Message-Id: <20190110110546.8101-3-quentin.perret@arm.com>
Date:   Thu, 10 Jan 2019 11:05:46 +0000
From:   Quentin Perret <quentin.perret@....com>
To:     corbet@....net, peterz@...radead.org, rjw@...ysocki.net
Cc:     mingo@...hat.com, morten.rasmussen@....com, qais.yousef@....com,
        patrick.bellasi@....com, dietmar.eggemann@....com,
        linux-doc@...r.kernel.org, linux-pm@...r.kernel.org,
        linux-kernel@...r.kernel.org, quentin.perret@....com
Subject: [PATCH 2/2] sched: Document Energy Aware Scheduling

Add some documentation detailing the main design points of EAS, as well
as a list of its dependencies.

Parts of this documentation are taken from Morten Rasmussen's original
EAS posting: https://lkml.org/lkml/2015/7/7/754

Reviewed-by: Qais Yousef <qais.yousef@....com>
Co-authored-by: Morten Rasmussen <morten.rasmussen@....com>
Signed-off-by: Quentin Perret <quentin.perret@....com>
---
 Documentation/scheduler/sched-energy.txt | 425 +++++++++++++++++++++++
 1 file changed, 425 insertions(+)
 create mode 100644 Documentation/scheduler/sched-energy.txt

diff --git a/Documentation/scheduler/sched-energy.txt b/Documentation/scheduler/sched-energy.txt
new file mode 100644
index 000000000000..197d81f4b836
--- /dev/null
+++ b/Documentation/scheduler/sched-energy.txt
@@ -0,0 +1,425 @@
+			   =======================
+			   Energy Aware Scheduling
+			   =======================
+
+1. Introduction
+---------------
+
+Energy Aware Scheduling (or EAS) gives the scheduler the ability to predict
+the impact of its decisions on the energy consumed by CPUs. EAS relies on an
+Energy Model (EM) of the CPUs to select an energy efficient CPU for each task,
+with a minimal impact on throughput. This document aims at providing an
+introduction on how EAS works, what are the main design decisions behind it, and
+details what is needed to get it to run.
+
+Before going any further, please note that at the time of writing:
+
+   /!\ EAS does not support platforms with symmetric CPU topologies /!\
+
+EAS operates only on heterogeneous CPU topologies (such as Arm big.LITTLE)
+because this is where the potential for saving energy through scheduling is
+the highest.
+
+The actual EM used by EAS is _not_ maintained by the scheduler, but by a
+dedicated framework. For details about this framework and what it provides,
+please refer to its documentation (see Documentation/power/energy-model.txt).
+
+
+2. Background and Terminology
+-----------------------------
+
+To make it clear from the start:
+ - energy = [joule] (resource like a battery on powered devices)
+ - power = energy/time = [joule/second] = [watt]
+
+The goal of EAS is to minimize energy, while still getting the job done. That
+is, we want to maximize:
+
+	performance [inst/s]
+	--------------------
+	    power [W]
+
+which is equivalent to minimizing:
+
+	energy [J]
+	-----------
+	instruction
+
+while still getting 'good' performance. It is essentially an alternative
+optimization objective to the current performance-only objective for the
+scheduler. This alternative considers two objectives: energy-efficiency and
+performance.
+
+The idea behind introducing an EM is to allow the scheduler to evaluate the
+implications of its decisions rather than blindly applying energy-saving
+techniques that may have positive effects only on some platforms. At the same
+time, the EM must be as simple as possible to minimize the scheduler latency
+impact.
+
+In short, EAS changes the way CFS tasks are assigned to CPUs. When it is time
+for the scheduler to decide where a task should run (during wake-up), the EM
+is used to break the tie between several good CPU candidates and pick the one
+that is predicted to yield the best energy consumption without harming the
+system's throughput. The predictions made by EAS rely on specific elements of
+knowledge about the platform's topology, which include the 'capacity' of CPUs,
+and their respective energy costs.
