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Bug 1082265 - Rewrite split-profile.pl in python.
This adds arguments for the paths to jprof, the program being profiled, and the jprof profile itself, so I don't need to modify my local copy of split-profile.pl to fix those. DONTBUILD --HG-- extra : transplant_source : %97%D5%A6%1C%A86%5E%AC%F7%FB%A7%8D%B0L%1D%17%FDK%EA%9F
This commit is contained in:
L. David Baron
parent
29be851cda
commit
456c443047
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#!/usr/bin/perl
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# This Source Code Form is subject to the terms of the Mozilla Public
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# License, v. 2.0. If a copy of the MPL was not distributed with this
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# file, You can obtain one at http://mozilla.org/MPL/2.0/.
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# split-profile.pl Documentation:
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#
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# This script uses jprof's includes (-i) and excludes (-e) options to
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# split profiles into segments. It takes as input a single text file,
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# and from that text file creates a series of jprof profiles in the
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# directory in which it is run. It expects the application binaries
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# with which the profile was made, including jprof, and the jprof
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# profile data, to be in a directory called "bin" that is a subdirectory
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# of the current directory, and it will output the profiles into the
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# current directory.
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#
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# The input file format looks like the following:
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#
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# poll g_main_poll
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# GetRuleCascade CSSRuleProcessor::GetRuleCascade(nsPresContext *, nsIAtom *)
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# RuleProcessorData RuleProcessorData::RuleProcessorData(nsPresContext *, nsIContent *, nsRuleWalker *, nsCompatibility *)
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#
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# From this input file, the script will construct a profile called
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# 00.html that contains the whole profile, a profile called 01-poll.html
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# that includes only stacks with g_main_poll, a profile called
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# 02-GetRuleCascade.html that includes only stacks that have
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# GetRuleCascade and do not have g_main_poll, a profile called
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# 03-RuleProcessorData.html that includes only stacks that have the
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# RuleProcessorData constructor and do not have GetRuleCascade or
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# g_main_poll, and a profile called 04.html that includes only stacks
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# that do not have any of the three functions in them.
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#
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# This means that all of the segments of the profile, except 00.html,
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# are mutually exclusive. Thus clever ordering of the functions in the
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# input file can lead to a logical splitting of the profile into
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# segments.
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use strict;
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my @names;
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my @sigs;
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sub read_info($) {
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my ($fname) = @_;
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open(INFO, "<$fname");
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my $i = 0;
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while (<INFO>) {
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chop;
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my $line = $_;
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my $idx = index($line, " ");
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my $name = substr($line, 0, $idx);
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my $sig = substr($line, $idx+1);
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$names[$i] = $name;
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$sigs[$i] = $sig;
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++$i;
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}
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}
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sub run_profile($$) {
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my ($options, $outfile) = @_;
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print "./jprof$options mozilla-bin jprof-log > ../$outfile.html\n";
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system "./jprof$options mozilla-bin jprof-log > ../$outfile.html";
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}
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sub run_profiles() {
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run_profile("", "00");
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for (my $i = 0; $i <= $#names + 1; ++$i) {
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my $options = "";
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for (my $j = 0; $j < $i; ++$j) {
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$options .= " -e\"$sigs[$j]\"";
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}
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if ($i <= $#names) {
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$options .= " -i\"$sigs[$i]\"";
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}
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my $num;
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my $n = $i + 1;
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if ($n < 10) {
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$num = "0$n";
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} else {
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$num = "$n";
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}
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if ($i <= $#names) {
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run_profile($options, "$num-$names[$i]");
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} else {
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run_profile($options, "$num");
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}
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}
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}
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($#ARGV == 0) || die "Usage: split-profile.pl <info-file>\n";
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read_info($ARGV[0]);
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chdir "bin" || die "Can't change directory to bin.";
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run_profiles();
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140
tools/jprof/split-profile.py
Executable file
140
tools/jprof/split-profile.py
Executable file
@ -0,0 +1,140 @@
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#!/usr/bin/python
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# This Source Code Form is subject to the terms of the Mozilla Public
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# License, v. 2.0. If a copy of the MPL was not distributed with this
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# file, You can obtain one at http://mozilla.org/MPL/2.0/.
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# This program splits up a jprof profile into multiple files based on a
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# list of functions in a text file. First, a complete profile is
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# generated. Then, for each line in the text file, a profile is
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# generated containing only stacks that go through that line, and also
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# excluding all stacks in earlier lines in the text file. This means
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# that the text file, from start to end, is splitting out pieces of the
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# profile in their own file. Finally, a final profile containing the
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# remainder is produced.
