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 <a name="Sampling-Error"></a>
 <div class="header">
 <p>
 Next: <a href="Assumptions.html#Assumptions" accesskey="n" rel="next">Assumptions</a>, Up: <a href="Inaccuracy.html#Inaccuracy" accesskey="u" rel="up">Inaccuracy</a> &nbsp; [<a href="index.html#SEC_Contents" title="Table of contents" rel="contents">Contents</a>]</p>
 </div>
 <hr>
 <a name="Statistical-Sampling-Error"></a>
 <h3 class="section">6.1 Statistical Sampling Error</h3>

 <p>The run-time figures that <code>gprof</code> gives you are based on a sampling
 process, so they are subject to statistical inaccuracy.  If a function runs
 only a small amount of time, so that on the average the sampling process
 ought to catch that function in the act only once, there is a pretty good
 chance it will actually find that function zero times, or twice.
 </p>
 <p>By contrast, the number-of-calls and basic-block figures are derived
 by counting, not sampling.  They are completely accurate and will not
 vary from run to run if your program is deterministic and single
 threaded.  In multi-threaded applications, or single threaded
 applications that link with multi-threaded libraries, the counts are
 only deterministic if the counting function is thread-safe.  (Note:
 beware that the mcount counting function in glibc is <em>not</em>
 thread-safe).  See <a href="Implementation.html#Implementation">Implementation of Profiling</a>.
 </p>
 <p>The <em>sampling period</em> that is printed at the beginning of the flat
 profile says how often samples are taken.  The rule of thumb is that a
 run-time figure is accurate if it is considerably bigger than the sampling
 period.
 </p>
 <p>The actual amount of error can be predicted.
 For <var>n</var> samples, the <em>expected</em> error
 is the square-root of <var>n</var>.  For example,
 if the sampling period is 0.01 seconds and <code>foo</code>&rsquo;s run-time is 1 second,
 <var>n</var> is 100 samples (1 second/0.01 seconds), sqrt(<var>n</var>) is 10 samples, so
 the expected error in <code>foo</code>&rsquo;s run-time is 0.1 seconds (10*0.01 seconds),
 or ten percent of the observed value.
 Again, if the sampling period is 0.01 seconds and <code>bar</code>&rsquo;s run-time is
 100 seconds, <var>n</var> is 10000 samples, sqrt(<var>n</var>) is 100 samples, so
 the expected error in <code>bar</code>&rsquo;s run-time is 1 second,
 or one percent of the observed value.
 It is likely to
 vary this much <em>on the average</em> from one profiling run to the next.
 (<em>Sometimes</em> it will vary more.)
 </p>
 <p>This does not mean that a small run-time figure is devoid of information.
 If the program&rsquo;s <em>total</em> run-time is large, a small run-time for one
 function does tell you that that function used an insignificant fraction of
 the whole program&rsquo;s time.  Usually this means it is not worth optimizing.
 </p>
 <p>One way to get more accuracy is to give your program more (but similar)
 input data so it will take longer.  Another way is to combine the data from
 several runs, using the &lsquo;<samp>-s</samp>&rsquo; option of <code>gprof</code>.  Here is how:
 </p>
 <ol>
 <li> Run your program once.

 </li><li> Issue the command &lsquo;<samp>mv gmon.out gmon.sum</samp>&rsquo;.

 </li><li> Run your program again, the same as before.

 </li><li> Merge the new data in <samp>gmon.out</samp> into <samp>gmon.sum</samp> with this command:

 <div class="example">
 <pre class="example">gprof -s <var>executable-file</var> gmon.out gmon.sum
 </pre></div>

 </li><li> Repeat the last two steps as often as you wish.

 </li><li> Analyze the cumulative data using this command:

 <div class="example">
 <pre class="example">gprof <var>executable-file</var> gmon.sum &gt; <var>output-file</var>
 </pre></div>
 </li></ol>

 <hr>
 <div class="header">
 <p>
 Next: <a href="Assumptions.html#Assumptions" accesskey="n" rel="next">Assumptions</a>, Up: <a href="Inaccuracy.html#Inaccuracy" accesskey="u" rel="up">Inaccuracy</a> &nbsp; [<a href="index.html#SEC_Contents" title="Table of contents" rel="contents">Contents</a>]</p>
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	<a name="Sampling-Error"></a>
	<div class="header">
	<p>
	Next: <a href="Assumptions.html#Assumptions" accesskey="n" rel="next">Assumptions</a>, Up: <a href="Inaccuracy.html#Inaccuracy" accesskey="u" rel="up">Inaccuracy</a>   [<a href="index.html#SEC_Contents" title="Table of contents" rel="contents">Contents</a>]</p>
	</div>
	<hr>
	<a name="Statistical-Sampling-Error"></a>
	<h3 class="section">6.1 Statistical Sampling Error</h3>

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

	</li><li> Issue the command ‘<samp>mv gmon.out gmon.sum</samp>’.

	</li><li> Run your program again, the same as before.

	</li><li> Merge the new data in <samp>gmon.out</samp> into <samp>gmon.sum</samp> with this command:

	<div class="example">
	<pre class="example">gprof -s <var>executable-file</var> gmon.out gmon.sum
	</pre></div>

	</li><li> Repeat the last two steps as often as you wish.

	</li><li> Analyze the cumulative data using this command:

	<div class="example">
	<pre class="example">gprof <var>executable-file</var> gmon.sum > <var>output-file</var>
	</pre></div>
	</li></ol>

	<hr>
	<div class="header">
	<p>
	Next: <a href="Assumptions.html#Assumptions" accesskey="n" rel="next">Assumptions</a>, Up: <a href="Inaccuracy.html#Inaccuracy" accesskey="u" rel="up">Inaccuracy</a>   [<a href="index.html#SEC_Contents" title="Table of contents" rel="contents">Contents</a>]</p>
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