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   4   The table below presents all the diagnostics we intend to implement.
   5   Each diagnostic has a corresponding compile time switch
   6   <code class="code">-D_GLIBCXX_PROFILE_&lt;diagnostic&gt;</code>.
   7   Groups of related diagnostics can be turned on with a single switch.
   8   For instance, <code class="code">-D_GLIBCXX_PROFILE_LOCALITY</code> is equivalent to
   9   <code class="code">-D_GLIBCXX_PROFILE_SOFTWARE_PREFETCH
  10   -D_GLIBCXX_PROFILE_RBTREE_LOCALITY</code>.
  11   </p><p>
  12   The benefit, cost, expected frequency and accuracy of each diagnostic
  13   was given a grade from 1 to 10, where 10 is highest.
  14   A high benefit means that, if the diagnostic is accurate, the expected
  15   performance improvement is high.
  16   A high cost means that turning this diagnostic on leads to high slowdown.
  17   A high frequency means that we expect this to occur relatively often.
  18   A high accuracy means that the diagnostic is unlikely to be wrong.
  19   These grades are not perfect.  They are just meant to guide users with
  20   specific needs or time budgets.
  21   </p><div class="table"><a id="id501260"></a><p class="title"><b>Table 19.2. Profile Diagnostics</b></p><div class="table-contents"><table summary="Profile Diagnostics" border="1"><colgroup><col align="left" /><col align="left" /><col align="left" /><col align="left" /><col align="left" /><col align="left" /><col align="left" /></colgroup><thead><tr><th align="left">Group</th><th align="left">Flag</th><th align="left">Benefit</th><th align="left">Cost</th><th align="left">Freq.</th><th align="left">Implemented</th><td class="auto-generated"> </td></tr></thead><tbody><tr><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.containers" target="_top">
  22     CONTAINERS</a></td><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.hashtable_too_small" target="_top">
  23     HASHTABLE_TOO_SMALL</a></td><td align="left">10</td><td align="left">1</td><td align="left"> </td><td align="left">10</td><td align="left">yes</td></tr><tr><td align="left"> </td><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.hashtable_too_large" target="_top">
  24     HASHTABLE_TOO_LARGE</a></td><td align="left">5</td><td align="left">1</td><td align="left"> </td><td align="left">10</td><td align="left">yes</td></tr><tr><td align="left"> </td><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.inefficient_hash" target="_top">
  25     INEFFICIENT_HASH</a></td><td align="left">7</td><td align="left">3</td><td align="left"> </td><td align="left">10</td><td align="left">yes</td></tr><tr><td align="left"> </td><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.vector_too_small" target="_top">
  26     VECTOR_TOO_SMALL</a></td><td align="left">8</td><td align="left">1</td><td align="left"> </td><td align="left">10</td><td align="left">yes</td></tr><tr><td align="left"> </td><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.vector_too_large" target="_top">
  27     VECTOR_TOO_LARGE</a></td><td align="left">5</td><td align="left">1</td><td align="left"> </td><td align="left">10</td><td align="left">yes</td></tr><tr><td align="left"> </td><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.vector_to_hashtable" target="_top">
  28     VECTOR_TO_HASHTABLE</a></td><td align="left">7</td><td align="left">7</td><td align="left"> </td><td align="left">10</td><td align="left">no</td></tr><tr><td align="left"> </td><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.hashtable_to_vector" target="_top">
  29     HASHTABLE_TO_VECTOR</a></td><td align="left">7</td><td align="left">7</td><td align="left"> </td><td align="left">10</td><td align="left">no</td></tr><tr><td align="left"> </td><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.vector_to_list" target="_top">
  30     VECTOR_TO_LIST</a></td><td align="left">8</td><td align="left">5</td><td align="left"> </td><td align="left">10</td><td align="left">yes</td></tr><tr><td align="left"> </td><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.list_to_vector" target="_top">
  31     LIST_TO_VECTOR</a></td><td align="left">10</td><td align="left">5</td><td align="left"> </td><td align="left">10</td><td align="left">no</td></tr><tr><td align="left"> </td><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.assoc_ord_to_unord" target="_top">
  32     ORDERED_TO_UNORDERED</a></td><td align="left">10</td><td align="left">5</td><td align="left"> </td><td align="left">10</td><td align="left">only map/unordered_map</td></tr><tr><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.algorithms" target="_top">
  33     ALGORITHMS</a></td><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.algorithms.sort" target="_top">
  34     SORT</a></td><td align="left">7</td><td align="left">8</td><td align="left"> </td><td align="left">7</td><td align="left">no</td></tr><tr><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.locality" target="_top">
  35     LOCALITY</a></td><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.locality.sw_prefetch" target="_top">
  36     SOFTWARE_PREFETCH</a></td><td align="left">8</td><td align="left">8</td><td align="left"> </td><td align="left">5</td><td align="left">no</td></tr><tr><td align="left"> </td><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.locality.linked" target="_top">
  37     RBTREE_LOCALITY</a></td><td align="left">4</td><td align="left">8</td><td align="left"> </td><td align="left">5</td><td align="left">no</td></tr><tr><td align="left"> </td><td align="left"><a class="ulink" href="#manual.ext.profile_mode.analysis.mthread.false_share" target="_top">
  38     FALSE_SHARING</a></td><td align="left">8</td><td align="left">10</td><td align="left"> </td><td align="left">10</td><td align="left">no</td></tr></tbody></table></div></div><br class="table-break" /><div class="sect2" title="Diagnostic Template"><div class="titlepage"><div><div><h3 class="title"><a id="manual.ext.profile_mode.analysis.template"></a>Diagnostic Template</h3></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
  39   <code class="code">_GLIBCXX_PROFILE_&lt;diagnostic&gt;</code>.