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# The program takes four arguments:
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# (1) The path to jprof.
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# (2) The path to the text file describing the splits. The output
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# will be placed in the same directory as this file.
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# (3) The program that was profiled.
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# (4) The jprof-log file generated by the profile, to be split up.
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# (Really, all arguments from (3) and later are passed through to
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# jprof, so additional arguments could be provided if you want to pass
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# additional arguments to jprof.)
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# In slightly more detail:
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#
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# This script uses jprof's includes (-i) and excludes (-e) options to
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# split profiles into segments. It takes as input a single text file,
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# and from that text file creates a series of jprof profiles in the
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# directory in which it is run. It expects the application binaries
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# with which the profile was made, including jprof, and the jprof
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# profile data, to be in a directory called "bin" that is a subdirectory
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# of the current directory, and it will output the profiles into the
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# current directory.
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#
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# The input file format looks like the following:
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#
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# poll g_main_poll
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# GetRuleCascade CSSRuleProcessor::GetRuleCascade(nsPresContext *, nsIAtom *)
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# RuleProcessorData RuleProcessorData::RuleProcessorData(nsPresContext *, nsIContent *, nsRuleWalker *, nsCompatibility *)
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#
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# From this input file, the script will construct a profile called
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# jprof-0.html that contains the whole profile, a profile called
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# jprof-1-poll.html that includes only stacks with g_main_poll, a
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# profile called jprof-2-GetRuleCascade.html that includes only stacks
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# that have GetRuleCascade and do not have g_main_poll, a profile called
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# jprof-3-RuleProcessorData.html that includes only stacks that have the
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# RuleProcessorData constructor and do not have GetRuleCascade or
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# g_main_poll, and a profile called jprof-4.html that includes only
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# stacks that do not have any of the three functions in them.
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#
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# This means that all of the segments of the profile, except
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# jprof-0.html, are mutually exclusive. Thus clever ordering of the
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# functions in the input file can lead to a logical splitting of the
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# profile into segments.
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import sys
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import subprocess
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import os.path
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if len(sys.argv) < 5:
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sys.stderr.write("Expected arguments: <jprof> <split-file> <program> <jprof-log>\n")
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sys.exit(1)
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jprof = sys.argv[1]
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splitfile = sys.argv[2]
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passthrough = sys.argv[3:]
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for f in [jprof, splitfile]:
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if not os.path.isfile(f):
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sys.stderr.write("could not find file: {0}\n".format(f))
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sys.exit(1)
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def read_splits(splitfile):
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"""
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Read splitfile (each line of which contains a name, a space, and
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then a function name to split on), and return a list of pairs
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representing exactly that. (Note that the name cannot contain
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spaces, but the function name can, and often does.)
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"""
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def line_to_split(line):
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line = line.strip("\r\n")
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idx = line.index(" ")
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return (line[0:idx], line[idx+1:])
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io = open(splitfile, "r")
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result = [line_to_split(line) for line in io]
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io.close()
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return result
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splits = read_splits(splitfile)
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def generate_profile(options, destfile):
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"""
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Run jprof to generate one split of the profile.
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"""
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args = [jprof] + options + passthrough
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print "Generating {0}".format(destfile)
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destio = open(destfile, "w")
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process = subprocess.Popen(args, stdout=destio)
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process.wait()
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destio.close()
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if process.returncode != 0:
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os.remove(destfile)
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sys.stderr.write("Error {0} from command:\n {1}\n".format(process.returncode, " ".join(args)))
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sys.exit(process.returncode)
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def output_filename(number, splitname):
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"""
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Return the filename (absolute path) we should use to output the
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profile segment with the given number and splitname. Splitname
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should be None for the complete profile and the remainder.
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"""
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def pad_count(i):
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result = str(i)
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# 0-pad to the same length
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result = "0" * (len(str(len(splits) + 1)) - len(result)) + result
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return result
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name = pad_count(number)
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if splitname is not None:
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name += "-" + splitname
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return os.path.join(os.path.dirname(splitfile),
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"jprof-{0}.html".format(name))
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# generate the complete profile
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generate_profile([], output_filename(0, None))
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# generate the listed splits
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count = 1
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excludes = []
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for (splitname, splitfunction) in splits:
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generate_profile(excludes + ["-i" + splitfunction],
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output_filename(count, splitname))
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excludes += ["-e" + splitfunction]
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count = count + 1
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# generate the remainder after the splits
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generate_profile(excludes, output_filename(count, None))
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