forked from rpms/glibc
67b30d7656
Create a new package glibc-benchtests with the benchmark binaries that one may download and run to benchmark glibc for their machine. More importantly, the glibc-bench-compare and bench.mk scripts can run benchmarks and compare performance of two arbitrary glibc versions as long as both versions have the glibc-benchtests package. Usage: Scenario 1: Compare two build numbers, e.g.: /usr/libexec/glibc-benchtests/glibc-bench-compare 2.20-1.fc21 2.21.90-11.fc22 If a second build is omitted, comparison is done with the currently installed glibc. Scenario 2: Compare two downloaded rpms - only glibc, glibc-benchtests and glibc-common are needed for both versions. e.g.: /usr/libexec/glibc-benchtests/glibc-bench-compare -p <dir1> <dir2>
413 lines
14 KiB
Diff
413 lines
14 KiB
Diff
diff -pruN a/benchtests/scripts/compare_bench.py b/benchtests/scripts/compare_bench.py
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--- a/benchtests/scripts/compare_bench.py 1970-01-01 05:30:00.000000000 +0530
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+++ b/benchtests/scripts/compare_bench.py 2015-05-07 15:32:41.843584024 +0530
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@@ -0,0 +1,184 @@
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+#!/usr/bin/python
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+# Copyright (C) 2015 Free Software Foundation, Inc.
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+# This file is part of the GNU C Library.
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+#
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+# The GNU C Library is free software; you can redistribute it and/or
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+# modify it under the terms of the GNU Lesser General Public
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+# License as published by the Free Software Foundation; either
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+# version 2.1 of the License, or (at your option) any later version.
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+#
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+# The GNU C Library is distributed in the hope that it will be useful,
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+# but WITHOUT ANY WARRANTY; without even the implied warranty of
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+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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+# Lesser General Public License for more details.
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+#
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+# You should have received a copy of the GNU Lesser General Public
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+# License along with the GNU C Library; if not, see
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+# <http://www.gnu.org/licenses/>.
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+"""Compare two benchmark results
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+
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+Given two benchmark result files and a threshold, this script compares the
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+benchmark results and flags differences in performance beyond a given
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+threshold.
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+"""
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+import sys
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+import os
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+import pylab
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+import import_bench as bench
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+
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+def do_compare(func, var, tl1, tl2, par, threshold):
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+ """Compare one of the aggregate measurements
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+
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+ Helper function to compare one of the aggregate measurements of a function
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+ variant.
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+
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+ Args:
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+ func: Function name
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+ var: Function variant name
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+ tl1: The first timings list
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+ tl2: The second timings list
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+ par: The aggregate to measure
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+ threshold: The threshold for differences, beyond which the script should
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+ print a warning.
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+ """
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+ d = abs(tl2[par] - tl1[par]) * 100 / tl1[str(par)]
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+ if d > threshold:
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+ if tl1[par] > tl2[par]:
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+ ind = '+++'
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+ else:
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+ ind = '---'
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+ print('%s %s(%s)[%s]: (%.2lf%%) from %g to %g' %
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+ (ind, func, var, par, d, tl1[par], tl2[par]))
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+
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+
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+def compare_runs(pts1, pts2, threshold):
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+ """Compare two benchmark runs
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+
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+ Args:
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+ pts1: Timing data from first machine
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+ pts2: Timing data from second machine
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+ """
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+
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+ # XXX We assume that the two benchmarks have identical functions and
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+ # variants. We cannot compare two benchmarks that may have different
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+ # functions or variants. Maybe that is something for the future.
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+ for func in pts1['functions'].keys():
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+ for var in pts1['functions'][func].keys():
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+ tl1 = pts1['functions'][func][var]
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+ tl2 = pts2['functions'][func][var]
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+
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+ # Compare the consolidated numbers
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+ # do_compare(func, var, tl1, tl2, 'max', threshold)
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+ do_compare(func, var, tl1, tl2, 'min', threshold)
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+ do_compare(func, var, tl1, tl2, 'mean', threshold)
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+
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+ # Skip over to the next variant or function if there is no detailed
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+ # timing info for the function variant.
