Speed April 2016

speed@python.org

7 participants
21 discussions

Re: [Speed] Performance comparison of regular expression engines

by Serhiy Storchaka

On 06.03.16 11:30, Maciej Fijalkowski wrote: > this is really difficult to read, can you tell me which column am I looking at? The first column is the searched pattern. The second column is the number of found matches (for control, it should be the same with all engines and versions). The third column, under the "re" header is the time in milliseconds. The column under the "str.find" header is the time of searching without using regular expressions. PyPy 2.2 usually is significantly faster than CPython 2.7, except searching plain string with regular expression. But thanks to Flexible String Representation searching plain string with and without regular expression is faster on CPython 3.6.

4 years, 11 months

Re: [Speed] When CPython performance depends on dead code...

by Victor Stinner

Hi, 2016-04-27 20:30 GMT+02:00 Brett Cannon <brett(a)python.org>: > My first intuition is some cache somewhere is unhappy w/ the varying sizes. > Have you tried any of this on another machine to see if the results are > consistent? On my laptop, the performance when I add deadcode doesn't seem to change much: the delta is smaller than 1%. I found a fix for my deadcode issue! Use "make profile-opt" rather than "make". Using PGO, GCC reorders hot functions to make them closer. I also read that it records statistics on branches to emit first the most frequent branch. I also modified bm_call_simple.py to use multiple processes and to use random hash seeds, rather than using a single process and disabling hash randomization. Comparison reference => fastcall (my whole fork, not just the tiny patches adding deadcode) using make (gcc -O3): Average: 1183.5 ms +/- 6.1 ms (min: 1173.3 ms, max: 1201.9 ms) - 15 processes x 5 loops => Average: 1121.2 ms +/- 7.4 ms (min: 1106.5 ms, max: 1142.0 ms) - 15 processes x 5 loops Comparison reference => fastcall using make profile-opt (PGO): Average: 962.7 ms +/- 17.8 ms (min: 952.6 ms, max: 998.6 ms) - 15 processes x 5 loops => Average: 961.1 ms +/- 18.6 ms (min: 949.0 ms, max: 1011.3 ms) - 15 processes x 5 loops Using make, fastcall *seems* to be faster, but in fact it looks more like random noise of deadcode. Using PGO, fastcall doesn't change performance at all. I expected fastcall to be faster, but it's the purpose of benchmarks: get real performance, not expectations :-) Next step: modify most benchmarks of perf.py to run multiple processes rather than a single process to test using multiple hash seeds. Victor

5 years

Re: [Speed] When CPython performance depends on dead code...

