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Message-ID: <20190428121721.GA121434@gmail.com>
Date: Sun, 28 Apr 2019 14:17:21 +0200
From: Ingo Molnar <mingo@...nel.org>
To: Aubrey Li <aubrey.intel@...il.com>
Cc: Julien Desfossez <jdesfossez@...italocean.com>,
Vineeth Remanan Pillai <vpillai@...italocean.com>,
Nishanth Aravamudan <naravamudan@...italocean.com>,
Peter Zijlstra <peterz@...radead.org>,
Tim Chen <tim.c.chen@...ux.intel.com>,
Thomas Gleixner <tglx@...utronix.de>,
Paul Turner <pjt@...gle.com>,
Linus Torvalds <torvalds@...ux-foundation.org>,
Linux List Kernel Mailing <linux-kernel@...r.kernel.org>,
Subhra Mazumdar <subhra.mazumdar@...cle.com>,
Frédéric Weisbecker <fweisbec@...il.com>,
Kees Cook <keescook@...omium.org>,
Greg Kerr <kerrnel@...gle.com>, Phil Auld <pauld@...hat.com>,
Aaron Lu <aaron.lwe@...il.com>,
Valentin Schneider <valentin.schneider@....com>,
Mel Gorman <mgorman@...hsingularity.net>,
Pawan Gupta <pawan.kumar.gupta@...ux.intel.com>,
Paolo Bonzini <pbonzini@...hat.com>
Subject: Re: [RFC PATCH v2 00/17] Core scheduling v2
* Aubrey Li <aubrey.intel@...il.com> wrote:
> On Sun, Apr 28, 2019 at 5:33 PM Ingo Molnar <mingo@...nel.org> wrote:
> > So because I'm a big fan of presenting data in a readable fashion, here
> > are your results, tabulated:
>
> I thought I tried my best to make it readable, but this one looks much better,
> thanks, ;-)
> >
> > #
> > # Sysbench throughput comparison of 3 different kernels at different
> > # load levels, higher numbers are better:
> > #
> >
> > .--------------------------------------|----------------------------------------------------------------.
> > | NA/AVX vanilla-SMT [stddev%] |coresched-SMT [stddev%] +/- | no-SMT [stddev%] +/- |
> > |--------------------------------------|----------------------------------------------------------------|
> > | 1/1 508.5 [ 0.2% ] | 504.7 [ 1.1% ] 0.8% | 509.0 [ 0.2% ] 0.1% |
> > | 2/2 1000.2 [ 1.4% ] | 1004.1 [ 1.6% ] 0.4% | 997.6 [ 1.2% ] 0.3% |
> > | 4/4 1912.1 [ 1.0% ] | 1904.2 [ 1.1% ] 0.4% | 1914.9 [ 1.3% ] 0.1% |
> > | 8/8 3753.5 [ 0.3% ] | 3748.2 [ 0.3% ] 0.1% | 3751.3 [ 0.4% ] 0.1% |
> > | 16/16 7139.3 [ 2.4% ] | 7137.9 [ 1.8% ] 0.0% | 7049.2 [ 2.4% ] 1.3% |
> > | 32/32 10899.0 [ 4.2% ] | 10780.3 [ 4.4% ] -1.1% | 10339.2 [ 9.6% ] -5.1% |
> > | 64/64 15086.1 [ 11.5% ] | 14262.0 [ 8.2% ] -5.5% | 11168.7 [ 22.2% ] -26.0% |
> > | 128/128 15371.9 [ 22.0% ] | 14675.8 [ 14.4% ] -4.5% | 10963.9 [ 18.5% ] -28.7% |
> > | 256/256 15990.8 [ 22.0% ] | 12227.9 [ 10.3% ] -23.5% | 10469.9 [ 19.6% ] -34.5% |
> > '--------------------------------------|----------------------------------------------------------------'
> >
> > One major thing that sticks out is that if we compare the stddev numbers
> > to the +/- comparisons then it's pretty clear that the benchmarks are
> > very noisy: in all but the last row stddev is actually higher than the
> > measured effect.
> >
> > So what does 'stddev' mean here, exactly? The stddev of multipe runs,
> > i.e. measured run-to-run variance? Or is it some internal metric of the
> > benchmark?
> >
>
> The benchmark periodically reports intermediate statistics in one second,
> the raw log looks like below:
> [ 11s ] thds: 256 eps: 14346.72 lat (ms,95%): 44.17
> [ 12s ] thds: 256 eps: 14328.45 lat (ms,95%): 44.17
> [ 13s ] thds: 256 eps: 13773.06 lat (ms,95%): 43.39
> [ 14s ] thds: 256 eps: 13752.31 lat (ms,95%): 43.39
> [ 15s ] thds: 256 eps: 15362.79 lat (ms,95%): 43.39
> [ 16s ] thds: 256 eps: 26580.65 lat (ms,95%): 35.59
> [ 17s ] thds: 256 eps: 15011.78 lat (ms,95%): 36.89
> [ 18s ] thds: 256 eps: 15025.78 lat (ms,95%): 39.65
> [ 19s ] thds: 256 eps: 15350.87 lat (ms,95%): 39.65
> [ 20s ] thds: 256 eps: 15491.70 lat (ms,95%): 36.89
>
> I have a python script to parse eps(events per second) and lat(latency)
> out, and compute the average and stddev. (And I can draw a curve locally).
>
> It's noisy indeed when tasks number is greater than the CPU number.
> It's probably caused by high frequent load balance and context switch.
Ok, so it's basically an internal workload noise metric, it doesn't
represent the run-to-run noise.
So it's the real stddev of the workload - but we don't know whether the
measured performance figure is exactly in the middle of the runtime
probability distribution.
> Do you have any suggestions? Or any other information I can provide?
Yeah, so we don't just want to know the "standard deviation" of the
measured throughput values, but also the "standard error of the mean".
I suspect it's pretty low, below 1% for all rows?
Thanks,
Ingo
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