  40   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span>  What problem will it diagnose?
  41   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span>.
  42   What is the fundamental reason why this is a problem</p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span>
  43   Percentage reduction in execution time.  When reduction is more than
  44   a constant factor, describe the reduction rate formula.
  45   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span>
  46   What would the advise look like?</p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span>
  47   What stdlibc++ components need to be instrumented?</p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
  48   How do we decide when to issue the advice?</p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
  49   How do we measure benefits?  Math goes here.</p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
  50 </p><pre class="programlisting">
  51 program code
  52 ...
  53 advice sample
  54 </pre><p>
  55 </p></li></ul></div></div><div class="sect2" title="Containers"><div class="titlepage"><div><div><h3 class="title"><a id="manual.ext.profile_mode.analysis.containers"></a>Containers</h3></div></div></div><p>
  56 <span class="emphasis"><em>Switch:</em></span>
  57   <code class="code">_GLIBCXX_PROFILE_CONTAINERS</code>.
  58 </p><div class="sect3" title="Hashtable Too Small"><div class="titlepage"><div><div><h4 class="title"><a id="manual.ext.profile_mode.analysis.hashtable_too_small"></a>Hashtable Too Small</h4></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
  59   <code class="code">_GLIBCXX_PROFILE_HASHTABLE_TOO_SMALL</code>.
  60   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span> Detect hashtables with many
  61   rehash operations, small construction size and large destruction size.
  62   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span> Rehash is very expensive.
  63   Read content, follow chains within bucket, evaluate hash function, place at
  64   new location in different order.</p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span> 36%.
  65   Code similar to example below.
  66   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span>
  67   Set initial size to N at construction site S.
  68   </p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span>
  69   <code class="code">unordered_set, unordered_map</code> constructor, destructor, rehash.
  70   </p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
  71   For each dynamic instance of <code class="code">unordered_[multi]set|map</code>,
  72   record initial size and call context of the constructor.
  73   Record size increase, if any, after each relevant operation such as insert.
  74   Record the estimated rehash cost.</p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
  75   Number of individual rehash operations * cost per rehash.</p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
  76 </p><pre class="programlisting">
  77 1 unordered_set&lt;int&gt; us;
  78 2 for (int k = 0; k &lt; 1000000; ++k) {
  79 3   us.insert(k);
  80 4 }
  81
  82 foo.cc:1: advice: Changing initial unordered_set size from 10 to 1000000 saves 1025530 rehash operations.
  83 </pre><p>
  84 </p></li></ul></div></div><div class="sect3" title="Hashtable Too Large"><div class="titlepage"><div><div><h4 class="title"><a id="manual.ext.profile_mode.analysis.hashtable_too_large"></a>Hashtable Too Large</h4></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
  85   <code class="code">_GLIBCXX_PROFILE_HASHTABLE_TOO_LARGE</code>.
  86   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span> Detect hashtables which are
  87   never filled up because fewer elements than reserved are ever
  88   inserted.
  89   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span> Save memory, which
  90   is good in itself and may also improve memory reference performance through
  91   fewer cache and TLB misses.</p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span> unknown.
  92   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span>
  93   Set initial size to N at construction site S.
  94   </p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span>
  95   <code class="code">unordered_set, unordered_map</code> constructor, destructor, rehash.
  96   </p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
  97   For each dynamic instance of <code class="code">unordered_[multi]set|map</code>,
  98   record initial size and call context of the constructor, and correlate it
  99   with its size at destruction time.