by Brett Cannon

My first intuition is some cache somewhere is unhappy w/ the varying sizes. Have you tried any of this on another machine to see if the results are consistent? On Wed, 27 Apr 2016 at 08:06 Victor Stinner <victor.stinner(a)gmail.com> wrote: > Hi, > > I'm working on an experimental change of CPython introducing a new > "fast call" calling convention for Python and C functions. It pass an > array of PyObject* and a number of arguments as an C int (PyObject > **stack, int nargs) instead of using a temporary tuple (PyObject > *args). The expectation is that avoiding the creation makes Python > faster. > http://bugs.python.org/issue26814 > > First microbenchmarks on optimized code are promising: between 18% and > 44% faster. > http://bugs.python.org/issue26814#msg263999 > http://bugs.python.org/issue26814#msg264003 > > But I was quickly blocked on "macrobenchmarks" (?): running the Python > benchmark suite says that many benchmarks are between 2% and 15% > slower. I spent hours (days) to investigate the issue using > Cachegrind, Callgrind, Linux perf, strace, ltrace, etc., but I was > unable to understand how my change can makes CPython slower. > > My change is quite big: "34 files changed, 3301 insertions(+), 730 > deletions(-)". In fact, the performance regression can be reproduced > easily with a few lines of C code: see attached patches. You only have > to add some *unused* (dead) code to see a "glitch" in performance. > It's even worse: the performance change depends on the size of unused > code. > > I done my best to isolate the microbenchmark to make it as reliable as > possible. Results of bm_call_simple on my desktop PC: > > (a) Reference: > Average: 1201.0 ms +/- 0.2 ms (min: 1200.7 ms, max: 1201.2 ms) > > (b) Add 2 unused functions, based on (a): > Average: 1273.0 ms +/- 1.8 ms (min: 1270.1 ms, max: 1274.4 ms) > > (c) Add 1 unused short function ("return NULL;"), based on (a): > Average: 1169.6 ms +/- 0.2 ms (min: 1169.3 ms, max: 1169.8 ms) > > (b) and (c) are 2 versions only adding unused code to (a). The > difference between (b) and (c) is the size of unused code. The problem > is that (b) makes the code slower and (c) makes the code faster (!), > whereas I would not expect any performance change. > > A sane person should ignore such minor performance delta (+72 ms = +6% > // -31.4 ms = -3%). Right. But for optimization patches on CPython, > we use the CPython benchmark suite as a proof that yeah, the change > really makes CPython faster, as announced. > > I compiled the C code using GCC (5.3) and Clang (3.7) using various > options: -O0, -O3, -fno-align-functions, -falign-functions=N (with > N=1, 2, 6, 12), -fomit-frame-pointer, -flto, etc. In short, the > performance looks "random". I'm unable to correlate the performance > with any Linux perf event. IMHO the performance depends on something > low level like L1 cache, CPU pipeline, branch prediction, etc. As I > wrote, I'm unable to verify that. > > To reproduce my issue, you can use the following commands: > --------------------------- > hg clone https://hg.python.org/cpython fastcall > # or: "hg clone (...)/cpython fastcall" > # if you already have a local copy of cpython ;-) > cd fastcall > ./configure -C > > # build reference binary > hg up -C -r 496e094f4734 > patch -p1 < prepare.patch > make && mv python python-ref > > # build binary with deadcode 1 > hg up -C -r 496e094f4734 > patch -p1 < prepare.patch > patch -p1 < deadcode1.patch > make && mv python python-deadcode1 > > # build binary with deadcode 2 > hg up -C -r 496e094f4734 > patch -p1 < prepare.patch > patch -p1 < deadcode2.patch > make && mv python python-deadcode2 > > # run benchmark > PYTHONHASHSEED=0 ./python-ref bm_call_simple.py > PYTHONHASHSEED=0 ./python-deadcode1 bm_call_simple.py > PYTHONHASHSEED=0 ./python-deadcode2 bm_call_simple.py > --------------------------- > > It suggest you to isolate at least one CPU and run the benchmark on > isolated CPUs to get reliable timings: > --------------------------- > # run benchmark on the CPU #2 > PYTHONHASHSEED=0 taskset -c 2 ./python-ref bm_call_simple.py > PYTHONHASHSEED=0 taskset -c 2 ./python-deadcode1 bm_call_simple.py > PYTHONHASHSEED=0 taskset -c 2 ./python-deadcode2 bm_call_simple.py > --------------------------- > My notes on CPU isolation: > http://haypo-notes.readthedocs.org/microbenchmark.html > > If you don't want to try CPU isolation, try to get an idle system > and/or run the benchmark many times until the standard deviation (the > "+/- ..." part) looks small enough... > > Don't try to run the microbenchmark without PYTHONHASHSEED=0 or you > will get random results depending on the secret hash key used by the > randomized hash function. (Or modify the code to spawn enough child > process to get an uniform distribution ;-)) > > I don't expect that you get the same numbers than me. For example, on > my laptop, the delta is very small (+/- 1%): > > $ PYTHONHASHSEED=0 taskset -c 2 ./python-ref bm_call_simple.py > Average: 1096.1 ms +/- 12.9 ms (min: 1079.5 ms, max: 1110.3 ms) > > $ PYTHONHASHSEED=0 taskset -c 2 ./python-deadcode1 bm_call_simple.py > Average: 1109.2 ms +/- 11.1 ms (min: 1095.8 ms, max: 1122.9 ms) > => +1% (+13 ms) > > $ PYTHONHASHSEED=0 taskset -c 2 ./python-deadcode2 bm_call_simple.py > Average: 1072.0 ms +/- 1.5 ms (min: 1070.0 ms, max: 1073.9 ms) > => -2% (-24 ms) > > CPU of my desktop: Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz - 4 > physical cores with hyper-threading > CPU of my laptop: Intel(R) Core(TM) i7-3520M CPU @ 2.90GHz - 2 > physical cores with hyper-threading > > I modified bm_call_simple.py to call foo() 100 times rather than 20 in > the loop to see the issue more easily. I also removed dependencies and > changed the output format to display average, standard deviation, > minimum and maximum. > > For more benchmarks, see attached deadcode1.log and deadcode2.log: > results of the CPython benchmark to compare deadcode1 VS reference, > and deadcode2 VS reference run on my desktop PC (perf.py --fast & CPU > isolation). Again, deadcode1 looks slower in most cases, whereas > deadcode2 looks faster in most cases, whereas the difference is still > dead code... > > Victor, disappointed > _______________________________________________ > Speed mailing list > Speed(a)python.org > https://mail.python.org/mailman/listinfo/speed >

