Messages in this thread | | | Date | Wed, 17 Apr 2024 11:38:20 +0530 | Subject | Re: [RFC PATCH 1/1] sched/eevdf: Skip eligibility check for current entity during wakeup preemption | From | K Prateek Nayak <> |
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Hello Youssef,
On 3/26/2024 8:36 AM, K Prateek Nayak wrote: >> [..snip..] >> >> Thanks for sharing this Prateek. >> We actually noticed we could also gain performance by disabling >> eligibility checks (but disable it on all paths). >> The following are a few threads we had on the topic: >> >> Discussion around eligibility: >> https://lore.kernel.org/lkml/CA+q576MS0-MV1Oy-eecvmYpvNT3tqxD8syzrpxQ-Zk310hvRbw@mail.gmail.com/ >> Some of our results: >> https://lore.kernel.org/lkml/CA+q576Mov1jpdfZhPBoy_hiVh3xSWuJjXdP3nS4zfpqfOXtq7Q@mail.gmail.com/ >> Sched feature to disable eligibility: >> https://lore.kernel.org/lkml/20231013030213.2472697-1-youssefesmat@chromium.org/ >> > > Thank you for pointing me to the discussions. I'll give this a spin on > my machine and report back what I see. Hope some of it will help during > the OSPM discussion :)
Sorry about the delay but on a positive note, I do not see any concerning regressions after dropping the eligibility criteria. I'll leave the full results from my testing below.
o System Details
- 3rd Generation EPYC System - 2 x 64C/128T - NPS1 mode
o Kernels
tip: tip:sched/core at commit 4475cd8bfd9b ("sched/balancing: Simplify the sg_status bitmask and use separate ->overloaded and ->overutilized flags")
eie: (everyone is eligible) tip + vruntime_eligible() and entity_eligible() always returns true.
o Results
================================================================== Test : hackbench Units : Normalized time in seconds Interpretation: Lower is better Statistic : AMean ================================================================== Case: tip[pct imp](CV) eie[pct imp](CV) 1-groups 1.00 [ -0.00]( 1.94) 0.95 [ 5.11]( 2.56) 2-groups 1.00 [ -0.00]( 2.41) 0.97 [ 2.80]( 1.52) 4-groups 1.00 [ -0.00]( 1.16) 0.95 [ 5.01]( 1.04) 8-groups 1.00 [ -0.00]( 1.72) 0.96 [ 4.37]( 1.01) 16-groups 1.00 [ -0.00]( 2.16) 0.94 [ 5.88]( 2.30)
================================================================== Test : tbench Units : Normalized throughput Interpretation: Higher is better Statistic : AMean ================================================================== Clients: tip[pct imp](CV) eie[pct imp](CV) 1 1.00 [ 0.00]( 0.69) 1.00 [ 0.05]( 0.61) 2 1.00 [ 0.00]( 0.25) 1.00 [ 0.06]( 0.51) 4 1.00 [ 0.00]( 1.04) 0.98 [ -1.69]( 1.21) 8 1.00 [ 0.00]( 0.72) 1.00 [ -0.13]( 0.56) 16 1.00 [ 0.00]( 2.40) 1.00 [ 0.43]( 0.63) 32 1.00 [ 0.00]( 0.62) 0.98 [ -1.80]( 2.18) 64 1.00 [ 0.00]( 1.19) 0.98 [ -2.13]( 1.26) 128 1.00 [ 0.00]( 0.91) 1.00 [ 0.37]( 0.50) 256 1.00 [ 0.00]( 0.52) 1.00 [ -0.11]( 0.21) 512 1.00 [ 0.00]( 0.36) 1.02 [ 1.54]( 0.58) 1024 1.00 [ 0.00]( 0.26) 1.01 [ 1.21]( 0.41)
