diff options
Diffstat (limited to 'gcc-4.8.3/contrib/compare_two_ftime_report_sets')
| -rwxr-xr-x | gcc-4.8.3/contrib/compare_two_ftime_report_sets | 605 |
1 files changed, 605 insertions, 0 deletions
diff --git a/gcc-4.8.3/contrib/compare_two_ftime_report_sets b/gcc-4.8.3/contrib/compare_two_ftime_report_sets new file mode 100755 index 000000000..384dfde1d --- /dev/null +++ b/gcc-4.8.3/contrib/compare_two_ftime_report_sets @@ -0,0 +1,605 @@ +#!/usr/bin/python + +# Script to statistically compare two sets of log files with -ftime-report +# output embedded within them. + +# Contributed by Lawrence Crowl <crowl@google.com> +# +# Copyright (C) 2012 Free Software Foundation, Inc. +# +# This file is part of GCC. +# +# GCC is free software; you can redistribute it and/or modify +# it under the terms of the GNU General Public License as published by +# the Free Software Foundation; either version 3, or (at your option) +# any later version. +# +# GCC is distributed in the hope that it will be useful, +# but WITHOUT ANY WARRANTY; without even the implied warranty of +# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the +# GNU General Public License for more details. +# +# You should have received a copy of the GNU General Public License +# along with GCC; see the file COPYING. If not, write to +# the Free Software Foundation, 51 Franklin Street, Fifth Floor, +# Boston, MA 02110-1301, USA. + + +""" Compare two sets of compile-time performance numbers. + +The intent of this script is to compare compile-time performance of two +different versions of the compiler. Each version of the compiler must be +run at least three times with the -ftime-report option. Each log file +represents a data point, or trial. The set of trials for each compiler +version constitutes a sample. The ouput of the script is a description +of the statistically significant difference between the two version of +the compiler. + +The parameters to the script are: + + Two file patterns that each match a set of log files. You will probably + need to quote the patterns before passing them to the script. + + Each pattern corresponds to a version of the compiler. + + A regular expression that finds interesting lines in the log files. + If you want to match the beginning of the line, you will need to add + the ^ operator. The filtering uses Python regular expression syntax. + + The default is "TOTAL". + + All of the interesting lines in a single log file are summed to produce + a single trial (data point). + + A desired statistical confidence within the range 60% to 99.9%. Due to + the implementation, this confidence will be rounded down to one of 60%, + 70%, 80%, 90%, 95%, 98%, 99%, 99.5%, 99.8%, and 99.9%. + + The default is 95. + + If the computed confidence is lower than desired, the script will + estimate the number of trials needed to meet the desired confidence. + This estimate is not very good, as the variance tends to change as + you increase the number of trials. + +The most common use of the script is total compile-time comparison between +logfiles stored in different directories. + +compare_two_ftime_report_sets "Log1/*perf" "Log2/*perf" + +One can also look at parsing time, but expecting a lower confidence. + +compare_two_ftime_report_sets "Log1/*perf" "Log2/*perf" "^phase parsing" 75 + +""" + + +import os +import sys +import fnmatch +import glob +import re +import math + + +####################################################################### Utility + + +def divide(dividend, divisor): + """ Return the quotient, avoiding division by zero. + """ + if divisor == 0: + return sys.float_info.max + else: + return dividend / divisor + + +################################################################# File and Line + + +# Should you repurpose this script, this code might help. +# +#def find_files(topdir, filepat): +# """ Find a set of file names, under a given directory, +# matching a Unix shell file pattern. +# Returns an iterator over the file names. +# """ +# for path, dirlist, filelist in os.walk(topdir): +# for name in fnmatch.filter(filelist, filepat): +# yield