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Parsing automata in Haskell (small project)
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Some things: Map benchmark. Applicative parsing of automata
8 years ago
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<h1>criterion performance measurements</h1>
<h2>overview</h2>
<p><a href="#grokularation">want to understand this report?</a></p>
<div id="overview" class="ovchart" style="width:900px;height:100px;"></div>
<h2><a name="b0">hashMap</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde0" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time0" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle0" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb0">xxx</span></td>
<td><span class="olstimept0">xxx</span></td>
<td><span class="confinterval olstimeub0">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb0">xxx</span></td>
<td><span class="olsr2pt0">xxx</span></td>
<td><span class="confinterval olsr2ub0">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">1.1079337686668929</span></td>
<td><span class="time">1.1118317350581899</span></td>
<td><span class="confinterval citime">1.1155279756712846</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">0.0</span></td>
<td><span class="time">6.2347239582959705e-3</span></td>
<td><span class="confinterval citime">6.402076538879691e-3</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.1875</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b1">lazyHashMap</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde1" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time1" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
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style="width:300px;height:278px;"></div></td>
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</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb1">xxx</span></td>
<td><span class="olstimept1">xxx</span></td>
<td><span class="confinterval olstimeub1">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb1">xxx</span></td>
<td><span class="olsr2pt1">xxx</span></td>
<td><span class="confinterval olsr2ub1">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">0.6050280515104536</span></td>
<td><span class="time">0.648151868254354</span></td>
<td><span class="confinterval citime">0.6811688430845001</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">0.0</span></td>
<td><span class="time">5.07514985589225e-2</span></td>
<td><span class="confinterval citime">5.718707791803603e-2</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.2101804941984909</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b2">treeMap</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde2" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time2" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle2" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb2">xxx</span></td>
<td><span class="olstimept2">xxx</span></td>
<td><span class="confinterval olstimeub2">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb2">xxx</span></td>
<td><span class="olsr2pt2">xxx</span></td>
<td><span class="confinterval olsr2ub2">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">0.6176181022485254</span></td>
<td><span class="time">0.6234788088583932</span></td>
<td><span class="confinterval citime">0.6346098493319007</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">0.0</span></td>
<td><span class="time">9.644282322289024e-3</span></td>
<td><span class="confinterval citime">9.81018975258874e-3</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.1875</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b3">lazyTreeMap</a></h2>
<table width="100%">
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<tr>
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<td><div id="time3" class="timechart"
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</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb3">xxx</span></td>
<td><span class="olstimept3">xxx</span></td>
<td><span class="confinterval olstimeub3">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb3">xxx</span></td>
<td><span class="olsr2pt3">xxx</span></td>
<td><span class="confinterval olsr2ub3">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">0.7115394046646545</span></td>
<td><span class="time">0.7382926173071612</span></td>
<td><span class="confinterval citime">0.7615077150888588</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">0.0</span></td>
<td><span class="time">3.752284661345008e-2</span></td>
<td><span class="confinterval citime">4.020972886057987e-2</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.1875</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b4">trieMap</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde4" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time4" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle4" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb4">xxx</span></td>
<td><span class="olstimept4">xxx</span></td>
<td><span class="confinterval olstimeub4">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb4">xxx</span></td>
<td><span class="olsr2pt4">xxx</span></td>
<td><span class="confinterval olsr2ub4">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">0.9874362552538516</span></td>
<td><span class="time">1.016644161750123</span></td>
<td><span class="confinterval citime">1.0402545899961197</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">0.0</span></td>
<td><span class="time">3.701183160024759e-2</span></td>
