зеркало из https://github.com/mozilla/pjs.git
798 строки
22 KiB
C++
798 строки
22 KiB
C++
/* ***** BEGIN LICENSE BLOCK *****
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* Version: MPL 1.1/GPL 2.0/LGPL 2.1
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*
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* The contents of this file are subject to the Mozilla Public License Version
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* 1.1 (the "License"); you may not use this file except in compliance with
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* the License. You may obtain a copy of the License at
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* http://www.mozilla.org/MPL/
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*
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* Software distributed under the License is distributed on an "AS IS" basis,
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* WITHOUT WARRANTY OF ANY KIND, either express or implied. See the License
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* for the specific language governing rights and limitations under the
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* License.
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*
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* The Original Code is ``garope''
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*
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* The Initial Developer of the Original Code is Netscape
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* Communications Corp. Portions created by the Initial Developer are
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* Copyright (C) 2001 the Initial Developer. All Rights Reserved.
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*
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* Contributor(s):
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* Chris Waterson <waterson@netscape.com>
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*
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* Alternatively, the contents of this file may be used under the terms of
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* either the GNU General Public License Version 2 or later (the "GPL"), or
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* the GNU Lesser General Public License Version 2.1 or later (the "LGPL"),
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* in which case the provisions of the GPL or the LGPL are applicable instead
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* of those above. If you wish to allow use of your version of this file only
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* under the terms of either the GPL or the LGPL, and not to allow others to
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* use your version of this file under the terms of the MPL, indicate your
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* decision by deleting the provisions above and replace them with the notice
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* and other provisions required by the GPL or the LGPL. If you do not delete
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* the provisions above, a recipient may use your version of this file under
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* the terms of any one of the MPL, the GPL or the LGPL.
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*
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* ***** END LICENSE BLOCK ***** */
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/*
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A program that attempts to find an optimal function ordering for an
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executable using a genetic algorithm whose fitness function is
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computed using runtime profile information.
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The fitness function was inspired by Nat Friedman's <nat@nat.org>
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work on `grope':
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_GNU Rope - A Subroutine Position Optimizer_
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<http://www.hungry.com/~shaver/grope/grope.ps>
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Brendan Eich <brendan@mozilla.org> told me tales about Scott Furman
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doing something like this, which sort of made me want to try it.
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As far as I can tell, it would take a lot of computers a lot of time
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to actually find something useful on a non-trivial program using
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this.
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*/
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#include <assert.h>
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#include <fstream>
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#include <hash_map>
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#include <vector>
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#include <limits.h>
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#include <unistd.h>
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#include <stdio.h>
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#include <fcntl.h>
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#include "elf_symbol_table.h"
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#define _GNU_SOURCE
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#include <getopt.h>
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#define PAGE_SIZE 4096
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#define SYMBOL_ALIGN 4
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//----------------------------------------------------------------------
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class call_pair
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{
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public:
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const Elf32_Sym *m_lo;
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const Elf32_Sym *m_hi;
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call_pair(const Elf32_Sym *site1, const Elf32_Sym *site2)
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{
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if (site1 < site2) {
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m_lo = site1;
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m_hi = site2;
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}
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else {
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m_hi = site1;
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m_lo = site2;
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}
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}
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friend bool
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operator==(const call_pair &lhs, const call_pair &rhs)
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{
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return (lhs.m_lo == rhs.m_lo) && (lhs.m_hi == rhs.m_hi);
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}
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};
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// Straight outta plhash.c!
