Hi,
A number of patch sets related to power-efficient scheduling have been posted over the last couple of months. Most of them do not have much data to back them up, so I decided to do some testing.
Common for all of the patch sets that I have tested, except one, is that they attempt to pack tasks on as few cpus as possible to allow the remaining cpus to enter deeper sleep states - a strategy that should make sense on most platforms that support per-cpu power gating and multi-socket machines.
Kernel: 3.9
Patch sets: rlb-v4: sched: use runnable load based balance (Alex Shi) https://lkml.org/lkml/2013/4/27/13 pas-v7: sched: power aware scheduling (Alex Shi) https://lkml.org/lkml/2013/4/3/732 pst-v3: sched: packing small tasks (Vincent Guittot) https://lkml.org/lkml/2013/3/22/183 pst-v4: sched: packing small tasks (Vincent Guittot) https://lkml.org/lkml/2013/4/25/396
Configuration: pas-v7: Set to "powersaving" mode. pst-v4: Set to "Full" packing mode.
Platform: ARM TC2 (test-chip), 2xCortex-A15 + 3xCortex-A7. Cortex-A15s disabled.
Measurement technique: Time spent non-idle (not in idle state) for each cpu based on cpuidle ftrace events. TC2 does not have per-core power-gating, so packing inside the A7 cluster does not lead to any significant power savings. Note that any product grade hardware (TC2 is a test-chip) will very likely have per-core power-gating, so in those cases packing will have an appreciable effect on power savings. Measuring non-idle time rather than power should give a more clear idea about the effect of the patch sets given that the idle back-end is highly implementation specific.
Benchmarks: audio playback (Android): 30s mp3 file playback on Android. bbench+audio (Android): Web page rendering while doing mp3 playback. andebench_native (Android): Android benchmark running in native mode. cyclictest: Short periodic tasks.
Results: Two runs for each patch set.
audio playback (Android) SMP non-idle % cpu 0 cpu 1 cpu 2 3.9_1 11.96 2.86 2.48 3.9_2 12.64 2.81 1.88 rlb-v4_1 12.61 2.44 1.90 rlb-v4_2 12.45 2.44 1.90 pas-v7_1 16.17 0.03 0.24 pas-v7_2 16.08 0.28 0.07 pst-v3_1 15.18 2.76 1.70 pst-v3_2 15.13 0.80 0.38 pst-v4_1 16.14 0.05 0.00 pst-v4_2 16.34 0.06 0.00
bbench+audio (Android) SMP non-idle % cpu 0 cpu 1 cpu 2 render time 3.9_1 25.00 20.73 21.22 812 3.9_2 24.29 19.78 22.34 795 rlb-v4_1 23.84 19.36 22.74 782 rlb-v4_2 24.07 19.36 22.74 797 pas-v7_1 28.29 17.86 16.01 869 pas-v7_2 28.62 18.54 15.05 908 pst-v3_1 29.14 20.59 21.72 830 pst-v3_2 27.69 18.81 20.06 830 pst-v4_1 42.20 13.63 2.29 880 pst-v4_2 41.56 14.40 2.17 935
andebench_native (8 threads) (Android) SMP non-idle % cpu 0 cpu 1 cpu 2 Score 3.9_1 99.22 98.88 99.61 4139 3.9_2 99.56 99.31 99.46 4148 rlb-v4_1 99.49 99.61 99.53 4153 rlb-v4_2 99.56 99.61 99.53 4149 pas-v7_1 99.53 99.59 99.29 4149 pas-v7_2 99.42 99.63 99.48 4150 pst-v3_1 97.89 99.33 99.42 4097 pst-v3_2 99.16 99.62 99.42 4097 pst-v4_1 99.34 99.01 99.59 4146 pst-v4_2 99.49 99.52 99.20 4146
cyclictest SMP non-idle % cpu 0 cpu 1 cpu 2 3.9_1 9.13 8.88 8.41 3.9_2 10.27 8.02 6.30 rlb-v4_1 8.88 8.09 8.11 rlb-v4_2 8.49 8.09 8.11 pas-v7_1 10.20 0.02 11.50 pas-v7_2 7.86 14.31 0.02 pst-v3_1 20.44 8.68 7.97 pst-v3_2 20.41 0.78 1.00 pst-v4_1 21.32 0.21 0.05 pst-v4_2 21.56 0.21 0.04
Overall, pas-v7 seems to do a fairly good job at packing. The idle time distribution seems to be somewhere between pst-v3 and the more aggressive pst-v4 for all the benchmarks. pst-v4 manages to keep two cpus nearly idle (<0.25% non-idle) for both cyclictest and audio, which is better than both pst-v3 and pas-v7. pas-v7 fails to pack cyclictest. Packing does come at at cost which can be seen for bbench+audio, where pst-v3 and rlb-v4 get better render times than pas-v7 and pst-v4 which do more aggressive packing. rlb-v4 does not pack, it is only included for reference.
From a packing perspective pst-v4 seems to do the best job for the
workloads that I have tested on ARM TC2. The less aggressive packing in pst-v3 may be a better choice for in terms of performance.
I'm well aware that these tests are heavily focused on mobile workloads. I would therefore encourage people to share your test results for your workloads on your platforms to complete the picture. Comments are also welcome.
Thanks, Morten