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fix ifdef indentation (#51)
2016-07-22 23:29:05 +02:00
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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">unary request</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">1.1127952052099233e-4</span></td>
<td><span class="time">1.1157779769569623e-4</span></td>
<td><span class="confinterval citime">1.1222578474109467e-4</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">7.006642862776891e-7</span></td>
<td><span class="time">1.6019470204849632e-6</span></td>
<td><span class="confinterval citime">3.0016649862884594e-6</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have slight
(<span class="percent">8.197243287309262e-2</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b1">client stream: 100 messages</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">1.1145656673217617e-3</span></td>
<td><span class="time">1.1225264180385309e-3</span></td>
<td><span class="confinterval citime">1.136100231001483e-3</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">2.1175077057984874e-5</span></td>
<td><span class="time">3.660369985889931e-5</span></td>
<td><span class="confinterval citime">6.0245576168224164e-5</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.2100730004584609</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b2">client stream: 1k messages</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>
<!--
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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">1.0274288101958585e-2</span></td>
<td><span class="time">1.0341389459833119e-2</span></td>
<td><span class="confinterval citime">1.0418416003070792e-2</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">1.5974251878414234e-4</span></td>
<td><span class="time">2.0913496502759373e-4</span></td>
<td><span class="confinterval citime">2.889361409397357e-4</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have slight
(<span class="percent">3.222222222222209e-2</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b3">client stream: 10k messages</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde3" class="kdechart"
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"
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<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">0.1035139522385206</span></td>
<td><span class="time">0.1044357553238654</span></td>
<td><span class="confinterval citime">0.10589733927787692</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">8.693634002187114e-4</span></td>
<td><span class="time">1.7581790983451108e-3</span></td>
<td><span class="confinterval citime">2.6984798923229657e-3</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have slight
(<span class="percent">9.876543209876533e-2</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b4">server stream: 100 messages</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">1.00500185533956e-3</span></td>
<td><span class="time">1.0219297837648412e-3</span></td>
<td><span class="confinterval citime">1.038431407924066e-3</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">4.534639216316358e-5</span></td>
<td><span class="time">5.8708277292223296e-5</span></td>
<td><span class="confinterval citime">7.292687430223964e-5</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have moderate
(<span class="percent">0.4697581699045145</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b5">server stream: 1k messages</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">8.263458511284804e-3</span></td>
<td><span class="time">8.29655676830788e-3</span></td>
<td><span class="confinterval citime">8.338747504141219e-3</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">7.989147957982453e-5</span></td>
<td><span class="time">1.1338955133128914e-4</span></td>
<td><span class="confinterval citime">1.6239809568186118e-4</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have slight
(<span class="percent">2.938475665748384e-2</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b6">server stream: 10k messages</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">8.139915127469478e-2</span></td>
<td><span class="time">8.203977915769449e-2</span></td>
<td><span class="confinterval citime">8.294207157255142e-2</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">8.712272680628149e-4</span></td>
