annotate www/design.html @ 402:2551e517b800

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author Rob Landley <rob@landley.net>
date Sat, 14 Jan 2012 23:28:15 -0600
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1 <!--#include file="header.html" -->
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3 <b><h2>Design goals</h2></b>
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4
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5 <p>Toybox should be simple, small, and fast. Often, these things need to be
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6 balanced off against each other. In general, simple is slightly more
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7 important than small, and small is slightly more important than fast, but
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8 it should be possible to get 80% of the way to each goal before they really
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9 start to fight.</p>
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10
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11 <b><h3>Fast</h3></b>
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13 <p>It's easy to say lots about optimizing for speed (which is why this section
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14 is so long), but at the same time it's the one we care the least about.
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15 The essence of speed is being as efficient as possible, which means doing as
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16 little work as possible. A design that's small and simple gets you 90% of the
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17 way there, and most of the rest is either fine-tuning or more trouble than
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18 it's worth (and often actually counterproductive). Still, here's some
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19 advice:</p>
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20
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21 <p>First, understand the darn problem you're trying to solve. You'd think
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22 I wouldn't have to say this, but I do. Trying to find a faster sorting
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23 algorithm is no substitute for figuring out a way to skip the sorting step
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24 entirely. The fastest way to do anything is not to have to do it at all,
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25 and _all_ optimization boils down to avoiding unnecessary work.</p>
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26
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27 <p>Speed is easy to measure; there are dozens of profiling tools for Linux
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28 (although personally I find the "time" command a good starting place).
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29 Don't waste too much time trying to optimize something you can't measure,
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30 and there's no much point speeding up things you don't spend much time doing
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31 anyway.</p>
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32
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33 <p>Understand the difference between throughput and latency. Faster
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34 processors improve throughput, but don't always do much for latency.
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35 After 30 years of Moore's Law, most of the remaining problems are latency,
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36 not throughput. (There are of course a few exceptions, like data compression
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37 code, encryption, rsync...) Worry about throughput inside long-running
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38 loops, and worry about latency everywhere else. (And don't worry too much
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39 about avoiding system calls or function calls or anything else in the name
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40 of speed unless you are in the middle of a tight loop that's you've already
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41 proven isn't running fast enough.)</p>
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42
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43 <p>"Locality of reference" is generally nice, in all sorts of contexts.
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44 It's obvious that waiting for disk access is 1000x slower than doing stuff in
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45 RAM (and making the disk seek is 10x slower than sequential reads/writes),
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46 but it's just as true that a loop which stays in L1 cache is many times faster
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47 than a loop that has to wait for a DRAM fetch on each iteration. Don't worry
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48 about whether "&" is faster than "%" until your executable loop stays in L1
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49 cache and the data access is fetching cache lines intelligently. (To
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50 understand DRAM, L1, and L2 cache, read Hannibal's marvelous ram guid at Ars
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51 Technica:
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52 <a href=http://arstechnica.com/paedia/r/ram_guide/ram_guide.part1-2.html>part one</a>,
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53 <a href=http://arstechnica.com/paedia/r/ram_guide/ram_guide.part2-1.html>part two</a>,
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54 <a href=http://arstechnica.com/paedia/r/ram_guide/ram_guide.part3-1.html>part three</a>,
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55 plus this
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56 <a href=http://arstechnica.com/articles/paedia/cpu/caching.ars/1>article on
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57 cacheing</a>, and this one on
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58 <a href=http://arstechnica.com/articles/paedia/cpu/bandwidth-latency.ars>bandwidth
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59 and latency</a>.
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07d8795fc19c Link to ars technica paedia broke because ars is now using Windows 2003 on
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60 And there's <a href=http://arstechnica.com/paedia/index.html>more where that came from</a>.)