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+ if 'timings' not in pts1['functions'][func][var].keys() or \
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+ 'timings' not in pts2['functions'][func][var].keys():
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+ continue
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+
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+ # If two lists do not have the same length then it is likely that
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+ # the performance characteristics of the function have changed.
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+ # XXX: It is also likely that there was some measurement that
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+ # strayed outside the usual range. Such ouiers should not
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+ # happen on an idle machine with identical hardware and
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+ # configuration, but ideal environments are hard to come by.
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+ if len(tl1['timings']) != len(tl2['timings']):
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+ print('* %s(%s): Timing characteristics changed' %
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+ (func, var))
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+ print('\tBefore: [%s]' %
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+ ', '.join([str(x) for x in tl1['timings']]))
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+ print('\tAfter: [%s]' %
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+ ', '.join([str(x) for x in tl2['timings']]))
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+ continue
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+
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+ # Collect numbers whose differences cross the threshold we have
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+ # set.
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+ issues = [(x, y) for x, y in zip(tl1['timings'], tl2['timings']) \
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+ if abs(y - x) * 100 / x > threshold]
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+
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+ # Now print them.
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+ for t1, t2 in issues:
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+ d = abs(t2 - t1) * 100 / t1
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+ if t2 > t1:
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+ ind = '-'
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+ else:
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+ ind = '+'
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+
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+ print("%s %s(%s): (%.2lf%%) from %g to %g" %
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+ (ind, func, var, d, t1, t2))
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+
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+
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+def plot_graphs(bench1, bench2):
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+ """Plot graphs for functions
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+
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+ Make scatter plots for the functions and their variants.
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+
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+ Args:
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+ bench1: Set of points from the first machine
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+ bench2: Set of points from the second machine.
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+ """
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+ for func in bench1['functions'].keys():
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+ for var in bench1['functions'][func].keys():
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+ # No point trying to print a graph if there are no detailed
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+ # timings.
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+ if u'timings' not in bench1['functions'][func][var].keys():
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+ print('Skipping graph for %s(%s)' % (func, var))
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+ continue
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+
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+ pylab.clf()
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+ pylab.ylabel('Time (cycles)')
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+
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+ # First set of points
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+ length = len(bench1['functions'][func][var]['timings'])
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+ X = [float(x) for x in range(length)]
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+ lines = pylab.scatter(X, bench1['functions'][func][var]['timings'],
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+ 1.5 + 100 / length)
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+ pylab.setp(lines, 'color', 'r')
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+
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+ # Second set of points
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+ length = len(bench2['functions'][func][var]['timings'])
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+ X = [float(x) for x in range(length)]
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+ lines = pylab.scatter(X, bench2['functions'][func][var]['timings'],
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+ 1.5 + 100 / length)
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+ pylab.setp(lines, 'color', 'g')
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+
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+ if var:
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+ filename = "%s-%s.png" % (func, var)
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+ else:
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+ filename = "%s.png" % func
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+ print('Writing out %s' % filename)
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+ pylab.savefig(filename)
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+
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+
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+def main(args):
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+ """Program Entry Point
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+
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+ Take two benchmark output files and compare their timings.
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+ """
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+ if len(args) > 4 or len(args) < 3:
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+ print('Usage: %s <schema> <file1> <file2> [threshold in %%]' % sys.argv[0])
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+ sys.exit(os.EX_USAGE)
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+
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+ bench1 = bench.parse_bench(args[1], args[0])
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+ bench2 = bench.parse_bench(args[2], args[0])
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+ if len(args) == 4:
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+ threshold = float(args[3])
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+ else:
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+ threshold = 10.0
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+
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+ if (bench1['timing_type'] != bench2['timing_type']):
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+ print('Cannot compare benchmark outputs: timing types are different')
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+ return
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+
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+ plot_graphs(bench1, bench2)
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+
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+ bench.compress_timings(bench1)
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+ bench.compress_timings(bench2)
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+
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+ compare_runs(bench1, bench2, threshold)
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+
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+
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+if __name__ == '__main__':
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+ main(sys.argv[1:])
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diff -pruN a/benchtests/scripts/import_bench.py b/benchtests/scripts/import_bench.py
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--- a/benchtests/scripts/import_bench.py 1970-01-01 05:30:00.000000000 +0530
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+++ b/benchtests/scripts/import_bench.py 2015-05-07 15:32:41.844584032 +0530
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@@ -0,0 +1,141 @@
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+#!/usr/bin/python
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+# Copyright (C) 2015 Free Software Foundation, Inc.