 100   </p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
 101   Number of iteration operations + memory saved.</p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
 102 </p><pre class="programlisting">
 103 1 vector&lt;unordered_set&lt;int&gt;&gt; v(100000, unordered_set&lt;int&gt;(100)) ;
 104 2 for (int k = 0; k &lt; 100000; ++k) {
 105 3   for (int j = 0; j &lt; 10; ++j) {
 106 4     v[k].insert(k + j);
 107 5  }
 108 6 }
 109
 110 foo.cc:1: advice: Changing initial unordered_set size from 100 to 10 saves N
 111 bytes of memory and M iteration steps.
 112 </pre><p>
 113 </p></li></ul></div></div><div class="sect3" title="Inefficient Hash"><div class="titlepage"><div><div><h4 class="title"><a id="manual.ext.profile_mode.analysis.inefficient_hash"></a>Inefficient Hash</h4></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
 114   <code class="code">_GLIBCXX_PROFILE_INEFFICIENT_HASH</code>.
 115   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span> Detect hashtables with polarized
 116   distribution.
 117   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span> A non-uniform
 118   distribution may lead to long chains, thus possibly increasing complexity
 119   by a factor up to the number of elements.
 120   </p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span> factor up
 121    to container size.
 122   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span> Change hash function
 123   for container built at site S.  Distribution score = N.  Access score = S.
 124   Longest chain = C, in bucket B.
 125   </p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span>
 126   <code class="code">unordered_set, unordered_map</code> constructor, destructor, [],
 127   insert, iterator.
 128   </p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
 129   Count the exact number of link traversals.
 130   </p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
 131   Total number of links traversed.</p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
 132 </p><pre class="programlisting">
 133 class dumb_hash {
 134  public:
 135   size_t operator() (int i) const { return 0; }
 136 };
 137 ...
 138   unordered_set&lt;int, dumb_hash&gt; hs;
 139   ...
 140   for (int i = 0; i &lt; COUNT; ++i) {
 141     hs.find(i);
 142   }
 143 </pre><p>
 144 </p></li></ul></div></div><div class="sect3" title="Vector Too Small"><div class="titlepage"><div><div><h4 class="title"><a id="manual.ext.profile_mode.analysis.vector_too_small"></a>Vector Too Small</h4></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
 145   <code class="code">_GLIBCXX_PROFILE_VECTOR_TOO_SMALL</code>.
 146   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span>Detect vectors with many
 147   resize operations, small construction size and large destruction size..
 148   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span>Resizing can be expensive.
 149   Copying large amounts of data takes time.  Resizing many small vectors may
 150   have allocation overhead and affect locality.</p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span>%.
 151   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span>
 152   Set initial size to N at construction site S.</p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span><code class="code">vector</code>.
 153   </p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
 154   For each dynamic instance of <code class="code">vector</code>,
 155   record initial size and call context of the constructor.
 156   Record size increase, if any, after each relevant operation such as
 157   <code class="code">push_back</code>.  Record the estimated resize cost.
 158   </p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
 159   Total number of words copied * time to copy a word.</p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
 160 </p><pre class="programlisting">
 161 1 vector&lt;int&gt; v;
 162 2 for (int k = 0; k &lt; 1000000; ++k) {
 163 3   v.push_back(k);
 164 4 }
 165
 166 foo.cc:1: advice: Changing initial vector size from 10 to 1000000 saves
 167 copying 4000000 bytes and 20 memory allocations and deallocations.
 168 </pre><p>
 169 </p></li></ul></div></div><div class="sect3" title="Vector Too Large"><div class="titlepage"><div><div><h4 class="title"><a id="manual.ext.profile_mode.analysis.vector_too_large"></a>Vector Too Large</h4></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
 170   <code class="code">_GLIBCXX_PROFILE_VECTOR_TOO_LARGE</code>
 171   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span>Detect vectors which are
 172   never filled up because fewer elements than reserved are ever
 173   inserted.
 174   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span>Save memory, which
 175   is good in itself and may also improve memory reference performance through
 176   fewer cache and TLB misses.</p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span>%.
 177   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span>
 178   Set initial size to N at construction site S.</p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span><code class="code">vector</code>.
