Re: [Patch v4 0/6] Introduce Thermal Pressure

[Daniel Lezcano]

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 â represents a count of 1
>>   790.0000 -   791.5000 [     2]: ââ
>>   791.5000 -   793.0000 [     2]: ââ
>>   793.0000 -   794.5000 [     2]: ââ
>>   794.5000 -   796.0000 [     1]: â
>>   796.0000 -   797.5000 [     0]:
>>   797.5000 -   799.0000 [     1]: â
>>   799.0000 -   800.5000 [     0]:
>>   800.5000 -   802.0000 [     0]:
>>   802.0000 -   803.5000 [     0]:
>>   803.5000 -   805.0000 [     1]: â
>>
>>
>> With thermal pressure:
>>
>> # NumSamples = 9; Min = 933.00; Max = 960.00
>> # Mean = 940.777778; Variance = 64.172840; SD = 8.010795; Median 937.000000
>> # each â represents a count of 1
>>   933.0000 -   935.7000 [     3]: âââ
>>   935.7000 -   938.4000 [     2]: ââ
>>   938.4000 -   941.1000 [     2]: ââ
>>   941.1000 -   943.8000 [     0]:
>>   943.8000 -   946.5000 [     0]:
>>   946.5000 -   949.2000 [     1]: â
>>   949.2000 -   951.9000 [     0]:
>>   951.9000 -   954.6000 [     0]:
>>   954.6000 -   957.3000 [     0]:
>>   957.3000 -   960.0000 [     1]: â
>>
> 
> 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.




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