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Web site updates, and a design document.
author Rob Landley <rob@landley.net>
date Thu, 09 Nov 2006 19:19:37 -0500
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children ce6956dfc0cf
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c8d0f1876c40 Web site updates, and a design document.
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1 <b><h2>Design goals</h2></b>
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2
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3 <p>Toybox should be simple, small, and fast. Often, these things need to be
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4 balanced off against each other. In general, simple is slightly more
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5 important than small, and small is slightly more important than fast, but
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6 it should be possible to get 80% of the way to each goal before they really
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7 start to fight.</p>
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8
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9 <b><h3>Fast</h3></b>
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10
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11 <p>It's easy to say lots about optimizing for speed (which is why this section
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12 is so long), but at the same time it's the one we care the least about.
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13 The essence of speed is being as efficient as possible, which means doing as
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14 little work as possible. A design that's small and simple gets you 90% of the
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15 way there, and most of the rest is either fine-tuning or more trouble than
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16 it's worth (and often actually counterproductive). Still, here's some
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17 advice:</p>
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18
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19 <p>First, understand the darn problem you're trying to solve. You'd think
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20 I wouldn't have to say this, but I do. Trying to find a faster sorting
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21 algorithm is no substitute for figuring out a way to skip the sorting step
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22 entirely. The fastest way to do anything is not to have to do it at all,
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23 and _all_ optimization boils down to avoiding unnecessary work.</p>
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24
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25 <p>Speed is easy to measure; there are dozens of profiling tools for Linux
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26 (although personally I find the "time" command a good starting place).
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27 Don't waste too much time trying to optimize something you can't measure,
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28 and there's no much point speeding up things you don't spend much time doing
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29 anyway.</p>
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30
c8d0f1876c40 Web site updates, and a design document.
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31 <p>Understand the difference between throughput and latency. Faster
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32 processors improve throughput, but don't always do much for latency.
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33 After 30 years of Moore's Law, most of the remaining problems are latency,
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34 not throughput. (There are of course a few exceptions, like data compression
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35 code, encryption, rsync...) Worry about throughput inside long-running
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36 loops, and worry about latency everywhere else. (And don't worry too much
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37 about avoiding system calls or function calls or anything else in the name
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38 of speed unless you are in the middle of a tight loop that's you've already
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39 proven isn't running fast enough.)</p>
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40
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41 <p>"Locality of reference" is generally nice, in all sorts of contexts.
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42 It's obvious that waiting for disk access is 1000x slower than doing stuff in
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43 RAM (and making the disk seek is 10x slower than sequential reads/writes),
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44 but it's just as true that a loop which stays in L1 cache is many times faster
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45 than a loop that has to wait for a DRAM fetch on each iteration. Don't worry
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46 about whether "&" is faster than "%" until your executable loop stays in L1
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47 cache and the data access is fetching cache lines intelligently. (To
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48 understand DRAM, L1, and L2 cache, read Hannibal's marvelous ram guid at Ars
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49 Technica:
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50 <a href=http://arstechnica.com/paedia/r/ram_guide/ram_guide.part1-2.html>part one</a>,
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51 <a href=http://arstechnica.com/paedia/r/ram_guide/ram_guide.part2-1.html>part two</a>,
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52 <a href=http://arstechnica.com/paedia/r/ram_guide/ram_guide.part3-1.html>part three</a>,
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53 plus this
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54 <a href=http://arstechnica.com/articles/paedia/cpu/caching.ars/1>article on
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55 cacheing</a>, and this one on
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56 <a href=http://arstechnica.com/articles/paedia/cpu/bandwidth-latency.ars>bandwidth
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57 and latency</a>.
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58 And there's <a href=http://arstechnica.com/paedia/>more where that came from</a>.)