os.path.join(path, name) + + +def match_files(fileglob): + """ Find a set of file names matching a Unix shell glob pattern. + Returns an iterator over the file names. + """ + return glob.iglob(os.path.expanduser(fileglob)) + + +def lines_in_file(filename): + """ Return an iterator over lines in the named file. """ + filedesc = open(filename, "r") + for line in filedesc: + yield line + filedesc.close() + + +def lines_containing_pattern(pattern, lines): + """ Find lines by a Python regular-expression. + Returns an iterator over lines containing the expression. + """ + parser = re.compile(pattern) + for line in lines: + if parser.search(line): + yield line + + +############################################################# Number Formatting + + +def strip_redundant_digits(numrep): + if numrep.find(".") == -1: + return numrep + return numrep.rstrip("0").rstrip(".") + + +def text_number(number): + return strip_redundant_digits("%g" % number) + + +def round_significant(digits, number): + if number == 0: + return 0 + magnitude = abs(number) + significance = math.floor(math.log10(magnitude)) + least_position = int(significance - digits + 1) + return round(number, -least_position) + + +def text_significant(digits, number): + return text_number(round_significant(digits, number)) + + +def text_percent(number): + return text_significant(3, number*100) + "%" + + +################################################################ T-Distribution + + +# This section of code provides functions for using Student's t-distribution. + + +# The functions are implemented using table lookup +# to facilitate implementation of inverse functions. + + +# The table is comprised of row 0 listing the alpha values, +# column 0 listing the degree-of-freedom values, +# and the other entries listing the corresponding t-distribution values. + +t_dist_table = [ +[ 0, 0.200, 0.150, 0.100, 0.050, 0.025, 0.010, 0.005, .0025, 0.001, .0005], +[ 1, 1.376, 1.963, 3.078, 6.314, 12.71, 31.82, 63.66, 127.3, 318.3, 636.6], +[ 2, 1.061, 1.386, 1.886, 2.920, 4.303, 6.965, 9.925, 14.09, 22.33, 31.60], +[ 3, 0.978, 1.250, 1.638, 2.353, 3.182, 4.541, 5.841, 7.453, 10.21, 12.92], +[ 4, 0.941, 1.190, 1.533, 2.132, 2.776, 3.747, 4.604, 5.598, 7.173, 8.610], +[ 5, 0.920, 1.156, 1.476, 2.015, 2.571, 3.365, 4.032, 4.773, 5.894, 6.869], +[ 6, 0.906, 1.134, 1.440, 1.943, 2.447, 3.143, 3.707, 4.317, 5.208, 5.959], +[ 7, 0.896, 1.119, 1.415, 1.895, 2.365, 2.998, 3.499, 4.029, 4.785, 5.408], +[ 8, 0.889, 1.108, 1.397, 1.860, 2.306, 2.896, 3.355, 3.833, 4.501, 5.041], +[ 9, 0.883, 1.100, 1.383, 1.833, 2.262, 2.821, 3.250, 3.690, 4.297, 4.781], +[ 10, 0.879, 1.093, 1.372, 1.812, 2.228, 2.764, 3.169, 3.581, 4.144, 4.587], +[ 11, 0.876, 1.088, 1.363, 1.796, 2.201, 2.718, 3.106, 3.497, 4.025, 4.437], +[ 12, 0.873, 1.083, 1.356, 1.782, 2.179, 2.681, 3.055, 3.428, 3.930, 4.318], +[ 13, 0.870, 1.079, 1.350, 1.771, 2.160, 2.650, 3.012, 3.372, 3.852, 4.221], +[ 14, 0.868, 1.076, 1.345, 1.761, 2.145, 2.624, 2.977, 3.326, 3.787, 4.140], +[ 15, 0.866, 1.074, 1.341, 1.753, 2.131, 2.602, 2.947, 3.286, 3.733, 4.073], +[ 16, 0.865, 1.071, 1.337, 1.746, 2.120, 2.583, 2.921, 3.252, 3.686, 4.015], +[ 17, 0.863, 1.069, 1.333, 1.740, 2.110, 2.567, 2.898, 3.222, 3.646, 3.965], +[ 18, 0.862, 1.067, 1.330, 1.734, 2.101, 2.552, 2.878, 3.197, 3.610, 3.922], +[ 19, 0.861, 1.066, 1.328, 1.729, 2.093, 2.539, 2.861, 3.174, 3.579, 3.883], +[ 20, 0.860, 1.064, 1.325, 1.725, 2.086, 2.528, 2.845, 3.153, 3.552, 3.850], +[ 21, 0.859, 1.063, 1.323, 1.721, 2.080, 2.518, 2.831, 3.135, 3.527, 3.819], +[ 22, 0.858, 1.061, 1.321, 1.717, 2.074, 2.508, 2.819, 3.119, 3.505, 3.792], +[ 23, 0.858, 1.060, 1.319, 1.714, 2.069, 2.500, 2.807, 3.104, 3.485, 3.768], +[ 24, 0.857, 1.059, 1.318, 1.711, 2.064, 2.492, 2.797, 3.091, 3.467, 3.745], +[ 25, 0.856, 1.058, 1.316, 1.708, 2.060, 2.485, 2.787, 3.078, 3.450, 3.725], +[ 26, 0.856, 1.058, 1.315, 1.706, 2.056, 2.479, 2.779, 3.067, 3.435, 3.707], +[ 27, 0.855, 1.057, 1.314, 1.703, 2.052, 2.473, 2.771, 3.057, 3.421, 3.689], +[ 28, 0.855, 1.056, 1.313, 1.701, 2.048, 2.467, 2.763, 3.047, 3.408, 3.674], +[ 