 179   </p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
 180   For each dynamic instance of <code class="code">vector</code>,
 181   record initial size and call context of the constructor, and correlate it
 182   with its size at destruction time.</p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
 183   Total amount of memory saved.</p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
 184 </p><pre class="programlisting">
 185 1 vector&lt;vector&lt;int&gt;&gt; v(100000, vector&lt;int&gt;(100)) ;
 186 2 for (int k = 0; k &lt; 100000; ++k) {
 187 3   for (int j = 0; j &lt; 10; ++j) {
 188 4     v[k].insert(k + j);
 189 5  }
 190 6 }
 191
 192 foo.cc:1: advice: Changing initial vector size from 100 to 10 saves N
 193 bytes of memory and may reduce the number of cache and TLB misses.
 194 </pre><p>
 195 </p></li></ul></div></div><div class="sect3" title="Vector to Hashtable"><div class="titlepage"><div><div><h4 class="title"><a id="manual.ext.profile_mode.analysis.vector_to_hashtable"></a>Vector to Hashtable</h4></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
 196   <code class="code">_GLIBCXX_PROFILE_VECTOR_TO_HASHTABLE</code>.
 197   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span> Detect uses of
 198   <code class="code">vector</code> that can be substituted with <code class="code">unordered_set</code>
 199   to reduce execution time.
 200   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span>
 201   Linear search in a vector is very expensive, whereas searching in a hashtable
 202   is very quick.</p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span>factor up
 203    to container size.
 204   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span>Replace
 205   <code class="code">vector</code> with <code class="code">unordered_set</code> at site S.
 206   </p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span><code class="code">vector</code>
 207   operations and access methods.</p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
 208   For each dynamic instance of <code class="code">vector</code>,
 209   record call context of the constructor.  Issue the advice only if the
 210   only methods called on this <code class="code">vector</code> are <code class="code">push_back</code>,
 211   <code class="code">insert</code> and <code class="code">find</code>.
 212   </p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
 213   Cost(vector::push_back) + cost(vector::insert) + cost(find, vector) -
 214   cost(unordered_set::insert) + cost(unordered_set::find).
 215   </p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
 216 </p><pre class="programlisting">
 217 1  vector&lt;int&gt; v;
 218 ...
 219 2  for (int i = 0; i &lt; 1000; ++i) {
 220 3    find(v.begin(), v.end(), i);
 221 4  }
 222
 223 foo.cc:1: advice: Changing "vector" to "unordered_set" will save about 500,000
 224 comparisons.
 225 </pre><p>
 226 </p></li></ul></div></div><div class="sect3" title="Hashtable to Vector"><div class="titlepage"><div><div><h4 class="title"><a id="manual.ext.profile_mode.analysis.hashtable_to_vector"></a>Hashtable to Vector</h4></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
 227   <code class="code">_GLIBCXX_PROFILE_HASHTABLE_TO_VECTOR</code>.
 228   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span> Detect uses of
 229   <code class="code">unordered_set</code> that can be substituted with <code class="code">vector</code>
 230   to reduce execution time.
 231   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span>
 232   Hashtable iterator is slower than vector iterator.</p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span>95%.
 233   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span>Replace
 234   <code class="code">unordered_set</code> with <code class="code">vector</code> at site S.
 235   </p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span><code class="code">unordered_set</code>
 236   operations and access methods.</p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
 237   For each dynamic instance of <code class="code">unordered_set</code>,
 238   record call context of the constructor.  Issue the advice only if the
 239   number of <code class="code">find</code>, <code class="code">insert</code> and <code class="code">[]</code>
 240   operations on this <code class="code">unordered_set</code> are small relative to the
 241   number of elements, and methods <code class="code">begin</code> or <code class="code">end</code>
 242   are invoked (suggesting iteration).</p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
 243   Number of .</p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
 244 </p><pre class="programlisting">
 245 1  unordered_set&lt;int&gt; us;
 246 ...
 247 2  int s = 0;
 248 3  for (unordered_set&lt;int&gt;::iterator it = us.begin(); it != us.end(); ++it) {
 249 4    s += *it;
 250 5  }
 251
 252 foo.cc:1: advice: Changing "unordered_set" to "vector" will save about N
 253 indirections and may achieve better data locality.
 254 </pre><p>
 255 </p></li></ul></div></div><div class="sect3" title="Vector to List"><div class="titlepage"><div><div><h4 class="title"><a id="manual.ext.profile_mode.analysis.vector_to_list"></a>Vector to List</h4></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
 256   <code class="code">_GLIBCXX_PROFILE_VECTOR_TO_LIST</code>.
 257   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span> Detect cases where
 258   <code class="code">vector</code> could be substituted with <code class="code">list</code> for
 259   better performance.
 260   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span>
 261   Inserting in the middle of a vector is expensive compared to inserting in a
 262   list.
 263   </p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span>factor up to
 264    container size.
