Re: [PATCH V3 1/2] sched: Reduce the default slice to avoid tasks getting an extra tick
From: K Prateek Nayak
Date: Thu Mar 06 2025 - 23:10:32 EST
Hello Zhou,
Sorry this slipped past me.
On 2/22/2025 8:32 AM, zihan zhou wrote:
Thank you for your reply, thank you for providing such a detailed test,
which also let me learn a lot.
Hello Zhou,
I'll leave some testing data below but overall, in my testing with
CONFIG_HZ=250 and CONFIG_HZ=10000, I cannot see any major regressions
(at least not for any stable data point) There are few small regressions
probably as a result of grater opportunity for wakeup preemption since
RUN_TO_PARITY will work for a slightly shorter duration now but I
haven't dug deeper to confirm if they are run to run variation or a
result of the larger number of wakeup preemption.
Since most servers run with CONFIG_HZ=250, and the tick is anyways 4ms
and with default base slice currently at 3ms, I don't think there will
be any discernible difference in most workloads (fingers crossed)
Please find full data below.
This should be CONFIG_HZ=250 and CONFIG_HZ=1000, is it wrong?
That is correct! My bad.
It seems that no performance difference is good news. This change will not
affect performance. This problem was first found in the openeuler 6.6
kernel. If one task runs all the time and the other runs for 3ms and then
sleeps for 1us, the running time of the two tasks will become 4:3, but 1:1
on orig cfs. This problem has disappeared in the mainline kernel.
o Benchmark results (CONFIG_HZ=1000)
==================================================================
Test : hackbench
Units : Normalized time in seconds
Interpretation: Lower is better
Statistic : AMean
==================================================================
Case: mainline[pct imp](CV) new_base_slice[pct imp](CV)
1-groups 1.00 [ -0.00]( 8.66) 1.05 [ -5.30](16.73)
2-groups 1.00 [ -0.00]( 5.02) 1.07 [ -6.54]( 7.29)
4-groups 1.00 [ -0.00]( 1.27) 1.02 [ -1.67]( 3.74)
8-groups 1.00 [ -0.00]( 2.75) 0.99 [ 0.78]( 2.61)
16-groups 1.00 [ -0.00]( 2.02) 0.97 [ 2.97]( 1.19)
==================================================================
Test : tbench
Units : Normalized throughput
Interpretation: Higher is better
Statistic : AMean
==================================================================
Clients: mainline[pct imp](CV) new_base_slice[pct imp](CV)
1 1.00 [ 0.00]( 0.40) 1.00 [ -0.44]( 0.47)
2 1.00 [ 0.00]( 0.49) 0.99 [ -0.65]( 1.39)
4 1.00 [ 0.00]( 0.94) 1.00 [ -0.34]( 0.09)
8 1.00 [ 0.00]( 0.64) 0.99 [ -0.77]( 1.57)
16 1.00 [ 0.00]( 1.04) 0.98 [ -2.00]( 0.98)
32 1.00 [ 0.00]( 1.13) 1.00 [ 0.34]( 1.31)
64 1.00 [ 0.00]( 0.58) 1.00 [ -0.28]( 0.80)
128 1.00 [ 0.00]( 1.40) 0.99 [ -0.91]( 0.51)
256 1.00 [ 0.00]( 1.14) 0.99 [ -1.48]( 1.17)
512 1.00 [ 0.00]( 0.51) 1.00 [ -0.25]( 0.66)
1024 1.00 [ 0.00]( 0.62) 0.99 [ -0.79]( 0.40)
==================================================================
Test : stream-10
Units : Normalized Bandwidth, MB/s
Interpretation: Higher is better
Statistic : HMean
==================================================================
Test: mainline[pct imp](CV) new_base_slice[pct imp](CV)
Copy 1.00 [ 0.00](16.03) 0.98 [ -2.33](17.69)
Scale 1.00 [ 0.00]( 6.26) 0.99 [ -0.60]( 7.94)
Add 1.00 [ 0.00]( 8.35) 1.01 [ 0.50](11.49)
Triad 1.00 [ 0.00]( 9.56) 1.01 [ 0.66]( 9.25)
==================================================================
