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@xxxxxxx> Co-authored-by: Morten Rasmussen <morten.rasmussen@xxxxxxx> Signed-off-by: Quentin Perret <quentin.perret@xxxxxxx> --- Documentation/scheduler/sched-energy.txt | 431 +++++++++++++++++++++++ 1 file changed, 431 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..b91899cb2846 --- /dev/null +++ b/Documentation/scheduler/sched-energy.txt @@ -0,0 +1,431 @@ + ======================= + 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 (which is available under +Documentation/driver-api/pm/energy-model.rst). + + +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 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. EAS is applied only to CFS tasks at the time of writing, +but it could be extended to other scheduling classes in the future. + +The predictions made by EAS rely on specific elements of knowledge about the +platform's topology, which include the 'capacity' of CPUs (defined in Section 3. +below), 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. 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 +Documentation/devicetree/bindings/arm/cpu-capacity.txt for more details). + +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 (for further details about performance +domains, see Documentation/driver-api/pm/energy-model.rst). + +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, the periodic and idle load-balance paths +are disabled (please see Section 5. for more details about this) so all the +balancing happens during wake-up. With EAS, 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 +off 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 severely 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 overrides 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. + +So, in order to use EAS, it is required from the architecture code to implement +the arch_scale_cpu_capacity() callback. Moreover, 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, the platform code (drivers) 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/driver-api/pm/energy-model.rst. + +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). + +In order to use EAS on a platform having a too complex EM, the only two possible +options are: + + 1. splitting the system into separate, smaller, root domains using exclusive + cpusets and enabling 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. submitting 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
