Hi All, This is Alecsandru from Server Scripting Languages Optimization team at Intel Corporation. I would like to submit a patch that improves the performance of the hash computation code on stringobject, bufferobject and unicodeobject. As can be seen from the attached sample performance results from the Grand Unified Python Benchmark, speedups up to 40% were observed. Furthermore, we see a 5-7% performance on OpenStack/Swift, where most of the code is in Python 2.7. Attached is the patch that modifies Object/stringobject.c, Object/bufferobject.c and Object/unicodeobject.c files. We built and tested this patch for Python 2.7 on our Linux machines (CentOS 7/Ubuntu Server 14.04, Intel Xeon Haswell/Broadwell with 18/8 cores). I've also opened an issue on the bug tracker: http://bugs.python.org/issue25106 Steps to apply the patch: 1. hg clone https://hg.python.org/cpython cpython 2. cd cpython 3. hg update 2.7 4. Copy hash8.patch to the current directory 5. hg import --no-commit hash8.patch 6. ./configure 7. make In the following, please find our sample performance results measured on a XEON Haswell machine. Hardware (HW): Intel XEON (Haswell) 18 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: 2.0GHz by echo 2000000 > /sys/devices/system/cpu/cpu*/cpufreq/scaling_min_freq echo 2000000 > /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 Benchmarks Speedup(%) unpack_sequence 40.32733766 chaos 24.84002537 chameleon 23.01392651 silent_logging 22.27202911 django 20.83842317 etree_process 20.46968294 nqueens 20.34234985 pathlib 19.63445919 pidigits 19.34722148 etree_generate 19.25836634 pybench 19.06895825 django_v2 18.06073108 etree_iterparse 17.3797149 fannkuch 17.08120879 pickle_list 16.60363602 raytrace 16.0316265 slowpickle 15.86611184 pickle_dict 15.30447114 call_simple 14.42909032 richards 14.2949594 simple_logging 13.6522626 etree_parse 13.38113097 json_dump_v2 12.26588885 float 11.88164311 mako 11.20606516 spectral_norm 11.04356684 hg_startup 10.57686164 mako_v2 10.37912648 slowunpickle 10.24030714 go 10.03567319 meteor_contest 9.956231435 normal_startup 9.607401586 formatted_logging 9.601244811 html5lib 9.082603748 2to3 8.741557816 html5lib_warmup 8.268150981 nbody 7.507012306 regex_compile 7.153922724 bzr_startup 7.140244739 telco 6.869411927 slowspitfire 5.746323922 tornado_http 5.24360121 rietveld 3.865704876 regex_v8 3.777622219 hexiom2 3.586305282 json_dump 3.477551682 spambayes 3.183991854 fastunpickle 2.971645347 fastpickle 0.673086656 regex_effbot 0.127946837 json_load 0.023727176 Thank you, Alecsandru