From patchwork Wed Oct 4 09:04:48 2023 Content-Type: text/plain; charset="utf-8" MIME-Version: 1.0 Content-Transfer-Encoding: 7bit X-Patchwork-Submitter: Hongyan Xia X-Patchwork-Id: 14899 Return-Path: Delivered-To: ouuuleilei@gmail.com Received: by 2002:a05:612c:2a8e:b0:403:3b70:6f57 with SMTP id in14csp2620899vqb; Wed, 4 Oct 2023 02:05:31 -0700 (PDT) X-Google-Smtp-Source: AGHT+IGCocMna/IZYc8d4lCn1KPkqrfS7F+xrnNIopGeK/hLEIhDbBpE9f93oSkSaXg6iSRuKumY X-Received: by 2002:a05:6a20:1447:b0:14e:3daf:fdb9 with SMTP id a7-20020a056a20144700b0014e3daffdb9mr2061521pzi.22.1696410331111; Wed, 04 Oct 2023 02:05:31 -0700 (PDT) ARC-Seal: i=1; a=rsa-sha256; t=1696410331; cv=none; d=google.com; s=arc-20160816; b=WZMuk7363JikRPl2nVTPh0swpRn0j3N0qsuTK6RZaj7NoFLYbm5Sl2Yzqgq2g6xTuq dTAB9yvP+EvMeHDauwY4emw2X5o2QbmzmQa+R9sMbW1mS4N3TXUA2MT7rneK2BV85F6E CmP0I3FrzV/MyJLLdaf6Y0nli9Bq1BorAns7OOXshhekwt1a+g6JrmwdhRNXdrWkL/EP ah4h0UuMtQhsHwGaLsFwN7keE/+QbxT7sr54Dfvvs1HIoAv36Da9/5fjKSsS1pM0kiWm iHd0KOqcr4OHP9gxFfMOsKYwUiKpM1LzvL8WygDZgHnBblczrWAByC22+Xri+3dmbgKM nW9w== ARC-Message-Signature: i=1; a=rsa-sha256; c=relaxed/relaxed; d=google.com; s=arc-20160816; h=list-id:precedence:content-transfer-encoding:mime-version :message-id:date:subject:cc:to:from; bh=Cs2LnarsSLmgQA3aeMA0KtzzS1klLtJObLVgOkkbOgQ=; fh=EztwlepW2jPibYOQJhdPP3KHZCP3+ZnpZcr08LILCr4=; b=G2I15X8ix/FMkiquVBUIAfDnhPUxexmzUB2mn10qPtbnhoMsetuI9jyLcKD8S0bPUc wR9uVrEAzcoPHRn69NAUE9X2k6tQa3vcNPIX9DFzV1FckcNStWXfXQd+G4yKXS9q82Aq 7MfvSeO9NiZBTZiDH68vUNa4e8uMy1btsuLlNnZ1233RfOjhFXskYsA+ftp6yt2ADwUK netfF+tzuIsgHt4KpXaWigdythTdZt8yrhDlVRbFqGqQ4WTUp1yC8YIcTWv0xx/zsCwg R16aMxl5eDKJ6MyOor20e8mQwE2qLlXNEv5e/JvA4A7Xuace9ISnABGwDpxsmvwpgQ8o igAw== ARC-Authentication-Results: i=1; mx.google.com; spf=pass (google.com: domain of linux-kernel-owner@vger.kernel.org designates 23.128.96.34 as permitted sender) smtp.mailfrom=linux-kernel-owner@vger.kernel.org; dmarc=fail (p=NONE sp=NONE dis=NONE) header.from=arm.com Received: from howler.vger.email (howler.vger.email. [23.128.96.34]) by mx.google.com with ESMTPS id fd2-20020a056a002e8200b0068e35d412c9si3476603pfb.323.2023.10.04.02.05.30 (version=TLS1_3 cipher=TLS_AES_256_GCM_SHA384 bits=256/256); Wed, 04 Oct 2023 02:05:31 -0700 (PDT) Received-SPF: pass (google.com: domain of linux-kernel-owner@vger.kernel.org designates 23.128.96.34 as permitted sender) client-ip=23.128.96.34; Authentication-Results: mx.google.com; spf=pass (google.com: domain of linux-kernel-owner@vger.kernel.org designates 23.128.96.34 as permitted sender) smtp.mailfrom=linux-kernel-owner@vger.kernel.org; dmarc=fail (p=NONE sp=NONE dis=NONE) header.from=arm.com Received: from out1.vger.email (depot.vger.email [IPv6:2620:137:e000::3:0]) by howler.vger.email (Postfix) with ESMTP id 285C1830D363; Wed, 4 Oct 2023 02:05:30 -0700 (PDT) X-Virus-Status: Clean X-Virus-Scanned: clamav-milter 0.103.10 at howler.vger.email Received: (majordomo@vger.kernel.org) by vger.kernel.org via listexpand id S241094AbjJDJF1 (ORCPT + 18 others); Wed, 4 Oct 2023 05:05:27 -0400 Received: from lindbergh.monkeyblade.net ([23.128.96.19]:42788 "EHLO lindbergh.monkeyblade.net" rhost-flags-OK-OK-OK-OK) by vger.kernel.org with ESMTP id S232554AbjJDJFY (ORCPT ); Wed, 4 Oct 2023 05:05:24 -0400 Received: from foss.arm.com (foss.arm.com [217.140.110.172]) by lindbergh.monkeyblade.net (Postfix) with ESMTP id 27316A6 for ; Wed, 4 Oct 2023 02:05:20 -0700 (PDT) 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 2F97DDA7; Wed, 4 Oct 2023 02:05:57 -0700 (PDT) Received: from e130256.cambridge.arm.com (usa-sjc-imap-foss1.foss.arm.com [10.121.207.14]) by usa-sjc-imap-foss1.foss.arm.com (Postfix) with ESMTPSA id 260423F59C; Wed, 4 Oct 2023 02:05:17 -0700 (PDT) From: Hongyan Xia To: Ingo Molnar , Peter Zijlstra , Vincent Guittot , Dietmar Eggemann Cc: Qais Yousef , Morten Rasmussen , Lukasz Luba , Christian Loehle , linux-kernel@vger.kernel.org, Hongyan Xia Subject: [RFC PATCH 0/6] sched: uclamp sum aggregation Date: Wed, 4 Oct 2023 10:04:48 +0100 Message-Id: X-Mailer: git-send-email 2.34.1 MIME-Version: 1.0 X-Spam-Status: No, score=-1.9 required=5.0 