+
+
+3. Topology information
+-----------------------
+
+EAS (as well as the rest of the scheduler) uses the notion of 'capacity' to
+differentiate CPUs with different computing throughput. The 'capacity' of a CPU
+represents the amount of work it can absorb when running at its highest
+frequency compared to the most capable CPU of the system. Capacity values are
+normalized in a 1024 range, and are comparable with the utilization signals of
+tasks and CPUs computed by the Per-Entity Load Tracking (PELT) mechanism. Thanks
+to capacity and utilization values, EAS is able to estimate how big/busy a
+task/CPU is, and to take this into consideration when evaluating performance vs
+energy trade-offs. The capacity of CPUs is provided via arch-specific code
+through the arch_scale_cpu_capacity() callback.
+
+The rest of platform knowledge used by EAS is directly read from the Energy
+Model (EM) framework. The EM of a platform is composed of a power cost table
+per 'performance domain' in the system (see Documentation/power/energy-model.txt
+for futher details about performance domains).
+
+The scheduler manages references to the EM objects in the topology code when the
+scheduling domains are built, or re-built. For each root domain (rd), the
+scheduler maintains a singly linked list of all performance domains intersecting
+the current rd->span. Each node in the list contains a pointer to a struct
+em_perf_domain as provided by the EM framework.
+
+The lists are attached to the root domains in order to cope with exclusive
+cpuset configurations. Since the boundaries of exclusive cpusets do not
+necessarily match those of performance domains, the lists of different root
+domains can contain duplicate elements.
+
+Example 1.
+    Let us consider a platform with 12 CPUs, split in 3 performance domains
+    (pd0, pd4 and pd8), organized as follows:
+
+	          CPUs:   0 1 2 3 4 5 6 7 8 9 10 11
+	          PDs:   |--pd0--|--pd4--|---pd8---|
+	          RDs:   |----rd1----|-----rd2-----|
+
+    Now, consider that userspace decided to split the system with two
+    exclusive cpusets, hence creating two independent root domains, each
+    containing 6 CPUs. The two root domains are denoted rd1 and rd2 in the
+    above figure. Since pd4 intersects with both rd1 and rd2, it will be
+    present in the linked list '->pd' attached to each of them:
+       * rd1->pd: pd0 -> pd4
+       * rd2->pd: pd4 -> pd8
+
+    Please note that the scheduler will create two duplicate list nodes for
+    pd4 (one for each list). However, both just hold a pointer to the same
+    shared data structure of the EM framework.
+
+Since the access to these lists can happen concurrently with hotplug and other
+things, they are protected by RCU, like the rest of topology structures
+manipulated by the scheduler.
+
+EAS also maintains a static key (sched_energy_present) which is enabled when at
+least one root domain meets all conditions for EAS to start. Those conditions
+are summarized in Section 6.
+
+
+4. Energy-Aware task placement
+------------------------------
+
+EAS overrides the CFS task wake-up balancing code. It uses the EM of the
+platform and the PELT signals to choose an energy-efficient target CPU during
+wake-up balance. When EAS is enabled, select_task_rq_fair() calls
+find_energy_efficient_cpu() to do the placement decision. This function looks
+for the CPU with the highest spare capacity (CPU capacity - CPU utilization) in
+each performance domain since it is the one which will allow us to keep the
+frequency the lowest. Then, the function checks if placing the task there could
+save energy compared to leaving it on prev_cpu, i.e. the CPU where the task ran
+in its previous activation.
+
+find_energy_efficient_cpu() uses compute_energy() to estimate what will be the
+energy consumed by the system if the waking task was migrated. compute_energy()
+looks at the current utilization landscape of the CPUs and adjusts it to
+'simulate' the task migration. The EM framework provides the em_pd_energy() API
+which computes the expected energy consumption of each performance domain for
+the given utilization landscape.
+
+An example of energy-optimized task placement decision is detailed below.
+
+Example 2.
+    Let us consider a (fake) platform with 2 independent performance domains
+    composed of two CPUs each. CPU0 and CPU1 are little CPUs; CPU2 and CPU3
+    are big.
