xref: /external/lisa/libs/utils/analysis/idle_analysis.py

# SPDX-License-Identifier: Apache-2.0
#
# Copyright (C) 2015, ARM Limited and contributors.
#
# 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.
#

""" Idle Analysis Module """

import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import pandas as pd
import pylab as pl

from analysis_module import AnalysisModule
from trace import ResidencyTime, ResidencyData
from trappy.utils import listify


class IdleAnalysis(AnalysisModule):
    """
    Support for plotting Idle Analysis data

    :param trace: input Trace object
    :type trace: :mod:`libs.utils.Trace`
    """

    def __init__(self, trace):
        super(IdleAnalysis, self).__init__(trace)

###############################################################################
# DataFrame Getter Methods
###############################################################################

    def _dfg_cpu_idle_state_residency(self, cpu):
        """
        Compute time spent by a given CPU in each idle state.

        :param entity: CPU ID
        :type entity: int

        :returns: :mod:`pandas.DataFrame` - idle state residency dataframe
        """
        if not self._trace.hasEvents('cpu_idle'):
            self._log.warning('Events [cpu_idle] not found, '
                              'idle state residency computation not possible!')
            return None

        idle_df = self._dfg_trace_event('cpu_idle')
        cpu_idle = idle_df[idle_df.cpu_id == cpu]

        cpu_is_idle = self._trace.getCPUActiveSignal(cpu) ^ 1

        # In order to compute the time spent in each idle state we
        # multiply 2 square waves:
        # - cpu_idle
        # - idle_state, square wave of the form:
        #     idle_state[t] == 1 if at time t CPU is in idle state i
        #     idle_state[t] == 0 otherwise
        available_idles = sorted(idle_df.state.unique())
        # Remove non-idle state from availables
        available_idles = available_idles[1:]
        cpu_idle = cpu_idle.join(cpu_is_idle.to_frame(name='is_idle'),
                                 how='outer')
        cpu_idle.fillna(method='ffill', inplace=True)

        # Extend the last cpu_idle event to the end of the time window under
        # consideration
        final_entry = pd.DataFrame([cpu_idle.iloc[-1]], index=[self._trace.x_max])
        cpu_idle = cpu_idle.append(final_entry)

        idle_time = []
        for i in available_idles:
            idle_state = cpu_idle.state.apply(
                lambda x: 1 if x == i else 0
            )
            idle_t = cpu_idle.is_idle * idle_state
            # Compute total time by integrating the square wave
            idle_time.append(self._trace.integrate_square_wave(idle_t))

        idle_time_df = pd.DataFrame({'time' : idle_time}, index=available_idles)
        idle_time_df.index.name = 'idle_state'
        return idle_time_df

    def _dfg_cluster_idle_state_residency(self, cluster):
        """
        Compute time spent by a given cluster in each idle state.

        :param cluster: cluster name or list of CPU IDs
        :type cluster: str or list(int)

        :returns: :mod:`pandas.DataFrame` - idle state residency dataframe
        """
        if not self._trace.hasEvents('cpu_idle'):
            self._log.warning('Events [cpu_idle] not found, '
                              'idle state residency computation not possible!')
            return None

        _cluster = cluster
        if isinstance(cluster, str) or isinstance(cluster, unicode):
            try:
                _cluster = self._platform['clusters'][cluster.lower()]
            except KeyError:
                self._log.warning('%s cluster not found!', cluster)
                return None

        idle_df = self._dfg_trace_event('cpu_idle')
        # Each core in a cluster can be in a different idle state, but the
        # cluster lies in the idle state with lowest ID, that is the shallowest
        # idle state among the idle states of its CPUs
        cl_idle = idle_df[idle_df.cpu_id == _cluster[0]].state.to_frame(
            name=_cluster[0])
        for cpu in _cluster[1:]:
            cl_idle = cl_idle.join(
                idle_df[idle_df.cpu_id == cpu].state.to_frame(name=cpu),
                how='outer'
            )
        cl_idle.fillna(method='ffill', inplace=True)
        cl_idle = pd.DataFrame(cl_idle.min(axis=1), columns=['state'])

        # Build a square wave of the form:
        #     cl_is_idle[t] == 1 if all CPUs in the cluster are reported
        #                      to be idle by cpufreq at time t
        #     cl_is_idle[t] == 0 otherwise
        cl_is_idle = self._trace.getClusterActiveSignal(_cluster) ^ 1

        # In order to compute the time spent in each idle statefrequency we
        # multiply 2 square waves:
        # - cluster_is_idle
        # - idle_state, square wave of the form:
        #     idle_state[t] == 1 if at time t cluster is in idle state i
        #     idle_state[t] == 0 otherwise
        available_idles = sorted(idle_df.state.unique())
        # Remove non-idle state from availables
        available_idles = available_idles[1:]
        cl_idle = cl_idle.join(cl_is_idle.to_frame(name='is_idle'),
                               how='outer')
        cl_idle.fillna(method='ffill', inplace=True)
        idle_time = []
        for i in available_idles:
            idle_state = cl_idle.state.apply(
                lambda x: 1 if x == i else 0
            )
            idle_t = cl_idle.is_idle * idle_state
            # Compute total time by integrating the square wave
            idle_time.append(self._trace.integrate_square_wave(idle_t))

        idle_time_df = pd.DataFrame({'time' : idle_time}, index=available_idles)
        idle_time_df.index.name = 'idle_state'
        return idle_time_df


###############################################################################
# Plotting Methods
###############################################################################

    def plotCPUIdleStateResidency(self, cpus=None, pct=False):
        """
        Plot per-CPU idle state residency. big CPUs are plotted first and then
        LITTLEs.

