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use nix
2020-08-01 08:00:18 +00:00
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<title>criterion report</title>
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<div id="main" class="body">
<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">parseCmd (foo &quot;bar&quot;)</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">4.28104689513341e-6</span></td>
<td><span class="time">4.52654834683996e-6</span></td>
<td><span class="confinterval citime">4.90588781266136e-6</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">1.0625068410346834e-6</span></td>
<td><span class="time">1.2545845308314575e-6</span></td>
<td><span class="confinterval citime">1.7504580108253014e-6</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.9824354319909373</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b1">parseCmd (f (f ..28x...))</a></h2>
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<td><div id="time1" class="timechart"
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<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">5.5674492706428895e-5</span></td>
<td><span class="time">6.147631818924635e-5</span></td>
<td><span class="confinterval citime">7.032176389854423e-5</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">1.475909426326071e-5</span></td>
<td><span class="time">2.5348526620781456e-5</span></td>
<td><span class="confinterval citime">3.795255320003821e-5</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have severe
(<span class="percent">0.9914806816493408</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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var reports = [{"reportAnalysis":{"anMean":{"estError":{"confIntLDX":2.455014517065499e-7,"confIntUDX":3.7933946582140027e-7,"confIntCL":5.0e-2},"estPoint":4.52654834683996e-6},"anRegress":[{"regRSquare":{"estError":{"confIntLDX":1.5711156446105323e-2,"confIntUDX":3.048875198566936e-2,"confIntCL":5.0e-2},"estPoint":0.9508664763735168},"regResponder":"time","regCoeffs":{"y":{"estError":{"confIntLDX":1.8673942753415584e-3,"confIntUDX":1.5088455797688093e-3,"confIntCL":5.0e-2},"estPoint":9.227267109763274e-4},"iters":{"estError":{"confIntLDX":3.526816471709262e-7,"confIntUDX":4.831048030199716e-7,"confIntCL":5.0e-2},"estPoint":4.047265762029648e-6}}}],"anStdDev":{"estError":{"confIntLDX":1.9207768979677413e-7,"confIntUDX":4.958734799938439e-7,"confIntCL":5.0e-2},"estPoint":1.2545845308314575e-6},"anOutlierVar":{"ovFraction":0.9824354319909373,"ovDesc":"severe","ovEffect":"Severe"}},"reportKDEs":[{"kdeValues":[2.5748789052526256e-6,2.6306448546663317e-6,2.6864108040800375e-6,2.7421767534937436e-6,2.7979427029074493e-6,2.8537086523211554e-6,2.909474601734861e-6,2.9652405511485673e-6,3.021006500562273e-6,3.076772449975979e-6,3.132538399389685e-6,3.188304348803391e-6,3.2440702982170967e-6,3.299836247630803e-6,3.3556021970445085e-6,3.4113681464582147e-6,3.4671340958719204e-6,3.5229000452856265e-6,3.5786659946993327e-6,3.6344319441130384e-6,3.690197893526744e-6,3.7459638429404502e-6,3.8017297923541564e-6,3.857495741767862e-6,3.913261691181568e-6,3.969027640595274e-6,4.02479359000898e-6,4.080559539422686e-6,4.1363254888363915e-6,4.192091438250097e-6,4.247857387663804e-6,4.3036233370775095e-6,4.359389286491215e-6,4.415155235904922e-6,4.4709211853186275e-6,4.526687134732333e-6,4.582453084146039e-6,4.638219033559745e-6,4.693984982973451e-6,4.749750932387157e-6,4.8055168818008634e-6,4.861282831214569e-6,4.917048780628275e-6,4.9728147300419806e-6,5.028580679455686e-6,5.084346628869392e-6,5.1401125782830985e-6,5.195878527696804e-6,5.251644477110511e-6,5.3074104265242165e-6,5.363176375937922e-6,5.418942325351628e-6,5.474708274765334e-6,5.530474224179039e-6,5.586240173592746e-6,5.642006123006452e-6,5.697772072420158e-6,5.753538021833864e-6,5.80930397124757e-6,5.865069920661275e-6,5.920835870074981e-6,5.976601819488687e-6,6.032367768902393e-6,6.088133718316099e-6,6.143