Message-ID: <93ed8dd3-7c27-40ab-ea2c-2f2530c75ebc@linaro.org>
Date:   Thu, 31 Oct 2019 18:48:29 +0100
From:   Daniel Lezcano <daniel.lezcano@...aro.org>
To:     Ionela Voinescu <ionela.voinescu@....com>
Cc:     Thara Gopinath <thara.gopinath@...aro.org>, mingo@...hat.com,
        peterz@...radead.org, vincent.guittot@...aro.org,
        rui.zhang@...el.com, edubezval@...il.com, qperret@...gle.com,
        linux-kernel@...r.kernel.org, amit.kachhap@...il.com,
        javi.merino@...nel.org
Subject: Re: [Patch v4 0/6] Introduce Thermal Pressure

On 31/10/2019 13:57, Ionela Voinescu wrote:
> On Thursday 31 Oct 2019 at 12:54:03 (+0100), Daniel Lezcano wrote:
>> Hi Ionela,
>>
>> On 31/10/2019 11:07, Ionela Voinescu wrote:
>>> Hi Daniel,
>>>
>>> On Tuesday 29 Oct 2019 at 16:34:11 (+0100), Daniel Lezcano wrote:
>>>> Hi Thara,
>>>>
>>>> On 22/10/2019 22:34, Thara Gopinath wrote:
>>>>> Thermal governors can respond to an overheat event of a cpu by
>>>>> capping the cpu's maximum possible frequency. This in turn
>>>>> means that the maximum available compute capacity of the
>>>>> cpu is restricted. But today in the kernel, task scheduler is 
>>>>> not notified of capping of maximum frequency of a cpu.
>>>>> In other words, scheduler is unware of maximum capacity
>>>>> restrictions placed on a cpu due to thermal activity.
>>>>> This patch series attempts to address this issue.
>>>>> The benefits identified are better task placement among available
>>>>> cpus in event of overheating which in turn leads to better
>>>>> performance numbers.
>>>>>
>>>>> The reduction in the maximum possible capacity of a cpu due to a 
>>>>> thermal event can be considered as thermal pressure. Instantaneous
>>>>> thermal pressure is hard to record and can sometime be erroneous
>>>>> as there can be mismatch between the actual capping of capacity
>>>>> and scheduler recording it. Thus solution is to have a weighted
>>>>> average per cpu value for thermal pressure over time.
>>>>> The weight reflects the amount of time the cpu has spent at a
>>>>> capped maximum frequency. Since thermal pressure is recorded as
>>>>> an average, it must be decayed periodically. Exisiting algorithm
>>>>> in the kernel scheduler pelt framework is re-used to calculate
>>>>> the weighted average. This patch series also defines a sysctl
>>>>> inerface to allow for a configurable decay period.
>>>>>
>>>>> Regarding testing, basic build, boot and sanity testing have been
>>>>> performed on db845c platform with debian file system.
>>>>> Further, dhrystone and hackbench tests have been
>>>>> run with the thermal pressure algorithm. During testing, due to
>>>>> constraints of step wise governor in dealing with big little systems,
>>>>> trip point 0 temperature was made assymetric between cpus in little
>>>>> cluster and big cluster; the idea being that
>>>>> big core will heat up and cpu cooling device will throttle the
>>>>> frequency of the big cores faster, there by limiting the maximum available
>>>>> capacity and the scheduler will spread out tasks to little cores as well.
>>>>>
>>>>> Test Results
>>>>>
>>>>> Hackbench: 1 group , 30000 loops, 10 runs       
>>>>>                                                Result         SD             
>>>>>                                                (Secs)     (% of mean)     
>>>>>  No Thermal Pressure                            14.03       2.69%           
>>>>>  Thermal Pressure PELT Algo. Decay : 32 ms      13.29       0.56%         
>>>>>  Thermal Pressure PELT Algo. Decay : 64 ms      12.57       1.56%           
>>>>>  Thermal Pressure PELT Algo. Decay : 128 ms     12.71       1.04%         
>>>>>  Thermal Pressure PELT Algo. Decay : 256 ms     12.29       1.42%           
>>>>>  Thermal Pressure PELT Algo. Decay : 512 ms     12.42       1.15%  
>>>>>
>>>>> Dhrystone Run Time  : 20 threads, 3000 MLOOPS
>>>>>                                                  Result      SD             
