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author | Tang Yizhou <tangyizhou@huawei.com> | 2022-03-12 15:07:51 +0800 |
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committer | Jonathan Corbet <corbet@lwn.net> | 2022-03-16 15:03:24 -0600 |
commit | b57b84968865780262f191cc95d688d7f82e681c (patch) | |
tree | 769c6d85e68b39a43f36efe36abce6a3c9f62187 /Documentation/scheduler/schedutil.rst | |
parent | ff1368763bb488783820c6ba68d73c03c562a9b7 (diff) | |
download | linux-b57b84968865780262f191cc95d688d7f82e681c.tar.bz2 |
docs: scheduler: Convert schedutil.txt to ReST
All other scheduler documents have been converted to *.rst. Let's do
the same for schedutil.txt.
Also fixed some typos.
Signed-off-by: Tang Yizhou <tangyizhou@huawei.com>
Link: https://lore.kernel.org/r/20220312070751.16844-1-tangyizhou@huawei.com
Signed-off-by: Jonathan Corbet <corbet@lwn.net>
Diffstat (limited to 'Documentation/scheduler/schedutil.rst')
-rw-r--r-- | Documentation/scheduler/schedutil.rst | 173 |
1 files changed, 173 insertions, 0 deletions
diff --git a/Documentation/scheduler/schedutil.rst b/Documentation/scheduler/schedutil.rst new file mode 100644 index 000000000000..32c7d69fc86c --- /dev/null +++ b/Documentation/scheduler/schedutil.rst @@ -0,0 +1,173 @@ +========= +Schedutil +========= + +.. note:: + + All this assumes a linear relation between frequency and work capacity, + we know this is flawed, but it is the best workable approximation. + + +PELT (Per Entity Load Tracking) +=============================== + +With PELT we track some metrics across the various scheduler entities, from +individual tasks to task-group slices to CPU runqueues. As the basis for this +we use an Exponentially Weighted Moving Average (EWMA), each period (1024us) +is decayed such that y^32 = 0.5. That is, the most recent 32ms contribute +half, while the rest of history contribute the other half. + +Specifically: + + ewma_sum(u) := u_0 + u_1*y + u_2*y^2 + ... + + ewma(u) = ewma_sum(u) / ewma_sum(1) + +Since this is essentially a progression of an infinite geometric series, the +results are composable, that is ewma(A) + ewma(B) = ewma(A+B). This property +is key, since it gives the ability to recompose the averages when tasks move +around. + +Note that blocked tasks still contribute to the aggregates (task-group slices +and CPU runqueues), which reflects their expected contribution when they +resume running. + +Using this we track 2 key metrics: 'running' and 'runnable'. 'Running' +reflects the time an entity spends on the CPU, while 'runnable' reflects the +time an entity spends on the runqueue. When there is only a single task these +two metrics are the same, but once there is contention for the CPU 'running' +will decrease to reflect the fraction of time each task spends on the CPU +while 'runnable' will increase to reflect the amount of contention. + +For more detail see: kernel/sched/pelt.c + + +Frequency / CPU Invariance +========================== + +Because consuming the CPU for 50% at 1GHz is not the same as consuming the CPU +for 50% at 2GHz, nor is running 50% on a LITTLE CPU the same as running 50% on +a big CPU, we allow architectures to scale the time delta with two ratios, one +Dynamic Voltage and Frequency Scaling (DVFS) ratio and one microarch ratio. + +For simple DVFS architectures (where software is in full control) we trivially +compute the ratio as:: + + f_cur + r_dvfs := ----- + f_max + +For more dynamic systems where the hardware is in control of DVFS we use +hardware counters (Intel APERF/MPERF, ARMv8.4-AMU) to provide us this ratio. +For Intel specifically, we use:: + + APERF + f_cur := ----- * P0 + MPERF + + 4C-turbo; if available and turbo enabled + f_max := { 1C-turbo; if turbo enabled + P0; otherwise + + f_cur + r_dvfs := min( 1, ----- ) + f_max + +We pick 4C turbo over 1C turbo to make it slightly more sustainable. + +r_cpu is determined as the ratio of highest performance level of the current +CPU vs the highest performance level of any other CPU in the system. + + r_tot = r_dvfs * r_cpu + +The result is that the above 'running' and 'runnable' metrics become invariant +of DVFS and CPU type. IOW. we can transfer and compare them between CPUs. + +For more detail see: + + - kernel/sched/pelt.h:update_rq_clock_pelt() + - arch/x86/kernel/smpboot.c:"APERF/MPERF frequency ratio computation." + - Documentation/scheduler/sched-capacity.rst:"1. CPU Capacity + 2. Task utilization" + + +UTIL_EST / UTIL_EST_FASTUP +========================== + +Because periodic tasks have their averages decayed while they sleep, even +though when running their expected utilization will be the same, they suffer a +(DVFS) ramp-up after they are running again. + +To alleviate this (a default enabled option) UTIL_EST drives an Infinite +Impulse Response (IIR) EWMA with the 'running' value on dequeue -- when it is +highest. A further default enabled option UTIL_EST_FASTUP modifies the IIR +filter to instantly increase and only decay on decrease. + +A further runqueue wide sum (of runnable tasks) is maintained of: + + util_est := \Sum_t max( t_running, t_util_est_ewma ) + +For more detail see: kernel/sched/fair.c:util_est_dequeue() + + +UCLAMP +====== + +It is possible to set effective u_min and u_max clamps on each CFS or RT task; +the runqueue keeps an max aggregate of these clamps for all running tasks. + +For more detail see: include/uapi/linux/sched/types.h + + +Schedutil / DVFS +================ + +Every time the scheduler load tracking is updated (task wakeup, task +migration, time progression) we call out to schedutil to update the hardware +DVFS state. + +The basis is the CPU runqueue's 'running' metric, which per the above it is +the frequency invariant utilization estimate of the CPU. From this we compute +a desired frequency like:: + + max( running, util_est ); if UTIL_EST + u_cfs := { running; otherwise + + clamp( u_cfs + u_rt , u_min, u_max ); if UCLAMP_TASK + u_clamp := { u_cfs + u_rt; otherwise + + u := u_clamp + u_irq + u_dl; [approx. see source for more detail] + + f_des := min( f_max, 1.25 u * f_max ) + +XXX IO-wait: when the update is due to a task wakeup from IO-completion we +boost 'u' above. + +This frequency is then used to select a P-state/OPP or directly munged into a +CPPC style request to the hardware. + +XXX: deadline tasks (Sporadic Task Model) allows us to calculate a hard f_min +required to satisfy the workload. + +Because these callbacks are directly from the scheduler, the DVFS hardware +interaction should be 'fast' and non-blocking. Schedutil supports +rate-limiting DVFS requests for when hardware interaction is slow and +expensive, this reduces effectiveness. + +For more information see: kernel/sched/cpufreq_schedutil.c + + +NOTES +===== + + - On low-load scenarios, where DVFS is most relevant, the 'running' numbers + will closely reflect utilization. + + - In saturated scenarios task movement will cause some transient dips, + suppose we have a CPU saturated with 4 tasks, then when we migrate a task + to an idle CPU, the old CPU will have a 'running' value of 0.75 while the + new CPU will gain 0.25. This is inevitable and time progression will + correct this. XXX do we still guarantee f_max due to no idle-time? + + - Much of the above is about avoiding DVFS dips, and independent DVFS domains + having to re-learn / ramp-up when load shifts. + |