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59 Running out of L1 cache can execute one instruction per clock cycle, going
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60 to L2 cache costs a dozen or so clock cycles, and waiting for a worst case dram
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61 fetch (round trip latency with a bank switch) can cost thousands of
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62 clock cycles. (Historically, this disparity has gotten worse with time,
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63 just like the speed hit for swapping to disk. These days, a _big_ L1 cache
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64 is 128k and a big L2 cache is a couple of megabytes. A cheap low-power
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65 embedded processor may have 8k of L1 cache and no L2.)</p>
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66
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67 <p>Learn how virtual memory and memory managment units work. Don't touch
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68 memory you don't have to. Even just reading memory evicts stuff from L1 and L2
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69 cache, which may have to be read back in later. Writing memory can force the
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70 operating system to break copy-on-write, which allocates more memory. (The
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71 memory returned by malloc() is only a virtual allocation, filled with lots of
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72 copy-on-write mappings of the zero page. Actual physical pages get allocated
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73 when the copy-on-write gets broken by writing to the virtual page. This
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74 is why checking the return value of malloc() isn't very useful anymore, it
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75 only detects running out of virtual memory, not physical memory.)</p>
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76
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77 <p>Don't think that just because you don't have a swap file the system can't
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78 start swap thrashing: any file backed page (ala mmap) can be evicted, and
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79 there's a reason all running programs require an executable file (they're
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80 mmaped, and can be flushed back to disk when memory is short). And long
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81 before that, disk cache gets reclaimed and has to be read back in. When the
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82 operating system really can't free up any more pages it triggers the out of
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83 memory killer to free up pages by killing processes (the alternative is the
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84 entire OS freezing solid). Modern operating systems seldom run out of
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85 memory gracefully.</p>
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86
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87 <p>Also, it's better to be simple than clever. Many people think that mmap()
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88 is faster than read() because it avoids a copy, but twiddling with the memory
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89 management is itself slow, and can cause unnecessary CPU cache flushes. And
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90 if a read faults in dozens of pages sequentially, but your mmap iterates
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91 backwards through a file (causing lots of seeks, each of which your program
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92 blocks waiting for), the read can be many times faster. On the other hand, the
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93 mmap can sometimes use less memory, since the memory provided by mmap
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94 comes from the page cache (allocated anyway), and it can be faster if you're
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95 doing a lot of different updates to the same area. The moral? Measure, then
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96 try to speed things up, and measure again to confirm it actually _did_ speed
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97 things up rather than made them worse. (And understanding what's really going
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98 on underneath is a big help to making it happen faster.)</p>
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99
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100 <p>In general, being simple is better than being clever. Optimization
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101 strategies change with time. For example, decades ago precalculating a table
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102 of results (for things like isdigit() or cosine(int degrees)) was clearly
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103 faster because processors were so slow. Then processors got faster and grew
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104 math coprocessors, and calculating the value each time became faster than
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105 the table lookup (because the calculation fit in L1 cache but the lookup
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106 had to go out to DRAM). Then cache sizes got bigger (the Pentium M has
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107 2 megabytes of L2 cache) and the table fit in cache, so the table became
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108 fast again... Predicting how changes in hardware will affect your algorithm
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109 is difficult, and using ten year old optimization advice and produce
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110 laughably bad results. But being simple and efficient is always going to
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111 give at least a reasonable result.</p>
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112
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113 <p>The famous quote from Ken Thompson, "When in doubt, use brute force",
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114 applies to toybox. Do the simple thing first, do as little of it as possible,
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115 and make sure it's right. You can always speed it up later.</p>
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116
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117 <b><h3>Small</h3></b>
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118 <p>Again, simple gives you most of this. An algorithm that does less work
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119 is generally smaller. Understand the problem, treat size as a cost, and
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120 get a good bang for the byte.</p>
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121
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122 <p>Understand the difference between binary size, heap size, and stack size.
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123 Your binary is the executable file on disk, your heap is where malloc() memory
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124 lives, and your stack is where local variables (and function call return
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125 addresses) live. Optimizing for binary size is generally good: executing
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126 fewer instructions makes your program run faster (and fits more of it in
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127 cache). On embedded systems, binary size is especially precious because
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128 flash is expensive (and its successor, MRAM, even more so). Small stack size
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129 is important for nommu systems because they have to preallocate their stack
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130 and can't make it bigger via page fault. And everybody likes a small heap.</p>
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131
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132 <p>Measure the right things. Especially with modern optimizers, expecting
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133 something to be smaller is no guarantee it will be after the compiler's done
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134 with it. Binary size isn't the most accurate indicator of the impact of a
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135 given change, because lots of things get combined and rounded during
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136 compilation and linking. Matt Mackall's bloat-o-meter is a python script
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137 which compares two versions of a program, and shows size changes in each
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138 symbol (using the "nm" command behind the scenes). To use this, run
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139 "make baseline" to build a baseline version to compare against, and
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140 then "make bloatometer" to compare that baseline version against the current
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141 code.</p>
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142
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143 <p>Avoid special cases. Whenever you see similar chunks of code in more than
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144 one place, it might be possible to combine them and have the users call shared
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145 code. (This is the most commonly cited trick, which doesn't make it easy.)</p>
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146
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147 <p>Some specific advice: Using a char in place of an int when doing math
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148 produces significantly larger code on some platforms (notably arm),
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149 because each time the compiler has to emit code to convert it to int, do the
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150 math, and convert it back. Bitfields have this problem on most platforms.