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+# This file is part of the GNU C Library.
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+#
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+# The GNU C Library is free software; you can redistribute it and/or
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+# modify it under the terms of the GNU Lesser General Public
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+# License as published by the Free Software Foundation; either
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+# version 2.1 of the License, or (at your option) any later version.
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+#
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+# The GNU C Library is distributed in the hope that it will be useful,
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+# but WITHOUT ANY WARRANTY; without even the implied warranty of
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+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the GNU
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+# Lesser General Public License for more details.
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+#
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+# You should have received a copy of the GNU Lesser General Public
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+# License along with the GNU C Library; if not, see
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+# <http://www.gnu.org/licenses/>.
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+"""Functions to import benchmark data and process it"""
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+
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+import json
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+try:
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+ import jsonschema as validator
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+except ImportError:
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+ print('Could not find jsonschema module.')
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+ raise
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+
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+
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+def mean(lst):
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+ """Compute and return mean of numbers in a list
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+
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+ The numpy average function has horrible performance, so implement our
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+ own mean function.
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+
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+ Args:
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+ lst: The list of numbers to average.
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+ Return:
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+ The mean of members in the list.
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+ """
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+ return sum(lst) / len(lst)
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+
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+
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+def split_list(bench, func, var):
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+ """ Split the list into a smaller set of more distinct points
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+
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+ Group together points such that the difference between the smallest
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+ point and the mean is less than 1/3rd of the mean. This means that
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+ the mean is at most 1.5x the smallest member of that group.
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+
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+ mean - xmin < mean / 3
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+ i.e. 2 * mean / 3 < xmin
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+ i.e. mean < 3 * xmin / 2
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+
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+ For an evenly distributed group, the largest member will be less than
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+ twice the smallest member of the group.
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+ Derivation:
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+
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+ An evenly distributed series would be xmin, xmin + d, xmin + 2d...
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+
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+ mean = (2 * n * xmin + n * (n - 1) * d) / 2 * n
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+ and max element is xmin + (n - 1) * d
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+
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+ Now, mean < 3 * xmin / 2
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+
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+ 3 * xmin > 2 * mean
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+ 3 * xmin > (2 * n * xmin + n * (n - 1) * d) / n
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+ 3 * n * xmin > 2 * n * xmin + n * (n - 1) * d
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+ n * xmin > n * (n - 1) * d
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+ xmin > (n - 1) * d
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+ 2 * xmin > xmin + (n-1) * d
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+ 2 * xmin > xmax
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+
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+ Hence, proved.
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+
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+ Similarly, it is trivial to prove that for a similar aggregation by using
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+ the maximum element, the maximum element in the group must be at most 4/3
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+ times the mean.
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+
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+ Args:
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+ bench: The benchmark object
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+ func: The function name
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+ var: The function variant name
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+ """
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+ means = []
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+ lst = bench['functions'][func][var]['timings']
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+ last = len(lst) - 1
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+ while lst:
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+ for i in range(last + 1):
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+ avg = mean(lst[i:])
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+ if avg > 0.75 * lst[last]:
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+ means.insert(0, avg)
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+ lst = lst[:i]
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+ last = i - 1
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+ break
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+ bench['functions'][func][var]['timings'] = means
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+
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+
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+def do_for_all_timings(bench, callback):
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+ """Call a function for all timing objects for each function and its
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+ variants.