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#define GOLDEN_RATIO 0x9E3779B9U
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template<>
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struct hash<call_pair>
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{
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size_t operator()(const call_pair &pair) const
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{
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size_t h = (reinterpret_cast<size_t>(pair.m_hi) >> 4);
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h += (reinterpret_cast<size_t>(pair.m_lo) >> 4);
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h *= GOLDEN_RATIO;
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return h;
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}
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};
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//----------------------------------------------------------------------
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struct hash<const Elf32_Sym *>
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{
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size_t operator()(const Elf32_Sym *sym) const
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{
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return (reinterpret_cast<size_t>(sym) >> 4) * GOLDEN_RATIO;
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}
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};
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//----------------------------------------------------------------------
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typedef hash_map<call_pair, unsigned int> call_graph_t;
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call_graph_t call_graph;
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typedef hash_map<const Elf32_Sym *, unsigned int> histogram_t;
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histogram_t histogram;
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long long total_calls = 0;
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elf_symbol_table symtab;
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bool opt_debug = false;
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int opt_generations = 10;
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int opt_mutate = 0;
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const char *opt_out = "order.out";
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int opt_population_size = 100;
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int opt_tick = 0;
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bool opt_verbose = false;
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int opt_window = 0;
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static struct option long_options[] = {
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{ "debug", no_argument, 0, 'd' },
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{ "exe", required_argument, 0, 'e' },
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{ "generations", required_argument, 0, 'g' },
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{ "help", no_argument, 0, '?' },
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{ "mutate", required_argument, 0, 'm' },
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{ "out", required_argument, 0, 'o' },
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{ "population", required_argument, 0, 'p' },
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{ "seed", required_argument, 0, 's' },
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{ "tick", optional_argument, 0, 't' },
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{ "verbose", no_argument, 0, 'v' },
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{ "window", required_argument, 0, 'w' },
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{ 0, 0, 0, 0 }
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};
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//----------------------------------------------------------------------
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static long long
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llrand()
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{
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long long result;
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result = (long long) rand();
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result *= (long long) (unsigned int) (RAND_MAX + 1);
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result += (long long) rand();
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return result;
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}
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//----------------------------------------------------------------------
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class symbol_order {
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public:
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typedef vector<const Elf32_Sym *> vector_t;
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typedef long long score_t;
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static const score_t max_score;
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/**
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* A vector of symbols that is this ordering.
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*/
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vector_t m_ordering;
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/**
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* The symbol ordering's score.
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*/
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score_t m_score;
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symbol_order() : m_score(0) {}
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/**
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* ``Shuffle'' a symbol ordering, randomizing it.
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*/
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void shuffle();
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/**
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* Initialize this symbol ordering by performing a crossover from
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* two ``parent'' symbol orderings.
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*/
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void crossover_from(const symbol_order *father, const symbol_order *mother);
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/**
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* Randomly mutate this symbol ordering.
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*/
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void mutate();
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/**
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* Score a symbol ordering based on paginated locality.
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*/
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score_t compute_score_page();
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/**
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* Score a symbol ordering based on a sliding window.
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*/
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score_t compute_score_window(int window_size);
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static score_t compute_score(symbol_order &order);
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/**
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* Use the symbol table to dump the ordered symbolic constants.
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*/
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void dump_symbols() const;
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friend ostream &
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operator<<(ostream &out, const symbol_order &order);
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};
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const symbol_order::score_t
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symbol_order::max_score = ~((symbol_order::score_t)1 << ((sizeof(symbol_order::score_t) * 8) - 1));
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symbol_order::score_t
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symbol_order::compute_score_page()
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{
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m_score = 0;
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unsigned int off = 0; // XXX in reality, probably not page-aligned to start
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vector_t::const_iterator end = m_ordering.end(),
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last = end,
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sym = m_ordering.begin();
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while (sym != end) {
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vector_t page;
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// If we had a symbol that spilled over from the last page,
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// then include it here.
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if (last != end)
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page.push_back(*last);
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// Pack symbols into the page
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do {
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page.push_back(*sym);
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int size = (*sym)->st_size;
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size += SYMBOL_ALIGN - 1;
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size &= ~(SYMBOL_ALIGN - 1);
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off += size;
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} while (++sym != end && off < PAGE_SIZE);
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// Remember if there was spill-over.
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off %= PAGE_SIZE;
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last = (off != 0) ? sym : end;
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// Now score the page as the count of all calls to symbols on
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// the page, less calls between the symbols on the page.