5 years

Re: [Speed] When CPython performance depends on dead code...

by Victor Stinner

> For more benchmarks, see attached deadcode1.log and deadcode2.log: > results of the CPython benchmark to compare deadcode1 VS reference, > and deadcode2 VS reference run on my desktop PC (perf.py --fast & CPU > isolation). Again, deadcode1 looks slower in most cases, whereas > deadcode2 looks faster in most cases, whereas the difference is still > dead code... Sorry, I forgot to attach these two files. They are now attached to this new email. Victor

5 years

When CPython performance depends on dead code...

by Victor Stinner

Hi, I'm working on an experimental change of CPython introducing a new "fast call" calling convention for Python and C functions. It pass an array of PyObject* and a number of arguments as an C int (PyObject **stack, int nargs) instead of using a temporary tuple (PyObject *args). The expectation is that avoiding the creation makes Python faster. http://bugs.python.org/issue26814 First microbenchmarks on optimized code are promising: between 18% and 44% faster. http://bugs.python.org/issue26814#msg263999 http://bugs.python.org/issue26814#msg264003 But I was quickly blocked on "macrobenchmarks" (?): running the Python benchmark suite says that many benchmarks are between 2% and 15% slower. I spent hours (days) to investigate the issue using Cachegrind, Callgrind, Linux perf, strace, ltrace, etc., but I was unable to understand how my change can makes CPython slower. My change is quite big: "34 files changed, 3301 insertions(+), 730 deletions(-)". In fact, the performance regression can be reproduced easily with a few lines of C code: see attached patches. You only have to add some *unused* (dead) code to see a "glitch" in performance. It's even worse: the performance change depends on the size of unused code. I done my best to isolate the microbenchmark to make it as reliable as possible. Results of bm_call_simple on my desktop PC: (a) Reference: Average: 1201.0 ms +/- 0.2 ms (min: 1200.7 ms, max: 1201.2 ms) (b) Add 2 unused functions, based on (a): Average: 1273.0 ms +/- 1.8 ms (min: 1270.1 ms, max: 1274.4 ms) (c) Add 1 unused short function ("return NULL;"), based on (a): Average: 1169.6 ms +/- 0.2 ms (min: 1169.3 ms, max: 1169.8 ms) (b) and (c) are 2 versions only adding unused code to (a). The difference between (b) and (c) is the size of unused code. The problem is that (b) makes the code slower and (c) makes the code faster (!), whereas I would not expect any performance change. A sane person should ignore such minor performance delta (+72 ms = +6% // -31.4 ms = -3%). Right. But for optimization patches on CPython, we use the CPython benchmark suite as a proof that yeah, the change really makes CPython faster, as announced. I compiled the C code using GCC (5.3) and Clang (3.7) using various options: -O0, -O3, -fno-align-functions, -falign-functions=N (with N=1, 2, 6, 12), -fomit-frame-pointer, -flto, etc. In short, the performance looks "random". I'm unable to correlate the performance with any Linux perf event. IMHO the performance depends on something low level like L1 cache, CPU pipeline, branch prediction, etc. As I wrote, I'm unable to verify that. To reproduce my issue, you can use the following commands: --------------------------- hg clone https://hg.python.org/cpython fastcall # or: "hg clone (...)