 265   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span>Replace vector with list
 266   at site S.</p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span><code class="code">vector</code>
 267   operations and access methods.</p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
 268   For each dynamic instance of <code class="code">vector</code>,
 269   record the call context of the constructor.  Record the overhead of each
 270   <code class="code">insert</code> operation based on current size and insert position.
 271   Report instance with high insertion overhead.
 272   </p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
 273   (Sum(cost(vector::method)) - Sum(cost(list::method)), for
 274   method in [push_back, insert, erase])
 275   + (Cost(iterate vector) - Cost(iterate list))</p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
 276 </p><pre class="programlisting">
 277 1  vector&lt;int&gt; v;
 278 2  for (int i = 0; i &lt; 10000; ++i) {
 279 3    v.insert(v.begin(), i);
 280 4  }
 281
 282 foo.cc:1: advice: Changing "vector" to "list" will save about 5,000,000
 283 operations.
 284 </pre><p>
 285 </p></li></ul></div></div><div class="sect3" title="List to Vector"><div class="titlepage"><div><div><h4 class="title"><a id="manual.ext.profile_mode.analysis.list_to_vector"></a>List to Vector</h4></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
 286   <code class="code">_GLIBCXX_PROFILE_LIST_TO_VECTOR</code>.
 287   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span> Detect cases where
 288   <code class="code">list</code> could be substituted with <code class="code">vector</code> for
 289   better performance.
 290   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span>
 291   Iterating through a vector is faster than through a list.
 292   </p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span>64%.
 293   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span>Replace list with vector
 294   at site S.</p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span><code class="code">vector</code>
 295   operations and access methods.</p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
 296   Issue the advice if there are no <code class="code">insert</code> operations.
 297   </p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
 298     (Sum(cost(vector::method)) - Sum(cost(list::method)), for
 299   method in [push_back, insert, erase])
 300   + (Cost(iterate vector) - Cost(iterate list))</p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
 301 </p><pre class="programlisting">
 302 1  list&lt;int&gt; l;
 303 ...
 304 2  int sum = 0;
 305 3  for (list&lt;int&gt;::iterator it = l.begin(); it != l.end(); ++it) {
 306 4    sum += *it;
 307 5  }
 308
 309 foo.cc:1: advice: Changing "list" to "vector" will save about 1000000 indirect
 310 memory references.
 311 </pre><p>
 312 </p></li></ul></div></div><div class="sect3" title="List to Forward List (Slist)"><div class="titlepage"><div><div><h4 class="title"><a id="manual.ext.profile_mode.analysis.list_to_slist"></a>List to Forward List (Slist)</h4></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
 313   <code class="code">_GLIBCXX_PROFILE_LIST_TO_SLIST</code>.
 314   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span> Detect cases where
 315   <code class="code">list</code> could be substituted with <code class="code">forward_list</code> for
 316   better performance.
 317   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span>
 318   The memory footprint of a forward_list is smaller than that of a list.
 319   This has beneficial effects on memory subsystem, e.g., fewer cache misses.
 320   </p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span>40%.
 321   Note that the reduction is only noticeable if the size of the forward_list
 322   node is in fact larger than that of the list node.  For memory allocators
 323   with size classes, you will only notice an effect when the two node sizes
 324   belong to different allocator size classes.
 325   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span>Replace list with
 326   forward_list at site S.</p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span><code class="code">list</code>
 327   operations and iteration methods.</p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
 328   Issue the advice if there are no <code class="code">backwards</code> traversals
 329   or insertion before a given node.
 330   </p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
 331   Always true.</p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
 332 </p><pre class="programlisting">
 333 1  list&lt;int&gt; l;
 334 ...
 335 2  int sum = 0;
 336 3  for (list&lt;int&gt;::iterator it = l.begin(); it != l.end(); ++it) {
 337 4    sum += *it;
 338 5  }
 339
 340 foo.cc:1: advice: Change "list" to "forward_list".
 341 </pre><p>
 342 </p></li></ul></div></div><div class="sect3" title="Ordered to Unordered Associative Container"><div class="titlepage"><div><div><h4 class="title"><a id="manual.ext.profile_mode.analysis.assoc_ord_to_unord"></a>Ordered to Unordered Associative Container</h4></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
 343   <code class="code">_GLIBCXX_PROFILE_ORDERED_TO_UNORDERED</code>.
 344   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span>  Detect cases where ordered
 345   associative containers can be replaced with unordered ones.
 346   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span>
 347   Insert and search are quicker in a hashtable than in
 348   a red-black tree.</p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span>52%.