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vector_t::const_iterator page_end = page.end();
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for (vector_t::const_iterator i = page.begin(); i != page_end; ++i) {
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histogram_t::const_iterator func = histogram.find(*i);
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if (func == histogram.end())
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continue;
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m_score += func->second;
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vector_t::const_iterator j = i;
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for (++j; j != page_end; ++j) {
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call_graph_t::const_iterator call =
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call_graph.find(call_pair(*i, *j));
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if (call != call_graph.end())
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m_score -= call->second;
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}
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}
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}
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assert(m_score >= 0);
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// Integer reciprocal so we minimize instead of maximize.
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if (m_score == 0)
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m_score = 1;
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m_score = (total_calls / m_score) + 1;
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return m_score;
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}
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symbol_order::score_t
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symbol_order::compute_score_window(int window_size)
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{
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m_score = 0;
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vector_t::const_iterator *window = new vector_t::const_iterator[window_size];
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int window_fill = 0;
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vector_t::const_iterator end = m_ordering.end(),
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sym = m_ordering.begin();
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for (; sym != end; ++sym) {
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histogram_t::const_iterator func = histogram.find(*sym);
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if (func != histogram.end()) {
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long long scale = ((long long) 1) << window_size;
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m_score += func->second * scale * 2;
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vector_t::const_iterator *limit = window + window_fill;
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vector_t::const_iterator *iter;
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for (iter = window ; iter < limit; ++iter) {
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call_graph_t::const_iterator call =
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call_graph.find(call_pair(*sym, **iter));
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if (call != call_graph.end())
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m_score -= (call->second * scale);
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scale >>= 1;
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}
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}
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// Slide the window.
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vector_t::const_iterator *begin = window;
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vector_t::const_iterator *iter;
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for (iter = window + (window_size - 1); iter > begin; --iter)
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*iter = *(iter - 1);
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if (window_fill < window_size)
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++window_fill;
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*window = sym;
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}
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delete[] window;
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assert(m_score >= 0);
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// Integer reciprocal so we minimize instead of maximize.
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if (m_score == 0)
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m_score = 1;
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m_score = (total_calls / m_score) + 1;
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return m_score;
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}
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symbol_order::score_t
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symbol_order::compute_score(symbol_order &order)
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{
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if (opt_window)
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return order.compute_score_window(opt_window);
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return order.compute_score_page();
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}
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void
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symbol_order::shuffle()
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{
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vector_t::iterator sym = m_ordering.begin();
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vector_t::iterator end = m_ordering.end();
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for (; sym != end; ++sym) {
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int i = rand() % m_ordering.size();
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const Elf32_Sym *temp = *sym;
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*sym = m_ordering[i];
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m_ordering[i] = temp;
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}
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}
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void
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symbol_order::crossover_from(const symbol_order *father, const symbol_order *mother)
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{
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histogram_t used;
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m_ordering = vector_t(father->m_ordering.size(), 0);
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vector_t::const_iterator parent_sym = father->m_ordering.begin();
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vector_t::iterator sym = m_ordering.begin();
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vector_t::iterator end = m_ordering.end();
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for (; sym != end; ++sym, ++parent_sym) {
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if (rand() % 2) {
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*sym = *parent_sym;
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used[*parent_sym] = 1;
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}
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}
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parent_sym = mother->m_ordering.begin();
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sym = m_ordering.begin();