/cpython fastcall" # if you already have a local copy of cpython ;-) cd fastcall ./configure -C # build reference binary hg up -C -r 496e094f4734 patch -p1 < prepare.patch make && mv python python-ref # build binary with deadcode 1 hg up -C -r 496e094f4734 patch -p1 < prepare.patch patch -p1 < deadcode1.patch make && mv python python-deadcode1 # build binary with deadcode 2 hg up -C -r 496e094f4734 patch -p1 < prepare.patch patch -p1 < deadcode2.patch make && mv python python-deadcode2 # run benchmark PYTHONHASHSEED=0 ./python-ref bm_call_simple.py PYTHONHASHSEED=0 ./python-deadcode1 bm_call_simple.py PYTHONHASHSEED=0 ./python-deadcode2 bm_call_simple.py --------------------------- It suggest you to isolate at least one CPU and run the benchmark on isolated CPUs to get reliable timings: --------------------------- # run benchmark on the CPU #2 PYTHONHASHSEED=0 taskset -c 2 ./python-ref bm_call_simple.py PYTHONHASHSEED=0 taskset -c 2 ./python-deadcode1 bm_call_simple.py PYTHONHASHSEED=0 taskset -c 2 ./python-deadcode2 bm_call_simple.py --------------------------- My notes on CPU isolation: http://haypo-notes.readthedocs.org/microbenchmark.html If you don't want to try CPU isolation, try to get an idle system and/or run the benchmark many times until the standard deviation (the "+/- ..." part) looks small enough... Don't try to run the microbenchmark without PYTHONHASHSEED=0 or you will get random results depending on the secret hash key used by the randomized hash function. (Or modify the code to spawn enough child process to get an uniform distribution ;-)) I don't expect that you get the same numbers than me. For example, on my laptop, the delta is very small (+/- 1%): $ PYTHONHASHSEED=0 taskset -c 2 ./python-ref bm_call_simple.py Average: 1096.1 ms +/- 12.9 ms (min: 1079.5 ms, max: 1110.3 ms) $ PYTHONHASHSEED=0 taskset -c 2 ./python-deadcode1 bm_call_simple.py Average: 1109.2 ms +/- 11.1 ms (min: 1095.8 ms, max: 1122.9 ms) => +1% (+13 ms) $ PYTHONHASHSEED=0 taskset -c 2 ./python-deadcode2 bm_call_simple.py Average: 1072.0 ms +/- 1.5 ms (min: 1070.0 ms, max: 1073.9 ms) => -2% (-24 ms) CPU of my desktop: Intel(R) Core(TM) i7-2600 CPU @ 3.40GHz - 4 physical cores with hyper-threading CPU of my laptop: Intel(R) Core(TM) i7-3520M CPU @ 2.90GHz - 2 physical cores with hyper-threading I modified bm_call_simple.py to call foo() 100 times rather than 20 in the loop to see the issue more easily. I also removed dependencies and changed the output format to display average, standard deviation, minimum and maximum. For more benchmarks, see attached deadcode1.log and deadcode2.log: results of the CPython benchmark to compare deadcode1 VS reference, and deadcode2 VS reference run on my desktop PC (perf.py --fast & CPU isolation). Again, deadcode1 looks slower in most cases, whereas deadcode2 looks faster in most cases, whereas the difference is still dead code... Victor, disappointed

5 years

Re: [Speed] Disable hash randomization to get reliable benchmarks

by Antoine Pitrou

On Tue, 26 Apr 2016 18:28:32 +0200 Maciej Fijalkowski <fijall(a)gmail.com> wrote: > > taking the minimum is a terrible idea anyway, none of the statistical > discussion makes sense if you do that The minimum is a reasonable metric for quick throwaway benchmarks as timeit is designed for, as it has a better hope of alleviating the impact of system load (as such throwaway benchmarks are often run on the developer's workstation). For a persistent benchmarks suite, where we can afford longer benchmark runtimes and are able to keep system noise to a minimum, we might prefer another metric. Regards Antoine.

