New submission from Yury Selivanov:
It's possible to significantly improve performance of shallow dict copy.
Currently, PyDict_Copy creates a new empty dict object and then inserts
key/values into it one by one.
My idea is to simply memcpy the whole keys/items region and do the necessary
increfs after it. This works just fine for non-key-sharing dicts.
With the following simple microbenchmark:
import time
N = 1000000
for size in [0, 1, 10, 20, 50, 100, 500, 1000]:
d = dict([(str(i), i) for i in range(size)])
t = time.monotonic()
for i in range(N):
d.copy()
e = time.monotonic() - t
print(f'dict(size={size}).copy() x {N} times:\t {e:.4f}')
Output for 3.7 master:
dict(size=0).copy() x 1000000 times: 0.1299
dict(size=1).copy() x 1000000 times: 0.1499
dict(size=10).copy() x 1000000 times: 0.3758
dict(size=20).copy() x 1000000 times: 0.7722
dict(size=50).copy() x 1000000 times: 1.2784
dict(size=100).copy() x 1000000 times: 2.5128
dict(size=500).copy() x 1000000 times: 12.8968
dict(size=1000).copy() x 1000000 times: 25.4276
Output for patched 3.7:
dict(size=0).copy() x 1000000 times: 0.1352
dict(size=1).copy() x 1000000 times: 0.1285
dict(size=10).copy() x 1000000 times: 0.1632
dict(size=20).copy() x 1000000 times: 0.3076
dict(size=50).copy() x 1000000 times: 0.3663
dict(size=100).copy() x 1000000 times: 0.5140
dict(size=500).copy() x 1000000 times: 2.3419
dict(size=1000).copy() x 1000000 times: 4.6176
----------
components: Interpreter Core
messages: 300117
nosy: haypo, inada.naoki, yselivanov
priority: normal
severity: normal
stage: patch review
status: open
title: Speed-up dict.copy() up to 5.5 times.
type: performance
versions: Python 3.7
_______________________________________
Python tracker <rep...@bugs.python.org>
<http://bugs.python.org/issue31179>
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