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61 Running out of L1 cache can execute one instruction per clock cycle, going
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62 to L2 cache costs a dozen or so clock cycles, and waiting for a worst case dram
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63 fetch (round trip latency with a bank switch) can cost thousands of
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64 clock cycles. (Historically, this disparity has gotten worse with time,
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65 just like the speed hit for swapping to disk. These days, a _big_ L1 cache
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66 is 128k and a big L2 cache is a couple of megabytes. A cheap low-power
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67 embedded processor may have 8k of L1 cache and no L2.)</p>
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68
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69 <p>Learn how virtual memory and memory managment units work. Don't touch
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70 memory you don't have to. Even just reading memory evicts stuff from L1 and L2
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71 cache, which may have to be read back in later. Writing memory can force the
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72 operating system to break copy-on-write, which allocates more memory. (The
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73 memory returned by malloc() is only a virtual allocation, filled with lots of
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74 copy-on-write mappings of the zero page. Actual physical pages get allocated
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75 when the copy-on-write gets broken by writing to the virtual page. This
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76 is why checking the return value of malloc() isn't very useful anymore, it
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77 only detects running out of virtual memory, not physical memory. Unless
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78 you're using a NOMMU system, where all bets are off.)</p>
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79
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80 <p>Don't think that just because you don't have a swap file the system can't
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81 start swap thrashing: any file backed page (ala mmap) can be evicted, and
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82 there's a reason all running programs require an executable file (they're
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83 mmaped, and can be flushed back to disk when memory is short). And long
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84 before that, disk cache gets reclaimed and has to be read back in. When the
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85 operating system really can't free up any more pages it triggers the out of
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86 memory killer to free up pages by killing processes (the alternative is the
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87 entire OS freezing solid). Modern operating systems seldom run out of
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88 memory gracefully.</p>
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89
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90 <p>Also, it's better to be simple than clever. Many people think that mmap()
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91 is faster than read() because it avoids a copy, but twiddling with the memory
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92 management is itself slow, and can cause unnecessary CPU cache flushes. And
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93 if a read faults in dozens of pages sequentially, but your mmap iterates
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94 backwards through a file (causing lots of seeks, each of which your program
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95 blocks waiting for), the read can be many times faster. On the other hand, the
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96 mmap can sometimes use less memory, since the memory provided by mmap
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97 comes from the page cache (allocated anyway), and it can be faster if you're
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98 doing a lot of different updates to the same area. The moral? Measure, then
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99 try to speed things up, and measure again to confirm it actually _did_ speed
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100 things up rather than made them worse. (And understanding what's really going
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101 on underneath is a big help to making it happen faster.)</p>
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102
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103 <p>In general, being simple is better than being clever. Optimization
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104 strategies change with time. For example, decades ago precalculating a table
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105 of results (for things like isdigit() or cosine(int degrees)) was clearly
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106 faster because processors were so slow. Then processors got faster and grew
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107 math coprocessors, and calculating the value each time became faster than
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108 the table lookup (because the calculation fit in L1 cache but the lookup
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109 had to go out to DRAM). Then cache sizes got bigger (the Pentium M has
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110 2 megabytes of L2 cache) and the table fit in cache, so the table became
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111 fast again... Predicting how changes in hardware will affect your algorithm
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112 is difficult, and using ten year old optimization advice and produce
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113 laughably bad results. But being simple and efficient is always going to
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114 give at least a reasonable result.</p>
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115
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116 <p>The famous quote from Ken Thompson, "When in doubt, use brute force",
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117 applies to toybox. Do the simple thing first, do as little of it as possible,
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118 and make sure it's right. You can always speed it up later.</p>
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119
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120 <b><h3>Small</h3></b>
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121 <p>Again, simple gives you most of this. An algorithm that does less work
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122 is generally smaller. Understand the problem, treat size as a cost, and
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123 get a good bang for the byte.</p>
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124
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125 <p>Understand the difference between binary size, heap size, and stack size.
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126 Your binary is the executable file on disk, your heap is where malloc() memory
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127 lives, and your stack is where local variables (and function call return
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128 addresses) live. Optimizing for binary size is generally good: executing
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129 fewer instructions makes your program run faster (and fits more of it in
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130 cache). On embedded systems, binary size is especially precious because
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131 flash is expensive (and its successor, MRAM, even more so). Small stack size
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132 is important for nommu systems because they have to preallocate their stack
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133 and can't make it bigger via page fault. And everybody likes a small heap.</p>
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135 <p>Measure the right things. Especially with modern optimizers, expecting
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136 something to be smaller is no guarantee it will be after the compiler's done
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137 with it. Binary size isn't the most accurate indicator of the impact of a
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138 given change, because lots of things get combined and rounded during
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139 compilation and linking. Matt Mackall's bloat-o-meter is a python script
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140 which compares two versions of a program, and shows size changes in each
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141 symbol (using the "nm" command behind the scenes). To use this, run
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142 "make baseline" to build a baseline version to compare against, and
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143 then "make bloatometer" to compare that baseline version against the current
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144 code.</p>
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145
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146 <p>Avoid special cases. Whenever you see similar chunks of code in more than
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147 one place, it might be possible to combine them and have the users call shared
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148 code. (This is the most commonly cited trick, which doesn't make it easy.)</p>
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149
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150 <p>Some specific advice: Using a char in place of an int when doing math
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151 produces significantly larger code on some platforms (notably arm),
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152 because each time the compiler has to emit code to convert it to int, do the
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153 math, and convert it back. Bitfields have this problem on most platforms.
