path torange_from_array (thecase 1: part), but that leads to a bit more duplicated code.

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In general LGTM. But please remove redundant spaces.

lstop = PyLong_AsLong(r->stop);
if (lstop == -1 && PyErr_Occurred()) {

if (r->start == _PyLong_GetZero() && r->step == _PyLong_GetOne() ) {

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How large is overhead of twoPyLong_AsLong() calls for 0 and 1? Is it noticeable?

Run something like:

./python -m timeit -s 'r = range(10)' 'for i in r: break'

AFAIK it is the fastest code that is affected by the iterator creation time.

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The overhead for the twoPyLong_AsLong calls (plus some bits and pieces) are from the first benchmark above:

range(1): Mean +- std dev: [main_pgo] 44.4 ns +- 1.0 ns -> [pr_pgo] 34.7 ns +- 4.0 ns: 1.28x faster

So about 10 ns.

The last benchmark from the first comment:

for loop: Mean +- std dev: [main_pgo] 295 ns +- 9 ns -> [pr_pgo] 267 ns +- 5 ns: 1.10x faster

executes:

def g():    x=0    for ii in range(10):        x += 1

So a small for loop with minimal work becomes 10% faster. (timings may vary, my system is noisy)

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Co-authored-by: Serhiy Storchaka <storchaka@gmail.com>
eendebakptand others added2 commitsApril 4, 2024 10:26
Co-authored-by: Erlend E. Aasland <erlend.aasland@protonmail.com>
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Looks good to me, but I'd like Serhiy's thumbs up before landing.

@erlend-aaslanderlend-aasland changed the titlegh-116477: Improve performance of rangegh-116477: Improve performance of range for single argumentsApr 4, 2024
@erlend-aaslanderlend-aasland changed the titlegh-116477: Improve performance of range for single argumentsgh-116477: Improve performance of range for the single argument caseApr 4, 2024
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I'll merge this later at the sprints. Thanks!

@erlend-aaslanderlend-aasland self-assigned thisMay 20, 2024
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I am not convinced that this change is worth to do, but I will not object against committing it if other core developer likes it.

eendebakptand others added2 commitsMay 23, 2024 22:43
Co-authored-by: Serhiy Storchaka <storchaka@gmail.com>
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@eendebakpt, are the benchmarks up to date?

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Here is a set of benchmarks for the PR rebased to main:

