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<a name="Sampling-Error"></a>
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<p>
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Next: <a rel="next" accesskey="n" href="Assumptions.html#Assumptions">Assumptions</a>,
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Up: <a rel="up" accesskey="u" href="Inaccuracy.html#Inaccuracy">Inaccuracy</a>
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<h3 class="section">6.1 Statistical Sampling Error</h3>
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<p>The run-time figures that <code>gprof</code> gives you are based on a sampling
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process, so they are subject to statistical inaccuracy. If a function runs
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only a small amount of time, so that on the average the sampling process
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ought to catch that function in the act only once, there is a pretty good
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chance it will actually find that function zero times, or twice.
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<p>By contrast, the number-of-calls and basic-block figures are derived
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by counting, not sampling. They are completely accurate and will not
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vary from run to run if your program is deterministic and single
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threaded. In multi-threaded applications, or single threaded
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applications that link with multi-threaded libraries, the counts are
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only deterministic if the counting function is thread-safe. (Note:
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beware that the mcount counting function in glibc is <em>not</em>
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thread-safe). See <a href="Implementation.html#Implementation">Implementation of Profiling</a>.
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<p>The <dfn>sampling period</dfn> that is printed at the beginning of the flat
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profile says how often samples are taken. The rule of thumb is that a
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run-time figure is accurate if it is considerably bigger than the sampling
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period.
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<p>The actual amount of error can be predicted.
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For <var>n</var> samples, the <em>expected</em> error
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is the square-root of <var>n</var>. For example,
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if the sampling period is 0.01 seconds and <code>foo</code>'s run-time is 1 second,
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<var>n</var> is 100 samples (1 second/0.01 seconds), sqrt(<var>n</var>) is 10 samples, so
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the expected error in <code>foo</code>'s run-time is 0.1 seconds (10*0.01 seconds),
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or ten percent of the observed value.
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Again, if the sampling period is 0.01 seconds and <code>bar</code>'s run-time is
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100 seconds, <var>n</var> is 10000 samples, sqrt(<var>n</var>) is 100 samples, so
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the expected error in <code>bar</code>'s run-time is 1 second,
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or one percent of the observed value.
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It is likely to
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vary this much <em>on the average</em> from one profiling run to the next.
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(<em>Sometimes</em> it will vary more.)
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<p>This does not mean that a small run-time figure is devoid of information.
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If the program's <em>total</em> run-time is large, a small run-time for one
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function does tell you that that function used an insignificant fraction of
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the whole program's time. Usually this means it is not worth optimizing.
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<p>One way to get more accuracy is to give your program more (but similar)
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input data so it will take longer. Another way is to combine the data from
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several runs, using the ‘<samp><span class="samp">-s</span></samp>’ option of <code>gprof</code>. Here is how:
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<ol type=1 start=1>
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<li>Run your program once.
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<li>Issue the command ‘<samp><span class="samp">mv gmon.out gmon.sum</span></samp>’.
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<li>Run your program again, the same as before.
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<li>Merge the new data in <samp><span class="file">gmon.out</span></samp> into <samp><span class="file">gmon.sum</span></samp> with this command:
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<pre class="example"> gprof -s <var>executable-file</var> gmon.out gmon.sum
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</pre>
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<li>Repeat the last two steps as often as you wish.
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<li>Analyze the cumulative data using this command:
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<pre class="example"> gprof <var>executable-file</var> gmon.sum > <var>output-file</var>
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</pre>
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</ol>
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</body></html>
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