5 years

Re: [Speed] Disable hash randomization to get reliable benchmarks

by Maciej Fijalkowski

On Tue, Apr 26, 2016 at 11:46 AM, Victor Stinner <victor.stinner(a)gmail.com> wrote: > Hi, > > 2016-04-26 10:56 GMT+02:00 Armin Rigo <arigo(a)tunes.org>: >> Hi, >> >> On 25 April 2016 at 08:25, Maciej Fijalkowski <fijall(a)gmail.com> wrote: >>> The problem with disabled ASLR is that you change the measurment from >>> a statistical distribution, to one draw from a statistical >>> distribution repeatedly. There is no going around doing multiple runs >>> and doing an average on that. >> >> You should mention that it is usually enough to do the following: >> instead of running once with PYTHONHASHSEED=0, run five or ten times >> with PYTHONHASHSEED in range(5 or 10). In this way, you get all >> benefits: not-too-long benchmarking, no randomness, but still some >> statistically relevant sampling. > > I guess that the number of required runs to get a nice distribution > depends on the size of the largest dictionary in the benchmark. I > mean, the dictionaries that matter in performance. > > The best would be to handle this transparently in perf.py. Either > disable all source of randomness, or run mutliple processes to have an > uniform distribution, rather than on only having one sample for one > specific config. Maybe it could be an option: by default, run multiple > processes, but have an option to only run one process using > PYTHONHASHSEED=0. > > By the way, timeit has a very similar issue. I'm quite sure that most > Python developers run "python -m timeit ..." at least 3 times and take > the minimum. "python -m timeit" could maybe be modified to also spawn > child processes to get a better distribution, and maybe also modified > to display the minimum, the average and the standard deviation? (not > only the minimum) taking the minimum is a terrible idea anyway, none of the statistical discussion makes sense if you do that > > Well, the question is also if it's a good thing to have such really > tiny microbenchmark like bm_call_simple in the Python benchmark suite. > I spend 2 or 3 days to analyze CPython running bm_call_simple with > Linux perf tool, callgrind and cachegrind. I'm still unable to > understand the link between my changes on the C code and the result. > IMHO this specific benchmark depends on very low-level things like the > CPU L1 cache. Maybe bm_call_simple helps in some very specific use > cases, like trying to make Python function calls faster. But in other > cases, it can be a source of noise, confusion and frustration... > > Victor maybe it's just a terrible benchmark (it surely is for pypy for example)

5 years

Re: [Speed] Disable hash randomization to get reliable benchmarks

by Victor Stinner

Hi, 2016-04-26 11:01 GMT+02:00 Antonio Cuni <anto.cuni(a)gmail.com>: > On Mon, Apr 25, 2016 at 12:49 AM, Victor Stinner <victor.stinner(a)gmail.com> > wrote: >> Last months, I spent a lot of time on microbenchmarks. Probably too >> much time :-) I found a great Linux config to get a much more stable >> system to get reliable microbenchmarks: >> https://haypo-notes.readthedocs.org/microbenchmark.html >> >> * isolate some CPU cores > > you might be interested in cpusets and the cset utility: in theory, they > allow you to isolate one CPU without having to reboot to change the kernel > parameters: > > http://skebanga.blogspot.it/2012/06/cset-shield-easily-configure-cpusets.ht… > https://github.com/lpechacek/cpuset Ah, I didn't know this tool. Basically, it looks similar to the Linux isolcpus command line parameter, but done in userpace. I see an advantage, it can be used temporary without having to reboot the kernel. > However, I never did a scientific comparison between cpusets and isolcpu to > see if the former behaves exactly like the latter. I have a simple test: * run a benchmark when the system is idle * run a benchmark when the system is *very* busy (ex: system load > 5) Using CPU isolation + nohz_full + blocking IRQ on isolated CPUs, the benchmark result is the *same* in two cases. Try on a Linux without any specific config to see a huge difference. For example, performance divided by two. I'm using CPU isolation to be able to run benchmarks while I'm still working on my PC: use firefox, thunderbird, run heavy unit tests, compile C code, etc. Right code, I dedicated 2 physical cores to benchmarks and kept 2 physical cores for regular work. Maybe it's too much. It looks like almost all benchmarks only use logical core in practice (whereas 2 physical cores give me 4 logical cores). Next time I will probably only dedicate 1 physical core. The advantage of having two dedicated physical cores is to be able to run two "isolated" benchmarks in parallel ;-) I wrote a simple tool to get a system load larger than a minimum: https://bitbucket.org/haypo/misc/src/tip/bin/system_load.py I also started to write a script to configure a system for CPU isolation: https://bitbucket.org/haypo/misc/src/tip/bin/isolcpus.py * Block IRQ on isolated CPu cores * Disable ASLR * Force performance CPU speed on isolated cores, but not on other cores. I don't want to burn my PC :-) Intel P-state is still enabled on all CPU cores, so the power state of isolated cores still change dynamically in practice. You can see it using powertop for example. CPU isolation is not perfect, you still have random source of noises. There are also System Management Interrupt (SMI) and other low-level things. I hope that running multiple iterations of the benchmark is be enough to reduce (or remove) other sources of noise. By the way, search "Linux realtime" to find good information about "sources of noise" on Linux. Example: https://rt.wiki.kernel.org/index.php/HOWTO:_Build_an_RT-application#Hardware Hopefully, my requirements on timing are more cool than hard realtime ;-) Victor