tests=BAYES_00, RCVD_IN_DNSWL_BLOCKED,SPF_HELO_NONE,SPF_NONE,URIBL_BLOCKED autolearn=ham autolearn_force=no version=3.4.6 X-Spam-Checker-Version: SpamAssassin 3.4.6 (2021-04-09) on lindbergh.monkeyblade.net Precedence: bulk List-ID: X-Mailing-List: linux-kernel@vger.kernel.org X-Greylist: Sender passed SPF test, not delayed by milter-greylist-4.6.4 (howler.vger.email [0.0.0.0]); Wed, 04 Oct 2023 02:05:30 -0700 (PDT) X-getmail-retrieved-from-mailbox: INBOX X-GMAIL-THRID: 1778815159303676964 X-GMAIL-MSGID: 1778815159303676964 Current implementation of uclamp leaves many things to be desired. There are several problems: 1. Max aggregation is fragile. A single task with a high UCLAMP_MAX (or with the default value, which is 1024) can ruin the previous settings the moment it is enqueued, as shown in the uclamp frequency spike problem in Section 5.2 of Documentation/scheduler/sched-util-clamp.rst. Constantly running at 1024 utilization implies that the CPU is driven at its max capacity. However, with UCLAMP_MAX, this assumption is no longer true. To mitigate this, one idea is to filter out uclamp values for short-running tasks. However, the effectiveness of this idea remains to be seen. 2. No way to differentiate between UCLAMP_MAX throttled CPU or a CPU running at its peak, as both show utilization of 1024. However, in certain cases it's important to tell the difference, as we still want to take the opportunity to enqueue tasks in the former case. It's also debatable whether uclamp should be a frequency hint. An example is a system with a mid CPU of capacity 500, and a little CPU with capacity 100. If we have 10 tasks with UCLAMP_MIN of 101, then util_fits_cpu() will schedule them on the mid CPU because feec() thinks they don't fit on the little, even though we should use the little core at some point instead of making the mid even more crowded. Of course, this in reality doesn't happen because of other mechanisms like load balancing, but it's also not good when other mechanisms can cancel the effect of uclamp in feec(). A CPU running at capacity 500 for 1ms or for 1000ms gives completely different performance levels, so trying to fit only the frequency does not give us any performance guarantees. If we then talk about not only running at some frequency but also for some amount of time, then what we really mean is a capacity hint, not a frequency hint. It's even worse when the tasks scheduled on the mid CPU also have UCLAMP_MAX values. In the previous example, instead of running at 500, a single UCLAMP_MAX, assuming it's 110, can make the mid CPU run at a much lower frequency than 500, so it is then a really bad decision to honor the UCLAMP_MIN frequency hint and place it on the mid CPU, instead of running it on the little CPU which is less crowded, and has pretty much the same capacity (100 vs 110) anyway. This series attempts to solve these problems by tracking a util_avg_uclamp signal in tasks and cfs_rq. At task level, p->se.avg.util_avg_uclamp is basically tracking the normal util_avg, but clamped within its uclamp min and max. At cfs_rq level, util_avg_uclamp must always be the sum of all util_avg_uclamp of all the entities on this cfs_rq. As a result, cfs_rq->avg.util_avg_uclamp is the sum aggregation of all the clamped values, which indicates the frequency this rq should run at and what the utilization is. This idea solves Problem 1 by capping the utilization of an always-running task throttled by UCLAMP_MAX. Although the task (denoted by Task A) has no idle time, the util_avg_uclamp signal gives its UCLAMP_MAX value instead of 1024, so even if another task (Task B) with a UCLAMP_MAX value at 1024 joins the rq, the util_avg_uclamp is A's UCLAMP_MAX plus B's utilization, instead of 1024 plus B's utilization, which means we no longer have the frequency spike problem caused by B. This should mean that we might completely avoid the need for uclamp filtering. It also solves Problem 2 by tracking the capped utilization of a rq. Using util_avg_uclamp, we are able to differentiate between a CPU throttled by UCLAMP_MAX and one that is actually running at its peak capacity. An