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for (; sym != end; ++sym) {
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if (! *sym) {
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while (used[*parent_sym])
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++parent_sym;
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*sym = *parent_sym++;
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}
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}
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}
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void
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symbol_order::mutate()
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{
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int i, j;
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i = rand() % m_ordering.size();
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j = rand() % m_ordering.size();
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const Elf32_Sym *temp = m_ordering[i];
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m_ordering[i] = m_ordering[j];
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m_ordering[j] = temp;
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}
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void
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symbol_order::dump_symbols() const
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{
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ofstream out(opt_out);
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vector_t::const_iterator sym = m_ordering.begin();
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vector_t::const_iterator end = m_ordering.end();
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for (; sym != end; ++sym)
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out << symtab.get_symbol_name(*sym) << endl;
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out.close();
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}
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ostream &
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operator<<(ostream &out, const symbol_order &order)
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{
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out << "symbol_order(" << order.m_score << ") ";
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symbol_order::vector_t::const_iterator sym = order.m_ordering.begin();
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symbol_order::vector_t::const_iterator end = order.m_ordering.end();
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for (; sym != end; ++sym)
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out.form("%08x ", *sym);
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out << endl;
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return out;
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}
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//----------------------------------------------------------------------
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static void
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usage(const char *name)
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{
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cerr << "usage: " << name << " [options] [<file> ...]" << endl;
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cerr << " Options:" << endl;
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cerr << " --debug, -d" << endl;
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cerr << " Print lots of verbose debugging cruft." << endl;
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cerr << " --exe=<image>, -e <image> (required)" << endl;
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cerr << " Specify the executable image from which to read symbol information." << endl;
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cerr << " --generations=<num>, -g <num>" << endl;
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cerr << " Specify the number of generations to run the GA (default is 10)." << endl;
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cerr << " --help, -?" << endl;
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cerr << " Print this message and exit." << endl;
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cerr << " --mutate=<num>, -m <num>" << endl;
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cerr << " Mutate every <num>th individual, or zero for no mutation (default)." << endl;
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cerr << " --out=<file>, -o <file>" << endl;
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cerr << " Specify the output file to which to dump the symbol ordering of the" << endl;
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cerr << " best individual (default is `order.out')." << endl;
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cerr << " --population=<num>, -p <num>" << endl;
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cerr << " Set the population size to <num> individuals (default is 100)." << endl;
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cerr << " --seed=<num>, -s <num>" << endl;
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cerr << " Specify a seed to srand()." << endl;
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cerr << " --tick[=<num>], -t [<num>]" << endl;
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cerr << " When reading address data, print a dot to stderr every <num>th" << endl;
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cerr << " address processed from the call trace. If specified with no argument," << endl;
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cerr << " a dot will be printed for every million addresses processed." << endl;
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cerr << " --verbose, -v" << endl;
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cerr << " Issue progress messages to stderr." << endl;
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cerr << " --window=<num>, -w <num>" << endl;
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cerr << " Use a sliding window instead of pagination to score orderings." << endl;
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cerr << endl;
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cerr << "This program uses a genetic algorithm to produce an `optimal' ordering for" << endl;
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cerr << "an executable based on call patterns." << endl;
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cerr << endl;
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cerr << "Addresses from a call trace are read as binary data from the files" << endl;
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cerr << "specified, or from stdin if no files are specified. These addresses" << endl;
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cerr << "are used with the symbolic information from the executable to create" << endl;
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cerr << "a call graph. This call graph is used to `score' arbitrary symbol" << endl;
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cerr << "orderings, and provides the fitness function for the GA." << endl;
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cerr << endl;
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}
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/**
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* Using the symbol table, map a stream of address references into a
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* callgraph and a histogram.
