EAS computes the energy impact of migrating a waking task when deciding
on which CPU it should run. However, the current approach is known to
have a high algorithmic complexity, which can result in prohibitively
high wake-up latencies on systems with complex energy models, such as
systems with per-CPU DVFS. On such systems, the algorithm complexity is
in O(n^2) (ignoring the cost of searching for performance states in the
EM) with 'n' the number of CPUs.

To address this, re-factor the EAS wake-up path to compute the energy
'delta' (with and without the task) on a per-performance domain basis,
rather than system-wide, which brings the complexity down to O(n).

No functional changes intended.

Test results
~~~~~~~~~~~~

* Setup: Tested on a Google Pixel 3, with a Snapdragon 845 (4+4 CPUs,
  A55/A75). Base kernel is 5.3-rc5 + Pixel3 specific patches. Android
  userspace, no graphics.

* Test case:  Run a periodic rt-app task, with 16ms period, ramping down
  from 70% to 10%, in 5% steps of 500 ms each (json avail. at [1]).
  Frequencies of all CPUs are pinned to max (using scaling_min_freq
  CPUFreq sysfs entries) to reduce variability. The time to run
  select_task_rq_fair() is measured using the function profiler
  (/sys/kernel/debug/tracing/trace_stat/function*). See the test script
  for more details [2].

Test 1:

I hacked the DT to 'fake' per-CPU DVFS. That is, we end up with one
CPUFreq policy per CPU (8 policies in total). Since all frequencies are
pinned to max for the test, this should have no impact on the actual
frequency selection, but it does in the EAS calculation.

      +---------------------------+----------------------------------+
      | Without patch             | With patch                       |
+-----+-----+----------+----------+-----+-----------------+----------+
| CPU | Hit | Avg (us) | s^2 (us) | Hit | Avg (us)        | s^2 (us) |
|-----+-----+----------+----------+-----+-----------------+----------+
|  0  | 274 | 38.303   | 1750.239 | 401 | 14.126 (-63.1%) | 146.625  |
|  1  | 197 | 49.529   | 1695.852 | 314 | 16.135 (-67.4%) | 167.525  |
|  2  | 142 | 34.296   | 1758.665 | 302 | 14.133 (-58.8%) | 130.071  |
|  3  | 172 | 31.734   | 1490.975 | 641 | 14.637 (-53.9%) | 139.189  |
|  4  | 316 | 7.834    | 178.217  | 425 | 5.413  (-30.9%) | 20.803   |
|  5  | 447 | 8.424    | 144.638  | 556 | 5.929  (-29.6%) | 27.301   |
|  6  | 581 | 14.886   | 346.793  | 456 | 5.711  (-61.6%) | 23.124   |
|  7  | 456 | 10.005   | 211.187  | 997 | 4.708  (-52.9%) | 21.144   |
+-----+-----+----------+----------+-----+-----------------+----------+
             * Hit, Avg and s^2 are as reported by the function profiler

Test 2:
I also ran the same test with a normal DT, with 2 CPUFreq policies, to
see if this causes regressions in the most common case.

      +---------------------------+----------------------------------+
      | Without patch             | With patch                       |
+-----+-----+----------+----------+-----+-----------------+----------+
| CPU | Hit | Avg (us) | s^2 (us) | Hit | Avg (us)        | s^2 (us) |
|-----+-----+----------+----------+-----+-----------------+----------+
|  0  | 345 | 22.184   | 215.321  | 580 | 18.635 (-16.0%) | 146.892  |
|  1  | 358 | 18.597   | 200.596  | 438 | 12.934 (-30.5%) | 104.604  |
|  2  | 359 | 25.566   | 200.217  | 397 | 10.826 (-57.7%) | 74.021   |
|  3  | 362 | 16.881   | 200.291  | 718 | 11.455 (-32.1%) | 102.280  |
|  4  | 457 | 3.822    | 9.895    | 757 | 4.616  (+20.8%) | 13.369   |
|  5  | 344 | 4.301    | 7.121    | 594 | 5.320  (+23.7%) | 18.798   |
|  6  | 472 | 4.326    | 7.849    | 464 | 5.648  (+30.6%) | 22.022   |
|  7  | 331 | 4.630    | 13.937   | 408 | 5.299  (+14.4%) | 18.273   |
+-----+-----+----------+----------+-----+-----------------+----------+
             * Hit, Avg and s^2 are as reported by the function profiler

In addition to these two tests, I also ran 50 iterations of the Lisa
EAS functional test suite [3] with this patch applied on Arm Juno r0,
Arm Juno r2, Arm TC2 and Hikey960, and could not see any regressions
(all EAS functional tests are passing).

 [1] https://paste.debian.net/1100055/
 [2] https://paste.debian.net/1100057/
 [3] https://github.com/ARM-software/lisa/blob/master/lisa/tests/scheduler/eas_behaviour.pySigned-off-by: Quentin Perret <quentin.perret@arm.com>
Cc: Linus Torvalds <torvalds@linux-foundation.org>
Cc: Peter Zijlstra <peterz@infradead.org>
Cc: Thomas Gleixner <tglx@linutronix.de>
Cc: dietmar.eggemann@arm.com
Cc: juri.lelli@redhat.com
Cc: morten.rasmussen@arm.com
Cc: qais.yousef@arm.com
Cc: qperret@qperret.net
Cc: rjw@rjwysocki.net
Cc: tkjos@google.com
Cc: valentin.schneider@arm.com
Cc: vincent.guittot@linaro.org
Link: https://lkml.kernel.org/r/20190912094404.13802-1-qperret@qperret.netSigned-off-by: Ingo Molnar <mingo@kernel.org>