Hi Andi,
On Mon, May 11, 2015 at 05:44:05PM +0200, Andi Kleen wrote:
> > The sampling ratio was useful for me to determine how often the event
> > was sampled - in this case the cpu cycles event was only sampled at 12%
>
> That's dangerous to determine without a plot. It could be that it was
> bimodal:
> 100% busy and then idle. You may want to add something like the spark
> plots I submitted for stat some time ago.
Right, we cannot know the exact situation from a single number. But
it was okay for me just to see overall status from the number. This
is what we cannot know from the output of 'perf report' easily, so I'd
like to add this info in some way.
Anyway, I wrote a script to plot the number of samples and periods
using python's matplotlib package. Maybe we can add it to the script
database.
Thanks,
Namhyung
/* sample-chart.py */
import os
import sys
import numpy as np
import matplotlib.pyplot as plt
sys.path.append(os.environ['PERF_EXEC_PATH'] + \
'/scripts/python/Perf-Trace-Util/lib/Perf/Trace')
from perf_trace_context import *
from EventClass import *
events = {}
mode = None # 'cpu' or 'task'
nr_events = 0
first_time = 0
last_time = 0
def trace_begin():
pass
def trace_end():
xcnt = last_time - first_time + 1
xpos = np.arange(xcnt)
times = np.arange(first_time, last_time + 1)
fig, plt_array = plt.subplots(nrows = nr_events, ncols = 2)
fig.suptitle("Event stat", fontsize=20)
n = 0
for e in events:
p1 = plt_array[n][0]
p2 = plt_array[n][1]
for k in events[e]: # key = cpu or tid
ev_stats = events[e][k]
samples = np.zeros(xcnt)
periods = np.zeros(xcnt)
for t in ev_stats:
samples[t - first_time] = ev_stats[t][0]
periods[t - first_time] = ev_stats[t][1]
key = "%s %d" % (mode, k)
p1.plot(times, samples, 'o', linewidth=2, label=key)
p2.plot(times, periods, '-', linewidth=2, label=key)
expect = 400 * np.ones(xcnt)
p1.plot(times, expect, '--')
p1.set_title("Number of samples in '%s'" % e)
p1.legend()
p2.set_title("Event values in '%s'" % e)
p2.legend()
n += 1
plt.show()
def process_event(param_dict):
evt = param_dict["ev_name"]
cpu = param_dict["sample"]["cpu"]
tid = param_dict["sample"]["tid"]
time = param_dict["sample"]["time"] / 100000000 # 100 ms
val = param_dict["sample"]["period"]
if evt not in events:
global nr_events
nr_events += 1
events[evt] = {}
global mode
if mode is None:
if cpu >= 10000000:
mode = 'task'
else:
mode = 'cpu'
key = cpu if mode == 'cpu' else tid
if key not in events[evt]:
events[evt][key] = {}
global first_time, last_time
if first_time == 0 or first_time > time:
first_time = time
if last_time < time:
last_time = time
ev_stat = events[evt][key]
if time not in ev_stat:
ev_stat[time] = [0, 0] # (nr_sample, period)
ev_stat[time][0] += 1
ev_stat[time][1] += val
def trace_unhandled(event_name, context, event_fields_dict):
pass
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