[ Upstream commit 7fcc17d0cb12938d2b3507973a6f93fc9ed2c7a1 ]
The Energy Model (EM) provides useful information about device power in
each performance state to other subsystems like: Energy Aware Scheduler
(EAS). The energy calculation in EAS does arithmetic operation based on
the EM em_cpu_energy(). Current implementation of that function uses
em_perf_state::cost as a pre-computed cost coefficient equal to:
cost = power * max_frequency / frequency.
The 'power' is expressed in milli-Watts (or in abstract scale).
There are corner cases when the EAS energy calculation for two Performance
Domains (PDs) return the same value. The EAS compares these values to
choose smaller one. It might happen that this values are equal due to
rounding error. In such scenario, we need better resolution, e.g. 1000
times better. To provide this possibility increase the resolution in the
em_perf_state::cost for 64-bit architectures. The cost of increasing
resolution on 32-bit is pretty high (64-bit division) and is not justified
since there are no new 32bit big.LITTLE EAS systems expected which would
benefit from this higher resolution.
This patch allows to avoid the rounding to milli-Watt errors, which might
occur in EAS energy estimation for each PD. The rounding error is common
for small tasks which have small utilization value.
There are two places in the code where it makes a difference:
1. In the find_energy_efficient_cpu() where we are searching for
best_delta. We might suffer there when two PDs return the same result,
like in the example below.
Scenario:
Low utilized system e.g. ~200 sum_util for PD0 and ~220 for PD1. There
are quite a few small tasks ~10-15 util. These tasks would suffer for
the rounding error. These utilization values are typical when running games
on Android. One of our partners has reported 5..10mA less battery drain
when running with increased resolution.
Some details:
We have two PDs: PD0 (big) and PD1 (little)
Let's compare w/o patch set ('old') and w/ patch set ('new')
We are comparing energy w/ task and w/o task placed in the PDs
a) 'old' w/o patch set, PD0
task_util = 13
cost = 480
sum_util_w/o_task = 215
sum_util_w_task = 228
scale_cpu = 1024
energy_w/o_task = 480 * 215 / 1024 = 100.78 => 100
energy_w_task = 480 * 228 / 1024 = 106.87 => 106
energy_diff = 106 - 100 = 6
(this is equal to 'old' PD1's energy_diff in 'c)')
b) 'new' w/ patch set, PD0
task_util = 13
cost = 480 * 1000 = 480000
sum_util_w/o_task = 215
sum_util_w_task = 228
energy_w/o_task = 480000 * 215 / 1024 = 100781
energy_w_task = 480000 * 228 / 1024 = 106875
energy_diff = 106875 - 100781 = 6094
(this is not equal to 'new' PD1's energy_diff in 'd)')
c) 'old' w/o patch set, PD1
task_util = 13
cost = 160
sum_util_w/o_task = 283
sum_util_w_task = 293
scale_cpu = 355
energy_w/o_task = 160 * 283 / 355 = 127.55 => 127
energy_w_task = 160 * 296 / 355 = 133.41 => 133
energy_diff = 133 - 127 = 6
(this is equal to 'old' PD0's energy_diff in 'a)')
d) 'new' w/ patch set, PD1
task_util = 13
cost = 160 * 1000 = 160000
sum_util_w/o_task = 283
sum_util_w_task = 293
scale_cpu = 355
energy_w/o_task = 160000 * 283 / 355 = 127549
energy_w_task = 160000 * 296 / 355 = 133408
energy_diff = 133408 - 127549 = 5859
(this is not equal to 'new' PD0's energy_diff in 'b)')
2. Difference in the 6% energy margin filter at the end of
find_energy_efficient_cpu(). With this patch the margin comparison also
has better resolution, so it's possible to have better task placement
thanks to that.
Fixes: 27871f7a8a ("PM: Introduce an Energy Model management framework")
Reported-by: CCJ Yeh <CCj.Yeh@mediatek.com>
Reviewed-by: Dietmar Eggemann <dietmar.eggemann@arm.com>
Signed-off-by: Lukasz Luba <lukasz.luba@arm.com>
Signed-off-by: Rafael J. Wysocki <rafael.j.wysocki@intel.com>
Signed-off-by: Sasha Levin <sashal@kernel.org>
Several subsystems in the kernel (task scheduler and/or thermal at the
time of writing) can benefit from knowing about the energy consumed by
CPUs. Yet, this information can come from different sources (DT or
firmware for example), in different formats, hence making it hard to
exploit without a standard API.