================================================================== Test : stream-10 Units : Normalized Bandwidth, MB/s Interpretation: Higher is better Statistic : HMean ================================================================== Test: tip[pct imp](CV) eie[pct imp](CV) Copy 1.00 [ 0.00]( 5.01) 1.01 [ 1.27]( 4.63) Scale 1.00 [ 0.00]( 6.93) 1.03 [ 2.66]( 5.20) Add 1.00 [ 0.00]( 5.94) 1.03 [ 3.41]( 4.99) Triad 1.00 [ 0.00]( 6.40) 0.95 [ -4.69]( 8.29)
================================================================== Test : stream-100 Units : Normalized Bandwidth, MB/s Interpretation: Higher is better Statistic : HMean ================================================================== Test: tip[pct imp](CV) eie[pct imp](CV) Copy 1.00 [ 0.00]( 2.84) 1.00 [ -0.37]( 2.44) Scale 1.00 [ 0.00]( 5.26) 1.00 [ 0.21]( 3.88) Add 1.00 [ 0.00]( 4.98) 1.00 [ 0.11]( 1.15) Triad 1.00 [ 0.00]( 1.60) 0.96 [ -3.72]( 5.26)
================================================================== Test : netperf Units : Normalized Througput Interpretation: Higher is better Statistic : AMean ================================================================== Clients: tip[pct imp](CV) eie[pct imp](CV) 1-clients 1.00 [ 0.00]( 0.90) 1.00 [ -0.09]( 0.16) 2-clients 1.00 [ 0.00]( 0.77) 0.99 [ -0.89]( 0.97) 4-clients 1.00 [ 0.00]( 0.63) 0.99 [ -1.03]( 1.53) 8-clients 1.00 [ 0.00]( 0.52) 0.99 [ -0.86]( 1.66) 16-clients 1.00 [ 0.00]( 0.43) 0.99 [ -0.91]( 0.79) 32-clients 1.00 [ 0.00]( 0.88) 0.98 [ -2.37]( 1.42) 64-clients 1.00 [ 0.00]( 1.63) 0.96 [ -4.07]( 0.91) * 128-clients 1.00 [ 0.00]( 0.94) 1.00 [ -0.30]( 0.94) 256-clients 1.00 [ 0.00]( 5.08) 0.95 [ -4.95]( 3.36) 512-clients 1.00 [ 0.00](51.89) 0.99 [ -0.93](51.00)
* This seems to be the only point of regression with low CV. I'll rerun this and report back if I see a consistent dip but for the time being I'm not worried.
================================================================== Test : schbench Units : Normalized 99th percentile latency in us Interpretation: Lower is better Statistic : Median ================================================================== #workers: tip[pct imp](CV) eie[pct imp](CV) 1 1.00 [ -0.00](30.01) 0.97 [ 3.12](14.32) 2 1.00 [ -0.00](26.14) 1.23 [-22.58](13.48) 4 1.00 [ -0.00](13.22) 1.00 [ -0.00]( 6.04) 8 1.00 [ -0.00]( 6.23) 1.00 [ -0.00](13.09) 16 1.00 [ -0.00]( 3.49) 1.02 [ -1.69]( 3.43) 32 1.00 [ -0.00]( 2.20) 0.98 [ 2.13]( 2.47) 64 1.00 [ -0.00]( 7.17) 0.88 [ 12.50]( 3.18) 128 1.00 [ -0.00]( 2.79) 1.02 [ -2.46]( 8.29) 256 1.00 [ -0.00](13.02) 1.01 [ -1.34](37.58) 512 1.00 [ -0.00]( 4.27) 0.79 [ 21.49]( 2.41)
================================================================== Test : DeathStarBench Units : Normalized throughput Interpretation: Higher is better Statistic : Mean ================================================================== Pinning scaling tip eie (pct imp) 1CCD 1 1.00 1.15 (%diff: 15.68%) 2CCD 2 1.00 0.99 (%diff: -1.12%) 4CCD 4 1.00 1.11 (%diff: 11.65%) 8CCD 8 1.00 1.05 (%diff: 4.98%)
--
> > [..snip..] >
I'll try to get data from more workloads, will update the thread with when it arrives.
-- Thanks and Regards, Prateek
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