29, 0.854, 1.055, 1.311, 1.699, 2.045, 2.462, 2.756, 3.038, 3.396, 3.660], +[ 30, 0.854, 1.055, 1.310, 1.697, 2.042, 2.457, 2.750, 3.030, 3.385, 3.646], +[ 31, 0.853, 1.054, 1.309, 1.696, 2.040, 2.453, 2.744, 3.022, 3.375, 3.633], +[ 32, 0.853, 1.054, 1.309, 1.694, 2.037, 2.449, 2.738, 3.015, 3.365, 3.622], +[ 33, 0.853, 1.053, 1.308, 1.692, 2.035, 2.445, 2.733, 3.008, 3.356, 3.611], +[ 34, 0.852, 1.052, 1.307, 1.691, 2.032, 2.441, 2.728, 3.002, 3.348, 3.601], +[ 35, 0.852, 1.052, 1.306, 1.690, 2.030, 2.438, 2.724, 2.996, 3.340, 3.591], +[ 36, 0.852, 1.052, 1.306, 1.688, 2.028, 2.434, 2.719, 2.990, 3.333, 3.582], +[ 37, 0.851, 1.051, 1.305, 1.687, 2.026, 2.431, 2.715, 2.985, 3.326, 3.574], +[ 38, 0.851, 1.051, 1.304, 1.686, 2.024, 2.429, 2.712, 2.980, 3.319, 3.566], +[ 39, 0.851, 1.050, 1.304, 1.685, 2.023, 2.426, 2.708, 2.976, 3.313, 3.558], +[ 40, 0.851, 1.050, 1.303, 1.684, 2.021, 2.423, 2.704, 2.971, 3.307, 3.551], +[ 50, 0.849, 1.047, 1.299, 1.676, 2.009, 2.403, 2.678, 2.937, 3.261, 3.496], +[ 60, 0.848, 1.045, 1.296, 1.671, 2.000, 2.390, 2.660, 2.915, 3.232, 3.460], +[ 80, 0.846, 1.043, 1.292, 1.664, 1.990, 2.374, 2.639, 2.887, 3.195, 3.416], +[100, 0.845, 1.042, 1.290, 1.660, 1.984, 2.364, 2.626, 2.871, 3.174, 3.390], +[150, 0.844, 1.040, 1.287, 1.655, 1.976, 2.351, 2.609, 2.849, 3.145, 3.357] ] + + +# The functions use the following parameter name conventions: +# alpha - the alpha parameter +# degree - the degree-of-freedom parameter +# value - the t-distribution value for some alpha and degree +# deviations - a confidence interval radius, +# expressed as a multiple of the standard deviation of the sample +# ax - the alpha parameter index +# dx - the degree-of-freedom parameter index + +# The interface to this section of code is the last three functions, +# find_t_dist_value, find_t_dist_alpha, and find_t_dist_degree. + + +def t_dist_alpha_at_index(ax): + if ax == 0: + return .25 # effectively no confidence + else: + return t_dist_table[0][ax] + + +def t_dist_degree_at_index(dx): + return t_dist_table[dx][0] + + +def t_dist_value_at_index(ax, dx): + return t_dist_table[dx][ax] + + +def t_dist_index_of_degree(degree): + limit = len(t_dist_table) - 1 + dx = 0 + while dx < limit and t_dist_degree_at_index(dx+1) <= degree: + dx += 1 + return dx + + +def t_dist_index_of_alpha(alpha): + limit = len(t_dist_table[0]) - 1 + ax = 0 + while ax < limit and t_dist_alpha_at_index(ax+1) >= alpha: + ax += 1 + return ax + + +def t_dist_index_of_value(dx, value): + limit = len(t_dist_table[dx]) - 1 + ax = 0 + while ax < limit and t_dist_value_at_index(ax+1, dx) < value: + ax += 1 + return ax + + +def t_dist_value_within_deviations(dx, ax, deviations): + degree = t_dist_degree_at_index(dx) + count = degree + 1 + root = math.sqrt(count) + value = t_dist_value_at_index(ax, dx) + nominal = value / root + comparison = nominal <= deviations + return comparison + + +def t_dist_index_of_degree_for_deviations(ax, deviations): + limit = len(t_dist_table) - 1 + dx = 1 + while dx < limit and not t_dist_value_within_deviations(dx, ax, deviations): + dx += 1 + return dx + + +def find_t_dist_value(alpha, degree): + """ Return the t-distribution value. + The parameters are alpha and degree of freedom. + """ + dx = t_dist_index_of_degree(degree) + ax = t_dist_index_of_alpha(alpha) + return t_dist_value_at_index(ax, dx) + + +def find_t_dist_alpha(value, degree): + """ Return the alpha. + The parameters are the t-distribution value for a given degree of freedom. + """ + dx = t_dist_index_of_degree(degree) + ax = t_dist_index_of_value(dx, value) + return t_dist_alpha_at_index(ax) + + +def find_t_dist_degree(alpha, deviations): + """ Return the degree-of-freedom. + The parameters are the desired alpha and the number of standard deviations + away from the mean that the degree should handle. + """ + ax = t_dist_index_of_alpha(alpha) + dx = t_dist_index_of_degree_for_deviations(ax, deviations) + return t_dist_degree_at_index(dx) + + +############################################################## Core Statistical + + +# This section provides the core statistical