On 04/28/2016 02:57 PM, Daniel Wagner wrote:
> As one can see above in the swait_stat output, the fork() path is
> using completion. A histogram of a fork bomp (1000 forks) benchmark
> shows a slight performance drop by 4%.
>
> [wagi@handman completion-test-5 (master)]$ cat forky-4.6.0-rc4.txt | perl histo -min 0.12 -max 0.20 -int 0.01 -stars -scale 10
> # NumSamples = 1000; Max = 0.208; Min = 0.123
> # Mean = 0.146406; Variance = 0.000275351163999956; SD = 0.0165937085668019
> # Each * represents a count of 10
> 0.1200 - 0.1300 [ 113]: ************
> 0.1300 - 0.1400 [ 324]: *********************************
> 0.1400 - 0.1500 [ 219]: **********************
> 0.1500 - 0.1600 [ 139]: **************
> 0.1600 - 0.1700 [ 94]: **********
> 0.1700 - 0.1800 [ 54]: ******
> 0.1800 - 0.1900 [ 37]: ****
> 0.1900 - 0.2000 [ 18]: **
>
> [wagi@handman completion-test-5 (master)]$ cat forky-4.6.0-rc4-00001-g0a16067.txt | perl histo -min 0.12 -max 0.20 -int 0.01 -stars -scale 10
> # NumSamples = 1000; Max = 0.207; Min = 0.121
> # Mean = 0.152056; Variance = 0.000295474863999994; SD = 0.0171893823042014
> # Each * represents a count of 10
> 0.1200 - 0.1300 [ 17]: **
> 0.1300 - 0.1400 [ 282]: *****************************
> 0.1400 - 0.1500 [ 240]: ************************
> 0.1500 - 0.1600 [ 158]: ****************
> 0.1600 - 0.1700 [ 114]: ************
> 0.1700 - 0.1800 [ 94]: **********
> 0.1800 - 0.1900 [ 66]: *******
> 0.1900 - 0.2000 [ 25]: ***
> 0.2000 - 0.2100 [ 1]: *
I redid the above test and changed my fork bomb to this:
for (i = 0; i < MAX_CHILDREN; i++) {
switch(fork()) {
case -1:
exit(1);
case 0:
_exit(0);
}
}
for (i = 0; i < MAX_CHILDREN; i++) {
do {
pid = waitpid(-1, &status, WUNTRACED );
if (pid < 0 && errno != ECHILD)
exit(1);
} while (!WIFEXITED(status) && !WIFSIGNALED(status));
}
Obviously, fork is not a very good benchmark since we might end up
into memory allocation etc. The distribution I get from baseline and
this batch look very similiar:
[wagi@handman completion (master)]$ cat results/forky-4.6.0-rc4.txt | perl histo -min 0.09 -max 0.11 -int 0.001 -stars -scale 100
0.0910 - 0.0920 [ 3]: *
0.0920 - 0.0930 [ 8]: *
0.0930 - 0.0940 [ 52]: *
0.0940 - 0.0950 [ 404]: *****
0.0950 - 0.0960 [ 1741]: ******************
0.0960 - 0.0970 [ 2221]: ***********************
0.0970 - 0.0980 [ 1612]: *****************
0.0980 - 0.0990 [ 1346]: **************
0.0990 - 0.1000 [ 1223]: *************
0.1000 - 0.1010 [ 724]: ********
0.1010 - 0.1020 [ 362]: ****
0.1020 - 0.1030 [ 186]: **
0.1030 - 0.1040 [ 71]: *
0.1040 - 0.1050 [ 29]: *
0.1050 - 0.1060 [ 12]: *
0.1060 - 0.1070 [ 4]: *
0.1080 - 0.1090 [ 2]: *
[wagi@handman completion (master)]$ cat results/forky-4.6.0-rc4-00001-gc4c770c.txt | perl histo -min 0.09 -max 0.11 -int 0.001 -stars -scale 100
0.0930 - 0.0940 [ 3]: *
0.0940 - 0.0950 [ 9]: *
0.0950 - 0.0960 [ 25]: *
0.0960 - 0.0970 [ 77]: *
0.0970 - 0.0980 [ 324]: ****
0.0980 - 0.0990 [ 1503]: ****************
0.0990 - 0.1000 [ 2247]: ***********************
0.1000 - 0.1010 [ 1708]: ******************
0.1010 - 0.1020 [ 1486]: ***************
0.1020 - 0.1030 [ 1215]: *************
0.1030 - 0.1040 [ 729]: ********
0.1040 - 0.1050 [ 368]: ****
0.1050 - 0.1060 [ 197]: **
0.1060 - 0.1070 [ 65]: *
0.1070 - 0.1080 [ 32]: *
0.1080 - 0.1090 [ 7]: *
0.1090 - 0.1100 [ 2]: *
A t-test (determine if two sets of data are significantly different)
returns a p value of 0 (< 1%). That means we reject the null
hypothesis of equal avarages. That means we have a 0.3% decrease in
perforamnce compared with the baseline.
