BPF CO-RE based sched-analyzer
This is a personal pet project and not affiliated with any employer or organization.
sched-analyzer connects to various points in the kernel to extract internal info and produce perfetto trace with additional collected events/tracks. Perfetto has integration with python for usage with libraries like pandas for more sophisticated post-processing option.
Each event recorded by sched-analyzer is processed in its own thread to ensure each BPF ringbuffer is emptied in parallel and reduce the chance of overflowing any of them and potentially lose data.
Since we peek inside kernel internals which are not ABI, there's no guarantee this will work on every kernel. Or won't silently fail if for instance some arguments to the one of the tracepoints we attach to changes.
The BPF backend, sched-analyzer, will collect data and generate perfetto
events for analysis and optionally additional post processing via python.
- load_avg and runnable_avg of FAIR at runqueue level
- util_avg of FAIR, RT, DL, IRQ and thermal pressure at runqueue level
- load_avg, runnable_avg and util_avg of tasks running
- uclamped util_avg of CPUs and tasks: clamp(util_avg, uclamp_min, uclamp_max)
- util_est at runqueue level and of tasks
- Number of tasks running for every runqueue
- Track cpu_idle and cpu_idle_miss events
- Track load balance entry/exit and some related info (Experimental)
- Track IPI related info (Experimental)
- Collect hard and soft irq entry/exit data (perfetto builtin functionality)
- Filter tasks per pid or comm
- Trace wake up path and why a task placement decision was made
- Better tracing of load balancer to understand when it kicks and what it performs when it runs
- Trace schedutil and its decision to select a frequency and when it's rate limited
- Trace TEO idle governor and its decision making process
- Add more python post processing tools to summarize task placement histogram for a sepcifc task(s) and residency of various PELT signals
- Add more python post processing tools to summarize softirq residencies and CPU histogram
- Track PELT at cgroup level (cfs_rq)
sudo apt install linux-tools-$(uname -r) git clang llvm libelf1 zlib1g libelf-dev bpftool
Download latest release of perfetto from github
You need a kernel compiled with BTF to Compile and Run.
Required kernel config to get BTF:
- CONFIG_DEBUG_INFO_BTF=y
make
make help // for generic help and how to static build and cross compile
g++-9 and g++-10 fail to create a working static build - see this issue.
First make sure perfetto is downloaded and in your PATH. You need to run the following commands once after every reboot:
sudo tracebox traced --background
sudo tracebox traced_probes --background
To collect data run:
sudo ./sched-analyzer --cpu_nr_running --util_avg
Press CTRL+c to stop. sched-analyzer.perfetto-trace will be in PWD that you
can open in ui.perfetto.dev
The captured data are under sched-analyzer tab in perfetto, expand it to see them
csv mode was deprecated and is no longer supported on main branch. You can still find it in csv-mode branch.
Post process the produced sched-analyzer.perfetto-trace to detect potential problems or summarizes some metrics.
pip install -r ./sched-analyzer-pp/requirements.txt
activate-global-python-argcomplete --user
complete -r sched-analyzer-pp
