forked from OSchip/llvm-project
Add summarizeStats.py to tools directory
The summarizeStats.py script processes raw data provided by the instrumented (stats-gathering) OpenMP* runtime library. It provides: 1) A radar chart which plots counters as frequency (per GigaTick) of use within the program. The frequencies are plotted as log10, however values less than one are kept as it is and represented in red color. This was done to help visualize the differences better. 2) Pie charts separating total time as compute and non-compute. The compute and non-compute times have their own pie charts showing the constructs that contributed to them. The percentages listed are with respect to the total time. 3) '.csv' file with percentage of time spent within the different constructs. The script can be used as: $ python $PATH_TO_SCRIPT/summarizeStats.py instrumented1.csv instrumented2.csv Patch by Taru Doodi Differential Revision: https://reviews.llvm.org/D41838 llvm-svn: 328568
This commit is contained in:
Jonathan Peyton
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#!/usr/bin/python
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import pandas as pd
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import numpy as np
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import re
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import sys
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import os
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import argparse
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import matplotlib
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from matplotlib import pyplot as plt
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from matplotlib.projections.polar import PolarAxes
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from matplotlib.projections import register_projection
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"""
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Read the stats file produced by the OpenMP runtime
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and produce a processed summary
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The radar_factory original code was taken from
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matplotlib.org/examples/api/radar_chart.html
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We added support to handle negative values for radar charts
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"""
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def radar_factory(num_vars, frame='circle'):
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"""Create a radar chart with num_vars axes."""
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# calculate evenly-spaced axis angles
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theta = 2*np.pi * np.linspace(0, 1-1./num_vars, num_vars)
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# rotate theta such that the first axis is at the top
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#theta += np.pi/2
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def draw_poly_frame(self, x0, y0, r):
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# TODO: use transforms to convert (x, y) to (r, theta)
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verts = [(r*np.cos(t) + x0, r*np.sin(t) + y0) for t in theta]
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return plt.Polygon(verts, closed=True, edgecolor='k')
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def draw_circle_frame(self, x0, y0, r):
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return plt.Circle((x0, y0), r)
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frame_dict = {'polygon': draw_poly_frame, 'circle': draw_circle_frame}
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if frame not in frame_dict:
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raise ValueError, 'unknown value for `frame`: %s' % frame
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class RadarAxes(PolarAxes):
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"""
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Class for creating a radar chart (a.k.a. a spider or star chart)
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http://en.wikipedia.org/wiki/Radar_chart
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"""
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name = 'radar'
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# use 1 line segment to connect specified points
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RESOLUTION = 1
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# define draw_frame method
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draw_frame = frame_dict[frame]
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def fill(self, *args, **kwargs):
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"""Override fill so that line is closed by default"""
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closed = kwargs.pop('closed', True)
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return super(RadarAxes, self).fill(closed=closed, *args, **kwargs)
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def plot(self, *args, **kwargs):
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"""Override plot so that line is closed by default"""
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lines = super(RadarAxes, self).plot(*args, **kwargs)
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#for line in lines:
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# self._close_line(line)
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def set_varlabels(self, labels):
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self.set_thetagrids(theta * 180/np.pi, labels,fontsize=14)
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def _gen_axes_patch(self):
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x0, y0 = (0.5, 0.5)
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r = 0.5
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return self.draw_frame(x0, y0, r)
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register_projection(RadarAxes)
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return theta
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# Code to read the raw stats
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def extractSI(s):
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"""Convert a measurement with a range suffix into a suitably scaled value"""
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du = s.split()
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num = float(du[0])
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units = du[1] if len(du) == 2 else ' '
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# http://physics.nist.gov/cuu/Units/prefixes.html
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factor = {'Y': 1e24,
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'Z': 1e21,
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'E': 1e18,
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'P': 1e15,
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'T': 1e12,
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'G': 1e9,
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'M': 1e6,
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'k': 1e3,
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' ': 1 ,
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'm': -1e3, # Yes, I do mean that, see below for the explanation.
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'u': -1e6,
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'n': -1e9,
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'p': -1e12,
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'f': -1e15,
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'a': -1e18,
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'z': -1e21,
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'y': -1e24}[units[0]]
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# Minor trickery here is an attempt to preserve accuracy by using a single
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# divide, rather than multiplying by 1/x, which introduces two roundings
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# since 1/10 is not representable perfectly in IEEE floating point. (Not
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# that this really matters, other than for cleanliness, since we're likely
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# reading numbers with at most five decimal digits of precision).