As an attempt to address this, introduce a centralized Energy Model
(EM) management framework which aggregates the power values provided
by drivers into a table for each performance domain in the system. The
power cost tables are made available to interested clients (e.g. task
scheduler or thermal) via platform-agnostic APIs. The overall design
is represented by the diagram below (focused on Arm-related drivers as
an example, but applicable to any architecture):
+---------------+ +-----------------+ +-------------+
| Thermal (IPA) | | Scheduler (EAS) | | Other |
+---------------+ +-----------------+ +-------------+
| | em_pd_energy() |
| | em_cpu_get() |
+-----------+ | +--------+
| | |
v v v
+---------------------+
| |
| Energy Model |
| |
| Framework |
| |
+---------------------+
^ ^ ^
| | | em_register_perf_domain()
+----------+ | +---------+
| | |
+---------------+ +---------------+ +--------------+
| cpufreq-dt | | arm_scmi | | Other |
+---------------+ +---------------+ +--------------+
^ ^ ^
| | |
+--------------+ +---------------+ +--------------+
| Device Tree | | Firmware | | ? |
+--------------+ +---------------+ +--------------+
Drivers (typically, but not limited to, CPUFreq drivers) can register
data in the EM framework using the em_register_perf_domain() API. The
calling driver must provide a callback function with a standardized
signature that will be used by the EM framework to build the power
cost tables of the performance domain. This design should offer a lot of
flexibility to calling drivers which are free of reading information
from any location and to use any technique to compute power costs.
Moreover, the capacity states registered by drivers in the EM framework
are not required to match real performance states of the target. This
is particularly important on targets where the performance states are
not known by the OS.
The power cost coefficients managed by the EM framework are specified in
milli-watts. Although the two potential users of those coefficients (IPA
and EAS) only need relative correctness, IPA specifically needs to
compare the power of CPUs with the power of other components (GPUs, for
example), which are still expressed in absolute terms in their
respective subsystems. Hence, specifying the power of CPUs in
milli-watts should help transitioning IPA to using the EM framework
without introducing new problems by keeping units comparable across
sub-systems.
On the longer term, the EM of other devices than CPUs could also be
managed by the EM framework, which would enable to remove the absolute
unit. However, this is not absolutely required as a first step, so this
extension of the EM framework is left for later.
On the client side, the EM framework offers APIs to access the power
cost tables of a CPU (em_cpu_get()), and to estimate the energy
consumed by the CPUs of a performance domain (em_pd_energy()). Clients
such as the task scheduler can then use these APIs to access the shared
data structures holding the Energy Model of CPUs.
Signed-off-by: Quentin Perret <quentin.perret@arm.com>
Signed-off-by: Peter Zijlstra (Intel) <peterz@infradead.org>
Cc: Linus Torvalds <torvalds@linux-foundation.org>
Cc: Mike Galbraith <efault@gmx.de>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Rafael J. Wysocki <rjw@rjwysocki.net>
Cc: Thomas Gleixner <tglx@linutronix.de>
Cc: adharmap@codeaurora.org
Cc: chris.redpath@arm.com
Cc: currojerez@riseup.net
Cc: dietmar.eggemann@arm.com
Cc: edubezval@gmail.com
Cc: gregkh@linuxfoundation.org
Cc: javi.merino@kernel.org
Cc: joel@joelfernandes.org
Cc: juri.lelli@redhat.com
Cc: morten.rasmussen@arm.com
Cc: patrick.bellasi@arm.com
Cc: pkondeti@codeaurora.org
Cc: skannan@codeaurora.org
Cc: smuckle@google.com
Cc: srinivas.pandruvada@linux.intel.com
Cc: thara.gopinath@linaro.org
Cc: tkjos@google.com
Cc: valentin.schneider@arm.com
Cc: vincent.guittot@linaro.org
Cc: viresh.kumar@linaro.org
Link: https://lkml.kernel.org/r/20181203095628.11858-4-quentin.perret@arm.com
Signed-off-by: Ingo Molnar <mingo@kernel.org>
Lukasz Luba