always-running rq with a task having UCLAMP_MAX at 300 will report a cfs_rq util_avg_uclamp at 300, not 1024, which means task placement sees spare capacity on this CPU and will allocate some tasks to it, instead of treating it the same way as a CPU actually running at the peak. This brings us closer to the benefits of Android's sum aggregation (see code related to CONFIG_USE_GROUP_THROTTLE at https://android.googlesource.com/kernel/gs/+/refs/heads/android-gs-raviole-5.10-android12-d1/drivers/soc/google/vh/kernel/sched/fair.c#510), which shows energy benefits because we are able to schedule tasks on smaller cores which are UCLAMP_MAX capped, instead of finding a fresh big CPU. However, a major difference is that this series has an amortized cost of O(1), instead of O(n) in cpu_util_next() in Android kernels. It avoids the shortcomings from uclamp-being-a-frequency-hint. Under sum aggregation, the scheduler sees the sum aggregated util_avg_uclamp and will avoid the problem of enqueueing too many tasks just to fit the frequency, and will place tasks on little CPUs in the previous example. Patch 2/6 tries to simulate PELT decay in the new util_avg_uclamp signal, as this gives us gradual decay of utilization which avoids premature CPU frequency drops. This is a major caveat of this series. We should explore if there's a better way to integrate uclamp directly into the util_avg signal, instead of introducing a new util_avg_uclamp and then simulate PELT on it. This new design significantly simplifies uclamp logic. Patch 4/6 removes the tri-state return value of util_fits_cpu(). Patch 5/6 then completely removes uclamp buckets. Because the util_avg_uclamp is already a clamped value propagated from bottom to top, there's no need to clamp anything at the top level and we can just use this value for frequency selection and spare capacity search. Basically, the idea is that all uclamp logic happens inside util_avg_uclamp, and other code using this new signal can just use it like util_avg, without even knowing that uclamp exists. This drastically reduces the complexity of uclamp and makes the code considering all the uclamp corner cases unnecessary. At the end of the series, we remove 749 lines of code while adding a bit more than 300 (although once we update Documentation.rst, it will be a bit more than that). Note that this series is still considered RFC status. TODO items are: 1. Implement sum aggregation for RT tasks. 2. Improve handling of cpu_util(boost). TESTING: Initial test and benchmark results suggest that sum aggregation not only is significantly simpler, but generally performs much better in achieving what uclamp is supposed to do. Two notebooks are shared at https://nbviewer.org/github/honxia02/sum_aggre_notebooks/blob/main/upstream.ipynb https://nbviewer.org/github/honxia02/sum_aggre_notebooks/blob/main/sum.ipynb The experiments done in notebooks are on Arm Juno r2 board. CPU0-3 are little cores with capacity of 383. CPU4-5 are big cores. The rt-app profiles used for these experiments are included in the notebooks. Scenario 1: Scheduling 4 always-running tasks with UCLAMP_MAX at 200. The scheduling decisions are plotted in Out[11] and Out[14] respectively. Max aggregation fails to recognize the opportunity to run all of them on the efficient little Power Domain (PD), whereas sum aggregation runs all 4 on the little PD, leaving the big PD completely powered off, satisfying uclamp requests while saving power. Scenario 2: Scheduling 4 tasks with UCLAMP_MIN and UCLAMP_MAX at a value slightly above the capacity of the little CPU. Results are in Out[17] and Out[82]. The purpose is to use UCLAMP_MIN to place tasks on the big core. Max aggregation is pretty much in an overutilized state the entire time. Sum aggregation sees that the big CPU can hold two such tasks (satisfying both UCLAMP_MIN and UCLAMP_MAX requests for all 4 tasks) on each big CPU and quickly settles down, and is still under EAS without overutilization. Scenario 3: Task A is a task with a small utilization pinned to CPU4. Task B is an always-running task pinned to CPU5, but UCLAMP_MAX