Change PyMem_Malloc to use pymalloc allocator

[vstinner]

BPO	26249
Nosy	@rhettinger, @pitrou, @vstinner, @serhiy-storchaka, @catalin-manciu
Files	pymem.patch python_memleak.py tu_malloc.c pymem_27.patch pymalloc.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 = None
closed_at = <Date 2016-04-26.11:37:12.570>
created_at = <Date 2016-01-31.17:48:24.357>
labels = ['performance']
title = 'Change PyMem_Malloc to use pymalloc allocator'
updated_at = <Date 2016-04-26.11:37:12.569>
user = 'https://github.com/vstinner'

bugs.python.org fields:

activity = <Date 2016-04-26.11:37:12.569>
actor = 'vstinner'
assignee = 'none'
closed = True
closed_date = <Date 2016-04-26.11:37:12.570>
closer = 'vstinner'
components = []
creation = <Date 2016-01-31.17:48:24.357>
creator = 'vstinner'
dependencies = []
files = ['41767', '41778', '41779', '42004', '42158']
hgrepos = []
issue_num = 26249
keywords = ['patch']
message_count = 51.0
messages = ['259290', '259297', '259376', '259377', '259378', '259379', '259382', '259383', '259384', '259385', '259389', '259390', '259391', '259392', '259393', '259395', '259440', '259441', '259445', '260674', '260675', '260681', '261430', '261431', '261433', '261445', '261446', '261447', '261448', '261449', '261450', '261452', '261453', '261454', '261455', '261456', '261457', '261458', '261459', '261488', '261749', '261766', '261788', '264020', '264027', '264130', '264132', '264174', '264245', '264251', '264252']
nosy_count = 8.0
nosy_names = ['rhettinger', 'pitrou', 'vstinner', 'python-dev', 'jtaylor', 'serhiy.storchaka', 'alecsandru.patrascu', 'catalin.manciu']
pr_nums = []
priority = 'normal'
resolution = 'fixed'
stage = None
status = 'closed'
superseder = None
type = 'performance'
url = 'https://bugs.python.org/issue26249'
versions = ['Python 3.6']

[vstinner]

The issue bpo-23601 showed speedup for the dict type by replacing PyMem_Malloc() with PyObject_Malloc() in dictobject.c.

When I worked on the PEP-445, it was discussed to use the Python fast memory allocator for small memory allocations (<= 512 bytes), but I think that nobody tested on benchmark.

So I open an issue to discuss that.

By the way, we should also benchmark the Windows memory allocator which limits fragmentations. Maybe we can skip the Python small memory allocator on recent version of Windows?

Attached patch implements the change. The main question is the speedup on various kinds of memory allocations (need a benchmark) :-)

I will try to run benchmarks.

--

If the patch slows down Python, maybe we can investigate if some Python types (like dict) mostly uses "small" memory blocks (<= 512 bytes).

[vstinner]

Ok, to avoid confusion, I opened an issue specific to Windows for its "Low-fragmentation Heap": issue bpo-26251.

Other issues related to memory allocators.

Merged:

• issue bpo-21233: Add *Calloc functions to CPython memory allocation API (extension of the PEP-445, asked by numpy)
• issue bpo-13483: Use VirtualAlloc to allocate memory arenas (implementation of the PEP-445)
• issue bpo-3329: API for setting the memory allocator used by Python

Open:

• issue bpo-18835: Add aligned memory variants to the suite of PyMem functions/macros => this one is still open, the status is unclear :-/

[pitrou]

Hum, the point of PyMem_Malloc() is that it's distinct from PyObject_Malloc(), right? Why would you redirect one to the other?

[pitrou]

(of course, we might question why we have two different families of allocation APIs...)

[vstinner]

Hum, the point of PyMem_Malloc() is that it's distinct from PyObject_Malloc(), right? Why would you redirect one to the other?

For performances.

(of course, we might question why we have two different families of allocation APIs...)

That's the real question: why does Python have PyMem family? Is it still justified in 2016?