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return num*factor if factor > 0 else num/-factor
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def readData(f):
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line = f.readline()
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fieldnames = [x.strip() for x in line.split(',')]
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line = f.readline().strip()
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data = []
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while line != "":
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if line[0] != '#':
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fields = line.split(',')
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data.append ((fields[0].strip(), [extractSI(v) for v in fields[1:]]))
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line = f.readline().strip()
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# Man, working out this next incantation out was non-trivial!
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# They really want you to be snarfing data in csv or some other
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# format they understand!
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res = pd.DataFrame.from_items(data, columns=fieldnames[1:], orient='index')
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return res
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def readTimers(f):
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"""Skip lines with leading #"""
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line = f.readline()
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while line[0] == '#':
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line = f.readline()
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line = line.strip()
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if line == "Statistics on exit\n" or "Aggregate for all threads\n":
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line = f.readline()
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return readData(f)
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def readCounters(f):
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"""This can be just the same!"""
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return readData(f)
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def readFile(fname):
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"""Read the statistics from the file. Return a dict with keys "timers", "counters" """
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res = {}
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try:
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with open(fname) as f:
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res["timers"] = readTimers(f)
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res["counters"] = readCounters(f)
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return res
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except (OSError, IOError):
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print "Cannot open " + fname
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return None
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def usefulValues(l):
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"""I.e. values which are neither null nor zero"""
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return [p and q for (p,q) in zip (pd.notnull(l), l != 0.0)]
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def uselessValues(l):
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"""I.e. values which are null or zero"""
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return [not p for p in usefulValues(l)]
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interestingStats = ("counters", "timers")
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statProperties = {"counters" : ("Count", "Counter Statistics"),
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"timers" : ("Time (ticks)", "Timer Statistics")
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}
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def drawChart(data, kind, filebase):
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"""Draw a summary bar chart for the requested data frame into the specified file"""
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data["Mean"].plot(kind="bar", logy=True, grid=True, colormap="GnBu",
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yerr=data["SD"], ecolor="black")
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plt.xlabel("OMP Constructs")
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plt.ylabel(statProperties[kind][0])
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plt.title (statProperties[kind][1])
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plt.tight_layout()
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plt.savefig(filebase+"_"+kind)
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def normalizeValues(data, countField, factor):
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"""Normalize values into a rate by dividing them all by the given factor"""
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data[[k for k in data.keys() if k != countField]] /= factor
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def setRadarFigure(titles):
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"""Set the attributes for the radar plots"""
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fig = plt.figure(figsize=(9,9))
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rect = [0.1, 0.1, 0.8, 0.8]
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labels = [0.2, 0.4, 0.6, 0.8, 1, 2, 3, 4, 5, 10]
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matplotlib.rcParams.update({'font.size':13})
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theta = radar_factory(len(titles))
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ax = fig.add_axes(rect, projection='radar')
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ax.set_rgrids(labels)
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ax.set_varlabels(titles)
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ax.text(theta[2], 1, "Linear->Log", horizontalalignment='center', color='green', fontsize=18)
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return {'ax':ax, 'theta':theta}
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def drawRadarChart(data, kind, filebase, params, color):
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"""Draw the radar plots"""
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tmp_lin = data * 0
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tmp_log = data * 0
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for key in data.keys():
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if data[key] >= 1:
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tmp_log[key] = np.log10(data[key])
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else:
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tmp_lin[key] = (data[key])
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params['ax'].plot(params['theta'], tmp_log, color='b', label=filebase+"_"+kind+"_log")
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params['ax'].plot(params['theta'], tmp_lin, color='r', label=filebase+"_"+kind+"_linear")
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params['ax'].legend(loc='best', bbox_to_anchor=(1.4,1.2))
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params['ax'].set_rlim((0, np.ceil(max(tmp_log))))
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def multiAppBarChartSettings(ax, plt, index, width, n, tmp, s):
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ax.set_yscale('log')
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ax.legend()
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ax.set_xticks(index + width * n / 2)
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ax.set_xticklabels(tmp[s]['Total'].keys(), rotation=50, horizontalalignment='right')
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plt.xlabel("OMP Constructs")
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plt.ylabel(statProperties[s][0])
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plt.title(statProperties[s][1])
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plt.tight_layout()
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def derivedTimerStats(data):
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stats = {}
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for key in data.keys():
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if key == 'OMP_worker_thread_life':
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totalRuntime = data['OMP_worker_thread_life']