> Compiling a kernel 100 times results in following statistics gather
> by 'time make -j200'
>
> user
> mean std var max min
> kernbech-4.6.0-rc4 9.126 0.2919 0.08523 9.92 8.55
> kernbech-4.6.0-rc4-00001-g0... 9.24 -1.25% 0.2768 5.17% 0.07664 10.07% 10.11 -1.92% 8.44 1.29%
>
>
> system
> mean std var max min
> kernbech-4.6.0-rc4 1.676e+03 2.409 5.804 1.681e+03 1.666e+03
> kernbech-4.6.0-rc4-00001-g0... 1.675e+03 0.07% 2.433 -1.01% 5.922 -2.03% 1.682e+03 -0.03% 1.67e+03 -0.20%
>
>
> elapsed
> mean std var max min
> kernbech-4.6.0-rc4 2.303e+03 26.67 711.1 2.357e+03 2.232e+03
> kernbech-4.6.0-rc4-00001-g0... 2.298e+03 0.23% 28.75 -7.83% 826.8 -16.26% 2.348e+03 0.38% 2.221e+03 0.49%
>
>
> CPU
> mean std var max min
> kernbech-4.6.0-rc4 4.418e+03 48.9 2.391e+03 4.565e+03 4.347e+03
> kernbech-4.6.0-rc4-00001-g0... 4.424e+03 -0.15% 55.73 -13.98% 3.106e+03 -29.90% 4.572e+03 -0.15% 4.356e+03 -0.21%
>
>
> While the mean is slightly less the var and std are increasing quite
> noticeable.
The idea behind doing the kernel builds is that I wanted to see if
there is an impact observable from a real work load. The above numbers are hard
to interpret, though if you only look at the elapsed time you see it takes
slightly longer. I repeated this test with 500 runs and the numbers I get
are the same as above. So at least it is consisted and repeatable experiment.
Obviously, I tried to micro benchmark what's going on, but so far I
have had any luck. A kernel module which has two threads which do a
ping-pong completion test, A typical trace looks like this:
trigger-2376 [000] 218.982609: sched_waking: comm=waiter/0 pid=2375 prio=120 target_cpu=000
trigger-2376 [000] 218.982609: sched_stat_runtime: comm=trigger pid=2376 runtime=1355 [ns] vruntime=40692621118 [ns]
trigger-2376 [000] 218.982609: sched_wakeup: waiter/0:2375 [120] success=1 CPU:000
trigger-2376 [000] 218.982610: rcu_utilization: Start context switch
trigger-2376 [000] 218.982610: rcu_utilization: End context switch
trigger-2376 [000] 218.982610: sched_stat_runtime: comm=trigger pid=2376 runtime=1072 [ns] vruntime=40692622190 [ns]
trigger-2376 [000] 218.982611: sched_switch: trigger:2376 [120] S ==> waiter/0:2375 [120]
waiter/0-2375 [000] 218.982611: latency_complete: latency=2285
waiter/0-2375 [000] 218.982611: sched_waking: comm=trigger pid=2376 prio=120 target_cpu=000
waiter/0-2375 [000] 218.982611: sched_stat_runtime: comm=waiter/0 pid=2375 runtime=1217 [ns] vruntime=40692622747 [ns]
waiter/0-2375 [000] 218.982612: sched_wakeup: trigger:2376 [120] success=1 CPU:000
waiter/0-2375 [000] 218.982612: rcu_utilization: Start context switch
waiter/0-2375 [000] 218.982612: rcu_utilization: End context switch
waiter/0-2375 [000] 218.982612: sched_stat_runtime: comm=waiter/0 pid=2375 runtime=1099 [ns] vruntime=40692623846 [ns]
waiter/0-2375 [000] 218.982613: sched_switch: waiter/0:2375 [120] S ==> trigger:2376 [120]
I have plotted the latency_complete (the time it takes from complete()
till the waiter is running)
https://www.monom.org/data/completion/completion-latency.png
The stats for the above plot are:
[wagi@handman results (master)]$ csvstat-3 completion-latency-4.6.0-rc4.txt
1. 805
<class 'int'>
Nulls: False
Min: 643
Max: 351709
Sum: 3396063015
Mean: 715.6573082933786
Median: 706.0
Standard Deviation: 385.24467795803787
Unique values: 4662
5 most frequent values:
697: 121547
703: 120730
693: 112609
699: 112543
701: 112370
Row count: 4745376
[wagi@handman results (master)]$ csvstat-3 completion-latency-4.6.0-rc4-00001-gc4c770c.txt
1. 4949
<class 'int'>
Nulls: False
Min: 660
Max: 376049
Sum: 3417112614
Mean: 710.0990997187752
Median: 696
Standard Deviation: 500.7461712849926
Unique values: 4930
5 most frequent values:
693: 188698
689: 165564
692: 158333
688: 156896
684: 155032
Row count: 4812163
In short, I haven't figured out yet why the kernel builds get slightly slower.
The first idea that the fork path is a problem is not 'proofable' with the
the fork bomb. At least if it is executed in a tight loop.
cheers,
daniel
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