--

Firefox uses jemalloc to limit the fragmentation of the heap memory. Once I spent a lot of time to try to understand the principle of fragmentation, and in my tiny benchmarks, jemalloc was *much* better than system allocator. By the way, jemalloc scales well on multiple threads ;-)

• http://www.canonware.com/jemalloc/
• https://github.com/jemalloc/jemalloc/wiki

My notes on heap memory fragmentation: http://haypo-notes.readthedocs.org/heap_fragmentation.html

[vstinner]

About heap memory fragmentation, see also my attached two "benchmarks" in Python and C: python_memleak.py and tu_malloc.c.

[vstinner]

So, I ran ssh://hg@hg.python.org/benchmarks with my patch. It looks like some benchmarks are up to 4% faster:

$ python3 -u perf.py ../default/python.orig ../default/python.pymem

INFO:root:Automatically selected timer: perf_counter
[ 1/10] 2to3...
INFO:root:Running ../default/python.pymem lib3/2to3/2to3 -f all lib/2to3
INFO:root:Running ../default/python.pymem lib3/2to3/2to3 -f all lib/2to3 1 time
INFO:root:Running ../default/python.orig lib3/2to3/2to3 -f all lib/2to3
INFO:root:Running ../default/python.orig lib3/2to3/2to3 -f all lib/2to3 1 time
[ 2/10] chameleon_v2...
INFO:root:Running ../default/python.pymem performance/bm_chameleon_v2.py -n 50 --timer perf_counter
INFO:root:Running ../default/python.orig performance/bm_chameleon_v2.py -n 50 --timer perf_counter
[ 3/10] django_v3...
INFO:root:Running ../default/python.pymem performance/bm_django_v3.py -n 50 --timer perf_counter
INFO:root:Running ../default/python.orig performance/bm_django_v3.py -n 50 --timer perf_counter
[ 4/10] fastpickle...
INFO:root:Running ../default/python.pymem performance/bm_pickle.py -n 50 --timer perf_counter --use_cpickle pickle
INFO:root:Running ../default/python.orig performance/bm_pickle.py -n 50 --timer perf_counter --use_cpickle pickle
[ 5/10] fastunpickle...
INFO:root:Running ../default/python.pymem performance/bm_pickle.py -n 50 --timer perf_counter --use_cpickle unpickle
INFO:root:Running ../default/python.orig performance/bm_pickle.py -n 50 --timer perf_counter --use_cpickle unpickle
[ 6/10] json_dump_v2...
INFO:root:Running ../default/python.pymem performance/bm_json_v2.py -n 50 --timer perf_counter
INFO:root:Running ../default/python.orig performance/bm_json_v2.py -n 50 --timer perf_counter
[ 7/10] json_load...
INFO:root:Running ../default/python.pymem performance/bm_json.py -n 50 --timer perf_counter json_load
INFO:root:Running ../default/python.orig performance/bm_json.py -n 50 --timer perf_counter json_load
[ 8/10] nbody...
INFO:root:Running ../default/python.pymem performance/bm_nbody.py -n 50 --timer perf_counter
INFO:root:Running ../default/python.orig performance/bm_nbody.py -n 50 --timer perf_counter
[ 9/10] regex_v8...
INFO:root:Running ../default/python.pymem performance/bm_regex_v8.py -n 50 --timer perf_counter
INFO:root:Running ../default/python.orig performance/bm_regex_v8.py -n 50 --timer perf_counter
[10/10] tornado_http...
INFO:root:Running ../default/python.pymem performance/bm_tornado_http.py -n 100 --timer perf_counter
INFO:root:Running ../default/python.orig performance/bm_tornado_http.py -n 100 --timer perf_counter

Report on Linux smithers 4.3.3-300.fc23.x86_64 #1 SMP Tue Jan 5 23:31:01 UTC 2016 x86_64 x86_64
Total CPU cores: 8

### 2to3 ###
6.880090 -> 6.818911: 1.01x faster

### fastpickle ###
Min: 0.453826 -> 0.442081: 1.03x faster
Avg: 0.456499 -> 0.443978: 1.03x faster
Significant (t=20.03)
Stddev: 0.00370 -> 0.00242: 1.5293x smaller

