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+#!/usr/bin/python
+# Copyright (C) 2014 Free Software Foundation, Inc.
+# This file is part of the GNU C Library.
+#
+# The GNU C Library is free software; you can redistribute it and/or
+# modify it under the terms of the GNU Lesser General Public
+# License as published by the Free Software Foundation; either
+# version 2.1 of the License, or (at your option) any later version.
+#
+# The GNU C Library 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
+# Lesser General Public License for more details.
+#
+# You should have received a copy of the GNU Lesser General Public
+# License along with the GNU C Library; if not, see
+# <http://www.gnu.org/licenses/>.
+"""Compare two benchmark results
+
+Given two benchmark result files and a threshold, this script compares the
+benchmark results and flags differences in performance beyond a given
+threshold.
+"""
+import sys
+import os
+import pylab
+import import_bench as bench
+
+def do_compare(func, var, tl1, tl2, par, threshold):
+ """Compare one of the aggregate measurements
+
+ Helper function to compare one of the aggregate measurements of a function
+ variant.
+
+ Args:
+ func: Function name
+ var: Function variant name
+ tl1: The first timings list
+ tl2: The second timings list
+ par: The aggregate to measure
+ threshold: The threshold for differences, beyond which the script should
+ print a warning.
+ """
+ d = abs(tl2[par] - tl1[par]) * 100 / tl1[str(par)]
+ if d > threshold:
+ if tl1[par] > tl2[par]:
+ ind = '+++'
+ else:
+ ind = '---'
+ print('%s %s(%s)[%s]: (%.2lf%%) from %g to %g' %
+ (ind, func, var, par, d, tl1[par], tl2[par]))
+
+
+def compare_runs(pts1, pts2, threshold):
+ """Compare two benchmark runs
+
+ For now, assume that machine1 and machine2 are the same. In future, we may
+ have to add an additional condition to ensure that we are comparing apples
+ to apples.
+
+ Args:
+ machine1: Machine info from the first machine
+ machine2: machine info from the second machine
+ pts1: Timing data from first machine
+ pts2: Timing data from second machine
+ """
+ # Just print the machines for now. We would want to do something more
+ # interesting with this in future.
+ print 'Machine 1:', pts1['machine']
+ print 'Machine 2:', pts2['machine']
+ print
+
+ # XXX We assume that the two benchmarks have identical functions and
+ # variants. We cannot compare two benchmarks that may have different
+ # functions or variants. Maybe that is something for the future.
+ for func in pts1['functions'].keys():
+ for var in pts1['functions'][func].keys():
+ tl1 = pts1['functions'][func][var]
+ tl2 = pts2['functions'][func][var]
+
+ # Compare the consolidated numbers
+ # do_compare(func, var, tl1, tl2, 'max', threshold)
+ do_compare(func, var, tl1, tl2, 'min', threshold)
+ do_compare(func, var, tl1, tl2, 'mean', threshold)
+
+ # Skip over to the next variant or function if there is no detailed
+ # timing info for the function variant.
+ if 'timings' not in pts1['functions'][func][var].keys() or \
+ 'timings' not in pts2['functions'][func][var].keys():
+ return
+
+ # If two lists do not have the same length then it is likely that
+ # the performance characteristics of the function have changed.
+ # XXX: It is also likely that there was some measurement that
+ # strayed outside the usual range. Such ouiers should not
+ # happen on an idle machine with identical hardware and
+ # configuration, but ideal environments are hard to come by.
+ if len(tl1['timings']) != len(tl2['timings']):
+ print('* %s(%s): Timing characteristics changed' %
+ (func, var))
+ print('\tBefore: [%s]' %
+ ', '.join([str(x) for x in tl1['timings']]))
+ print('\tAfter: [%s]' %
+ ', '.join([str(x) for x in tl2['timings']]))
+ continue
+
+ # Collect numbers whose differences cross the threshold we have
+ # set.
+ issues = [(x, y) for x, y in zip(tl1['timings'], tl2['timings']) \
+ if abs(y - x) * 100 / x > threshold]
+
+ # Now print them.
+ for t1, t2 in issues:
+ d = abs(t2 - t1) * 100 / t1
+ if t2 > t1:
+ ind = '-'
+ else:
+ ind = '+'
+
+ print("%s %s(%s): (%.2lf%%) from %g to %g" %
+ (ind, func, var, d, t1, t2))
+
+
+def plot_graphs(bench1, bench2):
+ """Plot graphs for functions
+
+ Make scatter plots for the functions and their variants.
+
+ Args:
+ bench1: Set of points from the first machine
+ bench2: Set of points from the second machine.
+ """
+ for func in bench1['functions'].keys():
+ for var in bench1['functions'][func].keys():
+ # No point trying to print a graph if there are no detailed
+ # timings.