        Requires cpu_idle trace events.

        :param cpus: list of CPU IDs. By default plot all CPUs
        :type cpus: list(int) or int

        :param pct: plot residencies in percentage
        :type pct: bool
        """
        if not self._trace.hasEvents('cpu_idle'):
            self._log.warning('Events [cpu_idle] not found, '
                              'plot DISABLED!')
            return

        if cpus is None:
            # Generate plots only for available CPUs
            cpuidle_data = self._dfg_trace_event('cpu_idle')
            _cpus = range(cpuidle_data.cpu_id.max() + 1)
        else:
            _cpus = listify(cpus)

        # Split between big and LITTLE CPUs ordered from higher to lower ID
        _cpus.reverse()
        big_cpus = [c for c in _cpus if c in self._big_cpus]
        little_cpus = [c for c in _cpus if c in self._little_cpus]
        _cpus = big_cpus + little_cpus

        residencies = []
        xmax = 0.0
        for cpu in _cpus:
            r = self._dfg_cpu_idle_state_residency(cpu)
            residencies.append(ResidencyData('CPU{}'.format(cpu), r))

            max_time = r.max().values[0]
            if xmax < max_time:
                xmax = max_time

        self._plotIdleStateResidency(residencies, 'cpu', xmax, pct=pct)

    def plotClusterIdleStateResidency(self, clusters=None, pct=False):
        """
        Plot per-cluster idle state residency in a given cluster, i.e. the
        amount of time cluster `cluster` spent in idle state `i`. By default,
        both 'big' and 'LITTLE' clusters data are plotted.

        Requires cpu_idle following trace events.
        :param clusters: name of the clusters to be plotted (all of them by
            default)
        :type clusters: str ot list(str)
        """
        if not self._trace.hasEvents('cpu_idle'):
            self._log.warning('Events [cpu_idle] not found, plot DISABLED!')
            return
        if 'clusters' not in self._platform:
            self._log.warning('No platform cluster info. Plot DISABLED!')
            return

        # Sanitize clusters
        if clusters is None:
            _clusters = self._platform['clusters'].keys()
        else:
            _clusters = listify(clusters)

        # Precompute residencies for each cluster
        residencies = []
        xmax = 0.0
        for c in _clusters:
            r = self._dfg_cluster_idle_state_residency(c.lower())
            residencies.append(ResidencyData('{} Cluster'.format(c), r))

            max_time = r.max().values[0]
            if xmax < max_time:
                xmax = max_time

        self._plotIdleStateResidency(residencies, 'cluster', xmax, pct=pct)

###############################################################################
# Utility Methods
###############################################################################

    def _plotIdleStateResidency(self, residencies, entity_name, xmax,
                                pct=False):
        """
        Generate Idle state residency plots for the given entities.

        :param residencies: list of residencies to be plot
        :type residencies: list(namedtuple(ResidencyData)) - each tuple
            contains:
            - a label to be used as subplot title
            - a dataframe with residency for each idle state

        :param entity_name: name of the entity ('cpu' or 'cluster') used in the
            figure name
        :type entity_name: str

        :param xmax: upper bound of x-axes
        :type xmax: double

        :param pct: plot residencies in percentage
        :type pct: bool
        """
        n_plots = len(residencies)
        gs = gridspec.GridSpec(n_plots, 1)
        fig = plt.figure()

        for idx, data in enumerate(residencies):
            r = data.residency
            if r is None:
                plt.close(fig)
                return

            axes = fig.add_subplot(gs[idx])
            is_first = idx == 0
            is_last = idx+1 == n_plots
            yrange = 0.4 * max(6, len(r)) * n_plots
            if pct:
                duration = r.time.sum()
                r_pct = r.apply(lambda x: x*100/duration)
                r_pct.columns = [data.label]
                r_pct.T.plot.barh(ax=axes, stacked=True, figsize=(16, yrange))

                axes.legend(loc='lower center', ncol=7)
                axes.set_xlim(0, 100)
            else:
                r.plot.barh(ax=axes, color='g',
                            legend=False, figsize=(16, yrange))

                axes.set_xlim(0, 1.05*xmax)
                axes.set_ylabel('Idle State')
                axes.set_title(data.label)

            axes.grid(True)
            if is_last:
                if pct:
                    axes.set_xlabel('Residency [%]')
                else:
                    axes.set_xlabel('Time [s]')
            else:
                axes.set_xticklabels([])

            if is_first:
                legend_y = axes.get_ylim()[1]
                axes.annotate('Idle State Residency Time', xy=(0, legend_y),
                              xytext=(-50, 45), textcoords='offset points',
                              fontsize=18)

        figname = '{}/{}{}_idle_state_residency.png'\
                  .format(self._trace.plots_dir,
                          self._trace.plots_prefix,
                          entity_name)

        pl.savefig(figname, bbox_inches='tight')

# vim :set tabstop=4 shiftwidth=4 expandtab