range(1): Mean +- std dev: [main_v2] 49.9 ns +- 1.1 ns -> [pr_v2] 39.7 ns +- 1.1 ns: 1.26x fasteriter(range(1)): Mean +- std dev: [main_v2] 82.1 ns +- 0.6 ns -> [pr_v2] 68.4 ns +- 1.7 ns: 1.20x fasterlist(range(1)): Mean +- std dev: [main_v2] 151 ns +- 1 ns -> [pr_v2] 134 ns +- 4 ns: 1.13x fasterlist(iter(range(1))): Mean +- std dev: [main_v2] 220 ns +- 1 ns -> [pr_v2] 204 ns +- 1 ns: 1.08x fasterrange(2, 1): Mean +- std dev: [main_v2] 56.8 ns +- 0.4 ns -> [pr_v2] 54.5 ns +- 1.1 ns: 1.04x fasteriter(range(2, 1)): Mean +- std dev: [main_v2] 90.1 ns +- 1.7 ns -> [pr_v2] 93.0 ns +- 3.5 ns: 1.03x slowerlist(iter(range(2, 1))): Mean +- std dev: [main_v2] 213 ns +- 6 ns -> [pr_v2] 218 ns +- 3 ns: 1.02x slowerfor loop length 1: Mean +- std dev: [main_v2] 140 ns +- 7 ns -> [pr_v2] 118 ns +- 3 ns: 1.19x fasterrange(10): Mean +- std dev: [main_v2] 49.8 ns +- 1.4 ns -> [pr_v2] 39.7 ns +- 1.2 ns: 1.25x fasteriter(range(10)): Mean +- std dev: [main_v2] 82.2 ns +- 0.8 ns -> [pr_v2] 69.3 ns +- 3.3 ns: 1.19x fasterlist(range(10)): Mean +- std dev: [main_v2] 192 ns +- 4 ns -> [pr_v2] 176 ns +- 4 ns: 1.09x fasterlist(iter(range(10))): Mean +- std dev: [main_v2] 262 ns +- 6 ns -> [pr_v2] 248 ns +- 6 ns: 1.05x fasterrange(2, 10): Mean +- std dev: [main_v2] 57.5 ns +- 0.8 ns -> [pr_v2] 62.0 ns +- 1.8 ns: 1.08x sloweriter(range(2, 10)): Mean +- std dev: [main_v2] 90.1 ns +- 2.2 ns -> [pr_v2] 106 ns +- 2 ns: 1.18x slowerlist(iter(range(2, 10))): Mean +- std dev: [main_v2] 264 ns +- 12 ns -> [pr_v2] 282 ns +- 11 ns: 1.07x slowerfor loop length 10: Mean +- std dev: [main_v2] 313 ns +- 18 ns -> [pr_v2] 292 ns +- 15 ns: 1.07x fasterrange(100): Mean +- std dev: [main_v2] 50.1 ns +- 2.7 ns -> [pr_v2] 40.6 ns +- 2.7 ns: 1.23x fasteriter(range(100)): Mean +- std dev: [main_v2] 82.7 ns +- 2.5 ns -> [pr_v2] 69.7 ns +- 4.2 ns: 1.19x fasterlist(range(100)): Mean +- std dev: [main_v2] 599 ns +- 26 ns -> [pr_v2] 581 ns +- 10 ns: 1.03x fasterlist(iter(range(100))): Mean +- std dev: [main_v2] 682 ns +- 45 ns -> [pr_v2] 663 ns +- 38 ns: 1.03x fasterrange(2, 100): Mean +- std dev: [main_v2] 57.4 ns +- 0.6 ns -> [pr_v2] 62.3 ns +- 1.3 ns: 1.08x sloweriter(range(2, 100)): Mean +- std dev: [main_v2] 90.3 ns +- 3.9 ns -> [pr_v2] 107 ns +- 5 ns: 1.19x slowerlist(iter(range(2, 100))): Mean +- std dev: [main_v2] 689 ns +- 41 ns -> [pr_v2] 681 ns +- 16 ns: 1.01x fasterfor loop length 100: Mean +- std dev: [main_v2] 1.97 us +- 0.12 us -> [pr_v2] 1.90 us +- 0.04 us: 1.04x fasterrange(400): Mean +- std dev: [main_v2] 63.2 ns +- 3.9 ns -> [pr_v2] 43.5 ns +- 3.3 ns: 1.45x fasteriter(range(400)): Mean +- std dev: [main_v2] 100 ns +- 7 ns -> [pr_v2] 73.1 ns +- 4.3 ns: 1.37x fasterrange(2, 400): Mean +- std dev: [main_v2] 71.6 ns +- 3.1 ns -> [pr_v2] 76.5 ns +- 3.6 ns: 1.07x sloweriter(range(2, 400)): Mean +- std dev: [main_v2] 107 ns +- 5 ns -> [pr_v2] 123 ns +- 4 ns: 1.15x slowerlist(iter(range(2, 400))): Mean +- std dev: [main_v2] 3.76 us +- 0.12 us -> [pr_v2] 3.72 us +- 0.08 us: 1.01x fasterBenchmark hidden because not significant (3): list(range(400)), list(iter(range(400))), for loop length 400Geometric mean: 1.06x faster
Benchmark script

(compiled with PGO, executed with--rigorous)

import pyperfrunner = pyperf.Runner()loop = """def g(n):    x=0    for ii in range(n):        x += 1"""        for s in [1, 10, 100, 400]:time = runner.timeit(name=f'range({s})', stmt=f"range({s})")time = runner.timeit(name=f'iter(range({s}))', stmt=f"iter(range({s}))")time = runner.timeit(name=f'list(range({s}))', stmt=f"list(range({s}))")time = runner.timeit(name=f'list(iter(range({s})))', stmt=f"list(iter(range({s})))")time = runner.timeit(name=f'range(2, {s})', stmt=f"range(2, {s})")time = runner.timeit(name=f'iter(range(2, {s}))', stmt=f"iter(range(2, {s}))")time = runner.timeit(name=f'list(iter(range(2, {s})))', stmt=f"list(iter(range(2, {s})))")time = runner.timeit(name=f'for loop length {s}', stmt=f"g({s})", setup=loop)

There is some noise in the data, but there are enough datapoints to make the trends clear. Most relevant for real applications are thefor loop length * benchmarks.

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So, if we exclude the iter optimisation, there will be no slowdowns (unlesss I misread the benchmark)?

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So, if we exclude the iter optimisation, there will be no slowdowns (unlesss I misread the benchmark)?

Both optimizations use the same check (two pointer comparisons to_PyLong_GetZero() and_PyLong_GetOne()), so I initially expected both to have the same (small) slowdown in the in slow path. But the test results above indeed suggest otherwise (although part if it is noise). I did more tests both with this PR and one with the iter optimalisation excluded and I think we can drop the iter optimization. The gain for the iter optimization is less than for the constructor and also in the range constructorPyLong_GetZero() is already available, whereas in the iter constructor it has to be called.

