[PATCH v10 00/16] Add utilization clamping support

From: Patrick Bellasi
Date: Fri Jun 21 2019 - 04:42:37 EST

Hi all, this is a respin of:

https://lore.kernel.org/lkml/20190515094459.10317-1-patrick.bellasi@xxxxxxx/

which addresses all Tejun's concerns:

- rename cgroup attributes to be cpu.uclamp.{min,max}
- update initialization of subgroups clamps to be "no clamps" by default
- use percentage rational numbers for clamp attributes, e.g. "12.34" for 12.34%.

by introducing modifications impacting only patches:

[PATCH v10 12/16] sched/core: uclamp: Extend CPU's cgroup controller
[PATCH v10 13/16] sched/core: uclamp: Propagate parent clamps

The rest of the patches are the same as per in v9, they have been just rebased
on top of:

tj/cgroup.git for-5.3
tip/tip.git sched/core

AFAIU, all the first 11 patches have been code reviewed and should be at a
"ready to merge" quality level. Please let me know if I'm wrong and there
is something else I need/can to do on those patches.

Otherwise, now that we should have settled all the behavioral aspects, I'm
looking forward for a code review on the last set of patches of this series.

Cheers,
Patrick


Series Organization
===================

The series is organized into these main sections:

- Patches [01-07]: Per task (primary) API
- Patches [08-09]: Schedutil integration for FAIR and RT tasks
- Patches [10-11]: Integration with EAS's energy_compute()
- Patches [12-16]: Per task group (secondary) API

It is based on today's tj/cgroup/for-5.3 and tip/sched/core and the full tree
is available here:

git://linux-arm.org/linux-pb.git lkml/utilclamp_v10
http://www.linux-arm.org/git?p=linux-pb.git;a=shortlog;h=refs/heads/lkml/utilclamp_v10


Newcomer's Short Abstract
=========================

The Linux scheduler tracks a "utilization" signal for each scheduling entity
(SE), e.g. tasks, to know how much CPU time they use. This signal allows the
scheduler to know how "big" a task is and, in principle, it can support
advanced task placement strategies by selecting the best CPU to run a task.
Some of these strategies are represented by the Energy Aware Scheduler [3].

When the schedutil cpufreq governor is in use, the utilization signal allows
the Linux scheduler to also drive frequency selection. The CPU utilization
signal, which represents the aggregated utilization of tasks scheduled on that
CPU, is used to select the frequency which best fits the workload generated by
the tasks.

The current translation of utilization values into a frequency selection is
simple: we go to max for RT tasks or to the minimum frequency which can
accommodate the utilization of DL+FAIR tasks.
However, utilization values by themselves cannot convey the desired
power/performance behaviors of each task as intended by user-space.
As such they are not ideally suited for task placement decisions.

Task placement and frequency selection policies in the kernel can be improved
by taking into consideration hints coming from authorized user-space elements,
like for example the Android middleware or more generally any "System
Management Software" (SMS) framework.

Utilization clamping is a mechanism which allows to "clamp" (i.e. filter) the
utilization generated by RT and FAIR tasks within a range defined by user-space.
The clamped utilization value can then be used, for example, to enforce a
minimum and/or maximum frequency depending on which tasks are active on a CPU.

The main use-cases for utilization clamping are:

- boosting: better interactive response for small tasks which
are affecting the user experience.

Consider for example the case of a small control thread for an external
accelerator (e.g. GPU, DSP, other devices). Here, from the task utilization
the scheduler does not have a complete view of what the task's requirements
are and, if it's a small utilization task, it keeps selecting a more energy
efficient CPU, with smaller capacity and lower frequency, thus negatively
impacting the overall time required to complete task activations.

- capping: increase energy efficiency for background tasks not affecting the
user experience.

Since running on a lower capacity CPU at a lower frequency is more energy
efficient, when the completion time is not a main goal, then capping the
utilization considered for certain (maybe big) tasks can have positive
effects, both on energy consumption and thermal headroom.
This feature allows also to make RT tasks more energy friendly on mobile
systems where running them on high capacity CPUs and at the maximum
frequency is not required.