+
+    The scheduler must decide where to place a task P whose util_avg = 200
+    and prev_cpu = 0.
+
+    The current utilization landscape of the CPUs is depicted on the graph
+    below. CPUs 0-3 have a util_avg of 400, 100, 600 and 500 respectively
+    Each performance domain has three Operating Performance Points (OPPs).
+    The CPU capacity and power cost associated with each OPP is listed in
+    the Energy Model table. The util_avg of P is shown on the figures
+    below as 'PP'.
+
+    CPU util.
+      1024                 - - - - - - -              Energy Model
+                                               +-----------+-------------+
+                                               |  Little   |     Big     |
+       768                 =============       +-----+-----+------+------+
+                                               | Cap | Pwr | Cap  | Pwr  |
+                                               +-----+-----+------+------+
+       512  ===========    - ##- - - - -       | 170 | 50  | 512  | 400  |
+                             ##     ##         | 341 | 150 | 768  | 800  |
+       341  -PP - - - -      ##     ##         | 512 | 300 | 1024 | 1700 |
+             PP              ##     ##         +-----+-----+------+------+
+       170  -## - - - -      ##     ##
+             ##     ##       ##     ##
+           ------------    -------------
+            CPU0   CPU1     CPU2   CPU3
+
+      Current OPP: =====       Other OPP: - - -     util_avg (100 each): ##
+
+
+    find_energy_efficient_cpu() will first look for the CPUs with the
+    maximum spare capacity in the two performance domains. In this example,
+    CPU1 and CPU3. Then it will estimate the energy of the system if P was
+    placed on either of them, and check if that would save some energy
+    compared to leaving P on CPU0. EAS assumes that OPPs follow utilization
+    (which is coherent with the behaviour of the schedutil CPUFreq
+    governor, see Section 6. for more details on this topic).
+
+    Case 1. P is migrated to CPU1
+    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+      1024                 - - - - - - -
+
+                                            Energy calculation:
+       768                 =============     * CPU0: 200 / 341 * 150 = 88
+                                             * CPU1: 300 / 341 * 150 = 131
+                                             * CPU2: 600 / 768 * 800 = 625
+       512  - - - - - -    - ##- - - - -     * CPU3: 500 / 768 * 800 = 520
+                             ##     ##          => total_energy = 1364
+       341  ===========      ##     ##
+                    PP       ##     ##
+       170  -## - - PP-      ##     ##
+             ##     ##       ##     ##
+           ------------    -------------
+            CPU0   CPU1     CPU2   CPU3
+
+
+    Case 2. P is migrated to CPU3
+    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+      1024                 - - - - - - -
+
+                                            Energy calculation:
+       768                 =============     * CPU0: 200 / 341 * 150 = 88
+                                             * CPU1: 100 / 341 * 150 = 43
+                                    PP       * CPU2: 600 / 768 * 800 = 625
+       512  - - - - - -    - ##- - -PP -     * CPU3: 700 / 768 * 800 = 729
+                             ##     ##          => total_energy = 1485
+       341  ===========      ##     ##
+                             ##     ##
+       170  -## - - - -      ##     ##
+             ##     ##       ##     ##
+           ------------    -------------
+            CPU0   CPU1     CPU2   CPU3
+
+
+    Case 3. P stays on prev_cpu / CPU 0
+    ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+      1024                 - - - - - - -
+
+                                            Energy calculation:
+       768                 =============     * CPU0: 400 / 512 * 300 = 234
+                                             * CPU1: 100 / 512 * 300 = 58
+                                             * CPU2: 600 / 768 * 800 = 625
+       512  ===========    - ##- - - - -     * CPU3: 500 / 768 * 800 = 520
+                             ##     ##          => total_energy = 1437
+       341  -PP - - - -      ##     ##
+             PP              ##     ##
+       170  -## - - - -      ##     ##
+             ##     ##       ##     ##
+           ------------    -------------
+            CPU0   CPU1     CPU2   CPU3
+
+
+    From these calculations, the Case 1 has the lowest total energy. So CPU 1
+    is be the best candidate from an energy-efficiency standpoint.
