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fdd0f81f |
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06-Dec-2022 |
Petar Gligoric <petar.gligoric@rohde-schwarz.com> |
perf script: task-analyzer add csv support
This patch adds the possibility to write the trace and the summary as csv files
to a user specified file. A format as such simplifies further data processing.
This is achieved by having ";" as separators instead of spaces and solely one
header per file.
Additional parameters are being considered, like in the normal usage of the
script. Colors are turned off in the case of a csv output, thus the highlight
option is also being ignored.
Usage:
Write standard task to csv file:
$ perf script report tasks-analyzer --csv <file>
write limited output to csv file in nanoseconds:
$ perf script report tasks-analyzer --csv <file> --ns --limit-to-tasks 1337
Write summary to a csv file:
$ perf script report tasks-analyzer --csv-summary <file>
Write summary to csv file with additional schedule information:
$ perf script report tasks-analyzer --csv-summary <file> --summary-extended
Write both summary and standard task to a csv file:
$ perf script report tasks-analyzer --csv --csv-summary
The following examples illustrate what is possible with the CSV output. The
first command sequence will record all scheduler switch events for 10 seconds,
the task-analyzer calculates task information like runtimes as CSV. A small
python snippet using pandas and matplotlib will visualize the most frequent
task (e.g. kworker/1:1) runtimes - each runtime as a bar in a bar chart:
$ perf record -e sched:sched_switch -a -- sleep 10
$ perf script report tasks-analyzer --ns --csv tasks.csv
$ cat << EOF > /tmp/freq-comm-runtimes-bar.py
import pandas as pd
import matplotlib.pyplot as plt
df = pd.read_csv("tasks.csv", sep=';')
most_freq_comm = df["COMM"].value_counts().idxmax()
most_freq_runtimes = df[df["COMM"]==most_freq_comm]["Runtime"]
plt.title(f"Runtimes for Task {most_freq_comm} in Nanoseconds")
plt.bar(range(len(most_freq_runtimes)), most_freq_runtimes)
plt.show()
$ python3 /tmp/freq-comm-runtimes-bar.py
As a seconds example, the subsequent script generates a pie chart of all
accumulated tasks runtimes for 10 seconds of system recordings:
$ perf record -e sched:sched_switch -a -- sleep 10
$ perf script report tasks-analyzer --csv-summary task-summary.csv
$ cat << EOF > /tmp/accumulated-task-pie.py
import pandas as pd
from matplotlib.pyplot import pie, axis, show
df = pd.read_csv("task-summary.csv", sep=';')
sums = df.groupby(df["Comm"])["Accumulated"].sum()
axis("equal")
pie(sums, labels=sums.index);
show()
EOF
$ python3 /tmp/accumulated-task-pie.py
A variety of other visualizations are possible in matplotlib and other
environments. Of course, pandas, numpy and co. also allow easy
statistical analysis of the data!
Signed-off-by: Petar Gligoric <petar.gligoric@rohde-schwarz.com>
Cc: Andi Kleen <ak@linux.intel.com>
Cc: Ian Rogers <irogers@google.com>
Cc: Jiri Olsa <jolsa@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Link: https://lore.kernel.org/r/20221206154406.41941-3-petar.gligor@gmail.com
Signed-off-by: Hagen Paul Pfeifer <hagen@jauu.net>
Signed-off-by: Arnaldo Carvalho de Melo <acme@redhat.com>
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| #
e76aff05 |
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06-Dec-2022 |
Hagen Paul Pfeifer <hagen@jauu.net> |
perf script: Introduce task analyzer python script
Introduce a new 'perf script' to analyze task scheduling behavior.
During the task analysis, some data is always needed - which goes beyond
the simple time of switching on and off a task (process/thread). This
concerns for example the runtime of a process or the frequency with
which the process was called. This script serves to simplify this
recurring analyze process. It immediately provides the user with helpful
task characteristic information about the tasks runtimes.
Usage:
Recorded can be in two ways:
$ perf script record tasks-analyzer -- sleep 10
$ perf record -e sched:sched_switch -a -- sleep 10
The script can parse all perf.data files, most important: sched:sched_switch
events are mandatory, other events will be ignored.
Most simple report use case is to just call the script without arguments:
$ perf script report tasks-analyzer
Switched-In Switched-Out CPU PID TID Comm Runtime Time Out-In
15576.658891407 15576.659156086 4 2412 2428 gdbus 265 1949
15576.659111320 15576.659455410 0 2412 2412 gnome-shell 344 2267
15576.659491326 15576.659506173 2 74 74 kworker/2:1 15 13145
15576.659506173 15576.659825748 2 2858 2858 gnome-terminal- 320 63263
15576.659871270 15576.659902872 6 20932 20932 kworker/u16:0 32 2314582
15576.659909951 15576.659945501 3 27264 27264 sh 36 -1
15576.659853285 15576.659971052 7 27265 27265 perf 118 5050741
[...]
What is not shown here are the ASCII color sequences. For example, if
the task consists of only one thread, the TID is grayed out.
Runtime is the time the task was running on the CPU, Time Out-In is the
time between the process being scheduled *out* and scheduled back *in*.
So the last time span between two executions. If -1 is printed, then the
task simply ran the first time in the measurements - a Out-In delta
could not be calculated.
In addition to the chronological representation, there is a summary on
task level. This output can be additionally switched on via the
--summary option and provides information such as max, min & average
runtime per process. The maximum runtime is often important for
debugging. The call looks like this:
$ perf script report tasks-analyzer --summary
Summary
Task Information Runtime Information
PID TID Comm Runs Accumulated Mean Median Min Max Max At
14 14 ksoftirqd/0 13 334 26 15 9 127 15571.621211956
15 15 rcu_preempt 133 1778 13 13 2 33 15572.581176024
16 16 migration/0 3 49 16 13 12 24 15571.608915425
20 20 migration/1 3 34 11 13 8 13 15571.639101555
25 25 migration/2 3 32 11 12 9 12 15575.639239896
[...]
Besides these two options, there are a number of other options that change the
output and behavior. This can be queried via --help. Options worth mentioning include:
- filter-tasks - filter out unneeded tasks, --filter-task 1337,/sbin/init
- highlight-tasks - more pleasant focusing, --highlight-tasks 1:red,mutt:yellow
- extended-times - show combinations of elapsed times between schedule in/schedule out
- summary-extended - summary with additional information, like maximum delta time statistics
- rename-comms-by-tids - handy for inexpressive processnames like python, --rename 1337:my-python-app
- ms - show timestamps in milliseconds, nanoseconds is also possible (--ns)
- time-limit - limit the analyzer to a time range, --time-limit 15576.0:15576.1
Script is tested and prime time ready for python2 & python3:
- make PYTHON=python3 prefix=/usr/local install
- make PYTHON=python2 prefix=/usr/local install
Signed-off-by: Hagen Paul Pfeifer <hagen@jauu.net>
Cc: Andi Kleen <ak@linux.intel.com>
Cc: Ian Rogers <irogers@google.com>
Cc: Jiri Olsa <jolsa@kernel.org>
Cc: Namhyung Kim <namhyung@kernel.org>
Link: https://lore.kernel.org/r/20221206154406.41941-2-petar.gligor@gmail.com
Signed-off-by: Petar Gligoric <petar.gligoric@rohde-schwarz.com>
Signed-off-by: Arnaldo Carvalho de Melo <acme@redhat.com>
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