>From these two use-cases, it's worth noticing that frequency selection
biasing, introduced by patches 9 and 10 of this series, is just one possible
usage of utilization clamping. Another compelling extension of utilization
clamping is in helping the scheduler in making tasks placement decisions.

Utilization is (also) a task specific property the scheduler uses to know
how much CPU bandwidth a task requires, at least as long as there is idle time.
Thus, the utilization clamp values, defined either per-task or per-task_group,
can represent tasks to the scheduler as being bigger (or smaller) than what
they actually are.

Utilization clamping thus enables interesting additional optimizations, for
example on asymmetric capacity systems like Arm big.LITTLE and DynamIQ CPUs,
where:

- boosting: try to run small/foreground tasks on higher-capacity CPUs to
complete them faster despite being less energy efficient.

- capping: try to run big/background tasks on low-capacity CPUs to save power
and thermal headroom for more important tasks

This series does not present this additional usage of utilization clamping but
it's an integral part of the EAS feature set, where [4] is one of its main
components.

Android kernels use SchedTune, a solution similar to utilization clamping, to
bias both 'frequency selection' and 'task placement'. This series provides the
foundation to add similar features to mainline while focusing, for the
time being, just on schedutil integration.


References
==========

[1] Message-ID: <20190509130215.GV2623@xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx>
https://lore.kernel.org/lkml/20190509130215.GV2623@xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx/

[2] Message-ID: <20180911162827.GJ1100574@xxxxxxxxxxxxxxxxxxxxxxxxxxx>
https://lore.kernel.org/lkml/20180911162827.GJ1100574@xxxxxxxxxxxxxxxxxxxxxxxxxxx/

[3] Energy Aware Scheduling
https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/tree/Documentation/scheduler/sched-energy.txt?h=v5.1

[4] Expressing per-task/per-cgroup performance hints
Linux Plumbers Conference 2018
https://linuxplumbersconf.org/event/2/contributions/128/


Patrick Bellasi (16):
sched/core: uclamp: Add CPU's clamp buckets refcounting
sched/core: uclamp: Add bucket local max tracking
sched/core: uclamp: Enforce last task's UCLAMP_MAX
sched/core: uclamp: Add system default clamps
sched/core: Allow sched_setattr() to use the current policy
sched/core: uclamp: Extend sched_setattr() to support utilization
clamping
sched/core: uclamp: Reset uclamp values on RESET_ON_FORK
sched/core: uclamp: Set default clamps for RT tasks
sched/cpufreq: uclamp: Add clamps for FAIR and RT tasks
sched/core: uclamp: Add uclamp_util_with()
sched/fair: uclamp: Add uclamp support to energy_compute()
sched/core: uclamp: Extend CPU's cgroup controller
sched/core: uclamp: Propagate parent clamps
sched/core: uclamp: Propagate system defaults to root group
sched/core: uclamp: Use TG's clamps to restrict TASK's clamps
sched/core: uclamp: Update CPU's refcount on TG's clamp changes

Documentation/admin-guide/cgroup-v2.rst | 50 ++
include/linux/log2.h | 34 +
include/linux/sched.h | 58 ++
include/linux/sched/sysctl.h | 11 +
include/linux/sched/topology.h | 6 -
include/uapi/linux/sched.h | 14 +-
include/uapi/linux/sched/types.h | 66 +-
init/Kconfig | 75 +++
kernel/sched/core.c | 797 +++++++++++++++++++++++-
kernel/sched/cpufreq_schedutil.c | 22 +-
kernel/sched/fair.c | 44 +-
kernel/sched/rt.c | 4 +
kernel/sched/sched.h | 123 +++-
kernel/sysctl.c | 16 +
14 files changed, 1276 insertions(+), 44 deletions(-)

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2.21.0