Two runs of benchmarks of main vs. the updated PR

range(10): Mean +- std dev: [main] 51.4 ns +- 2.8 ns -> [v3] 37.4 ns +- 1.7 ns: 1.38x fasteriter(range(10)): Mean +- std dev: [main] 83.8 ns +- 2.9 ns -> [v3] 70.3 ns +- 2.6 ns: 1.19x fasterlist(range(10)): Mean +- std dev: [main] 154 ns +- 6 ns -> [v3] 128 ns +- 6 ns: 1.21x fasterlist(iter(range(10))): Mean +- std dev: [main] 204 ns +- 12 ns -> [v3] 186 ns +- 7 ns: 1.10x fasterlist(iter(range(2, 10))): Mean +- std dev: [main] 206 ns +- 10 ns -> [v3] 198 ns +- 8 ns: 1.04x fasterfor loop length 10: Mean +- std dev: [main] 304 ns +- 14 ns -> [v3] 284 ns +- 14 ns: 1.07x fasterfor loop range(2, 10): Mean +- std dev: [main] 279 ns +- 9 ns -> [v3] 274 ns +- 11 ns: 1.02x fasterrange(400): Mean +- std dev: [main] 59.5 ns +- 2.6 ns -> [v3] 37.8 ns +- 1.5 ns: 1.58x fasteriter(range(400)): Mean +- std dev: [main] 96.0 ns +- 4.0 ns -> [v3] 70.5 ns +- 3.3 ns: 1.36x fasterlist(range(400)): Mean +- std dev: [main] 2.37 us +- 0.10 us -> [v3] 2.20 us +- 0.09 us: 1.08x fasterlist(iter(range(400))): Mean +- std dev: [main] 2.48 us +- 0.10 us -> [v3] 2.32 us +- 0.12 us: 1.07x fasteriter(range(2, 400)): Mean +- std dev: [main] 105 ns +- 4 ns -> [v3] 103 ns +- 4 ns: 1.02x fasterlist(iter(range(2, 400))): Mean +- std dev: [main] 2.51 us +- 0.14 us -> [v3] 2.38 us +- 0.12 us: 1.06x fasterBenchmark hidden because not significant (5): range(2, 10), iter(range(2, 10)), range(2, 400), for loop length 400, for loop range(2, 400)Geometric mean: 1.11x faster
range(10): Mean +- std dev: [maint] 47.5 ns +- 2.1 ns -> [v3tt] 37.3 ns +- 1.8 ns: 1.27x fasteriter(range(10)): Mean +- std dev: [maint] 82.2 ns +- 3.5 ns -> [v3tt] 70.6 ns +- 2.7 ns: 1.16x fasterlist(range(10)): Mean +- std dev: [maint] 152 ns +- 5 ns -> [v3tt] 131 ns +- 5 ns: 1.16x fasterlist(iter(range(10))): Mean +- std dev: [maint] 200 ns +- 8 ns -> [v3tt] 186 ns +- 6 ns: 1.07x fasterrange(2, 10): Mean +- std dev: [maint] 59.1 ns +- 2.6 ns -> [v3tt] 58.4 ns +- 2.8 ns: 1.01x fasteriter(range(2, 10)): Mean +- std dev: [maint] 92.8 ns +- 3.2 ns -> [v3tt] 93.8 ns +- 3.2 ns: 1.01x slowerlist(iter(range(2, 10))): Mean +- std dev: [maint] 206 ns +- 8 ns -> [v3tt] 202 ns +- 10 ns: 1.02x fasterfor loop length 10: Mean +- std dev: [maint] 311 ns +- 17 ns -> [v3tt] 288 ns +- 10 ns: 1.08x fasterfor loop range(2, 10): Mean +- std dev: [maint] 285 ns +- 13 ns -> [v3tt] 278 ns +- 12 ns: 1.02x fasterrange(400): Mean +- std dev: [maint] 81.6 ns +- 23.9 ns -> [v3tt] 38.3 ns +- 1.8 ns: 2.13x fasteriter(range(400)): Mean +- std dev: [maint] 115 ns +- 20 ns -> [v3tt] 72.5 ns +- 3.0 ns: 1.58x fasterlist(range(400)): Mean +- std dev: [maint] 2.52 us +- 0.14 us -> [v3tt] 2.34 us +- 0.11 us: 1.08x fasterlist(iter(range(400))): Mean +- std dev: [maint] 2.46 us +- 0.13 us -> [v3tt] 2.43 us +- 0.13 us: 1.01x fasterrange(2, 400): Mean +- std dev: [maint] 70.6 ns +- 2.9 ns -> [v3tt] 73.9 ns +- 3.5 ns: 1.05x slowerlist(iter(range(2, 400))): Mean +- std dev: [maint] 2.59 us +- 0.23 us -> [v3tt] 2.51 us +- 0.14 us: 1.03x fasterfor loop range(2, 400): Mean +- std dev: [maint] 9.91 us +- 0.55 us -> [v3tt] 11.1 us +- 1.5 us: 1.12x slowerBenchmark hidden because not significant (2): iter(range(2, 400)), for loop length 400Geometric mean: 1.11x faster

