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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">1.183382868328787</span></td>
<td><span class="time">1.215732274359608</span></td>
<td><span class="confinterval citime">1.2389196604156985</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">0.0</span></td>
<td><span class="time">3.503516182025675e-2</span></td>
<td><span class="confinterval citime">4.016173074386313e-2</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="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>
<!--
<td><div id="cycle1" 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 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">0.6865059789976155</span></td>
<td><span class="time">0.7542026497750826</span></td>
<td><span class="confinterval citime">0.8048248111077276</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">0.0</span></td>
<td><span class="time">7.726176646268411e-2</span></td>
<td><span class="confinterval citime">8.76801554170899e-2</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.22673586586940853</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">0.8238558324788979</span></td>
<td><span class="time">0.8307417458265022</span></td>
<td><span class="confinterval citime">0.8342716353335188</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">1.3597399555105182e-16</span></td>
<td><span class="time">5.969075961189514e-3</span></td>
<td><span class="confinterval citime">6.1139479712569505e-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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style="width:450px;height:278px;"></div></td>
<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">0.9354499509970536</span></td>
<td><span class="time">0.9851443474133057</span></td>
<td><span class="confinterval citime">1.0627382844955944</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">0.0</span></td>
<td><span class="time">6.807619435779887e-2</span></td>
<td><span class="confinterval citime">7.348995361008136e-2</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.19887312451224287</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b4">ekmett nub</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">0.9545364879865016</span></td>
<td><span class="time">1.5538256277149696</span></td>
<td><span class="confinterval citime">1.7950628808842766</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">0.0</span></td>
<td><span class="time">0.5477803725389206</span></td>
<td><span class="confinterval citime">0.6201640592663545</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.7323203286478808</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>
-->
</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 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">0.7222421209859246</span></td>
<td><span class="time">0.7364937729645803</span></td>
<td><span class="confinterval citime">0.7451906630518073</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">0.0</span></td>
<td><span class="time">1.321159981561309e-2</span></td>
<td><span class="confinterval citime">1.5063455498919222e-2</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="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">4.307012264498212</span></td>
<td><span class="time">4.448000653498998</span></td>
<td><span class="confinterval citime">4.5421990593317405</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">0.0</span></td>
<td><span class="time">0.14129862380354494</span></td>
<td><span class="confinterval citime">0.16315642489430177</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>
</div>
<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>