 349   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span>
 350   Replace set with unordered_set at site S.</p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span>
 351   <code class="code">set</code>, <code class="code">multiset</code>, <code class="code">map</code>,
 352   <code class="code">multimap</code> methods.</p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
 353   Issue the advice only if we are not using operator <code class="code">++</code> on any
 354   iterator on a particular <code class="code">[multi]set|map</code>.
 355   </p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
 356   (Sum(cost(hashtable::method)) - Sum(cost(rbtree::method)), for
 357   method in [insert, erase, find])
 358   + (Cost(iterate hashtable) - Cost(iterate rbtree))</p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
 359 </p><pre class="programlisting">
 360 1  set&lt;int&gt; s;
 361 2  for (int i = 0; i &lt; 100000; ++i) {
 362 3    s.insert(i);
 363 4  }
 364 5  int sum = 0;
 365 6  for (int i = 0; i &lt; 100000; ++i) {
 366 7    sum += *s.find(i);
 367 8  }
 368 </pre><p>
 369 </p></li></ul></div></div></div><div class="sect2" title="Algorithms"><div class="titlepage"><div><div><h3 class="title"><a id="manual.ext.profile_mode.analysis.algorithms"></a>Algorithms</h3></div></div></div><p><span class="emphasis"><em>Switch:</em></span>
 370   <code class="code">_GLIBCXX_PROFILE_ALGORITHMS</code>.
 371   </p><div class="sect3" title="Sort Algorithm Performance"><div class="titlepage"><div><div><h4 class="title"><a id="manual.ext.profile_mode.analysis.algorithms.sort"></a>Sort Algorithm Performance</h4></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
 372   <code class="code">_GLIBCXX_PROFILE_SORT</code>.
 373   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span> Give measure of sort algorithm
 374   performance based on actual input.  For instance, advise Radix Sort over
 375   Quick Sort for a particular call context.
 376   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span>
 377   See papers:
 378   <a class="ulink" href="http://portal.acm.org/citation.cfm?doid=1065944.1065981" target="_top">
 379   A framework for adaptive algorithm selection in STAPL</a> and
 380   <a class="ulink" href="http://ieeexplore.ieee.org/search/wrapper.jsp?arnumber=4228227" target="_top">
 381   Optimizing Sorting with Machine Learning Algorithms</a>.
 382   </p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span>60%.
 383   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span> Change sort algorithm
 384   at site S from X Sort to Y Sort.</p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span> <code class="code">sort</code>
 385   algorithm.</p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
 386   Issue the advice if the cost model tells us that another sort algorithm
 387   would do better on this input.  Requires us to know what algorithm we
 388   are using in our sort implementation in release mode.</p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
 389   Runtime(algo) for algo in [radix, quick, merge, ...]</p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
 390 </p><pre class="programlisting">
 391 </pre><p>
 392 </p></li></ul></div></div></div><div class="sect2" title="Data Locality"><div class="titlepage"><div><div><h3 class="title"><a id="manual.ext.profile_mode.analysis.locality"></a>Data Locality</h3></div></div></div><p><span class="emphasis"><em>Switch:</em></span>
 393   <code class="code">_GLIBCXX_PROFILE_LOCALITY</code>.
 394   </p><div class="sect3" title="Need Software Prefetch"><div class="titlepage"><div><div><h4 class="title"><a id="manual.ext.profile_mode.analysis.locality.sw_prefetch"></a>Need Software Prefetch</h4></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
 395   <code class="code">_GLIBCXX_PROFILE_SOFTWARE_PREFETCH</code>.
 396   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span> Discover sequences of indirect
 397   memory accesses that are not regular, thus cannot be predicted by
 398   hardware prefetchers.
 399   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span>
 400   Indirect references are hard to predict and are very expensive when they
 401   miss in caches.</p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span>25%.
 402   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span> Insert prefetch
 403   instruction.</p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span> Vector iterator and
 404   access operator [].
 405   </p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
 406   First, get cache line size and page size from system.
 407   Then record iterator dereference sequences for which the value is a pointer.
 408   For each sequence within a container, issue a warning if successive pointer
 409   addresses are not within cache lines and do not form a linear pattern
 410   (otherwise they may be prefetched by hardware).
 411   If they also step across page boundaries, make the warning stronger.
 412   </p><p>The same analysis applies to containers other than vector.
 413   However, we cannot give the same advice for linked structures, such as list,
 414   as there is no random access to the n-th element.  The user may still be
 415   able to benefit from this information, for instance by employing frays (user
 416   level light weight threads) to hide the latency of chasing pointers.
