440 строки
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440 строки
16 KiB
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=========================
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Capacity Aware Scheduling
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=========================
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1. CPU Capacity
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===============
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1.1 Introduction
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----------------
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Conventional, homogeneous SMP platforms are composed of purely identical
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CPUs. Heterogeneous platforms on the other hand are composed of CPUs with
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different performance characteristics - on such platforms, not all CPUs can be
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considered equal.
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CPU capacity is a measure of the performance a CPU can reach, normalized against
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the most performant CPU in the system. Heterogeneous systems are also called
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asymmetric CPU capacity systems, as they contain CPUs of different capacities.
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Disparity in maximum attainable performance (IOW in maximum CPU capacity) stems
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from two factors:
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- not all CPUs may have the same microarchitecture (µarch).
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- with Dynamic Voltage and Frequency Scaling (DVFS), not all CPUs may be
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physically able to attain the higher Operating Performance Points (OPP).
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Arm big.LITTLE systems are an example of both. The big CPUs are more
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performance-oriented than the LITTLE ones (more pipeline stages, bigger caches,
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smarter predictors, etc), and can usually reach higher OPPs than the LITTLE ones
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can.
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CPU performance is usually expressed in Millions of Instructions Per Second
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(MIPS), which can also be expressed as a given amount of instructions attainable
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per Hz, leading to::
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capacity(cpu) = work_per_hz(cpu) * max_freq(cpu)
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1.2 Scheduler terms
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-------------------
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Two different capacity values are used within the scheduler. A CPU's
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``capacity_orig`` is its maximum attainable capacity, i.e. its maximum
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attainable performance level. A CPU's ``capacity`` is its ``capacity_orig`` to
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which some loss of available performance (e.g. time spent handling IRQs) is
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subtracted.
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Note that a CPU's ``capacity`` is solely intended to be used by the CFS class,
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while ``capacity_orig`` is class-agnostic. The rest of this document will use
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the term ``capacity`` interchangeably with ``capacity_orig`` for the sake of
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brevity.
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1.3 Platform examples
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---------------------
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1.3.1 Identical OPPs
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~~~~~~~~~~~~~~~~~~~~
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Consider an hypothetical dual-core asymmetric CPU capacity system where
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- work_per_hz(CPU0) = W
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- work_per_hz(CPU1) = W/2
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- all CPUs are running at the same fixed frequency
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By the above definition of capacity:
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- capacity(CPU0) = C
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- capacity(CPU1) = C/2
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To draw the parallel with Arm big.LITTLE, CPU0 would be a big while CPU1 would
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be a LITTLE.
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With a workload that periodically does a fixed amount of work, you will get an
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execution trace like so::
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CPU0 work ^
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| ____ ____ ____
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+----+----+----+----+----+----+----+----+----+----+-> time
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CPU1 work ^
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| _________ _________ ____
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+----+----+----+----+----+----+----+----+----+----+-> time
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CPU0 has the highest capacity in the system (C), and completes a fixed amount of
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work W in T units of time. On the other hand, CPU1 has half the capacity of
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CPU0, and thus only completes W/2 in T.
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1.3.2 Different max OPPs
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~~~~~~~~~~~~~~~~~~~~~~~~
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Usually, CPUs of different capacity values also have different maximum
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OPPs. Consider the same CPUs as above (i.e. same work_per_hz()) with:
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- max_freq(CPU0) = F
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- max_freq(CPU1) = 2/3 * F
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This yields:
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- capacity(CPU0) = C
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- capacity(CPU1) = C/3
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Executing the same workload as described in 1.3.1, which each CPU running at its
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maximum frequency results in::
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CPU0 work ^
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| ____ ____ ____
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+----+----+----+----+----+----+----+----+----+----+-> time
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workload on CPU1
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CPU1 work ^
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| ______________ ______________ ____
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+----+----+----+----+----+----+----+----+----+----+-> time
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1.4 Representation caveat
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-------------------------
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It should be noted that having a *single* value to represent differences in CPU
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performance is somewhat of a contentious point. The relative performance
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difference between two different µarchs could be X% on integer operations, Y% on
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floating point operations, Z% on branches, and so on. Still, results using this
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simple approach have been satisfactory for now.
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2. Task utilization
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===================
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2.1 Introduction
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----------------
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Capacity aware scheduling requires an expression of a task's requirements with
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regards to CPU capacity. Each scheduler class can express this differently, and
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while task utilization is specific to CFS, it is convenient to describe it here
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in order to introduce more generic concepts.
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Task utilization is a percentage meant to represent the throughput requirements
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of a task. A simple approximation of it is the task's duty cycle, i.e.::
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task_util(p) = duty_cycle(p)
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On an SMP system with fixed frequencies, 100% utilization suggests the task is a
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busy loop. Conversely, 10% utilization hints it is a small periodic task that
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spends more time sleeping than executing. Variable CPU frequencies and
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asymmetric CPU capacities complexify this somewhat; the following sections will
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expand on these.