899667729806e-6,6.199665617143511e-6,6.255431566557217e-6,6.311197515970923e-6,6.3669634653846285e-6,6.422729414798335e-6,6.478495364212041e-6,6.5342613136257464e-6,6.590027263039453e-6,6.645793212453159e-6,6.7015591618668644e-6,6.75732511128057e-6,6.813091060694277e-6,6.868857010107982e-6,6.924622959521688e-6,6.980388908935394e-6,7.0361548583491e-6,7.091920807762806e-6,7.147686757176512e-6,7.2034527065902175e-6,7.259218656003924e-6,7.31498460541763e-6,7.3707505548313355e-6,7.426516504245041e-6,7.482282453658748e-6,7.5380484030724535e-6,7.593814352486159e-6,7.649580301899865e-6,7.705346251313572e-6,7.761112200727278e-6,7.816878150140984e-6,7.87264409955469e-6,7.928410048968395e-6,7.984175998382101e-6,8.039941947795807e-6,8.095707897209512e-6,8.151473846623218e-6,8.207239796036924e-6,8.26300574545063e-6,8.318771694864337e-6,8.374537644278043e-6,8.430303593691748e-6,8.486069543105454e-6,8.541835492519161e-6,8.597601441932867e-6,8.653367391346573e-6,8.709133340760279e-6,8.764899290173984e-6,8.82066523958769e-6,8.876431189001396e-6,8.932197138415101e-6,8.987963087828807e-6,9.043729037242513e-6,9.099494986656219e-6,9.155260936069924e-6,9.211026885483632e-6,9.266792834897337e-6,9.322558784311043e-6,9.378324733724749e-6,9.434090683138456e-6,9.489856632552162e-6,9.545622581965868e-6,9.601388531379573e-6,9.657154480793279e-6],"kdeType":"time","kdePDF":[3108.0630247569256,4462.928861782218,7452.151682195453,12621.496340017029,20758.488148123473,32860.829451741796,50101.684119018784,73787.35643069167,105284.19838978672,145870.8727876051,196468.79340983412,257237.6207622822,327096.68581357563,403320.3900980505,481407.19877937733,555393.7206626342,618667.7980106461,665163.0944888566,690657.4821540348,693812.4438911312,676617.8589640092,644046.8735340246,602946.0667784587,560425.6700466
reports.map(mangulate);
var benches = ["parseCmd (foo &quot;bar&quot;)","parseCmd (f (f ..28x...))",];
var ylabels = [[-0,"<a href=\"#b0\">parseCmd (foo &quot;bar&quot;)</a>"],[-1,"<a href=\"#b1\">parseCmd (f (f ..28x...))</a>"],];
var means = $.scaleTimes([4.52654834683996e-6,6.147631818924635e-5,]);
var stddevs = [1.2545845308314575e-6,2.5348526620781456e-5,];
stddevs = $.scaleBy(means[1], stddevs);
var xs = [];
var prev = null;
var colors = ["#edc240","#afd8f8","#cb4b4b","#4da74d","#9440ed"];
var max_time = 0;
for (var i = 0; i < means[0].length; i++) {
var name = benches[i].split(/\//);
name.pop();
name = name.join('/');
if (name != prev) {
var color = colors[xs.length / 2 % colors.length];
xs.push({ data: [[means[0][i], -i]], label: name, color: color,
bars: { show: true, horizontal: true, barWidth: 0.75,
align: "center" } });
xs.push({ data: [[means[0][i], -i, stddevs[i]]], color: color,
lines: { show: false },
points: { radius: 0, errorbars: "x",
xerr: { show: true, radius: 5,
lowerCap: "-", upperCap: "-" } } });
prev = name;
} else {
xs[xs.length - 2].data.push([means[0][i], -i]);
xs[xs.length - 1].data.push([means[0][i], -i, stddevs[i]]);
}
max_time = Math.max(max_time, means[0][i] + stddevs[i]);
}
var oq = $("#overview");
var max_x = max_time * 1.02;
o = $.plot(oq, xs, { grid: { borderColor: "#777", hoverable: true },
legend: { show: xs.length > 1 },
xaxis: { max: max_x, zoomRange: [false, max_x], panRange: [0, max_x] },
yaxis: { ticks: ylabels, tickColor: '#ffffff', zoomRange: false, panRange: false },
zoom: { interactive: true, amount: 1.05 },
pan: { interactive: true } });
if (benches.length > 3)
o.getPlaceholder().height(28*benches.length);
o.resize();
o.setupGrid();
o.draw();
$.addTooltip("#overview", function(x,y) { return $.renderTime(x / means[1]); });
});
$(document).ready(function () {
$(".time").text(function(_, text) {
return $.renderTime(text);
});
$(".citime").text(function(_, text) {
return $.renderTime(text);
});
$(".percent").text(function(_, text) {
return (text*100).toFixed(1);
});
});
</script>
</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>
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