<td><span class="time">1.303323554210239e-3</span></td>
<td><span class="confinterval citime">1.75288845055683e-3</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have slight
(<span class="percent">9.000000000000001e-2</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b7">bidi stream: 50 messages up, 50 down</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde7" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time7" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle7" 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 olstimelb7">xxx</span></td>
<td><span class="olstimept7">xxx</span></td>
<td><span class="confinterval olstimeub7">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb7">xxx</span></td>
<td><span class="olsr2pt7">xxx</span></td>
<td><span class="confinterval olsr2ub7">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">3.6837263323658102e-3</span></td>
<td><span class="time">3.690185989917211e-3</span></td>
<td><span class="confinterval citime">3.7016024806948455e-3</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">1.87435230068336e-5</span></td>
<td><span class="time">2.7406270029965376e-5</span></td>
<td><span class="confinterval citime">3.868689618608243e-5</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have slight
(<span class="percent">2.1266540642722116e-2</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b8">bidi stream: 500 message up, 500 down</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde8" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time8" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle8" 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 olstimelb8">xxx</span></td>
<td><span class="olstimept8">xxx</span></td>
<td><span class="confinterval olstimeub8">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb8">xxx</span></td>
<td><span class="olsr2pt8">xxx</span></td>
<td><span class="confinterval olsr2ub8">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">3.539779838485038e-2</span></td>
<td><span class="time">3.5467452958929724e-2</span></td>
<td><span class="confinterval citime">3.5585300045038584e-2</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">1.1602363892840695e-4</span></td>
<td><span class="time">1.603301186146598e-4</span></td>
<td><span class="confinterval citime">2.1296560804213925e-4</span></td>
</tr>
</tbody>
</table>
<span class="outliers">
<p>Outlying measurements have slight
(<span class="percent">5.8593749999999986e-2</span>%)
effect on estimated standard deviation.</p>
</span>
<h2><a name="b9">bidi stream: 5000 messages up, 5000 down</a></h2>
<table width="100%">
<tbody>
<tr>
<td><div id="kde9" class="kdechart"
style="width:450px;height:278px;"></div></td>
<td><div id="time9" class="timechart"
style="width:450px;height:278px;"></div></td>
<!--
<td><div id="cycle9" 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 olstimelb9">xxx</span></td>
<td><span class="olstimept9">xxx</span></td>
<td><span class="confinterval olstimeub9">xxx</span></td>
</tr>
<tr>
<td>R&#xb2; goodness-of-fit</td>
<td><span class="confinterval olsr2lb9">xxx</span></td>
<td><span class="olsr2pt9">xxx</span></td>
<td><span class="confinterval olsr2ub9">xxx</span></td>
</tr>
<tr>
<td>Mean execution time</td>
<td><span class="confinterval citime">0.3519412043621479</span></td>
<td><span class="time">0.35207846750875343</span></td>
<td><span class="confinterval citime">0.3521320771337429</span></td>
</tr>
<tr>
<td>Standard deviation</td>
<td><span class="confinterval citime">0.0</span></td>
<td><span class="time">1.2712910344311448e-4</span></td>
<td><span class="confinterval citime">1.4489214967143587e-4</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="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>
<script type="text/javascript">
$(function () {
function mangulate(rpt) {
var measured = function(key) {
var idx = rpt.reportKeys.indexOf(key);
return rpt.reportMeasured.map(function(r) { return r[idx]; });
};
var number = rpt.reportNumber;
var name = rpt.reportName;
var mean = rpt.reportAnalysis.anMean.estPoint;
var iters = measured("iters");
var times = measured("time");
var kdetimes = rpt.reportKDEs[0].kdeValues;
var kdepdf = rpt.reportKDEs[0].kdePDF;
var meanSecs = mean;
var units = $.timeUnits(mean);
var rgrs = rpt.reportAnalysis.anRegress[0];
var scale = units[0];
var olsTime = rgrs.regCoeffs.iters;