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*/
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static void
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map_addrs(int fd)
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{
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const Elf32_Sym *last = 0;
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unsigned int buf[128];
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ssize_t cb;
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unsigned int count = 0;
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while ((cb = read(fd, buf, sizeof buf)) > 0) {
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if (cb % sizeof buf[0])
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fprintf(stderr, "unaligned read\n");
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unsigned int *addr = buf;
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unsigned int *limit = buf + (cb / 4);
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for (; addr < limit; ++addr) {
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const Elf32_Sym *sym = symtab.lookup(*addr);
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if (last && sym && last != sym) {
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++total_calls;
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++histogram[sym];
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++call_graph[call_pair(last, sym)];
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if (opt_tick && (++count % opt_tick == 0)) {
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cerr << ".";
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flush(cerr);
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}
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}
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last = sym;
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}
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}
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if (opt_tick)
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cerr << endl;
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cerr << "Total calls: " << total_calls << endl;
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total_calls *= 1024;
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if (opt_window)
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total_calls <<= (opt_window + 1);
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}
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static symbol_order *
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pick_parent(symbol_order *ordering, int max, int index)
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{
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while (1) {
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index -= ordering->m_score;
|
|
if (index < 0)
|
|
break;
|
|
|
|
++ordering;
|
|
}
|
|
|
|
return ordering;
|
|
}
|
|
|
|
/**
|
|
* The main program
|
|
*/
|
|
int
|
|
main(int argc, char *argv[])
|
|
{
|
|
const char *opt_exe = 0;
|
|
|
|
int c;
|
|
while (1) {
|
|
int option_index = 0;
|
|
c = getopt_long(argc, argv, "?de:g:m:o:p:s:t:vw:", long_options, &option_index);
|
|
|
|
if (c < 0)
|
|
break;
|
|
|
|
switch (c) {
|
|
case '?':
|
|
usage(argv[0]);
|
|
return 0;
|
|
|
|
case 'd':
|
|
opt_debug = true;
|
|
break;
|
|
|
|
case 'e':
|
|
opt_exe = optarg;
|
|
break;
|
|
|
|
case 'g':
|
|
opt_generations = atoi(optarg);
|
|
break;
|
|
|
|
case 'm':
|
|
opt_mutate = atoi(optarg);
|
|
break;
|
|
|
|
case 'o':
|
|
opt_out = optarg;
|
|
break;
|
|
|
|
case 'p':
|
|
opt_population_size = atoi(optarg);
|
|
break;
|
|
|
|
case 's':
|
|
srand(atoi(optarg));
|
|
break;
|
|
|
|
case 't':
|
|
opt_tick = optarg ? atoi(optarg) : 1000000;
|
|
break;
|
|
|
|
case 'v':
|
|
opt_verbose = true;
|
|
break;
|
|
|
|
case 'w':
|
|
opt_window = atoi(optarg);
|
|
if (opt_window < 0 || opt_window > 8) {
|
|
cerr << "invalid window size: " << opt_window << endl;
|
|
return 1;
|
|
}
|
|
|
|
break;
|
|
|
|
default:
|
|
usage(argv[0]);
|
|
return 1;
|
|
}
|
|
}
|
|
|
|
// Make sure an image was specified
|
|
if (! opt_exe) {
|
|
usage(argv[0]);
|
|
return 1;
|
|
}
|
|
|
|
// Read the sym table.
|
|
symtab.init(opt_exe);
|
|
|
|
// Process addresses to construct the call graph.
|
|
if (optind >= argc) {
|
|
map_addrs(STDIN_FILENO);
|
|
}
|
|
else {
|
|
do {
|
|
int fd = open(argv[optind], O_RDONLY);
|
|
if (fd < 0) {
|
|
perror(argv[optind]);
|
|
return 1;
|
|
}
|
|
|
|
map_addrs(fd);
|
|
close(fd);
|
|
} while (++optind < argc);
|
|
}
|
|
|
|
if (opt_debug) {
|
|
cerr << "Call graph:" << endl;
|
|
|
|
call_graph_t::const_iterator limit = call_graph.end();
|
|
call_graph_t::const_iterator i;
|
|
for (i = call_graph.begin(); i != limit; ++i) {
|
|
const call_pair& pair = i->first;
|
|
cerr.form("%08x %08x %10d\n",
|
|
pair.m_lo->st_value,
|
|
pair.m_hi->st_value,
|
|
i->second);
|
|
}
|
|
}
|
|
|
|
// Collect the symbols into a vector
|
|
symbol_order::vector_t ordering;
|
|
elf_symbol_table::const_iterator end = symtab.end();
|
|
for (elf_symbol_table::const_iterator sym = symtab.begin(); sym != end; ++sym) {
|
|
if (symtab.is_function(sym))
|
|
ordering.push_back(sym);
|
|
}
|
|
|
|
if (opt_verbose) {
|
|
symbol_order initial;
|
|
initial.m_ordering = ordering;
|
|
cerr << "created initial ordering, score=" << symbol_order::compute_score(initial) << endl;
|
|
|
|
if (opt_debug)
|
|
cerr << initial;
|
|
}
|
|
|
|
// Create a population.
