The example script plots the distribution (histogram) of the pulseaudio
wake-up times and finds the biggest latency. The script also generates
a KernelShark session descriptor file (JSON). The session descriptor file
can be used by the KernelSherk GUI to open a session which will directly
visualize the largest wake-up latency.
Signed-off-by: Yordan Karadzhov <ykaradzhov@xxxxxxxxxx>
---
kernel-shark/bin/sched_wakeup.py | 96 ++++++++++++++++++++++++++++++++
1 file changed, 96 insertions(+)
create mode 100755 kernel-shark/bin/sched_wakeup.py
diff --git a/kernel-shark/bin/sched_wakeup.py b/kernel-shark/bin/sched_wakeup.py
new file mode 100755
index 0000000..79dbedd
--- /dev/null
+++ b/kernel-shark/bin/sched_wakeup.py
@@ -0,0 +1,96 @@
+#!/usr/bin/env python2
+
+import matplotlib.pyplot as plt
+import libkshark_wrapper as ks
+import scipy.stats as st
+import numpy as np
+import array
+import json
+import sys
+
+fname = str(sys.argv[1])
+
+ks.open_file(fname)
+ofst, cpu, evt, pid, ts = ks.load_data()
+tasks = ks.get_tasks()
+task_pid = tasks["pulseaudio"]
+
+ss_eid = ks.event_id("sched", "sched_switch");
+w_eid = ks.event_id("sched", "sched_waking");
+
+i = evt.size - 1
+dt = []
+delta_max = i_ss_max = i_sw_max = 0
+
+while (i >= 0):
+ if (evt[i] == ss_eid):
+ next_pid = ks.read_event_field(offset=ofst[i],
+ sys="sched",
+ event="sched_switch",
+ field="next_pid")
+
+ if (next_pid == task_pid):
+ time_ss = ts[i]
+ index_ss = i
+
+ while(i >= 0):
+ i = i - 1
+ if (evt[i] == w_eid):
+ waking_pid = ks.read_event_field(offset=ofst[i],
+ sys="sched",
+ event="sched_waking",
+ field="pid")
+
+ if (waking_pid == task_pid):
+ delta = (time_ss - ts[i]) / 1000.
+ #print delta
+ dt.append(delta);
+ if (delta > delta_max):
+ print "lat. max: ", delta
+ i_ss_max = index_ss
+ i_sw_max = i
+ delta_max = delta
+
+ break
+
+ i = i - 1
+
+desc = st.describe(np.array(dt))
+print desc
+
+fig, ax = plt.subplots(nrows=1, ncols=1)
+fig.set_figheight(6)
+fig.set_figwidth(7)
+
+rect = fig.patch
+rect.set_facecolor('white')
+
+ax.set_xlabel('latency [$\mu$s]')
+plt.yscale('log')
+ax.hist(dt, bins=(200), histtype=u'step');
+plt.show()
+
+sname = 'sched.json'
+ks.new_session(fname, sname)
+
+with open(sname, "r+") as s:
+ session = json.load(s)
+ session['TaskPlots'] = [task_pid]
+ session['CPUPlots'] = [int(cpu[i_sw_max]), int(cpu[i_ss_max])]
+
+ delta = ts[i_ss_max] - ts[i_sw_max]
+ tmin = int(ts[i_sw_max] - delta)
+ tmax = int(ts[i_ss_max] + delta)
+ session['Model']['range'] = [tmin, tmax]
+
+ session['Markers']['markA']['isSet'] = True
+ session['Markers']['markA']['row'] = int(i_sw_max)
+
+ session['Markers']['markB']['isSet'] = True
+ session['Markers']['markB']['row'] = int(i_ss_max)
+
+ session['ViewTop'] = int(i_sw_max) - 5
+
+ ks.save_session(session, s)
+
+ks.close()
--
2.19.1
|