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+
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+ Args:
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+ bench: The benchmark object
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+ callback: The callback function
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+ """
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+ for func in bench['functions'].keys():
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+ for k in bench['functions'][func].keys():
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+ if 'timings' not in bench['functions'][func][k].keys():
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+ continue
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+
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+ callback(bench, func, k)
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+
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+
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+def compress_timings(points):
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+ """Club points with close enough values into a single mean value
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+
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+ See split_list for details on how the clubbing is done.
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+
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+ Args:
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+ points: The set of points.
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+ """
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+ do_for_all_timings(points, split_list)
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+
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+
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+def parse_bench(filename, schema_filename):
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+ """Parse the input file
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+
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+ Parse and validate the json file containing the benchmark outputs. Return
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+ the resulting object.
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+ Args:
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+ filename: Name of the benchmark output file.
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+ Return:
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+ The bench dictionary.
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+ """
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+ with open(schema_filename, 'r') as schemafile:
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+ schema = json.load(schemafile)
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+ with open(filename, 'r') as benchfile:
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+ bench = json.load(benchfile)
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+ validator.validate(bench, schema)
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+ do_for_all_timings(bench, lambda b, f, v:
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+ b['functions'][f][v]['timings'].sort())
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+ return bench
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diff -pruN a/benchtests/scripts/validate_benchout.py b/benchtests/scripts/validate_benchout.py
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--- a/benchtests/scripts/validate_benchout.py 2015-05-07 11:58:40.000000000 +0530
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+++ b/benchtests/scripts/validate_benchout.py 2015-05-07 15:32:41.844584032 +0530
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@@ -27,37 +27,26 @@ import sys
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import os
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try:
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- import jsonschema
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+ import import_bench as bench
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except ImportError:
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- print('Could not find jsonschema module. Output not validated.')
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+ print('Import Error: Output will not be validated.')
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# Return success because we don't want the bench target to fail just
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# because the jsonschema module was not found.
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sys.exit(os.EX_OK)
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-def validate_bench(benchfile, schemafile):
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- """Validate benchmark file
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-
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- Validate a benchmark output file against a JSON schema.
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+def print_and_exit(message, exitcode):
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+ """Prints message to stderr and returns the exit code.
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Args:
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- benchfile: The file name of the bench.out file.
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- schemafile: The file name of the JSON schema file to validate
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- bench.out against.
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+ message: The message to print
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+ exitcode: The exit code to return
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- Exceptions:
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- jsonschema.ValidationError: When bench.out is not valid
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- jsonschema.SchemaError: When the JSON schema is not valid
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- IOError: If any of the files are not found.
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+ Returns:
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+ The passed exit code
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"""
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- with open(benchfile, 'r') as bfile:
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- with open(schemafile, 'r') as sfile:
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- bench = json.load(bfile)
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- schema = json.load(sfile)
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- jsonschema.validate(bench, schema)
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-
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- # If we reach here, we're all good.
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- print("Benchmark output in %s is valid." % benchfile)
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+ print(message, file=sys.stderr)
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+ return exitcode
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def main(args):
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@@ -73,11 +62,23 @@ def main(args):
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Exceptions thrown by validate_bench
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"""
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if len(args) != 2:
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- print("Usage: %s <bench.out file> <bench.out schema>" % sys.argv[0],
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- file=sys.stderr)
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- return os.EX_USAGE
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+ return print_and_exit("Usage: %s <bench.out file> <bench.out schema>"
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+ % sys.argv[0], os.EX_USAGE)
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+
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+ try:
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+ bench.parse_bench(args[0], args[1])
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+ except IOError as e:
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+ return print_and_exit("IOError(%d): %s" % (e.errno, e.strerror),
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+ os.EX_OSFILE)
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+
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+ except bench.validator.ValidationError as e:
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+ return print_and_exit("Invalid benchmark output: %s" % e.message,
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+ os.EX_DATAERR)
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+
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+ except bench.validator.SchemaError as e:
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+ return print_and_exit("Invalid schema: %s" % e.message, os.EX_DATAERR)
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- validate_bench(args[0], args[1])
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+ print("Benchmark output in %s is valid." % args[0])
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return os.EX_OK
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