+
+Big CPUs are generally more power hungry than the little ones and are thus used
+mainly when a task doesn't fit the littles. However, little CPUs aren't always
+necessarily more energy-efficient than big CPUs. For some systems, the high OPPs
+of the little CPUs can be less energy-efficient than the lowest OPPs of the
+bigs, for example. So, if the little CPUs happen to have enough utilization at
+a specific point in time, a small task waking up at that moment could be better
+of executing on the big side in order to save energy, even though it would fit
+on the little side.
+
+And even in the case where all OPPs of the big CPUs are less energy-efficient
+than those of the little, using the big CPUs for a small task might still, under
+specific conditions, save energy. Indeed, placing a task on a little CPU can
+result in raising the OPP of the entire performance domain, and that will
+increase the cost of the tasks already running there. If the waking task is
+placed on a big CPU, its own execution cost might be higher than if it was
+running on a little, but it won't impact the other tasks of the little CPUs
+which will keep running at a lower OPP. So, when considering the total energy
+consumed by CPUs, the extra cost of running that one task on a big core can be
+smaller than the cost of raising the OPP on the little CPUs for all the other
+tasks.
+
+The examples above would be nearly impossible to get right in a generic way, and
+for all platforms, without knowing the cost of running at different OPPs on all
+CPUs of the system. Thanks to its EM-based design, EAS should cope with them
+correctly without too many troubles. However, in order to ensure a minimal
+impact on throughput for high-utilization scenarios, EAS also implements another
+mechanism called 'over-utilization'.
+
+
+5. Over-utilization
+-------------------
+
+From a general standpoint, the use-cases where EAS can help the most are those
+involving a light/medium CPU utilization. Whenever long CPU-bound tasks are
+being run, they will require all of the available CPU capacity, and there isn't
+much that can be done by the scheduler to save energy without severly harming
+throughput. In order to avoid hurting performance with EAS, CPUs are flagged as
+'over-utilized' as soon as they are used at more than 80% of their compute
+capacity. As long as no CPUs are over-utilized in a root domain, load balancing
+is disabled and EAS overridess the wake-up balancing code. EAS is likely to load
+the most energy efficient CPUs of the system more than the others if that can be
+done without harming throughput. So, the load-balancer is disabled to prevent
+it from breaking the energy-efficient task placement found by EAS. It is safe to
+do so when the system isn't overutilized since being below the 80% tipping point
+implies that:
+
+    a. there is some idle time on all CPUs, so the utilization signals used by
+       EAS are likely to accurately represent the 'size' of the various tasks
+       in the system;
+    b. all tasks should already be provided with enough CPU capacity,
+       regardless of their nice values;
+    c. since there is spare capacity all tasks must be blocking/sleeping
+       regularly and balancing at wake-up is sufficient.
+
+As soon as one CPU goes above the 80% tipping point, at least one of the three
+assumptions above becomes incorrect. In this scenario, the 'overutilized' flag
+is raised for the entire root domain, EAS is disabled, and the load-balancer is
+re-enabled. By doing so, the scheduler falls back onto load-based algorithms for
+wake-up and load balance under CPU-bound conditions. This provides a better
+respect of the nice values of tasks.
+
+Since the notion of overutilization largely relies on detecting whether or not
+there is some idle time in the system, the CPU capacity 'stolen' by higher
+(than CFS) scheduling classes (as well as IRQ) must be taken into account. As
+such, the detection of overutilization accounts for the capacity used not only
+by CFS tasks, but also by the other scheduling classes and IRQ.
+
+
+6. Dependencies and requirements for EAS
+----------------------------------------
+
+Energy Aware Scheduling depends on the CPUs of the system having specific
+hardware properties and on other features of the kernel being enabled. This
+section lists these dependencies and provides hints as to how they can be met.
