Optimize PyLong_AsDouble for single-digit longs

[1st1]

BPO	26288
Nosy	@mdickinson, @pitrou, @vstinner, @skrah, @serhiy-storchaka, @1st1
Files	as_double.patch

^{Note: these values reflect the state of the issue at the time it was migrated and might not reflect the current state.}

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GitHub fields:

assignee = 'https://github.com/1st1'
closed_at = <Date 2016-02-08.21:13:53.105>
created_at = <Date 2016-02-05.01:55:00.043>
labels = ['interpreter-core', 'performance']
title = 'Optimize PyLong_AsDouble for single-digit longs'
updated_at = <Date 2016-02-08.21:13:53.104>
user = 'https://github.com/1st1'

bugs.python.org fields:

activity = <Date 2016-02-08.21:13:53.104>
actor = 'yselivanov'
assignee = 'yselivanov'
closed = True
closed_date = <Date 2016-02-08.21:13:53.105>
closer = 'yselivanov'
components = ['Interpreter Core']
creation = <Date 2016-02-05.01:55:00.043>
creator = 'yselivanov'
dependencies = []
files = ['41812']
hgrepos = []
issue_num = 26288
keywords = ['patch']
message_count = 15.0
messages = ['259615', '259703', '259704', '259715', '259723', '259724', '259736', '259737', '259738', '259739', '259740', '259741', '259777', '259785', '259886']
nosy_count = 7.0
nosy_names = ['mark.dickinson', 'pitrou', 'vstinner', 'skrah', 'python-dev', 'serhiy.storchaka', 'yselivanov']
pr_nums = []
priority = 'normal'
resolution = 'fixed'
stage = 'resolved'
status = 'closed'
superseder = None
type = 'performance'
url = 'https://bugs.python.org/issue26288'
versions = ['Python 3.6']

[1st1]

The attached patch drastically speeds up PyLong_AsDouble for single digit longs:

-m timeit -s "x=2" "x*2.2 + 2 + x*2.5 + 1.0 - x / 2.0 + (x+0.1)/(x-0.1)2 + (x+10)(x-30)"

with patch: 0.414
without: 0.612

spectral_norm: 1.05x faster. The results are even better when paired with patch from issue bpo-21955.

[python-dev]

New changeset 986184c355e8 by Yury Selivanov in branch 'default':
Issue bpo-26288: Optimize PyLong_AsDouble.
https://hg.python.org/cpython/rev/986184c355e8

[1st1]

Thanks a lot for the review, Serhiy!

[vstinner]

Nice enhancement.

/* Fast path; single digit will always fit decimal.
This improves performance of FP/long operations by at
least 20%. This is even visible on macro-benchmarks.
*/

I'm not sure that "spectral_norm" can be qualified as macro-benchmark. It's more a microbenchmark on numerics, no? I'm just nitpicking, your patch is fine ;-)

[pitrou]

Actually, please fix the comment. We don't want someone wondering what those "macro-benchmarks" are.

[skrah]

Additionally, "single digit will always fit a double"?

[1st1]

Actually, please fix the comment. We don't want someone wondering what those "macro-benchmarks" are.

If spectral-norm and nbody aren't good benchmarks then let's remove them from our benchmarks suite.

I'll remove that comment anyways, as it doesn't make a lot of sense :)

Additionally, "single digit will always fit a double"?

What's wrong about that phrase? Would this be better: "It's safe to cast a single-digit long (31 bits) to double"?

[skrah]

Sorry, I was a bit brief: The current comment says "decimal" instead of "double". It should be changed to "double".

[pitrou]

Le 06/02/2016 18:07, Yury Selivanov a écrit :

> Actually, please fix the comment. We don't want someone wondering what those "macro-benchmarks" are.

If spectral-norm and nbody aren't good benchmarks then let's remove
them from our benchmarks suite.

Probably, yes.

[pitrou]

Actually, let me refine that. nbody and spectral-norm don't make sense
for people running CPython. Perhaps people running PyPy might care about
their performance... (though PyPy is supposed to support a subset of
Numpy too)