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

[Daniel Lezcano]

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



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