+
+
+  6.1 - Asymmetric CPU topology
+
+As mentioned in the introduction, EAS is only supported on platforms with
+asymmetric CPU topologies for now. This requirement is checked at run-time by
+looking for the presence of the SD_ASYM_CPUCAPACITY flag when the scheduling
+domains are built.
+
+The flag is set/cleared automatically by the scheduler topology code whenever
+there are CPUs with different capacities in a root domain. The capacities of
+CPUs are provided by arch-specific code through the arch_scale_cpu_capacity()
+callback. As an example, arm and arm64 share an implementation of this callback
+which uses a combination of CPUFreq data and device-tree bindings to compute the
+capacity of CPUs (see drivers/base/arch_topology.c for more details).
+
+So, in order to use EAS on your platform your architecture must implement the
+arch_scale_cpu_capacity() callback, and some of the CPUs must have a lower
+capacity than others.
+
+Please note that EAS is not fundamentally incompatible with SMP, but no
+significant savings on SMP platforms have been observed yet. This restriction
+could be amended in the future if proven otherwise.
+
+
+  6.2 - Energy Model presence
+
+EAS uses the EM of a platform to estimate the impact of scheduling decisions on
+energy. So, your platform must provide power cost tables to the EM framework in
+order to make EAS start. To do so, please refer to documentation of the
+independent EM framework in Documentation/power/energy-model.txt.
+
+Please also note that the scheduling domains need to be re-built after the
+EM has been registered in order to start EAS.
+
+
+  6.3 - Energy Model complexity
+
+The task wake-up path is very latency-sensitive. When the EM of a platform is
+too complex (too many CPUs, too many performance domains, too many performance
+states, ...), the cost of using it in the wake-up path can become prohibitive.
+The energy-aware wake-up algorithm has a complexity of:
+
+	C = Nd * (Nc + Ns)
+
+with: Nd the number of performance domains; Nc the number of CPUs; and Ns the
+total number of OPPs (ex: for two perf. domains with 4 OPPs each, Ns = 8).
+
+A complexity check is performed at the root domain level, when scheduling
+domains are built. EAS will not start on a root domain if its C happens to be
+higher than the completely arbitrary EM_MAX_COMPLEXITY threshold (2048 at the
+time of writing).
+
+If you really want to use EAS but the complexity of your platform's Energy
+Model is too high to be used with a single root domain, you're left with only
+two possible options:
+
+    1. split your system into separate, smaller, root domains using exclusive
+       cpusets and enable EAS locally on each of them. This option has the
+       benefit to work out of the box but the drawback of preventing load
+       balance between root domains, which can result in an unbalanced system
+       overall;
+    2. submit patches to reduce the complexity of the EAS wake-up algorithm,
+       hence enabling it to cope with larger EMs in reasonable time.
+
+
+  6.4 - Schedutil governor
+
+EAS tries to predict at which OPP will the CPUs be running in the close future
+in order to estimate their energy consumption. To do so, it is assumed that OPPs
+of CPUs follow their utilization.
+
+Although it is very difficult to provide hard guarantees regarding the accuracy
+of this assumption in practice (because the hardware might not do what it is
+told to do, for example), schedutil as opposed to other CPUFreq governors at
+least _requests_ frequencies calculated using the utilization signals.
+Consequently, the only sane governor to use together with EAS is schedutil,
+because it is the only one providing some degree of consistency between
+frequency requests and energy predictions.
+
+Using EAS with any other governor than schedutil is not supported.
+
+
+  6.5 Scale-invariant utilization signals
+
+In order to make accurate prediction across CPUs and for all performance
+states, EAS needs frequency-invariant and CPU-invariant PELT signals. These can
+be obtained using the architecture-defined arch_scale{cpu,freq}_capacity()
+callbacks.
+
+Using EAS on a platform that doesn't implement these two callbacks is not
+supported.
+
+
+  6.6 Multithreading (SMT)
+
+EAS in its current form is SMT unaware and is not able to leverage
+multithreaded hardware to save energy. EAS considers threads as independent
+CPUs, which can actually be counter-productive for both performance and energy.
+
+EAS on SMT is not supported.
-- 
2.20.1

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