Test : stream-100
Units : Normalized Bandwidth, MB/s
Interpretation: Higher is better
Statistic : HMean
==================================================================
Test: mainline[pct imp](CV) new_base_slice[pct imp](CV)
Copy 1.00 [ 0.00]( 6.03) 1.02 [ 1.58]( 2.27)
Scale 1.00 [ 0.00]( 5.78) 1.02 [ 1.64]( 4.50)
Add 1.00 [ 0.00]( 5.25) 1.01 [ 1.37]( 4.17)
Triad 1.00 [ 0.00]( 5.25) 1.03 [ 3.35]( 1.18)
==================================================================
Test : netperf
Units : Normalized Througput
Interpretation: Higher is better
Statistic : AMean
==================================================================
Clients: mainline[pct imp](CV) new_base_slice[pct imp](CV)
1-clients 1.00 [ 0.00]( 0.06) 1.01 [ 0.66]( 0.75)
2-clients 1.00 [ 0.00]( 0.80) 1.01 [ 0.79]( 0.31)
4-clients 1.00 [ 0.00]( 0.65) 1.01 [ 0.56]( 0.73)
8-clients 1.00 [ 0.00]( 0.82) 1.01 [ 0.70]( 0.59)
16-clients 1.00 [ 0.00]( 0.68) 1.01 [ 0.63]( 0.77)
32-clients 1.00 [ 0.00]( 0.95) 1.01 [ 0.87]( 1.06)
64-clients 1.00 [ 0.00]( 1.55) 1.01 [ 0.66]( 1.60)
128-clients 1.00 [ 0.00]( 1.23) 1.00 [ -0.28]( 1.58)
256-clients 1.00 [ 0.00]( 4.92) 1.00 [ 0.25]( 4.47)
512-clients 1.00 [ 0.00](57.12) 1.00 [ 0.24](62.52)
==================================================================
Test : schbench
Units : Normalized 99th percentile latency in us
Interpretation: Lower is better
Statistic : Median
==================================================================
#workers: mainline[pct imp](CV) new_base_slice[pct imp](CV)
1 1.00 [ -0.00](27.55) 0.81 [ 19.35](31.80)
2 1.00 [ -0.00](19.98) 0.87 [ 12.82]( 9.17)
4 1.00 [ -0.00](10.66) 1.09 [ -9.09]( 6.45)
8 1.00 [ -0.00]( 4.06) 0.90 [ 9.62]( 6.38)
16 1.00 [ -0.00]( 5.33) 0.98 [ 1.69]( 1.97)
32 1.00 [ -0.00]( 8.92) 0.97 [ 3.16]( 1.09)
64 1.00 [ -0.00]( 6.06) 0.97 [ 3.30]( 2.97)
128 1.00 [ -0.00](10.15) 1.05 [ -5.47]( 4.75)
256 1.00 [ -0.00](27.12) 1.00 [ -0.20](13.52)
512 1.00 [ -0.00]( 2.54) 0.80 [ 19.75]( 0.40)
==================================================================
Test : new-schbench-requests-per-second
Units : Normalized Requests per second
Interpretation: Higher is better
Statistic : Median
==================================================================
#workers: mainline[pct imp](CV) new_base_slice[pct imp](CV)
1 1.00 [ 0.00]( 0.15) 1.00 [ 0.00]( 0.46)
2 1.00 [ 0.00]( 0.15) 1.00 [ 0.00]( 0.15)
4 1.00 [ 0.00]( 0.15) 1.00 [ 0.00]( 0.15)
8 1.00 [ 0.00]( 0.00) 1.00 [ 0.00]( 0.15)
16 1.00 [ 0.00]( 0.00) 1.00 [ 0.00]( 0.00)
32 1.00 [ 0.00]( 0.43) 1.01 [ 0.63]( 0.28)
64 1.00 [ 0.00]( 1.17) 1.00 [ 0.00]( 0.20)
128 1.00 [ 0.00]( 0.20) 1.00 [ 0.00]( 0.20)
256 1.00 [ 0.00]( 0.27) 1.00 [ 0.00]( 1.69)
512 1.00 [ 0.00]( 0.21) 0.95 [ -4.70]( 0.34)
==================================================================
Test : new-schbench-wakeup-latency
Units : Normalized 99th percentile latency in us
Interpretation: Lower is better
Statistic : Median
==================================================================
#workers: mainline[pct imp](CV) new_base_slice[pct imp](CV)
1 1.00 [ -0.00](11.08) 1.33 [-33.33](15.78)
2 1.00 [ -0.00]( 4.08) 1.08 [ -7.69](10.00)
4 1.00 [ -0.00]( 6.39) 1.21 [-21.43](22.13)
8 1.00 [ -0.00]( 6.88) 1.15 [-15.38](11.93)
16 1.00 [ -0.00](13.62) 1.08 [ -7.69](10.33)
32 1.00 [ -0.00]( 0.00) 1.00 [ -0.00]( 3.87)
64 1.00 [ -0.00]( 8.13) 1.00 [ -0.00]( 2.38)