 417   </p><p>
 418   This analysis is a little oversimplified.  A better cost model could be
 419   created by understanding the capability of the hardware prefetcher.
 420   This model could be trained automatically by running a set of synthetic
 421   cases.
 422   </p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
 423   Total distance between pointer values of successive elements in vectors
 424   of pointers.</p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
 425 </p><pre class="programlisting">
 426 1 int zero = 0;
 427 2 vector&lt;int*&gt; v(10000000, &amp;zero);
 428 3 for (int k = 0; k &lt; 10000000; ++k) {
 429 4   v[random() % 10000000] = new int(k);
 430 5 }
 431 6 for (int j = 0; j &lt; 10000000; ++j) {
 432 7   count += (*v[j] == 0 ? 0 : 1);
 433 8 }
 434
 435 foo.cc:7: advice: Insert prefetch instruction.
 436 </pre><p>
 437 </p></li></ul></div></div><div class="sect3" title="Linked Structure Locality"><div class="titlepage"><div><div><h4 class="title"><a id="manual.ext.profile_mode.analysis.locality.linked"></a>Linked Structure Locality</h4></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
 438   <code class="code">_GLIBCXX_PROFILE_RBTREE_LOCALITY</code>.
 439   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span> Give measure of locality of
 440   objects stored in linked structures (lists, red-black trees and hashtables)
 441   with respect to their actual traversal patterns.
 442   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span>Allocation can be tuned
 443   to a specific traversal pattern, to result in better data locality.
 444   See paper:
 445   <a class="ulink" href="http://www.springerlink.com/content/8085744l00x72662/" target="_top">
 446   Custom Memory Allocation for Free</a>.
 447   </p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span>30%.
 448   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span>
 449   High scatter score N for container built at site S.
 450   Consider changing allocation sequence or choosing a structure conscious
 451   allocator.</p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span> Methods of all
 452   containers using linked structures.</p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
 453   First, get cache line size and page size from system.
 454   Then record the number of successive elements that are on different line
 455   or page, for each traversal method such as <code class="code">find</code>.  Give advice
 456   only if the ratio between this number and the number of total node hops
 457   is above a threshold.</p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
 458   Sum(same_cache_line(this,previous))</p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
 459 </p><pre class="programlisting">
 460  1  set&lt;int&gt; s;
 461  2  for (int i = 0; i &lt; 10000000; ++i) {
 462  3    s.insert(i);
 463  4  }
 464  5  set&lt;int&gt; s1, s2;
 465  6  for (int i = 0; i &lt; 10000000; ++i) {
 466  7    s1.insert(i);
 467  8    s2.insert(i);
 468  9  }
 469 ...
 470       // Fast, better locality.
 471 10    for (set&lt;int&gt;::iterator it = s.begin(); it != s.end(); ++it) {
 472 11      sum += *it;
 473 12    }
 474       // Slow, elements are further apart.
 475 13    for (set&lt;int&gt;::iterator it = s1.begin(); it != s1.end(); ++it) {
 476 14      sum += *it;
 477 15    }
 478
 479 foo.cc:5: advice: High scatter score NNN for set built here.  Consider changing
 480 the allocation sequence or switching to a structure conscious allocator.
 481 </pre><p>
 482 </p></li></ul></div></div></div><div class="sect2" title="Multithreaded Data Access"><div class="titlepage"><div><div><h3 class="title"><a id="manual.ext.profile_mode.analysis.mthread"></a>Multithreaded Data Access</h3></div></div></div><p>
 483   The diagnostics in this group are not meant to be implemented short term.
 484   They require compiler support to know when container elements are written
 485   to.  Instrumentation can only tell us when elements are referenced.
 486   </p><p><span class="emphasis"><em>Switch:</em></span>
 487   <code class="code">_GLIBCXX_PROFILE_MULTITHREADED</code>.
 488   </p><div class="sect3" title="Data Dependence Violations at Container Level"><div class="titlepage"><div><div><h4 class="title"><a id="manual.ext.profile_mode.analysis.mthread.ddtest"></a>Data Dependence Violations at Container Level</h4></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
 489   <code class="code">_GLIBCXX_PROFILE_DDTEST</code>.
 490   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span> Detect container elements
 491   that are referenced from multiple threads in the parallel region or
 492   across parallel regions.
 493   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span>
 494   Sharing data between threads requires communication and perhaps locking,
 495   which may be expensive.
 496   </p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span>?%.
 497   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span> Change data
 498   distribution or parallel algorithm.</p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span> Container access methods
 499   and iterators.