### fastunpickle ###
Min: 0.547908 -> 0.526027: 1.04x faster
Avg: 0.554663 -> 0.528686: 1.05x faster
Significant (t=15.95)
Stddev: 0.00893 -> 0.00728: 1.2260x smaller

### json_dump_v2 ###
Min: 2.733907 -> 2.627718: 1.04x faster
Avg: 2.762473 -> 2.664675: 1.04x faster
Significant (t=11.99)
Stddev: 0.03796 -> 0.04341: 1.1435x larger

### regex_v8 ###
Min: 0.042438 -> 0.042581: 1.00x slower
Avg: 0.042805 -> 0.044078: 1.03x slower
Significant (t=-2.12)
Stddev: 0.00171 -> 0.00388: 2.2694x larger

### tornado_http ###
Min: 0.254089 -> 0.246088: 1.03x faster
Avg: 0.257046 -> 0.249033: 1.03x faster
Significant (t=15.83)
Stddev: 0.00401 -> 0.00310: 1.2930x smaller

The following not significant results are hidden, use -v to show them:
chameleon_v2, django_v3, json_load, nbody.

real 19m13.413s
user 18m50.024s
sys 0m22.507s

[YurySelivanov]

On Feb 2, 2016, at 7:00 AM, STINNER Victor <report@bugs.python.org> wrote:

So, I ran ssh://hg@hg.python.org/benchmarks with my patch. It looks like some benchmarks are up to 4% faster:

Please use -r flag for perf.py

[pitrou]

It looks like some benchmarks are up to 4% faster:

What this says is that some internals uses of PyMem_XXX should be replaced with PyObject_XXX.

[vstinner]

FYI benchmark result to compare Python with and without pymalloc (fast memory allocator for block <= 512 bytes). As expected, no pymalloc is slower, up to 30% slower (and it's never faster).

Report on Linux smithers 4.3.3-300.fc23.x86_64 #1 SMP Tue Jan 5 23:31:01 UTC 2016 x86_64 x86_64
Total CPU cores: 8

### 2to3 ###
7.253671 -> 7.558993: 1.04x slower

### chameleon_v2 ###
Min: 5.598481 -> 5.794526: 1.04x slower
Avg: 5.714233 -> 5.922142: 1.04x slower
Significant (t=-8.01)
Stddev: 0.15956 -> 0.09048: 1.7636x smaller

### django_v3 ###
Min: 0.574221 -> 0.606462: 1.06x slower
Avg: 0.579659 -> 0.612088: 1.06x slower
Significant (t=-28.44)
Stddev: 0.00605 -> 0.00532: 1.1371x smaller

### fastpickle ###
Min: 0.450852 -> 0.502645: 1.11x slower
Avg: 0.455619 -> 0.513777: 1.13x slower
Significant (t=-26.24)
Stddev: 0.00696 -> 0.01404: 2.0189x larger

### fastunpickle ###
Min: 0.544064 -> 0.696306: 1.28x slower
Avg: 0.552459 -> 0.705372: 1.28x slower
Significant (t=-85.52)
Stddev: 0.00798 -> 0.00980: 1.2281x larger

### json_dump_v2 ###
Min: 2.780312 -> 3.265531: 1.17x slower
Avg: 2.830463 -> 3.370060: 1.19x slower
Significant (t=-23.73)
Stddev: 0.04190 -> 0.15521: 3.7046x larger

### json_load ###
Min: 0.428893 -> 0.558956: 1.30x slower
Avg: 0.431941 -> 0.569441: 1.32x slower
Significant (t=-74.76)
Stddev: 0.00791 -> 0.01033: 1.3060x larger

### regex_v8 ###
Min: 0.043439 -> 0.044614: 1.03x slower
Avg: 0.044388 -> 0.046487: 1.05x slower
Significant (t=-4.95)
Stddev: 0.00215 -> 0.00209: 1.0283x smaller

### tornado_http ###
Min: 0.264603 -> 0.278840: 1.05x slower
Avg: 0.270153 -> 0.285263: 1.06x slower
Significant (t=-23.04)
Stddev: 0.00489 -> 0.00436: 1.1216x smaller

The following not significant results are hidden, use -v to show them:
nbody.