5 years

Re: [Speed] Disable hash randomization to get reliable benchmarks

by Victor Stinner

Hi, 2016-04-26 10:56 GMT+02:00 Armin Rigo <arigo(a)tunes.org>: > Hi, > > On 25 April 2016 at 08:25, Maciej Fijalkowski <fijall(a)gmail.com> wrote: >> The problem with disabled ASLR is that you change the measurment from >> a statistical distribution, to one draw from a statistical >> distribution repeatedly. There is no going around doing multiple runs >> and doing an average on that. > > You should mention that it is usually enough to do the following: > instead of running once with PYTHONHASHSEED=0, run five or ten times > with PYTHONHASHSEED in range(5 or 10). In this way, you get all > benefits: not-too-long benchmarking, no randomness, but still some > statistically relevant sampling. I guess that the number of required runs to get a nice distribution depends on the size of the largest dictionary in the benchmark. I mean, the dictionaries that matter in performance. The best would be to handle this transparently in perf.py. Either disable all source of randomness, or run mutliple processes to have an uniform distribution, rather than on only having one sample for one specific config. Maybe it could be an option: by default, run multiple processes, but have an option to only run one process using PYTHONHASHSEED=0. By the way, timeit has a very similar issue. I'm quite sure that most Python developers run "python -m timeit ..." at least 3 times and take the minimum. "python -m timeit" could maybe be modified to also spawn child processes to get a better distribution, and maybe also modified to display the minimum, the average and the standard deviation? (not only the minimum) Well, the question is also if it's a good thing to have such really tiny microbenchmark like bm_call_simple in the Python benchmark suite. I spend 2 or 3 days to analyze CPython running bm_call_simple with Linux perf tool, callgrind and cachegrind. I'm still unable to understand the link between my changes on the C code and the result. IMHO this specific benchmark depends on very low-level things like the CPU L1 cache. Maybe bm_call_simple helps in some very specific use cases, like trying to make Python function calls faster. But in other cases, it can be a source of noise, confusion and frustration... Victor

5 years

Re: [Speed] Disable hash randomization to get reliable benchmarks

by Antonio Cuni

Hi Armin, On Tue, Apr 26, 2016 at 10:56 AM, Armin Rigo <arigo(a)tunes.org> wrote: > Hi, > > On 25 April 2016 at 08:25, Maciej Fijalkowski <fijall(a)gmail.com> wrote: > > The problem with disabled ASLR is that you change the measurment from > > a statistical distribution, to one draw from a statistical > > distribution repeatedly. There is no going around doing multiple runs > > and doing an average on that. > > You should mention that it is usually enough to do the following: > instead of running once with PYTHONHASHSEED=0, run five or ten times > with PYTHONHASHSEED in range(5 or 10). In this way, you get all > benefits: not-too-long benchmarking, no randomness, but still some > statistically relevant sampling. > note that here there are two sources of "randomness": one is PYTHONHASHSEED (which you can control with the env variable), the other is ASLR which, AFAIK, you cannot control in the same fine way: you can only either enable or disable it.

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Speed April 2016