$(".olstimept" + number).text(function() {
return $.renderTime(olsTime.estPoint);
});
$(".olstimelb" + number).text(function() {
return $.renderTime(olsTime.estLowerBound);
});
$(".olstimeub" + number).text(function() {
return $.renderTime(olsTime.estUpperBound);
});
$(".olsr2pt" + number).text(function() {
return rgrs.regRSquare.estPoint.toFixed(3);
});
$(".olsr2lb" + number).text(function() {
return rgrs.regRSquare.estLowerBound.toFixed(3);
});
$(".olsr2ub" + number).text(function() {
return rgrs.regRSquare.estUpperBound.toFixed(3);
});
mean *= scale;
kdetimes = $.scaleBy(scale, kdetimes);
var kq = $("#kde" + number);
var k = $.plot(kq,
[{ label: name + " time densities",
data: $.zip(kdetimes, kdepdf),
}],
{ xaxis: { tickFormatter: $.unitFormatter(scale) },
yaxis: { ticks: false },
grid: { borderColor: "#777",
hoverable: true, markings: [ { color: '#6fd3fb',
lineWidth: 1.5, xaxis: { from: mean, to: mean } } ] },
});
var o = k.pointOffset({ x: mean, y: 0});
kq.append('<div class="meanlegend" title="' + $.renderTime(meanSecs) +
'" style="position:absolute;left:' + (o.left + 4) +
'px;bottom:139px;">mean</div>');
$.addTooltip("#kde" + number,
function(secs) { return $.renderTime(secs / scale); });
var timepairs = new Array(times.length);
var lastiter = iters[iters.length-1];
var olspairs = [[0,0], [lastiter, lastiter * scale * olsTime.estPoint]];
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timepairs[i] = [iters[i],times[i]*scale];
iterFormatter = function() {
var denom = 0;
return function(iters) {
if (iters == 0)
return '';
if (denom > 0)
return (iters / denom).toFixed();
var power;
if (iters >= 1e9) {
denom = '1e9'; power = '&#x2079;';
}
if (iters >= 1e6) {
denom = '1e6'; power = '&#x2076;';
}
else if (iters >= 1e3) {
denom = '1e3'; power = '&#xb3;';
}
else denom = 1;
if (denom > 1) {
iters = (iters / denom).toFixed();
iters += '&times;10' + power + ' iters';
} else {
iters += ' iters';
}
return iters;
};
};
$.plot($("#time" + number),
[{ label: "regression", data: olspairs,
lines: { show: true } },
{ label: name + " times", data: timepairs,
points: { show: true } }],
{ grid: { borderColor: "#777", hoverable: true },
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$.addTooltip("#time" + number,
function(iters,secs) {
return ($.renderTime(secs / scale) + ' / ' +
iters.toLocaleString() + ' iters');
});
if (0) {
var cyclepairs = new Array(cycles.length);
for (var i = 0; i < cycles.length; i++)
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[{ label: name + " cycles",
data: cyclepairs }],
{ points: { show: true },
grid: { borderColor: "#777", hoverable: true },
xaxis: { tickFormatter:
function(cycles,axis) { return cycles + ' cycles'; }},
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});
$.addTooltip("#cycles" + number, function(x,y) { return x + ' cycles'; });
}
};
var reports = [{"reportAnalysis":{"anMean":{"estUpperBound":1.1222578474109467e-4,"estLowerBound":1.1127952052099233e-4,"estPoint":1.1157779769569623e-4,"estConfidenceLevel":0.95},"anRegress":[{"regRSquare":{"estUpperBound":0.9999354162380549,"estLowerBound":0.999466948473189,"estPoint":0.9997801565701177,"estConfidenceLevel":0.95},"regResponder":"time","regCoeffs":{"y":{"estUpperBound":2.200073377691192e-4,"estLowerBound":8.683885397991465e-6,"estPoint":1.2231967168686375e-4,"estConfidenceLevel":0.95},"iters":{"estUpperBound":1.1208100853828737e-4,"estLowerBound":1.1122669199887345e-4,"estPoint":1.1155395813237895e-4,"estConfidenceLevel":0.95}}}],"anStdDev":{"estUpperBound":3.0016649862884594e-6,"estLowerBound":7.006642862776891e-7,"estPoint":1.6019470204849632e-6,"estConfidenceLevel":0.95},"anOutlierVar":{"ovFraction":8.197243287309262e-2,"ovDesc":"slight","ovEffect":"Slight"},"anOverhead":2.7205617842992416e-6},"reportKDEs":[{"kdeValues":[1.0958968858953292e-4,1.0968162805448001e-4,1.0977356751942709e-4,1.0986550698437418e-4,1.0995744644932126e-4,1.1004938591426835e-4,1.1014132537921542e-4,1.1023326484416251e-4,1.1032520430910959e-4,1.1041714377405668e-4,1.1050908323900375e-4,1.1060102270395084e-4,1.1069296216889792e-4,1.10784901633845e-4,1.1087684109879208e-4,1.1096878056373917e-4,1.1106072002868625e-4,1.1115265949363334e-4,1.1124459895858041e-4,1.113365384235275e-4,1.1142847788847458e-4,1.1152041735342167e-4,1.1161235681836874e-4,1.1170429628331583e-4,1.1179623574826291e-4,1.1188817521321e-4,1.1198011467815708e-4,1.1207205414310