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Message-ID: <3d0f9c2b-8498-4405-b178-9f6c8615f73b@amd.com> Date: Fri, 7 Mar 2025 09:40:11 +0530 From: K Prateek Nayak <kprateek.nayak@....com> To: zihan zhou <15645113830zzh@...il.com> CC: <bsegall@...gle.com>, <dietmar.eggemann@....com>, <gautham.shenoy@....com>, <juri.lelli@...hat.com>, <linux-kernel@...r.kernel.org>, <mgorman@...e.de>, <mingo@...hat.com>, <peterz@...radead.org>, <rostedt@...dmis.org>, <vincent.guittot@...aro.org>, <vschneid@...hat.com> Subject: Re: [PATCH V3 1/2] sched: Reduce the default slice to avoid tasks getting an extra tick 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@....com> > > 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
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