<td><span class="confinterval citime">4.089446131052591e-2</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.1875</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b5">group . sort</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde5" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time5" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle5" class="cyclechart"
style="width:300px;height:278px;"></div></td>
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</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb5">xxx</span></td>
<td><span class="olstimept5">xxx</span></td>
<td><span class="confinterval olstimeub5">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb5">xxx</span></td>
<td><span class="olsr2pt5">xxx</span></td>
<td><span class="confinterval olsr2ub5">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">0.38601253600209046</span></td>
<td><span class="time">0.398264101588575</span></td>
<td><span class="confinterval citime">0.4043930002590062</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">0.0</span></td>
<td><span class="time">1.0610171151690466e-2</span></td>
<td><span class="confinterval citime">1.0615563891628281e-2</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.18749999999999994</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b6">nub</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde6" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time6" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle6" class="cyclechart"
style="width:300px;height:278px;"></div></td>
-->
</tr>
</tbody>
</table>
<table>
<thead class="analysis">
<th></th>
<th class="cibound"
title="0.95 confidence level">lower bound</th>
<th>estimate</th>
<th class="cibound"
title="0.95 confidence level">upper bound</th>
</thead>
<tbody>
<tr>
<td>OLS regression</td>
<td><span class="confinterval olstimelb6">xxx</span></td>
<td><span class="olstimept6">xxx</span></td>
<td><span class="confinterval olstimeub6">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb6">xxx</span></td>
<td><span class="olsr2pt6">xxx</span></td>
<td><span class="confinterval olsr2ub6">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">2.146072198999658</span></td>
<td><span class="time">2.1540133390033995</span></td>
<td><span class="confinterval citime">2.159312120340474</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">0.0</span></td>
<td><span class="time">7.94818133776119e-3</span></td>
<td><span class="confinterval citime">9.177758494010351e-3</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.18749999999999997</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="grokularation">understanding this report</a></h2>
<p>In this report, each function benchmarked by criterion is assigned
a section of its own. The charts in each section are active; if
you hover your mouse over data points and annotations, you will see
more details.</p>
<ul>
<li>The chart on the left is a
<a href="http://en.wikipedia.org/wiki/Kernel_density_estimation">kernel
density estimate</a> (also known as a KDE) of time
measurements. This graphs the probability of any given time
measurement occurring. A spike indicates that a measurement of a
particular time occurred; its height indicates how often that
measurement was repeated.</li>
<li>The chart on the right is the raw data from which the kernel
density estimate is built. The <i>x</i> axis indicates the
number of loop iterations, while the <i>y</i> axis shows measured
execution time for the given number of loop iterations. The
line behind the values is the linear regression prediction of
execution time for a given number of iterations. Ideally, all
measurements will be on (or very near) this line.</li>
</ul>
<p>Under the charts is a small table.
The first two rows are the results of a linear regression run
on the measurements displayed in the right-hand chart.</p>
<ul>
<li><i>OLS regression</i> indicates the
time estimated for a single loop iteration using an ordinary
least-squares regression model. This number is more accurate
than the <i>mean</i> estimate below it, as it more effectively
eliminates measurement overhead and other constant factors.</li>
<li><i>R&#xb2; goodness-of-fit</i> is a measure of how
accurately the linear regression model fits the observed
measurements. If the measurements are not too noisy, R&#xb2;
should lie between 0.99 and 1, indicating an excellent fit. If
the number is below 0.99, something is confounding the accuracy
of the linear model.</li>
<li><i>Mean execution time</i> and <i>standard deviation</i> are
statistics calculated from execution time
divided by number of iterations.</li>
</ul>
<p>We use a statistical technique called
the <a href="http://en.wikipedia.org/wiki/Bootstrapping_(statistics)">bootstrap</a>
to provide confidence intervals on our estimates. The
bootstrap-derived upper and lower bounds on estimates let you see
how accurate we believe those estimates to be. (Hover the mouse
over the table headers to see the confidence levels.)</p>
<p>A noisy benchmarking environment can cause some or many
measurements to fall far from the mean. These outlying
measurements can have a significant inflationary effect on the
estimate of the standard deviation. We calculate and display an
estimate of the extent to which the standard deviation has been
inflated by outliers.</p>
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<div id="footer">
<div class="body">
<div class="footfirst">
<h2>colophon</h2>
<p>This report was created using the
<a href="http://hackage.haskell.org/package/criterion">criterion</a>
benchmark execution and performance analysis tool.</p>
<p>Criterion is developed and maintained
by <a href="http://www.serpentine.com/blog/">Bryan O'Sullivan</a>.</p>
</div>
</div>
</div>
</body>
</html>