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([2a01:e34:ed2f:f020:bc1d:8e1f:c99f:e225]) by smtp.googlemail.com with ESMTPSA id v8sm5073331wra.79.2019.10.31.10.48.29 (version=TLS1_2 cipher=ECDHE-RSA-AES128-GCM-SHA256 bits=128/128); Thu, 31 Oct 2019 10:48:30 -0700 (PDT) Subject: Re: [Patch v4 0/6] Introduce Thermal Pressure To: Ionela Voinescu Cc: Thara Gopinath , mingo@redhat.com, peterz@infradead.org, vincent.guittot@linaro.org, rui.zhang@intel.com, edubezval@gmail.com, qperret@google.com, linux-kernel@vger.kernel.org, amit.kachhap@gmail.com, javi.merino@kernel.org References: <1571776465-29763-1-git-send-email-thara.gopinath@linaro.org> <20191031100631.GC19197@e108754-lin> <2009bac3-405a-c60e-a1dd-191625ff3fc5@linaro.org> <20191031125536.GA9817@e108754-lin> From: Daniel Lezcano Openpgp: preference=signencrypt Autocrypt: addr=daniel.lezcano@linaro.org; prefer-encrypt=mutual; keydata= mQINBFv/yykBEADDdW8RZu7iZILSf3zxq5y8YdaeyZjI/MaqgnvG/c3WjFaunoTMspeusiFE sXvtg3ehTOoyD0oFjKkHaia1Zpa1m/gnNdT/WvTveLfGA1gH+yGes2Sr53Ht8hWYZFYMZc8V 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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. -- Linaro.org │ Open source software for ARM SoCs Follow Linaro: Facebook | Twitter | Blog