|
|
if (opt_verbose)
|
|
cerr << "creating population" << endl;
|
|
|
|
symbol_order *population = new symbol_order[opt_population_size];
|
|
|
|
symbol_order::score_t total = 0, min = symbol_order::max_score, max = 0;
|
|
|
|
// Score it.
|
|
symbol_order *order = population;
|
|
symbol_order *limit = population + opt_population_size;
|
|
for (; order < limit; ++order) {
|
|
order->m_ordering = ordering;
|
|
order->shuffle();
|
|
|
|
symbol_order::score_t score = symbol_order::compute_score(*order);
|
|
|
|
if (opt_debug)
|
|
cerr << *order;
|
|
|
|
if (min > score)
|
|
min = score;
|
|
if (max < score)
|
|
max = score;
|
|
|
|
total += score;
|
|
}
|
|
|
|
if (opt_verbose) {
|
|
cerr << "Initial population";
|
|
cerr << ": min=" << min;
|
|
cerr << ", max=" << max;
|
|
cerr << " mean=" << (total / opt_population_size);
|
|
cerr << endl;
|
|
}
|
|
|
|
|
|
// Run the GA.
|
|
if (opt_verbose)
|
|
cerr << "begininng ga" << endl;
|
|
|
|
symbol_order::score_t best = 0;
|
|
|
|
for (int generation = 1; generation <= opt_generations; ++generation) {
|
|
// Create a new population.
|
|
symbol_order *offspring = new symbol_order[opt_population_size];
|
|
|
|
symbol_order *kid = offspring;
|
|
symbol_order *offspring_limit = offspring + opt_population_size;
|
|
for (; kid < offspring_limit; ++kid) {
|
|
// Pick parents.
|
|
symbol_order *father, *mother;
|
|
father = pick_parent(population, max, llrand() % total);
|
|
mother = pick_parent(population, max, llrand() % total);
|
|
|
|
// Create a kid.
|
|
kid->crossover_from(father, mother);
|
|
|
|
// Mutate, possibly.
|
|
if (opt_mutate) {
|
|
if (rand() % opt_mutate == 0)
|
|
kid->mutate();
|
|
}
|
|
}
|
|
|
|
delete[] population;
|
|
population = offspring;
|
|
|
|
// Score the new population.
|
|
total = 0;
|
|
min = symbol_order::max_score;
|
|
max = 0;
|
|
|
|
symbol_order *fittest = 0;
|
|
|
|
limit = offspring_limit;
|
|
for (order = population; order < limit; ++order) {
|
|
symbol_order::score_t score = symbol_order::compute_score(*order);
|
|
|
|
if (opt_debug)
|
|
cerr << *order;
|
|
|
|
if (min > score)
|
|
min = score;
|
|
|
|
if (max < score)
|
|
max = score;
|
|
|
|
if (best < score) {
|
|
best = score;
|
|
fittest = order;
|
|
}
|
|
|
|
total += score;
|
|
}
|
|
|
|
if (opt_verbose) {
|
|
cerr << "Generation " << generation;
|
|
cerr << ": min=" << min;
|
|
cerr << ", max=" << max;
|
|
if (fittest)
|
|
cerr << "*";
|
|
cerr << " mean=" << (total / opt_population_size);
|
|
cerr << endl;
|
|
}
|
|
|
|
// If we've found a new ``best'' individual, dump it.
|
|
if (fittest)
|
|
fittest->dump_symbols();
|
|
}
|
|
|
|
delete[] population;
|
|
return 0;
|
|
}
|