 500   </p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
 501   Keep a shadow for each container.  Record iterator dereferences and
 502   container member accesses.  Issue advice for elements referenced by
 503   multiple threads.
 504   See paper: <a class="ulink" href="http://portal.acm.org/citation.cfm?id=207110.207148" target="_top">
 505   The LRPD test: speculative run-time parallelization of loops with
 506   privatization and reduction parallelization</a>.
 507   </p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
 508   Number of accesses to elements referenced from multiple threads
 509   </p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
 510 </p><pre class="programlisting">
 511 </pre><p>
 512 </p></li></ul></div></div><div class="sect3" title="False Sharing"><div class="titlepage"><div><div><h4 class="title"><a id="manual.ext.profile_mode.analysis.mthread.false_share"></a>False Sharing</h4></div></div></div><div class="itemizedlist"><ul class="itemizedlist" type="disc"><li class="listitem"><p><span class="emphasis"><em>Switch:</em></span>
 513   <code class="code">_GLIBCXX_PROFILE_FALSE_SHARING</code>.
 514   </p></li><li class="listitem"><p><span class="emphasis"><em>Goal:</em></span> Detect elements in the
 515   same container which share a cache line, are written by at least one
 516   thread, and accessed by different threads.
 517   </p></li><li class="listitem"><p><span class="emphasis"><em>Fundamentals:</em></span> Under these assumptions,
 518   cache protocols require
 519   communication to invalidate lines, which may be expensive.
 520   </p></li><li class="listitem"><p><span class="emphasis"><em>Sample runtime reduction:</em></span>68%.
 521   </p></li><li class="listitem"><p><span class="emphasis"><em>Recommendation:</em></span> Reorganize container
 522   or use padding to avoid false sharing.</p></li><li class="listitem"><p><span class="emphasis"><em>To instrument:</em></span> Container access methods
 523   and iterators.
 524   </p></li><li class="listitem"><p><span class="emphasis"><em>Analysis:</em></span>
 525   First, get the cache line size.
 526   For each shared container, record all the associated iterator dereferences
 527   and member access methods with the thread id.  Compare the address lists
 528   across threads to detect references in two different threads to the same
 529   cache line.  Issue a warning only if the ratio to total references is
 530   significant.  Do the same for iterator dereference values if they are
 531   pointers.</p></li><li class="listitem"><p><span class="emphasis"><em>Cost model:</em></span>
 532   Number of accesses to same cache line from different threads.
 533   </p></li><li class="listitem"><p><span class="emphasis"><em>Example:</em></span>
 534 </p><pre class="programlisting">
 535 1     vector&lt;int&gt; v(2, 0);
 536 2 #pragma omp parallel for shared(v, SIZE) schedule(static, 1)
 537 3     for (i = 0; i &lt; SIZE; ++i) {
 538 4       v[i % 2] += i;
 539 5     }
 540
 541 OMP_NUM_THREADS=2 ./a.out
 542 foo.cc:1: advice: Change container structure or padding to avoid false
 543 sharing in multithreaded access at foo.cc:4.  Detected N shared cache lines.
 544 </pre><p>
 545 </p></li></ul></div></div></div><div class="sect2" title="Statistics"><div class="titlepage"><div><div><h3 class="title"><a id="manual.ext.profile_mode.analysis.statistics"></a>Statistics</h3></div></div></div><p>
 546 <span class="emphasis"><em>Switch:</em></span>
 547   <code class="code">_GLIBCXX_PROFILE_STATISTICS</code>.
 548 </p><p>
 549   In some cases the cost model may not tell us anything because the costs
 550   appear to offset the benefits.  Consider the choice between a vector and
 551   a list.  When there are both inserts and iteration, an automatic advice
 552   may not be issued.  However, the programmer may still be able to make use
 553   of this information in a different way.
 554 </p><p>
 555   This diagnostic will not issue any advice, but it will print statistics for
 556   each container construction site.  The statistics will contain the cost
 557   of each operation actually performed on the container.
 558 </p></div></div><div class="navfooter"><hr /><table width="100%" summary="Navigation footer"><tr><td width="40%" align="left"><a accesskey="p" href="bk01pt03ch19s06.html">Prev</a> </td><td width="20%" align="center"><a accesskey="u" href="profile_mode.html">Up</a></td><td width="40%" align="right"> <a accesskey="n" href="ext_allocators.html">Next</a></td></tr><tr><td width="40%" align="left" valign="top">Developer Information </td><td width="20%" align="center"><a accesskey="h" href="../spine.html">Home</a></td><td width="40%" align="right" valign="top"> Chapter 20. Allocators</td></tr></table></div></body></html>