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154 Because of this, using char to index a for() loop is probably not a net win,
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155 although using char (or a bitfield) to store a value in a structure that's
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156 repeated hundreds of times can be a good tradeoff of binary size for heap
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157 space.</p>
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158
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159 <b><h3>Simple</h3></b>
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160
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161 <p>Complexity is a cost, just like code size or runtime speed. Treat it as
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162 a cost, and spend your complexity budget wisely.</p>
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163
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164 <p>Simplicity has lots of benefits. Simple code is easy to maintain, easy to
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165 port to new processors, easy to audit for security holes, and easy to
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166 understand. (Comments help, but they're no substitute for simple code.)</p>
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167
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168 <p><a href=http://www.joelonsoftware.com/articles/fog0000000069.html>Joel
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169 Spolsky argues against throwing code out and starting over</a>, and he has
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170 good points: an existing debugged codebase contains a huge amount of baked
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171 in knowledge about strange real-world use cases that the designers didn't
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172 know about until users hit the bugs, and most of this knowledge is never
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173 explicitly stated anywhere except in the source code.</p>
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174
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175 <p>That said, the Mythical Man-Month's "build one to throw away" advice points
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176 out that until you've solved the problem you don't properly understand it, and
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177 about the time you finish your first version is when you've finally figured
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178 out what you _should_ have done. (The corrolary is that if you build one
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179 expecting to throw it away, you'll actually wind up throwing away two. You
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180 don't understand the problem until you _have_ solved it.)</p>
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181
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182 <p>Joel is talking about what closed source software can afford to do: Code
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183 that works and has been paid for is a corporate asset not lightly abandoned.
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184 Open source software can afford to re-implement code that works, over and
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185 over from scratch, for incremental gains. Before toybox, the unix command line
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186 has already been reimplemented from scratch several times in a row (the
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187 original AT&amp;T Unix command line in assembly and then in C, the BSD
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188 versions, the GNU tools, BusyBox...) but maybe toybox can do a better job. :)</p>
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189
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190 <p>P.S. How could I resist linking to an article about
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191 <a href=http://blog.outer-court.com/archive/2005-08-24-n14.html>why
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192 programmers should strive to be lazy and dumb</a>?</p>
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193
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194 <b><h2>Portability issues</h2></b>
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195
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196 <b><h3>Platforms</h3></b>
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197 <p>Toybox should run on every hardware platform Linux runs on. Other
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198 posix/susv3 environments (perhaps MacOS X or newlib+libgloss) are vaguely
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199 interesting but only if they're easy to support; I'm not going to spend much
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200 effort on them.</p>
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201
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202 <p>I don't do windows.</p>
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203
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204 <b><h3>32/64 bit</h3></b>
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205 <p>Toybox should work on both 32 bit and 64 bit systems. By the end of 2008
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206 64 bit hardware will be the new desktop standard, but 32 bit hardware will
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207 continue to be important in embedded devices for years to come.</p>
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208
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209 <p>Toybox relies on the fact that on any Unix-like platform, pointer and long
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210 are always the same size (on both 32 and 64 bit). Pointer and int are _not_
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211 the same size on 64 bit systems, but pointer and long are.</p>
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212
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213 <p>This is guaranteed by the LP64 memory model, a Unix standard (which Linux
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214 and MacOS X both implement). See
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215 <a href=http://www.unix.org/whitepapers/64bit.html>the LP64 standard</a> and
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216 <a href=http://www.unix.org/version2/whatsnew/lp64_wp.html>the LP64
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217 rationale</a> for details.</p>
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218
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219 <p>Note that Windows doesn't work like this, and I don't care.
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220 <a href=http://blogs.msdn.com/oldnewthing/archive/2005/01/31/363790.aspx>The
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221 insane legacy reasons why this is broken on Windows are explained here.</a></p>
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222
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223 <b><h3>Signedness of char</h3></b>
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224 <p>On platforms like x86, variables of type char default to unsigned. On
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225 platforms like arm, char defaults to signed. This difference can lead to
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226 subtle portability bugs, and to avoid them we specify which one we want by
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227 feeding the compiler -funsigned-char.</p>
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228
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229 <p>The reason to pick "unsigned" is that way we're 8-bit clean by default.</p>
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230
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231 <!--#include file="footer.html" -->