416e-4,1.1216399360805124e-4,1.1225593307299833e-4,1.123478725379454e-4,1.124398120028925e-4,1.1253175146783957e-4,1.1262369093278666e-4,1.1271563039773374e-4,1.1280756986268083e-4,1.128995093276279e-4,1.1299144879257499e-4,1.1308338825752207e-4,1.1317532772246916e-4,1.1326726718741623e-4,1.1335920665236332e-4,1.134511461173104e-4,1.1354308558225749e-4,1.1363502504720456e-4,1.1372696451215165e-4,1.1381890397709873e-4,1.1391084344204582e-4,1.140027829069929e-4,1.1409472237193998e-4,1.1418666183688706e-4,1.1427860130183415e-4,1.1437054076678123e-4,1.1446248023172832e-4,1.1455441969667539e-4,1.1464635916162248e-4,1.1473829862656956e-4,1.1483023809151665e-4,1.1492217755646372e-4,1.1501411702141081e-4,1.1510605648635789e-4,1.1519799595130498e-4,1.1528993541625205e-4,1.1538187488119914e-4,1.1547381434614622e-4,1.1556575381109331e-4,1.1565769327604038e-4,1.1574963274098747e-4,1.1584157220593455e-4,1.1593351167088164e-4,1.1602545113582871e-4,1.161173906007758e-4,1.1620933006572288e-4,1.1630126953066997e-4,1.1639320899561705e-4,1.1648514846056413e-4,1.1657708792551121e-4,1.166690273904583e-4,1.1676096685540538e-4,1.1685290632035247e-4,1.1694484578529954e-4,1.1703678525024663e-4,1.1712872471519371e-4,1.172206641801408e-4,1.1731260364508787e-4,1.1740454311003496e-4,1.1749648257498204e-4,1.1758842203992913e-4,1.176803615048762e-4,1.1777230096982329e-4,1.1786424043477037e-4,1.1795617989971746e-4,1.1804811936466453e-4,1.1814005882961162e-4,1.182319982945587e-4,1.1832393775950579e-4,1.1841587722445286e-4,1.1850781668939995e-4,1.1859975615434703e-4,1.1869169561929412e-4,1.187836350842412e-4,1.1887557454918829e-4,1.1896751401413536e-4,1.1905945347908245e-4,1.1915139294402953e-4,1.1924333240897662e-4,1.1933527187392369e-4,1.1942721133887078e-4,1.1951915080381786e-4,1.1961109026876495e-4,1.1970302973371202e-4,1.1979496919865911e-4,1.198869086636062e-4,1.1997884812855328e-4,1.2007078759350035e-4,1.2016272705844744e-4,1.2025466652339453e-4,1.2034660598834161e-4,1.2043854545328868e-4,1.2053048491823577e-4,1.2062242438318286e-4,1.2071436384812994e-4,1.2080630331307702e-4,1.208982427780241e-4,1.209901822429712e-4,1.2108212170791827e-4,1.2117406117286535e-4,1.2126600063781244e-4],"kdeType":"time","kdePDF":[51.690454333104654,194.53748670444952,745.983137073066,2499.4864826075727,7293.696723264001,18606.324592414974,41706.149316044,82677.72281313274,146161.11659285624,232859.96212733764,338651.566371352,456128.61223905405,577010.8651797776,692726.3660815249,792641.2232350251,863025.6262160137,890227.8130958664,8671
reports.map(mangulate);
var benches = ["unary request","client stream: 100 messages","client stream: 1k messages","client stream: 10k messages","server stream: 100 messages","server stream: 1k messages","server stream: 10k messages","bidi stream: 50 messages up, 50 down","bidi stream: 500 message up, 500 down","bidi stream: 5000 messages up, 5000 down",];
var ylabels = [[-0,'<a href="#b0">unary request</a>'],[-1,'<a href="#b1">client stream: 100 messages</a>'],[-2,'<a href="#b2">client stream: 1k messages</a>'],[-3,'<a href="#b3">client stream: 10k messages</a>'],[-4,'<a href="#b4">server stream: 100 messages</a>'],[-5,'<a href="#b5">server stream: 1k messages</a>'],[-6,'<a href="#b6">server stream: 10k messages</a>'],[-7,'<a href="#b7">bidi stream: 50 messages up, 50 down</a>'],[-8,'<a href="#b8">bidi stream: 500 message up, 500 down</a>'],[-9,'<a href="#b9">bidi stream: 5000 messages up, 5000 down</a>'],];
var means = $.scaleTimes([1.1157779769569623e-4,1.1225264180385309e-3,1.0341389459833119e-2,0.1044357553238654,1.0219297837648412e-3,8.29655676830788e-3,8.203977915769449e-2,3.690185989917211e-3,3.5467452958929724e-2,0.35207846750875343,]);
var xs = [];
var prev = null;
for (var i = 0; i < means[0].length; i++) {
var name = benches[i].split(/\//);
name.pop();
name = name.join('/');
if (name != prev) {
xs.push({ label: name, data: [[means[0][i], -i]]});
prev = name;
}
else
xs[xs.length-1].data.push([means[0][i],-i]);
}
var oq = $("#overview");
o = $.plot(oq, xs, { bars: { show: true, horizontal: true,
barWidth: 0.75, align: "center" },
grid: { borderColor: "#777", hoverable: true },
legend: { show: xs.length > 1 },
xaxis: { max: Math.max.apply(undefined,means[0]) * 1.02 },
yaxis: { ticks: ylabels, tickColor: '#ffffff' } });
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>
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