def get_sched_task_signals(self, experiment, signals):
"""
Get a pandas.DataFrame with the sched signals for the workload task
This examines scheduler load tracking trace events, supporting either
sched_load_avg_task or sched_pelt_se. You will need a target kernel that
includes these events.
:param experiment: Experiment to get trace for
:param signals: List of load tracking signals to extract. Probably a
subset of ``['util_avg', 'load_avg']``
:returns: :class:`pandas.DataFrame` with a column for each signal for
the experiment's workload task
"""
[task] = experiment.wload.tasks.keys()
trace = self.get_trace(experiment)
# There are two different scheduler trace events that expose the load
# tracking signals. Neither of them is in mainline. Eventually they
# should be unified but for now we'll just check for both types of
# event.
# TODO: Add support for this parsing in Trappy and/or tasks_analysis
if 'sched_load_avg_task' in trace.available_events:
event = 'sched_load_avg_task'
elif 'sched_pelt_se' in trace.available_events:
event = 'sched_pelt_se'
else:
raise ValueError('No sched_load_avg_task or sched_pelt_se events. '
'Does the kernel support them?')
df = getattr(trace.ftrace, event).data_frame
signals = df[df['comm'] == task][signals]
return select_window(signals, self.get_window(experiment))
def get_signal_mean(self, experiment, signal,
ignore_first_s=UTIL_AVG_CONVERGENCE_TIME):
"""
Get the mean of a scheduler signal for the experiment's task
Ignore the first `ignore_first_s` seconds of the signal.
"""
(wload_start, wload_end) = self.get_window(experiment)
window = (wload_start + ignore_first_s, wload_end)
signal = self.get_sched_task_signals(experiment, [signal])[signal]
signal = select_window(signal, window)
return area_under_curve(signal) / (window[1] - window[0])
def get_task_sched_signal(self, trace, cpu, task_name, signal):
"""
Get a :class:`pandas.DataFrame` with the sched signals for the workload task
This examines scheduler load tracking trace events. You will need a
target kernel that includes the required events.
:returns: :class:`pandas.DataFrame` with a column for each signal for
the workload task
"""
df = trace.analysis.load_tracking.df_tasks_signal(signal)
df = df[df['comm'] == task_name]
window = self.get_task_window(trace, task_name, cpu)
df = select_window(df, window)
# Normalize the signal with the detected task execution start
df.index -= window[0]
return df
def get_sched_task_signals(self, experiment, signals):
"""
Get a pandas.DataFrame with the sched signals for the workload task
This examines scheduler load tracking trace events, supporting either
sched_load_avg_task or sched_pelt_se. You will need a target kernel that
includes these events.
:param experiment: Experiment to get trace for
:param signals: List of load tracking signals to extract. Probably a
subset of ``['util_avg', 'load_avg']``
:returns: :class:`pandas.DataFrame` with a column for each signal for
the experiment's workload task
"""
[task] = experiment.wload.tasks.keys()
trace = self.get_trace(experiment)
# There are two different scheduler trace events that expose the load
# tracking signals. Neither of them is in mainline. Eventually they
# should be unified but for now we'll just check for both types of
# event.
# TODO: Add support for this parsing in Trappy and/or tasks_analysis
signal_fields = signals
if 'sched_load_avg_task' in trace.available_events:
event = 'sched_load_avg_task'
elif 'sched_load_se' in trace.available_events:
event = 'sched_load_se'
# sched_load_se uses 'util' and 'load' instead of 'util_avg' and
# 'load_avg'
signal_fields = [s.replace('_avg', '') for s in signals]
elif 'sched_pelt_se' in trace.available_events:
event = 'sched_pelt_se'
else:
raise ValueError('No sched_load_avg_task or sched_pelt_se events. '
'Does the kernel support them?')
df = getattr(trace.ftrace, event).data_frame
df = df[df['comm'] == task][signal_fields]
df = select_window(df, self.get_window(experiment))
return df.rename(columns=dict(zip(signal_fields, signals)))