[Python-Dev] Profile Guided Optimization active by-default
Patrascu, Alecsandru
alecsandru.patrascu at intel.com
Sat Aug 22 18:40:52 CEST 2015
Hello and thank you for your feedback.
We have measured PGO gain using other workloads also. Our initial choice for this optimization was pybench, but the speedup obtained was lower than using regrtest and it didn't cover a lot of Python scenarios. Instead, regrtest has an uniform distribution for the tests and the resulting binary is overall much faster than the default, or trained using other workloads, and thus covering a larger pool of Python loads. This optimization was also tested on a production environments running OpenStack Swift and got up to 9% improvements.
The reason we proposed this target to be always on is that the obtained optimized binary is better out of the box for the general cases.
Alecsandru
From: gvanrossum at gmail.com [mailto:gvanrossum at gmail.com] On Behalf Of Guido van Rossum
Sent: Saturday, August 22, 2015 7:15 PM
To: Patrascu, Alecsandru
Cc: python-dev at python.org
Subject: Re: [Python-Dev] Profile Guided Optimization active by-default
How about we first add a new Makefile target that enables PGO, without turning it on by default? Then later we can enable it by default.
Also, I have my doubts about regrtest. How sure are we that it represents a typical Python load? Tests are often using a different mix of operations than production code.
On Sat, Aug 22, 2015 at 7:46 AM, Patrascu, Alecsandru <alecsandru.patrascu at intel.com> wrote:
Hi All,
This is Alecsandru from Server Scripting Languages Optimization team at Intel Corporation.
I would like to submit a request to turn-on Profile Guided Optimization or PGO as the default build option for Python (both 2.7 and 3.6), given its performance benefits on a wide variety of workloads and hardware. For instance, as shown from attached sample performance results from the Grand Unified Python Benchmark, >20% speed up was observed. In addition, we are seeing 2-9% performance boost from OpenStack/Swift where more than 60% of the codes are in Python 2.7. Our analysis indicates the performance gain was mainly due to reduction of icache misses and CPU front-end stalls.
Attached is the Makefile patches that modify the all build target and adds a new one called "disable-profile-opt". We built and tested this patch for Python 2.7 and 3.6 on our Linux machines (CentOS 7/Ubuntu Server 14.04, Intel Xeon Haswell/Broadwell with 18/8 cores). We use "regrtest" suite for training as it provides the best performance improvement. Some of the test programs in the suite may fail which leads to build fail. One solution is to disable the specific failed test using the "-x " flag (as shown in the patch)
Steps to apply the patch:
1. hg clone https://hg.python.org/cpython cpython
2. cd cpython
3. hg update 2.7 (needed for 2.7 only)
4. Copy *.patch to the current directory
5. patch < python2.7-pgo.patch (or patch < python3.6-pgo.patch)
6. ./configure
7. make
To disable PGO
7b. make disable-profile-opt
In the following, please find our sample performance results from latest XEON machine, XEON Broadwell EP.
Hardware (HW): Intel XEON (Broadwell) 8 Cores
BIOS settings: Intel Turbo Boost Technology: false
Hyper-Threading: false
Operating System: Ubuntu 14.04.3 LTS trusty
OS configuration: CPU freq set at fixed: 2.6GHz by
echo 2600000 > /sys/devices/system/cpu/cpu*/cpufreq/scaling_min_freq
echo 2600000 > /sys/devices/system/cpu/cpu*/cpufreq/scaling_max_freq
Address Space Layout Randomization (ASLR) disabled (to reduce run to run variation) by
echo 0 > /proc/sys/kernel/randomize_va_space
GCC version: gcc version 4.8.4 (Ubuntu 4.8.4-2ubuntu1~14.04)
Benchmark: Grand Unified Python Benchmark (GUPB)
GUPB Source: https://hg.python.org/benchmarks/
Python2.7 results:
Python source: hg clone https://hg.python.org/cpython cpython
Python Source: hg update 2.7
hg id: 0511b1165bb6 (2.7)
hg id -r 'ancestors(.) and tag()': 15c95b7d81dc (2.7) v2.7.10
hg --debug id -i: 0511b1165bb6cf40ada0768a7efc7ba89316f6a5
Benchmarks Speedup(%)
simple_logging 20
raytrace 20
silent_logging 19
richards 19
chaos 16
formatted_logging 16
json_dump 15
hexiom2 13
pidigits 12
slowunpickle 12
django_v2 12
unpack_sequence 11
float 11
mako 11
slowpickle 11
fastpickle 11
django 11
go 10
json_dump_v2 10
pathlib 10
regex_compile 10
pybench 9.9
etree_process 9
regex_v8 8
bzr_startup 8
2to3 8
slowspitfire 8
telco 8
pickle_list 8
fannkuch 8
etree_iterparse 8
nqueens 8
mako_v2 8
etree_generate 8
call_method_slots 7
html5lib_warmup 7
html5lib 7
nbody 7
spectral_norm 7
spambayes 7
fastunpickle 6
meteor_contest 6
chameleon 6
rietveld 6
tornado_http 5
unpickle_list 5
pickle_dict 4
regex_effbot 3
normal_startup 3
startup_nosite 3
etree_parse 2
call_method_unknown 2
call_simple 1
json_load 1
call_method 1
Python3.6 results
Python source: hg clone https://hg.python.org/cpython cpython
hg id: 96d016f78726 tip
hg id -r 'ancestors(.) and tag()': 1a58b1227501 (3.5) v3.5.0rc1
hg --debug id -i: 96d016f78726afbf66d396f084b291ea43792af1
Benchmark Speedup(%)
fastunpickle 22.94
fastpickle 21.67
json_load 17.64
simple_logging 17.49
meteor_contest 16.67
formatted_logging 15.33
etree_process 14.61
raytrace 13.57
etree_generate 13.56
chaos 12.09
hexiom2 12
nbody 11.88
json_dump_v2 11.24
richards 11.02
nqueens 10.96
fannkuch 10.79
go 10.77
float 10.26
regex_compile 9.8
silent_logging 9.63
pidigits 9.58
etree_iterparse 9.48
2to3 8.44
regex_v8 8.09
regex_effbot 7.88
call_simple 7.63
tornado_http 7.38
etree_parse 4.92
spectral_norm 4.72
normal_startup 4.39
telco 3.88
startup_nosite 3.7
call_method 3.63
unpack_sequence 3.6
call_method_slots 2.91
call_method_unknown 2.59
iterative_count 0.45
threaded_count -2.79
Thank you,
Alecsandru
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