New submission from Dimitar Tasev:
Hello, I have noticed a significant performance regression when allocating a
large shared array in Python 3.x versus Python 2.7. The affected module seems
to be `multiprocessing`.
The function I used for benchmarking:
from timeit import timeit
timeit('sharedctypes.Array(ctypes.c_float, 500*2048*2048)', 'from
multiprocessing import sharedctypes; import ctypes', number=1)
And the results from executing it:
Python 3.5.2
Out[2]: 182.68500420999771
-------------------
Python 2.7.12
Out[6]: 2.124835968017578
I will try to provide any information you need. Right now I am looking at
callgrind/cachegrind without Debug symbols, and can post that, in the meantime
I am building Python with Debug and will re-run the callgrind/cachegrind.
Allocating the same-size array with numpy doesn't seem to have a difference
between Python versions. The numpy command used was
`numpy.full((500,2048,2048), 5.0)`. Allocating the same number of list members
also doesn't have a difference - `arr = [5.0]*(500*2048*2048)`
----------
files: shared_array_alloc.py
messages: 298285
nosy: dtasev
priority: normal
severity: normal
status: open
title: Shared Array Memory Allocation Regression
type: performance
versions: Python 2.7, Python 3.5, Python 3.6
Added file: http://bugs.python.org/file47009/shared_array_alloc.py
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<http://bugs.python.org/issue30919>
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