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LGTM.@serhiy-storchaka, any objections?

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Please repeat benchmarks with the current code.

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eendebakpt commentedFeb 6, 2025•
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New benchmarks (Ubuntu, with the new tail calling interpreter, non-PGO):

range(1): Mean +- std dev: [main] 52.0 ns +- 1.1 ns -> [pr] 38.8 ns +- 0.9 ns: 1.34x fasteriter(range(1)): Mean +- std dev: [main] 99.4 ns +- 2.6 ns -> [pr] 88.2 ns +- 2.9 ns: 1.13x fasterlist(range(1)): Mean +- std dev: [main] 177 ns +- 2 ns -> [pr] 160 ns +- 2 ns: 1.11x fasteriter(range(2, 1)): Mean +- std dev: [main] 101 ns +- 1 ns -> [pr] 103 ns +- 2 ns: 1.03x slowerfor loop length 1: Mean +- std dev: [main] 155 ns +- 8 ns -> [pr] 149 ns +- 5 ns: 1.04x fasterrange(10): Mean +- std dev: [main] 52.2 ns +- 1.2 ns -> [pr] 41.2 ns +- 2.8 ns: 1.27x fasteriter(range(10)): Mean +- std dev: [main] 99.5 ns +- 2.5 ns -> [pr] 86.9 ns +- 1.5 ns: 1.15x fasterlist(range(10)): Mean +- std dev: [main] 226 ns +- 12 ns -> [pr] 201 ns +- 3 ns: 1.13x fasteriter(range(2, 10)): Mean +- std dev: [main] 113 ns +- 4 ns -> [pr] 110 ns +- 2 ns: 1.03x fasterfor loop length 10: Mean +- std dev: [main] 332 ns +- 11 ns -> [pr] 326 ns +- 15 ns: 1.02x fasterrange(100): Mean +- std dev: [main] 54.0 ns +- 2.2 ns -> [pr] 39.6 ns +- 1.4 ns: 1.36x fasteriter(range(100)): Mean +- std dev: [main] 98.0 ns +- 1.0 ns -> [pr] 88.4 ns +- 2.6 ns: 1.11x fasterfor loop length 100: Mean +- std dev: [main] 2.09 us +- 0.11 us -> [pr] 1.99 us +- 0.11 us: 1.05x fasterrange(400): Mean +- std dev: [main] 65.9 ns +- 2.4 ns -> [pr] 40.9 ns +- 1.2 ns: 1.61x fasteriter(range(400)): Mean +- std dev: [main] 118 ns +- 10 ns -> [pr] 92.5 ns +- 5.2 ns: 1.28x fasterlist(range(400)): Mean +- std dev: [main] 3.05 us +- 0.13 us -> [pr] 3.19 us +- 0.31 us: 1.05x slowerrange(2, 400): Mean +- std dev: [main] 73.9 ns +- 7.7 ns -> [pr] 71.2 ns +- 2.0 ns: 1.04x fasteriter(range(2, 400)): Mean +- std dev: [main] 132 ns +- 7 ns -> [pr] 124 ns +- 3 ns: 1.07x fasterfor loop length 400: Mean +- std dev: [main] 9.37 us +- 0.34 us -> [pr] 9.54 us +- 0.25 us: 1.02x slowerBenchmark hidden because not significant (5): range(2, 1), range(2, 10), list(range(100)), range(2, 100), iter(range(2, 100))Geometric mean: 1.10x faster
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Pieter, can you fix the linting CI? Let's land this!

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I am not convinced that this change is worth to do, but I will not object against committing it if other core developer likes it.

I understand your concern. For example, we did not optimisezip creation using vector calls, since normally the loop itself would eat up the small performance gain. However, contrary to vectorcall optimisations, this is a trivial modification. It may be worth it for smaller loops. Benchmarks for such cases could be helpful.

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