>>>>>                                                  (Secs)    (% of mean)     
>>>>>  No Thermal Pressure                              9.452      4.49%
>>>>>  Thermal Pressure PELT Algo. Decay : 32 ms        8.793      5.30%
>>>>>  Thermal Pressure PELT Algo. Decay : 64 ms        8.981      5.29%
>>>>>  Thermal Pressure PELT Algo. Decay : 128 ms       8.647      6.62%
>>>>>  Thermal Pressure PELT Algo. Decay : 256 ms       8.774      6.45%
>>>>>  Thermal Pressure PELT Algo. Decay : 512 ms       8.603      5.41%  
>>>>
>>>> I took the opportunity to try glmark2 on the db845c platform with the
>>>> default decay and got the following glmark2 scores:
>>>>
>>>> Without thermal pressure:
>>>>
>>>> # NumSamples = 9; Min = 790.00; Max = 805.00
>>>> # Mean = 794.888889; Variance = 19.209877; SD = 4.382907; Median 794.000000
>>>> # each ∎ represents a count of 1
>>>>   790.0000 -   791.5000 [     2]: ∎∎
>>>>   791.5000 -   793.0000 [     2]: ∎∎
>>>>   793.0000 -   794.5000 [     2]: ∎∎
>>>>   794.5000 -   796.0000 [     1]: ∎
>>>>   796.0000 -   797.5000 [     0]:
>>>>   797.5000 -   799.0000 [     1]: ∎
>>>>   799.0000 -   800.5000 [     0]:
>>>>   800.5000 -   802.0000 [     0]:
>>>>   802.0000 -   803.5000 [     0]:
>>>>   803.5000 -   805.0000 [     1]: ∎
>>>>
>>>>
>>>> With thermal pressure:
>>>>
>>>> # NumSamples = 9; Min = 933.00; Max = 960.00
>>>> # Mean = 940.777778; Variance = 64.172840; SD = 8.010795; Median 937.000000
>>>> # each ∎ represents a count of 1
>>>>   933.0000 -   935.7000 [     3]: ∎∎∎
>>>>   935.7000 -   938.4000 [     2]: ∎∎
>>>>   938.4000 -   941.1000 [     2]: ∎∎
>>>>   941.1000 -   943.8000 [     0]:
>>>>   943.8000 -   946.5000 [     0]:
>>>>   946.5000 -   949.2000 [     1]: ∎
>>>>   949.2000 -   951.9000 [     0]:
>>>>   951.9000 -   954.6000 [     0]:
>>>>   954.6000 -   957.3000 [     0]:
>>>>   957.3000 -   960.0000 [     1]: ∎
>>>>
>>>
>>> Interesting! If I'm interpreting these correctly there seems to be
>>> significant improvement when applying thermal pressure.
>>>
>>> I'm not familiar with glmark2, can you tell me more about the process
>>> and the work that the benchmark does?
>>
>> glmark2 is a 3D benchmark. I ran it without parameters, so all tests are
>> run. At the end, it gives a score which are the values given above.
>>
>>> I assume this is a GPU benchmark,
>>> but not knowing more about it I fail to see the correlation between
>>> applying thermal pressure to CPU capacities and the improvement of GPU
>>> performance.
>>> Do you happen to know more about the behaviour that resulted in these
>>> benchmark scores?
>>
>> My hypothesis is glmark2 makes the GPU to contribute a lot to the
>> heating effect, thus increasing the temperature to the CPU close to it.
>>
> 
> Hhmm.. yes, I am assuming that there is some thermal mitigation (CPU
> frequency capping) done as a result of the heat inflicted by the work
> on the GPU, but these patches do not result in better thermal
> management as for the GPU to perform better. They only inform the
> scheduler in regards to reduced capacity of CPUs so it can decide to
> better use the compute capacity that it has available.
> 
> There could be a second hand effect of the more efficient use of the
> CPUs which would release thermal headroom for the GPU to use, but I
> would not expect the differences to be as high as in the results above.

Indeed, you may be right.

> Another possibility is that work on the CPUs impacts the scores more
> than I would expect for such a benchmark but again I would not
> expect the work on the CPUs to be significant as to result in such
> differences in the scores.
> 
> If you have the chance to look more into exactly what is the behaviour,
> with and without thermal pressure - cooling states, average frequency,
> use of CPUs, use of GPU, etc, it would be very valuable.

Not sure I have enough bandwidth to do all. I'll double check if there
is a difference when testing both versions.



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