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2.2 Frequency invariance
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------------------------
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One issue that needs to be taken into account is that a workload's duty cycle is
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directly impacted by the current OPP the CPU is running at. Consider running a
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periodic workload at a given frequency F::
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CPU work ^
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| ____ ____ ____
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+----+----+----+----+----+----+----+----+----+----+-> time
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This yields duty_cycle(p) == 25%.
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Now, consider running the *same* workload at frequency F/2::
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CPU work ^
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| _________ _________ ____
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+----+----+----+----+----+----+----+----+----+----+-> time
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This yields duty_cycle(p) == 50%, despite the task having the exact same
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behaviour (i.e. executing the same amount of work) in both executions.
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The task utilization signal can be made frequency invariant using the following
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formula::
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task_util_freq_inv(p) = duty_cycle(p) * (curr_frequency(cpu) / max_frequency(cpu))
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Applying this formula to the two examples above yields a frequency invariant
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task utilization of 25%.
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2.3 CPU invariance
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------------------
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CPU capacity has a similar effect on task utilization in that running an
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identical workload on CPUs of different capacity values will yield different
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duty cycles.
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Consider the system described in 1.3.2., i.e.::
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- capacity(CPU0) = C
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- capacity(CPU1) = C/3
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Executing a given periodic workload on each CPU at their maximum frequency would
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result in::
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CPU0 work ^
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| ____ ____ ____
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+----+----+----+----+----+----+----+----+----+----+-> time
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CPU1 work ^
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| ______________ ______________ ____
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+----+----+----+----+----+----+----+----+----+----+-> time
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IOW,
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- duty_cycle(p) == 25% if p runs on CPU0 at its maximum frequency
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- duty_cycle(p) == 75% if p runs on CPU1 at its maximum frequency
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The task utilization signal can be made CPU invariant using the following
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formula::
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task_util_cpu_inv(p) = duty_cycle(p) * (capacity(cpu) / max_capacity)
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with ``max_capacity`` being the highest CPU capacity value in the
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system. Applying this formula to the above example above yields a CPU
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invariant task utilization of 25%.
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2.4 Invariant task utilization
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------------------------------
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Both frequency and CPU invariance need to be applied to task utilization in
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order to obtain a truly invariant signal. The pseudo-formula for a task
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utilization that is both CPU and frequency invariant is thus, for a given
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task p::
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curr_frequency(cpu) capacity(cpu)
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task_util_inv(p) = duty_cycle(p) * ------------------- * -------------
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max_frequency(cpu) max_capacity
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In other words, invariant task utilization describes the behaviour of a task as
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if it were running on the highest-capacity CPU in the system, running at its
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maximum frequency.
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Any mention of task utilization in the following sections will imply its
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invariant form.
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2.5 Utilization estimation
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--------------------------
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Without a crystal ball, task behaviour (and thus task utilization) cannot
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accurately be predicted the moment a task first becomes runnable. The CFS class
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maintains a handful of CPU and task signals based on the Per-Entity Load
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Tracking (PELT) mechanism, one of those yielding an *average* utilization (as
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opposed to instantaneous).
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This means that while the capacity aware scheduling criteria will be written
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considering a "true" task utilization (using a crystal ball), the implementation
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will only ever be able to use an estimator thereof.
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3. Capacity aware scheduling requirements
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=========================================
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3.1 CPU capacity
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----------------
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Linux cannot currently figure out CPU capacity on its own, this information thus
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needs to be handed to it. Architectures must define arch_scale_cpu_capacity()
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for that purpose.
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The arm and arm64 architectures directly map this to the arch_topology driver
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CPU scaling data, which is derived from the capacity-dmips-mhz CPU binding; see
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Documentation/devicetree/bindings/arm/cpu-capacity.txt.
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3.2 Frequency invariance
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------------------------
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As stated in 2.2, capacity-aware scheduling requires a frequency-invariant task
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utilization. Architectures must define arch_scale_freq_capacity(cpu) for that
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purpose.
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Implementing this function requires figuring out at which frequency each CPU
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have been running at. One way to implement this is to leverage hardware counters
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whose increment rate scale with a CPU's current frequency (APERF/MPERF on x86,
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AMU on arm64). Another is to directly hook into cpufreq frequency transitions,
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when the kernel is aware of the switched-to frequency (also employed by
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arm/arm64).
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4. Scheduler topology
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=====================
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During the construction of the sched domains, the scheduler will figure out
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whether the system exhibits asymmetric CPU capacities. Should that be the
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case:
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- The sched_asym_cpucapacity static key will be enabled.
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- The SD_ASYM_CPUCAPACITY flag will be set at the lowest sched_domain level that
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spans all unique CPU capacity values.