classes and functions. + + +class Accumulator: + + """ An accumulator for statistical information using arithmetic mean. """ + + def __init__(self): + self.count = 0 + self.mean = 0 + self.sumsqdiff = 0 + + def insert(self, value): + self.count += 1 + diff = value - self.mean + self.mean += diff / self.count + self.sumsqdiff += (self.count - 1) * diff * diff / self.count + + +def fill_accumulator_from_values(values): + accumulator = Accumulator() + for value in values: + accumulator.insert(value) + return accumulator + + +def alpha_from_confidence(confidence): + scrubbed = min(99.99, max(confidence, 60)) + return (100.0 - scrubbed) / 200.0 + + +def confidence_from_alpha(alpha): + return 100 - 200 * alpha + + +class Sample: + + """ A description of a sample using an arithmetic mean. """ + + def __init__(self, accumulator, alpha): + if accumulator.count < 3: + sys.exit("Samples must contain three trials.") + self.count = accumulator.count + self.mean = accumulator.mean + variance = accumulator.sumsqdiff / (self.count - 1) + self.deviation = math.sqrt(variance) + self.error = self.deviation / math.sqrt(self.count) + self.alpha = alpha + self.radius = find_t_dist_value(alpha, self.count - 1) * self.error + + def alpha_for_radius(self, radius): + return find_t_dist_alpha(divide(radius, self.error), self.count) + + def degree_for_radius(self, radius): + return find_t_dist_degree(self.alpha, divide(radius, self.deviation)) + + def __str__(self): + text = "trial count is " + text_number(self.count) + text += ", mean is " + text_number(self.mean) + text += " (" + text_number(confidence_from_alpha(self.alpha)) +"%" + text += " confidence in " + text_number(self.mean - self.radius) + text += " to " + text_number(self.mean + self.radius) + ")" + text += ",\nstd.deviation is " + text_number(self.deviation) + text += ", std.error is " + text_number(self.error) + return text + + +def sample_from_values(values, alpha): + accumulator = fill_accumulator_from_values(values) + return Sample(accumulator, alpha) + + +class Comparison: + + """ A comparison of two samples using arithmetic means. """ + + def __init__(self, first, second, alpha): + if first.mean > second.mean: + self.upper = first + self.lower = second + self.larger = "first" + else: + self.upper = second + self.lower = first + self.larger = "second" + self.a_wanted = alpha + radius = self.upper.mean - self.lower.mean + rising = self.lower.alpha_for_radius(radius) + falling = self.upper.alpha_for_radius(radius) + self.a_actual = max(rising, falling) + rising = self.lower.degree_for_radius(radius) + falling = self.upper.degree_for_radius(radius) + self.count = max(rising, falling) + 1 + + def __str__(self): + message = "The " + self.larger + " sample appears to be " + change = divide(self.upper.mean, self.lower.mean) - 1 + message += text_percent(change) + " larger,\n" + confidence = confidence_from_alpha(self.a_actual) + if confidence >= 60: + message += "with " + text_number(confidence) + "% confidence" + message += " of being larger." + else: + message += "but with no confidence of actually being larger." + if self.a_actual > self.a_wanted: + confidence = confidence_from_alpha(self.a_wanted) + message += "\nTo reach " + text_number(confidence) + "% confidence," + if self.count < 100: + message += " you need roughly " + text_number(self.count) + " trials,\n" + message += "assuming the standard deviation is stable, which is iffy." + else: + message += "\nyou need to reduce the larger deviation" + message += " or increase the number of trials." + return message + + +############################################################ Single Value Files + + +# This section provides functions to compare two raw data files, +# each containing a whole sample consisting of single number per line. + + +# Should you repurpose this script, this code might help. +# +#def values_from_data_file(filename): +# for line in lines_in_file(filename): +# yield float(line) + + +# Should you repurpose this script, this code might help. +# +#def sample_from_data_file(filename, alpha): +# confidence = confidence_from_alpha(alpha) +# text = "\nArithmetic sample for data file\n\"" + filename + "\"" +# text += " with desired confidence " + text_number(confidence) + " is " +# print text +# values = values_from_data_file(filename) +# sample = sample_from_values(values, alpha) +# print sample +# return sample + + +# Should you repurpose this script, this code might help. +# +#def compare_two_data_files(filename1, filename2, confidence): +# alpha = alpha_from_confidence(confidence) +# sample1 = sample_from_data_file(filename1, alpha) +# sample2 = sample_from_data_file(filename2, alpha) +# print +# print Comparison(sample1, sample2, alpha) + + +# Should you repurpose this script, this code might help. +# +#def command_two_data_files(): +# argc = len(sys.argv) +# if argc < 2 or 4 < argc: +# message = "usage: " + sys.argv[0] +# message += " file-name file-name [confidence]" +# print message +# else: +# filename1 = sys.argv[1] +# filename2 = sys.argv[2] +# if len(sys.argv) >= 4: +# confidence = int(sys.argv[3]) +# else: +# confidence = 95 +# compare_two_data_files(filename1, filename2, confidence) + + +############################################### -ftime-report TimeVar Log Files + + +# This section provides functions to compare two sets of -ftime-report log +# files. Each set is a sample, where each data point is derived from the +# sum of values in a single log file. + + +label = r"^ *([^:]*[^: ]) *:" +number = r" *([0-9.]*) *" +percent = r"\( *[0-9]*\%\)" +numpct = number + percent +total_format = label + number + number + number + number + " kB\n" +total_parser = re.compile(total_format) +tmvar_format = label + numpct + " usr" + numpct + " sys" +tmvar_format += numpct + " wall" + number + " kB " + percent + " ggc\n" +tmvar_parser = re.compile(tmvar_format) +replace = r"\2\t\3\t\4\t\5\t\1" + + +def split_time_report(lines, pattern): + if pattern == "TOTAL": + parser = total_parser + else: + parser = tmvar_parser + for line in lines: + modified = parser.sub(replace, line) + if modified != line: + yield re.split("\t", modified) + + +def extract_cpu_time(tvtuples): + for tuple in tvtuples: + yield float(tuple[0]) + float(tuple[1]) + + +def sum_values(values): + sum = 0 + for value in values: + sum += value + return sum + + +def extract_time_for_timevar_log(filename, pattern): + lines = lines_in_file(filename) + tmvars = lines_containing_pattern(pattern, lines) + tuples = split_time_report(tmvars, pattern) + times = extract_cpu_time(tuples) + return sum_values(times) + + +def extract_times_for_timevar_logs(filelist, pattern): + for filename in filelist: + yield extract_time_for_timevar_log(filename, pattern) + + +def sample_from_timevar_logs(fileglob, pattern, alpha): + confidence = confidence_from_alpha(alpha) + text = "\nArithmetic sample for timevar log files\n\"" + fileglob + "\"" + text += "\nand selecting lines containing \"" + pattern + "\"" + text += " with desired confidence " + text_number(confidence) + " is " + print text + filelist = match_files(fileglob) + values = extract_times_for_timevar_logs(filelist, pattern) + sample = sample_from_values(values, alpha) + print sample + return sample + + +def compare_two_timevar_logs(fileglob1, fileglob2, pattern, confidence): + alpha = alpha_from_confidence(confidence) + sample1 = sample_from_timevar_logs(fileglob1, pattern, alpha) + sample2 = sample_from_timevar_logs(fileglob2, pattern, alpha) + print + print Comparison(sample1, sample2, alpha) + + +def command_two_timevar_logs(): + argc = len(sys.argv) + if argc < 3 or 5 < argc: + message = "usage: " + sys.argv[0] + message += " file-pattern file-pattern [line-pattern [confidence]]" + print message + else: + filepat1 = sys.argv[1] + filepat2 = sys.argv[2] + if len(sys.argv) >= 5: + confidence = int(sys.argv[4]) + else: + confidence = 95 + if len(sys.argv) >= 4: + linepat = sys.argv[3] + else: + linepat = "TOTAL" + compare_two_timevar_logs(filepat1, filepat2, linepat, confidence) + + +########################################################################## Main + + +# This section is the main code, implementing the command. + + +command_two_timevar_logs() |