[vstinner]

Test with jemalloc using the shell script "python.jemalloc":
---
#!/bin/sh
LD_PRELOAD=/usr/lib64/libjemalloc.so /home/haypo/prog/python/default/python "$@"
---

Memory consumption:
python3 -u perf.py -m ../default/python ../default/python.jemalloc

Report on Linux smithers 4.3.3-300.fc23.x86_64 #1 SMP Tue Jan 5 23:31:01 UTC 2016 x86_64 x86_64
Total CPU cores: 8

### 2to3 ###
Mem max: 43100.000 -> 220.000: 195.9091x smaller

### chameleon_v2 ###
Mem max: 367276.000 -> 224.000: 1639.6250x smaller

### django_v3 ###
Mem max: 24136.000 -> 284.000: 84.9859x smaller

### fastpickle ###
Mem max: 8692.000 -> 284.000: 30.6056x smaller

### fastunpickle ###
Mem max: 8704.000 -> 216.000: 40.2963x smaller

### json_dump_v2 ###
Mem max: 10448.000 -> 216.000: 48.3704x smaller

### json_load ###
Mem max: 8444.000 -> 220.000: 38.3818x smaller

### nbody ###
Mem max: 7388.000 -> 220.000: 33.5818x smaller

### regex_v8 ###
Mem max: 12764.000 -> 220.000: 58.0182x smaller

### tornado_http ###
Mem max: 28216.000 -> 228.000: 123.7544x smaller

---

Performance:

Report on Linux smithers 4.3.3-300.fc23.x86_64 #1 SMP Tue Jan 5 23:31:01 UTC 2016 x86_64 x86_64
Total CPU cores: 8

### 2to3 ###
7.413484 -> 7.189792: 1.03x faster

### chameleon_v2 ###
Min: 5.559697 -> 5.869468: 1.06x slower
Avg: 5.672448 -> 6.033152: 1.06x slower
Significant (t=-13.67)
Stddev: 0.12098 -> 0.14203: 1.1740x larger

### nbody ###
Min: 0.242194 -> 0.229747: 1.05x faster
Avg: 0.244991 -> 0.235297: 1.04x faster
Significant (t=9.75)
Stddev: 0.00262 -> 0.00652: 2.4861x larger

### regex_v8 ###
Min: 0.042532 -> 0.046920: 1.10x slower
Avg: 0.043249 -> 0.047907: 1.11x slower
Significant (t=-13.23)
Stddev: 0.00180 -> 0.00172: 1.0503x smaller

### tornado_http ###
Min: 0.265755 -> 0.274526: 1.03x slower
Avg: 0.273617 -> 0.284186: 1.04x slower
Significant (t=-6.67)
Stddev: 0.00583 -> 0.01474: 2.5297x larger

The following not significant results are hidden, use -v to show them:
django_v3, fastpickle, fastunpickle, json_dump_v2, json_load.

[vstinner]

> It looks like some benchmarks are up to 4% faster:

What this says is that some internals uses of PyMem_XXX should be replaced with PyObject_XXX.

Why not changing PyMem_XXX to use the same fast allocator than PyObject_XXX? (as proposed in this issue)

FYI we now also have the PyMem_RawXXX family :)

[pitrou]

Le 02/02/2016 15:47, STINNER Victor a écrit :

2to3

Mem max: 43100.000 -> 220.000: 195.9091x smaller

chameleon_v2

Mem max: 367276.000 -> 224.000: 1639.6250x smaller

django_v3

Mem max: 24136.000 -> 284.000: 84.9859x smaller

These figures are not even remotely believable.
It would make sense to investigate them before posting such numbers ;-)

[pitrou]

Le 02/02/2016 15:48, STINNER Victor a écrit :

> What this says is that some internals uses of PyMem_XXX should be replaced with PyObject_XXX.

Why not changing PyMem_XXX to use the same fast allocator than
PyObject_XXX? (as proposed in this issue)

Why have two sets of functions doing exactly the same thing?