You might need to add the following to your ~/.bash_completion to get it to work:
for file in ~/.bash_completion.d/*
do
. $file
done
See ./sched-analyzer-pp/sched-analyzer-pp --help for list of all available
options
- Plot frequency selection and residency on TUI
- Plot idle residencies on TUI
- Save produced TUI output as png image
- Save raw data as csv
./sched-analyzer-pp/sched-analyzer-pp --freq --freq-residency --idle-residency sched-analyzer.perfetto-trace
CPU0 Frequency
┌──────────────────────────────────────────────────────────────────────────────────────────────┐
2.06┤ ▗▄▄▄▟▗▌▟▟▟▟▗▌▟ ▗▌│
1.82┤ ▗▄▄▄▄▀▀▀▀▘ ▐▐▌████▐▌█ ▐▌│
1.58┤ ▗ ▗▄▄▄▄▀▀▀▀▘ ▐▐▌████▐▌█ ▌▌│
1.09┤ ▄▄▄▄▄▀▀▀▀▜ ▄▄▄▄▞▀▀▀▘ ▐▌█▐███▌█ ▌▌▌│
0.84┤ ▟ ▄▄▄▄▄▀▀▀▀▀ ▐ ▄▄▄▄▞▀▀▀▀ ▐▌█▐███▌█ █ ▌│
0.60┤▄▀▝▄▄▄▄▄▀▀▀▀▀ ▝▄▄▄▄▞▀▀▀▀ ▝▌▜▝▛▛▛▌▜ ▜ ▚│
└┬──────────────────────┬───────────────────────┬──────────────────────┬──────────────────────┬┘
1.79 2.28 2.78 3.27 3.76
CPU4 Frequency
┌──────────────────────────────────────────────────────────────────────────────────────────────┐
3.20┤ ▞│
2.77┤▖ ▌│
2.34┤▌ ▗ ▗▘│
1.47┤▌ ▗▄▄▄▄▄▄▞▀▀▀▀▀▀▀▖ ▗▄▌ ▐ │
1.03┤▌ ▄▄▄▄▄▄▄▀▀▀▀▀▀▀▘ ▌ ▗▄▀▘ ▚ ▗▄▄▄▄▄▄▄▄▄▄▄▄▞ │
0.60┤▚▄▄▄▄▄▄▄▀▀▀▀▀▀▀ ▚▄▀▘ ▝▄▄▄▄▄▄▄▄▄▄▄▄▄▞▀▀▀▀▀▀▀▀▀▀▀▀▘ │
└┬──────────────────────┬───────────────────────┬──────────────────────┬──────────────────────┬┘
0.00 0.45 0.90 1.34 1.79
CPU0 Frequency residency %
┌──────────────────────────────────────────────────────────────────────────────────────────────┐
93.3┤███████ │
77.7┤███████ │
62.2┤███████ │
31.1┤███████ │
15.5┤███████ │
0.0┤███████ ███████ ██████ ███████│
└───┬─────────────────────┬─────────────────────┬──────────────────────────────────────────┬───┘
0.6 0.972 1.332 2.064
CPU4 Frequency residency %
┌──────────────────────────────────────────────────────────────────────────────────────────────┐
95.7┤████ │
79.7┤████ │
63.8┤████ │
31.9┤████ │
15.9┤████ │
0.0┤████ ███ ████ ████ ████ ████ ████ │
└─┬───────────────────────┬───────┬──────┬───────┬───────┬──────┬────────────────────────────┬─┘
0.6 1.284 1.5 1.728 1.956 2.184 2.388 3.204
CPU0 Idle residency % CPU1 Idle residency % CPU2 Idle residency % CPU3 Idle residency %
┌────────────────────────┐ ┌────────────────────────┐ ┌────────────────────────┐ ┌────────────────────────┐
70.1┤ ███│82.2┤ ███│60.8┤ ███│86.3┤ ███│
58.4┤ ███│68.5┤ ███│50.6┤ ███│71.9┤ ███│
46.7┤ ███│54.8┤ ███│40.5┤ ████ ███│57.5┤ ███│
23.4┤ ████ ███│27.4┤ ███│20.3┤ ████ ███│28.8┤ ███│
11.7┤ ████ ███│13.7┤ ████ ███│10.1┤ ████ ███│14.4┤ ████ ███│
0.0┤███ ████ ███│ 0.0┤███ ████ ███│ 0.0┤███ ████ ███│ 0.0┤███ ████ ███│
└─┬──────────┬─────────┬─┘ └─┬──────────┬─────────┬─┘ └─┬──────────┬─────────┬─┘ └─┬──────────┬─────────┬─┘
-1.0 0.0 1.0 -1.0 0.0 1.0 -1.0 0.0 1.0 -1.0 0.0 1.0
CPU4 Idle residency % CPU5 Idle residency % CPU6 Idle residency % CPU7 Idle residency %
┌────────────────────────┐ ┌────────────────────────┐ ┌────────────────────────┐ ┌────────────────────────┐
56.1┤ ███│79.3┤ ███│72.2┤ ███│97.0┤ ███│
46.7┤ ████ ███│66.1┤ ███│60.1┤ ███│80.8┤ ███│
37.4┤ ████ ███│52.9┤ ███│48.1┤ ███│64.6┤ ███│
18.7┤ ████ ███│26.4┤ ███│24.1┤ ████ ███│32.3┤ ███│
9.3┤ ████ ███│13.2┤ ████ ███│12.0┤ ████ ███│16.2┤ ███│
0.0┤███ ████ ███│ 0.0┤███ ████ ███│ 0.0┤███ ████ ███│ 0.0┤███ ████ ███│
└─┬──────────┬─────────┬─┘ └─┬──────────┬─────────┬─┘ └─┬──────────┬─────────┬─┘ └─┬──────────┬─────────┬─┘
-1.0 0.0 1.0 -1.0 0.0 1.0 -1.0 0.0 1.0 -1.0 0.0 1.0