+ if u'timings' not in bench1['functions'][func][var].keys():
+ print('Skipping graph for %s(%s)' % (func, var))
+ continue
+
+ pylab.clf()
+ pylab.ylabel('Time (cycles)')
+
+ # First set of points
+ length = len(bench1['functions'][func][var]['timings'])
+ X = [float(x) for x in range(length)]
+ lines = pylab.scatter(X, bench1['functions'][func][var]['timings'],
+ 1.5 + 100 / length)
+ pylab.setp(lines, 'color', 'r')
+
+ # Second set of points
+ length = len(bench2['functions'][func][var]['timings'])
+ X = [float(x) for x in range(length)]
+ lines = pylab.scatter(X, bench2['functions'][func][var]['timings'],
+ 1.5 + 100 / length)
+ pylab.setp(lines, 'color', 'g')
+
+ if var:
+ filename = "%s-%s.png" % (func, var)
+ else:
+ filename = "%s.png" % func
+ print('Writing out %s' % filename)
+ pylab.savefig(filename)
+
+
+def main(args):
+ """Program Entry Point
+
+ Take two benchmark output files and compare their timings.
+ """
+ if len(args) > 4 or len(args) < 3:
+ print('Usage: %s <schema> <file1> <file2> [threshold in %%]' % sys.argv[0])
+ sys.exit(os.EX_USAGE)
+
+ bench1 = bench.parse_bench(args[1], args[0])
+ bench2 = bench.parse_bench(args[2], args[0])
+ if len(args) == 4:
+ threshold = float(args[3])
+ else:
+ threshold = 10.0
+
+ if (bench1['timing-type'] != bench2['timing-type']):
+ print('Cannot compare benchmark outputs: timing types are different')
+ return
+
+ plot_graphs(bench1, bench2)
+
+ bench.compress_timings(bench1)
+ bench.compress_timings(bench2)
+
+ compare_runs(bench1, bench2, threshold)
+
+
+if __name__ == '__main__':
+ main(sys.argv[1:])
@@ -25,6 +25,102 @@ except ImportError:
raise
+def mean(lst):
+ """Compute and return mean of numbers in a list
+
+ The numpy average function has horrible performance, so implement our
+ own mean function.
+
+ Args:
+ lst: The list of numbers to average.
+ Return:
+ The mean of members in the list.
+ """
+ return sum(lst) / len(lst)
+
+
+def split_list(bench, func, var):
+ """ Split the list into a smaller set of more distinct points
+
+ Group together points such that the difference between the smallest
+ point and the mean is less than 1/3rd of the mean. This means that
+ the mean is at most 1.5x the smallest member of that group.
+
+ mean - xmin < mean / 3
+ i.e. 2 * mean / 3 < xmin
+ i.e. mean < 3 * xmin / 2
+
+ For an evenly distributed group, the largest member will be less than
+ twice the smallest member of the group.
+ Derivation:
+
+ An evenly distributed series would be xmin, xmin + d, xmin + 2d...
+
+ mean = (2 * n * xmin + n * (n - 1) * d) / 2 * n
+ and max element is xmin + (n - 1) * d
+
+ Now, mean < 3 * xmin / 2
+
+ 3 * xmin > 2 * mean
+ 3 * xmin > (2 * n * xmin + n * (n - 1) * d) / n
+ 3 * n * xmin > 2 * n * xmin + n * (n - 1) * d
+ n * xmin > n * (n - 1) * d
+ xmin > (n - 1) * d
+ 2 * xmin > xmin + (n-1) * d
+ 2 * xmin > xmax
+
+ Hence, proved.
+
+ Similarly, it is trivial to prove that for a similar aggregation by using
+ the maximum element, the maximum element in the group must be at most 4/3
+ times the mean.
+
+ Args:
+ bench: The benchmark object
+ func: The function name
+ var: The function variant name
+ """
+ means = []
+ lst = bench['functions'][func][var]['timings']
+ last = len(lst) - 1
+ while lst:
+ for i in range(last + 1):
+ avg = mean(lst[i:])
+ if avg > 0.75 * lst[last]:
+ means.insert(0, avg)
+ lst = lst[:i]
+ last = i - 1
+ break
+ bench['functions'][func][var]['timings'] = means
+
+
+def do_for_all_timings(bench, callback):
+ """Call a function for all timing objects for each function and its
+ variants.
+
+ Args:
+ bench: The benchmark object
+ callback: The callback function
+ """
+ for func in bench['functions'].keys():
+ for k in bench['functions'][func].keys():
+ if 'timings' not in bench['functions'][func][k].keys():
+ continue
+
+ callback(bench, func, k)
+
+
+def compress_timings(points):
+ """Club points with close enough values into a single mean value
+
+ See split_list for details on how the clubbing is done.
+
+ Args:
+ points: The set of points.
+ """
+ do_for_all_timings(points, split_list)
+
+
def parse_bench(filename, schema_filename):
"""Parse the input file