Content-Type: multipart/mixed; boundary="------------7IAMPpMUIkfR98baKy0F251W"
Message-ID: <6e38d7d7-9991-b0a6-2855-0c8838cce142@gmail.com>
Date:   Fri, 10 Mar 2023 09:11:54 +0900
MIME-Version: 1.0
User-Agent: Mozilla/5.0 (X11; Linux x86_64; rv:102.0) Gecko/20100101
 Thunderbird/102.7.1
To:     paulmck@kernel.org, qiuxu.zhuo@intel.com
Cc:     frederic@kernel.org, jiangshanlai@gmail.com,
        joel@joelfernandes.org, linux-doc@vger.kernel.org,
        linux-kernel@vger.kernel.org, rcu@vger.kernel.org,
        urezki@gmail.com, Akira Yokosawa <akiyks@gmail.com>
References: <1a4b1362-defb-4464-9217-32e6d3d4a8d2@paulmck-laptop>
Subject: Re: [PATCH v3] rcu: Add a minimum time for marking boot as completed
Content-Language: en-US
From:   Akira Yokosawa <akiyks@gmail.com>
In-Reply-To: <1a4b1362-defb-4464-9217-32e6d3d4a8d2@paulmck-laptop>
Precedence: bulk

This is a multi-part message in MIME format.
--------------7IAMPpMUIkfR98baKy0F251W
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 7bit

Hi,

Let me chime in this interesting thread.

On Thu, 9 Mar 2023 13:53:39 -0800, Paul E. McKenney wrote:
> On Thu, Mar 09, 2023 at 03:17:09PM +0000, Zhuo, Qiuxu wrote:
>> > From: Paul E. McKenney <paulmck@kernel.org>
>> > [...]
>> > >
>> > > a's standard deviation is ~0.4.
>> > > b's standard deviation is ~0.5.
>> > >
>> > > a's average 9.0 is at the upbound of the standard deviation of b's [8.0, 9].
>> > > So, the measurements should be statistically significant to some degree.
>> > 
>> > That single standard deviation means that you have 68% confidence that the
>> > difference is real.  This is not far above the 50% leval of random noise.
>> > 95% is the lowest level that is normally considered to be statistically
>> > significant.
>> 
>> 95% means there is no overlap between two standard deviations of a
>> and two standard deviations of b.
>> 
>> This relies on either much less noise during testing or a big enough 
>> difference between a and b. 

Appended is a histogram comparing 2 data sets.

As you see, the one with v2 patch is far from normal distribution.
I think there is at least two peaks.
The one at the right around 9.7 seems not affected by the patch.
In such a case, average and standard deviation of all the data don't
tell much.

It is hard to say anything for sure with such small set of samples.
And the shape of the plot is likely to be highly dependent on machine
setups.

Hope this helps.

        Thanks, Akira

>> 
[...]
--------------7IAMPpMUIkfR98baKy0F251W
Content-Type: image/png; name="time-to-boot.png"
Content-Disposition: attachment; filename="time-to-boot.png"
Content-Transfer-Encoding: base64
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=

--------------7IAMPpMUIkfR98baKy0F251W--