Source code for trappy.cpu_power
# Copyright 2015-2016 ARM Limited
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""Process the output of the cpu_cooling devices in the current
directory's trace.dat"""
import pandas as pd
from trappy.base import Base
from trappy.dynamic import register_ftrace_parser
[docs]def pivot_with_labels(dfr, data_col_name, new_col_name, mapping_label):
"""Pivot a :mod:`pandas.DataFrame` row into columns
:param dfr: The :mod:`pandas.DataFrame` to operate on.
:param data_col_name: The name of the column in the :mod:`pandas.DataFrame`
which contains the values.
:param new_col_name: The name of the column in the :mod:`pandas.DataFrame` that will
become the new columns.
:param mapping_label: A dictionary whose keys are the values in
new_col_name and whose values are their
corresponding name in the :mod:`pandas.DataFrame` to be returned.
:type dfr: :mod:`pandas.DataFrame`
:type data_col_name: str
:type new_col_name: str
:type mapping_label: dict
Example:
>>> dfr_in = pd.DataFrame({'cpus': ["000000f0",
>>> "0000000f",
>>> "000000f0",
>>> "0000000f"
>>> ],
>>> 'freq': [1, 3, 2, 6]})
>>> dfr_in
cpus freq
0 000000f0 1
1 0000000f 3
2 000000f0 2
3 0000000f 6
>>> map_label = {"000000f0": "A15", "0000000f": "A7"}
>>> power.pivot_with_labels(dfr_in, "freq", "cpus", map_label)
A15 A7
0 1 NaN
1 1 3
2 2 3
3 2 6
"""
# There has to be a more "pandas" way of doing this.
col_set = set(dfr[new_col_name])
ret_series = {}
for col in col_set:
try:
label = mapping_label[col]
except KeyError:
available_keys = ", ".join(mapping_label.keys())
error_str = '"{}" not found, available keys: {}'.format(col,
available_keys)
raise KeyError(error_str)
data = dfr[dfr[new_col_name] == col][data_col_name]
ret_series[label] = data
return pd.DataFrame(ret_series).fillna(method="pad")
[docs]def num_cpus_in_mask(mask):
"""Return the number of cpus in a cpumask"""
mask = mask.replace(",", "")
value = int(mask, 16)
return bin(value).count("1")
[docs]class CpuOutPower(Base):
"""Process the cpufreq cooling power actor data in a ftrace dump"""
unique_word = "thermal_power_cpu_limit"
"""The unique word that will be matched in a trace line"""
name = "cpu_out_power"
"""The name of the :mod:`pandas.DataFrame` member that will be created in a
:mod:`trappy.ftrace.FTrace` object"""
pivot = "cpus"
"""The Pivot along which the data is orthogonal"""
[docs] def get_all_freqs(self, mapping_label):
"""Get a :mod:`pandas.DataFrame` with the maximum frequencies allowed by the governor
:param mapping_label: A dictionary that maps cpumasks to name
of the cpu.
:type mapping_label: dict
:return: freqs are in MHz
"""
dfr = self.data_frame
return pivot_with_labels(dfr, "freq", "cpus", mapping_label) / 1000
register_ftrace_parser(CpuOutPower, "thermal")
[docs]class CpuInPower(Base):
"""Process the cpufreq cooling power actor data in a ftrace dump
"""
unique_word = "thermal_power_cpu_get"
"""The unique word that will be matched in a trace line"""
name = "cpu_in_power"
"""The name of the :mod:`pandas.DataFrame` member that will be created in a
:mod:`trappy.ftrace.FTrace` object"""
pivot = "cpus"
"""The Pivot along which the data is orthogonal"""
def _get_load_series(self):
"""get a :mod:`pandas.Series` with the aggregated load"""
dfr = self.data_frame
load_cols = [s for s in dfr.columns if s.startswith("load")]
load_series = dfr[load_cols[0]].copy()
for col in load_cols[1:]:
load_series += dfr[col]
return load_series
[docs] def get_load_data(self, mapping_label):
"""Return :mod:`pandas.DataFrame` suitable for plot_load()
:param mapping_label: A Dictionary mapping cluster cpumasks to labels
:type mapping_label: dict
"""
dfr = self.data_frame
load_series = self._get_load_series()
load_dfr = pd.DataFrame({"cpus": dfr["cpus"], "load": load_series})
return pivot_with_labels(load_dfr, "load", "cpus", mapping_label)
[docs] def get_normalized_load_data(self, mapping_label):
"""Return a :mod:`pandas.DataFrame` for plotting normalized load data
:param mapping_label: should be a dictionary mapping cluster cpumasks
to labels
:type mapping_label: dict
"""
dfr = self.data_frame
load_series = self._get_load_series()
load_series *= dfr['freq']
for cpumask in mapping_label:
num_cpus = num_cpus_in_mask(cpumask)
idx = dfr["cpus"] == cpumask
max_freq = max(dfr[idx]["freq"])
load_series[idx] = load_series[idx] / (max_freq * num_cpus)
load_dfr = pd.DataFrame({"cpus": dfr["cpus"], "load": load_series})
return pivot_with_labels(load_dfr, "load", "cpus", mapping_label)
[docs] def get_all_freqs(self, mapping_label):
"""get a :mod:`pandas.DataFrame` with the "in" frequencies as seen by the governor
.. note::
Frequencies are in MHz
"""
dfr = self.data_frame
return pivot_with_labels(dfr, "freq", "cpus", mapping_label) / 1000
register_ftrace_parser(CpuInPower, "thermal")