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151 Because of this, using char to index a for() loop is probably not a net win,
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152 although using char (or a bitfield) to store a value in a structure that's
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153 repeated hundreds of times can be a good tradeoff of binary size for heap
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154 space.</p>
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155
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156 <b><h3>Simple</h3></b>
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157
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158 <p>Complexity is a cost, just like code size or runtime speed. Treat it as
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159 a cost, and spend your complexity budget wisely.</p>
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160
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161 <p>Simplicity has lots of benefits. Simple code is easy to maintain, easy to
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162 port to new processors, easy to audit for security holes, and easy to
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163 understand. (Comments help, but they're no substitute for simple code.)</p>
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164
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165 <p><a href=http://www.joelonsoftware.com/articles/fog0000000069.html>Joel
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166 Spolsky argues against throwing code out and starting over</a>, and he has
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167 good points: an existing debugged codebase contains a huge amount of baked
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168 in knowledge about strange real-world use cases that the designers didn't
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169 know about until users hit the bugs, and most of this knowledge is never
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170 explicitly stated anywhere except in the source code.</p>
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171
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172 <p>That said, the Mythical Man-Month's "build one to throw away" advice points
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173 out that until you've solved the problem you don't properly understand it, and
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174 about the time you finish your first version is when you've finally figured
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175 out what you _should_ have done. (The corrolary is that if you build one
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176 expecting to throw it away, you'll actually wind up throwing away two. You
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177 don't understand the problem until you _have_ solved it.)</p>
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178
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179 <p>Joel is talking about what closed source software can afford to do: Code
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180 that works and has been paid for is a corporate asset not lightly abandoned.
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181 Open source software can afford to re-implement code that works, over and
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182 over from scratch, for incremental gains. Before toybox, the unix command line
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183 has already been reimplemented from scratch several in a row (the
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184 original Unix and BSD tools, the GNU tools, BusyBox...)
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185 but maybe toybox can do a better job. :)</p>
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186
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187 <p>P.S. How could I resist linking to an article about
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188 <a href=http://blog.outer-court.com/archive/2005-08-24-n14.html>why
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189 programmers should strive to be lazy and dumb</a>?</p>
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190
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191 <b><h2>Portability issues</h2></b>
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192
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193 <b><h3>Platforms</h3></b>
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194 <p>Toybox should run on every hardware platform Linux runs on. Other
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195 posix/susv3 environments (perhaps MacOS X or newlib+libgloss) are vaguely
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196 interesting but only if they're easy to support, I'm not going to spend much
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197 effort on them.</p>
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198
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199 <p>I don't do windows.</p>
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200
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201 <b><h3>32/64 bit</h3></b>
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202 <p>Toybox should work on both 32 bit and 64 bit systems. By the end of 2008
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203 64 bit hardware will be the new desktop standard, but 32 bit hardware will
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204 continue to be important in embedded devices for years to come.</p>
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205
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206 <p>Toybox relies on the fact that on any Unix-like platform, pointer and long
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207 are always the same size (on both 32 and 64 bit). Pointer and int are _not_
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208 the same size on 64 bit systems, but pointer and long are.</p>
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209
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210 <p>This is guaranteed by the LP64 memory model, a Unix standard (which Linux
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211 and MacOS X implements). See
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212 <a href=http://www.unix.org/whitepapers/64bit.html>the LP64 standard</a> and
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213 <a href=http://www.unix.org/version2/whatsnew/lp64_wp.html>the LP64
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214 rationale</a> for details.</p>
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215
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216 <p>Note that Windows doesn't work like this, and I don't care.
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217 <a href=http://blogs.msdn.com/oldnewthing/archive/2005/01/31/363790.aspx>The
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218 insane legacy reasons why this is broken on Windows are explained here.</a></p>
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219
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220 <b><h3>Signedness of char</h3></b>
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221 <p>On platforms like x86, variables of type char default to unsigned. On
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222 platforms like arm, char defaults to signed. This difference can lead to
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223 subtle portability bugs, and to avoid them we specify which one we want by
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224 feeding the compiler -funsigned-char.</p>
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225
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226 <p>The reason to pick "unsigned" is that way we're 8-bit clean by default.</p>