128 1.00 [ -0.00]( 5.26) 0.98 [ 2.11]( 1.92)
256 1.00 [ -0.00]( 1.00) 0.78 [ 22.36](14.65)
512 1.00 [ -0.00]( 0.48) 0.73 [ 27.15]( 6.75)
==================================================================
Test : new-schbench-request-latency
Units : Normalized 99th percentile latency in us
Interpretation: Lower is better
Statistic : Median
==================================================================
#workers: mainline[pct imp](CV) new_base_slice[pct imp](CV)
1 1.00 [ -0.00]( 1.53) 1.00 [ -0.00]( 1.77)
2 1.00 [ -0.00]( 0.50) 1.01 [ -1.35]( 1.19)
4 1.00 [ -0.00]( 0.14) 1.00 [ -0.00]( 0.42)
8 1.00 [ -0.00]( 0.24) 1.00 [ -0.27]( 1.37)
16 1.00 [ -0.00]( 0.00) 1.00 [ 0.27]( 0.14)
32 1.00 [ -0.00]( 0.66) 1.01 [ -1.48]( 2.65)
64 1.00 [ -0.00]( 5.72) 0.96 [ 4.32]( 5.64)
128 1.00 [ -0.00]( 0.10) 1.00 [ -0.20]( 0.18)
256 1.00 [ -0.00]( 2.52) 0.96 [ 4.04]( 9.70)
512 1.00 [ -0.00]( 0.68) 1.06 [ -5.52]( 0.36)
==================================================================
Test : longer running benchmarks
Units : Normalized throughput
Interpretation: Higher is better
Statistic : Median
==================================================================
Benchmark pct imp
ycsb-cassandra -0.64%
ycsb-mongodb 0.56%
deathstarbench-1x 0.30%
deathstarbench-2x 3.21%
deathstarbench-3x 2.18%
deathstarbench-6x -0.40%
mysql-hammerdb-64VU -0.63%
---
It seems that new_base_slice has made some progress in high load/latency
and regressed a bit on low load.
It seems that slice should not only be related to the number of cpus, but
also to the corresponding relationship between the overall load and the
number of cpus. The load is relatively heavy, so the slice should be
smaller. The load is relatively light, so the slice should be larger.
Fixing it to a value may not be the optimal solution.
We've seen that assumptions go wrong in our experiments; some benchmarks
really love their time on the CPU without any preemptions :)
With that overwhelming amount of data out of the way, please feel free
to add:
Tested-by: K Prateek Nayak <kprateek.nayak@xxxxxxx>
I think you're worth it, but it seems a bit late. I have received the email
of tip-bot2, I am not sure if there can still add it.
That is fine as long as there is a record on lore :)
Your email made me realize that I should establish a systematic testing
method. Can you give me some useful projects?
We use selective benchmarks from LKP: https://github.com/intel/lkp-tests
Then there are some larger benchmarks we run based on previous regression
reports and debugs. some of them are:
YCSB: https://github.com/brianfrankcooper/YCSB
netperf: https://github.com/HewlettPackard/netperf
DeathStarBench: https://github.com/delimitrou/DeathStarBench
HammerDB: https://github.com/TPC-Council/HammerDB.git
tbench (part of dbench): https://dbench.samba.org/web/download.html
schbench: https://git.kernel.org/pub/scm/linux/kernel/git/mason/schbench.git
sched-messaging: https://git.kernel.org/pub/scm/linux/kernel/git/torvalds/linux.git/tree/tools/perf/bench/sched-messaging.c?h=v6.14-rc4
Some of them are hard to setup the first time; we internally have some
tools that have made it easy to run these benchmarks in a way that
stresses the system but we keep an eye out for regression reports to
understand what benchmarks folks are running in the field.
Sorry again for the delay and thank you.
Thanks!
--
Thanks and Regards,
Prateek