capped at 200. After a while, task A is then pinned to CPU5, joining B. Results are in Out[23] and Out[95]. The blue util_avg signal is the original root CFS util_avg. The yellow line is the root CFS utilization after considering uclamp. Max aggregation sees a frequency spike at 579.1s. When zoomed in, one can see square-wave-like utilization values because of A periodically going to sleep. When A wakes up, its default UCLAMP_MAX of 1024 will uncap B and reach the highest CPU frequency. When A sleeps, B's UCLAMP_MAX will be in effect and will reduce rq utilization to 200. This happens repeatedly, hence the square wave. In contrast, sum aggregation sees a normal increase in utilization when A joins B, at around 2507s, without any square-wave behavior. Scenario 4: 4 always-running tasks with UCLAMP_MAX of 120 pinned to the little PD (CPU0-3). 4 same tasks pinned to the big PD (CPU4-5). After a while, remove the CPU pinning of the 4 tasks on the big PD. Results are in Out[29] and Out[101]. Max aggregation fails to identify that all 8 tasks can be packed on the little PD, whereas sum aggregation immediately moves the 4 tasks pinned to big PD to the little PD the moment pinning is removed. Sum aggregation understands that even when the rq seems to have utilization of 1024, this is because of UCLAMP_MAX and there's still opportunity to pack 2 such tasks on each little CPU, leaving the big PD untouched, saving power. BENCHMARKS: One TODO item is to handle cpu_util(boost) better. The current handling of boost is far from ideal and is a known caveat. All below benchmarks numbers are done without any boosting in cpu_util(), in both max and sum aggregation. Geekbench 6 (on Rock-5B board) +-----+-------------+------------+ | | Single-core | Multi-core | +-----+-------------+------------+ | Max | 800.6 | 2977.0 | | Sum | 801.2 | 2981.4 | +-----+-------------+------------+ No regression is seen after switching to sum aggregation. Jankbench (backporting sum aggregation to Android 5.18 mainline kernel) +------+-----------------+-------+-----------+ | | variable | value | perc_diff | +------+-----------------+-------+-----------+ | main | jank_percentage | 1.1 | 0.00% | | sum | jank_percentage | 0.5 | -53.92% | +------+-----------------+-------+-----------+ +------------+--------+------+-------+-----------+ | | metric | tag | value | perc_diff | +------------+--------+------+-------+-----------+ | CPU | gmean | main | 166.1 | 0.00% | | CPU-Big | gmean | main | 55.1 | 0.00% | | CPU-Little | gmean | main | 91.7 | 0.00% | | CPU-Mid | gmean | main | 19.2 | 0.00% | | CPU | gmean | sum | 161.3 | -2.85% | | CPU-Big | gmean | sum | 52.9 | -3.97% | | CPU-Little | gmean | sum | 86.7 | -5.39% | | CPU-Mid | gmean | sum | 21.6 | 12.63% | +------------+--------+------+-------+-----------+ The TL;DR of Jankbench is that sum aggregation reduces jank by 54% while with 2.85% less CPU power. Note that this benchmark on Pixel 6 by default has some sort of uclamp feedback applied to it. When jank is detected, certain display and benchmark threads are applied with a UCLAMP_MIN value of 512 (any help in identifying where these UCLAMP_MIN values come from in the mainline Android kernel is appreciated). In contrast, we constantly apply a UCLAMP_MIN value of 60 to these threads without any feedback loop. If a similar feedback loop is involved to only apply UCLAMP_MIN when needed, power consumption can be expected to be even lower. Hongyan Xia (6): sched/uclamp: Track uclamped util_avg in sched_avg sched/uclamp: Simulate PELT decay in util_avg_uclamp sched/fair: Use CFS util_avg_uclamp for utilization and frequency sched/fair: Rewrite util_fits_cpu() sched/uclamp: Remove all uclamp bucket logic sched/uclamp: Simplify uclamp_eff_value() include/linux/sched.h | 13 +- init/Kconfig | 32 --- kernel/sched/core.c | 312 ++------------------------- kernel/sched/cpufreq_schedutil.c | 19 +- kernel/sched/fair.c | 354 +++++++++---------------------- kernel/sched/pelt.c | 146 ++++++++++++- kernel/sched/rt.c | 4 - kernel/sched/sched.h | 208 ++++++------------ 8 files changed, 339 insertions(+), 749 deletions(-)