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elif key in ('FOR_static_iterations', 'OMP_PARALLEL_args',
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'OMP_set_numthreads', 'FOR_dynamic_iterations'):
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break
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else:
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stats[key] = 100 * data[key] / totalRuntime
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return stats
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def compPie(data):
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compKeys = {}
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nonCompKeys = {}
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for key in data.keys():
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if key in ('OMP_critical', 'OMP_single', 'OMP_serial',
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'OMP_parallel', 'OMP_master', 'OMP_task_immediate',
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'OMP_task_taskwait', 'OMP_task_taskyield', 'OMP_task_taskgroup',
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'OMP_task_join_bar', 'OMP_task_plain_bar', 'OMP_task_taskyield'):
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compKeys[key] = data[key]
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else:
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nonCompKeys[key] = data[key]
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print "comp keys:", compKeys, "\n\n non comp keys:", nonCompKeys
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return [compKeys, nonCompKeys]
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def drawMainPie(data, filebase, colors):
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sizes = [sum(data[0].values()), sum(data[1].values())]
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explode = [0,0]
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labels = ["Compute - " + "%.2f" % sizes[0], "Non Compute - " + "%.2f" % sizes[1]]
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patches = plt.pie(sizes, explode, colors=colors, startangle=90)
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plt.title("Time Division")
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plt.axis('equal')
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plt.legend(patches[0], labels, loc='best', bbox_to_anchor=(-0.1,1), fontsize=16)
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plt.savefig(filebase+"_main_pie", bbox_inches='tight')
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def drawSubPie(data, tag, filebase, colors):
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explode = []
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labels = data.keys()
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sizes = data.values()
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total = sum(sizes)
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percent = []
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for i in range(len(sizes)):
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explode.append(0)
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percent.append(100 * sizes[i] / total)
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labels[i] = labels[i] + " - %.2f" % percent[i]
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patches = plt.pie(sizes, explode=explode, colors=colors, startangle=90)
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plt.title(tag+"(Percentage of Total:"+" %.2f" % (sum(data.values()))+")")
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plt.tight_layout()
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plt.axis('equal')
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plt.legend(patches[0], labels, loc='best', bbox_to_anchor=(-0.1,1), fontsize=16)
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plt.savefig(filebase+"_"+tag, bbox_inches='tight')
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def main():
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parser = argparse.ArgumentParser(description='''This script takes a list
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of files containing each of which contain output from a stats-gathering
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enabled OpenMP runtime library. Each stats file is read, parsed, and
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used to produce a summary of the statistics''')
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parser.add_argument('files', nargs='+',
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help='files to parse which contain stats-gathering output')
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command_args = parser.parse_args()
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colors = ['orange', 'b', 'r', 'yellowgreen', 'lightsage', 'lightpink',
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'green', 'purple', 'yellow', 'cyan', 'mediumturquoise',
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'olive']
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stats = {}
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matplotlib.rcParams.update({'font.size':22})
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for s in interestingStats:
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fig, ax = plt.subplots()
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width = 0.45
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n = 0
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index = 0
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for f in command_args.files:
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filebase = os.path.splitext(f)[0]
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tmp = readFile(f)
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data = tmp[s]['Total']
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"""preventing repetition by removing rows similar to Total_OMP_work
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as Total_OMP_work['Total'] is same as OMP_work['Total']"""
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if s == 'counters':
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elapsedTime = tmp["timers"]["Mean"]["OMP_worker_thread_life"]
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normalizeValues(tmp["counters"], "SampleCount",
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elapsedTime / 1.e9)
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"""Plotting radar charts"""
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params = setRadarFigure(data.keys())
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chartType = "radar"
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drawRadarChart(data, s, filebase, params, colors[n])
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"""radar Charts finish here"""
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plt.savefig(filebase+"_"+s+"_"+chartType, bbox_inches='tight')
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elif s == 'timers':
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print "overheads in "+filebase
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numThreads = tmp[s]['SampleCount']['Total_OMP_parallel']
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for key in data.keys():
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if key[0:5] == 'Total':
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del data[key]
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stats[filebase] = derivedTimerStats(data)
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dataSubSet = compPie(stats[filebase])
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drawMainPie(dataSubSet, filebase, colors)
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plt.figure(0)
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drawSubPie(dataSubSet[0], "Computational Time", filebase, colors)
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plt.figure(1)
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drawSubPie(dataSubSet[1], "Non Computational Time", filebase, colors)
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with open('derivedStats_{}.csv'.format(filebase), 'w') as f:
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f.write('================={}====================\n'.format(filebase))
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f.write(pd.DataFrame(stats[filebase].items()).to_csv()+'\n')
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n += 1
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plt.close()
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if __name__ == "__main__":
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main()
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