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The sched_asym_cpucapacity static key is intended to guard sections of code that
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cater to asymmetric CPU capacity systems. Do note however that said key is
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*system-wide*. Imagine the following setup using cpusets::
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capacity C/2 C
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________ ________
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/ \ / \
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CPUs 0 1 2 3 4 5 6 7
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\__/ \______________/
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cpusets cs0 cs1
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Which could be created via:
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.. code-block:: sh
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mkdir /sys/fs/cgroup/cpuset/cs0
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echo 0-1 > /sys/fs/cgroup/cpuset/cs0/cpuset.cpus
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echo 0 > /sys/fs/cgroup/cpuset/cs0/cpuset.mems
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mkdir /sys/fs/cgroup/cpuset/cs1
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echo 2-7 > /sys/fs/cgroup/cpuset/cs1/cpuset.cpus
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echo 0 > /sys/fs/cgroup/cpuset/cs1/cpuset.mems
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echo 0 > /sys/fs/cgroup/cpuset/cpuset.sched_load_balance
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Since there *is* CPU capacity asymmetry in the system, the
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sched_asym_cpucapacity static key will be enabled. However, the sched_domain
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hierarchy of CPUs 0-1 spans a single capacity value: SD_ASYM_CPUCAPACITY isn't
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set in that hierarchy, it describes an SMP island and should be treated as such.
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Therefore, the 'canonical' pattern for protecting codepaths that cater to
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asymmetric CPU capacities is to:
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- Check the sched_asym_cpucapacity static key
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- If it is enabled, then also check for the presence of SD_ASYM_CPUCAPACITY in
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the sched_domain hierarchy (if relevant, i.e. the codepath targets a specific
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CPU or group thereof)
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5. Capacity aware scheduling implementation
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===========================================
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5.1 CFS
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-------
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5.1.1 Capacity fitness
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~~~~~~~~~~~~~~~~~~~~~~
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The main capacity scheduling criterion of CFS is::
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task_util(p) < capacity(task_cpu(p))
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This is commonly called the capacity fitness criterion, i.e. CFS must ensure a
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task "fits" on its CPU. If it is violated, the task will need to achieve more
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work than what its CPU can provide: it will be CPU-bound.
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Furthermore, uclamp lets userspace specify a minimum and a maximum utilization
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value for a task, either via sched_setattr() or via the cgroup interface (see
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Documentation/admin-guide/cgroup-v2.rst). As its name imply, this can be used to
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clamp task_util() in the previous criterion.
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5.1.2 Wakeup CPU selection
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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CFS task wakeup CPU selection follows the capacity fitness criterion described
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above. On top of that, uclamp is used to clamp the task utilization values,
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which lets userspace have more leverage over the CPU selection of CFS
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tasks. IOW, CFS wakeup CPU selection searches for a CPU that satisfies::
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clamp(task_util(p), task_uclamp_min(p), task_uclamp_max(p)) < capacity(cpu)
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By using uclamp, userspace can e.g. allow a busy loop (100% utilization) to run
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on any CPU by giving it a low uclamp.max value. Conversely, it can force a small
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periodic task (e.g. 10% utilization) to run on the highest-performance CPUs by
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giving it a high uclamp.min value.
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.. note::
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Wakeup CPU selection in CFS can be eclipsed by Energy Aware Scheduling
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(EAS), which is described in Documentation/scheduler/sched-energy.rst.
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5.1.3 Load balancing
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~~~~~~~~~~~~~~~~~~~~
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A pathological case in the wakeup CPU selection occurs when a task rarely
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sleeps, if at all - it thus rarely wakes up, if at all. Consider::
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w == wakeup event
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capacity(CPU0) = C
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capacity(CPU1) = C / 3
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workload on CPU0
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CPU work ^
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| _________ _________ ____
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+----+----+----+----+----+----+----+----+----+----+-> time
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w w w
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workload on CPU1
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CPU work ^
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+----+----+----+----+----+----+----+----+----+----+->
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w
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This workload should run on CPU0, but if the task either:
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- was improperly scheduled from the start (inaccurate initial
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utilization estimation)
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- was properly scheduled from the start, but suddenly needs more
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processing power
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then it might become CPU-bound, IOW ``task_util(p) > capacity(task_cpu(p))``;
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the CPU capacity scheduling criterion is violated, and there may not be any more
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wakeup event to fix this up via wakeup CPU selection.
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Tasks that are in this situation are dubbed "misfit" tasks, and the mechanism
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put in place to handle this shares the same name. Misfit task migration
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leverages the CFS load balancer, more specifically the active load balance part
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(which caters to migrating currently running tasks). When load balance happens,
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a misfit active load balance will be triggered if a misfit task can be migrated
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to a CPU with more capacity than its current one.
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5.2 RT
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------
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5.2.1 Wakeup CPU selection
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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RT task wakeup CPU selection searches for a CPU that satisfies::
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task_uclamp_min(p) <= capacity(task_cpu(cpu))
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while still following the usual priority constraints. If none of the candidate
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CPUs can satisfy this capacity criterion, then strict priority based scheduling
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is followed and CPU capacities are ignored.
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5.3 DL
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------
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5.3.1 Wakeup CPU selection
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~~~~~~~~~~~~~~~~~~~~~~~~~~
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DL task wakeup CPU selection searches for a CPU that satisfies::
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task_bandwidth(p) < capacity(task_cpu(p))
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while still respecting the usual bandwidth and deadline constraints. If
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none of the candidate CPUs can satisfy this capacity criterion, then the
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task will remain on its current CPU.
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