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[209.132.180.67]) by mx.google.com with ESMTP id d24si4072974eda.162.2019.10.31.05.59.30; Thu, 31 Oct 2019 05:59:54 -0700 (PDT) Received-SPF: pass (google.com: best guess record for domain of linux-kernel-owner@vger.kernel.org designates 209.132.180.67 as permitted sender) client-ip=209.132.180.67; Authentication-Results: mx.google.com; spf=pass (google.com: best guess record for domain of linux-kernel-owner@vger.kernel.org designates 209.132.180.67 as permitted sender) smtp.mailfrom=linux-kernel-owner@vger.kernel.org Received: (majordomo@vger.kernel.org) by vger.kernel.org via listexpand id S1726836AbfJaM5Z (ORCPT + 99 others); Thu, 31 Oct 2019 08:57:25 -0400 Received: from foss.arm.com ([217.140.110.172]:48146 "EHLO foss.arm.com" rhost-flags-OK-OK-OK-OK) by vger.kernel.org with ESMTP id S1726735AbfJaM5Z (ORCPT ); Thu, 31 Oct 2019 08:57:25 -0400 Received: from usa-sjc-imap-foss1.foss.arm.com (unknown [10.121.207.14]) by usa-sjc-mx-foss1.foss.arm.com (Postfix) with ESMTP id A16541FB; Thu, 31 Oct 2019 05:57:24 -0700 (PDT) Received: from localhost (e108754-lin.cambridge.arm.com [10.1.199.68]) by usa-sjc-imap-foss1.foss.arm.com (Postfix) with ESMTPSA id 416C13F71E; Thu, 31 Oct 2019 05:57:24 -0700 (PDT) Date: Thu, 31 Oct 2019 12:57:22 +0000 From: Ionela Voinescu To: Daniel Lezcano 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 Subject: Re: [Patch v4 0/6] Introduce Thermal Pressure Message-ID: <20191031125536.GA9817@e108754-lin> References: <1571776465-29763-1-git-send-email-thara.gopinath@linaro.org> <20191031100631.GC19197@e108754-lin> <2009bac3-405a-c60e-a1dd-191625ff3fc5@linaro.org> MIME-Version: 1.0 Content-Type: text/plain; charset=utf-8 Content-Disposition: inline Content-Transfer-Encoding: 8bit In-Reply-To: <2009bac3-405a-c60e-a1dd-191625ff3fc5@linaro.org> User-Agent: Mutt/1.9.4 (2018-02-28) Sender: linux-kernel-owner@vger.kernel.org Precedence: bulk List-ID: X-Mailing-List: linux-kernel@vger.kernel.org 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. 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. Thank you, Ionela. > > > > -- > Linaro.org │ Open source software for ARM SoCs > > Follow Linaro: Facebook | > Twitter | > Blog >