larskuhtz/README.md

## README.md

      
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              README.md
            
          
    Performance of http-streams and http-client

We compare the performance of the
http-streams and the
http-client libraries. The
focus is on making a large number of small concurrent requests. This typically
occurs in services that use HTTP to connect to backend services, such as a
database, message queue, or log-message sink. Often those backend services use
relatively small JSON encoded messages.
Description and Results

Each benchmark initializes a number of threads. Each threads sequentially
performs the same number of identical synchronous HTTP requests to the same
HTTP server.
The results of some benchmarks can be viewed rendered as HTML here:
https://rawgit.com/larskuhtz/d935b119f8b5790e2cda/raw/results.html
The following benchmarks are obtained with the faster-timeout branch
of the http-client package.
Note that all benchmarks are compiled with that branch. The Benchmarks that are labeled
http-client-local-manager-no-timeout use managerResponseTimeout = Nothing in ManagerSettings.
https://rawgit.com/larskuhtz/d935b119f8b5790e2cda/raw/results-no-timeout.html
These results may indidcate the issue is not caused by the implementation of
timeout in System.Timeout but is sitting deeper somewhere in
getSystemTimerManager from GHC.Event. The latter is used by
System.Timeout as well as the timeout implementation the the
faster-timeout branch branch
of http-client.
Setup

The results are generated on a 4 core mac-book pro. The interest is on the
comparision of both libraries. No attempt has been made to reduce environment
noise or maximize performance.
The http-client library is benchmarked with two setups:


Each thread receives it's own manager instance (connection pool) that keeps
a single connection open.


All threads share a common manager isntance (connection pool) that keeps as
many connections open as there are threads.


The http-streams library is benchmarked with each thread allocating a single
static TCP connection. There is some logic in place to ensure that the
connection is reset in case an error occurs. This is not production level code,
but we think that a production grade implementation may have similar
performance. We implemented the following two setups:


The TCP connection is stored in an MVar.


The TCP connection is stored in an IORef.


The HTTP server is a wai application
that just echos back the request body. It is running on
warp. For these results the server
with running in the same process as the benchmarks. We also run benchmarks
where the HTTP server was running as a different process on the same or another
machine. The results for those setups were similar to the results presented
here.
Code

The
code
is compiled with
ghc --make -O2 StreamsVsClient.hs -D'MIN_VERSION_http_streams(x,y,z)=1' -Wall -fforce-recomp -threaded
and executed as
./StreamsVsClient -o results.html +RTS -N

  
## results-no-timeout.html
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    <title>criterion report</title>
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series=[],options={colors:["#edc240","#afd8f8","#cb4b4b","#4da74d","#9440ed"],legend:{show:true,noColumns:1,labelFormatter:null,labelBoxBorderColor:"#ccc",container:null,position:"ne",margin:5,backgroundColor:null,backgroundOpacity:.85,sorted:null},xaxis:{show:null,position:"bottom",mode:null,font:null,color:null,tickColor:null,transform:null,inverseTransform:null,min:null,max:null,autoscaleMargin:null,ticks:null,tickFormatter:null,labelWidth:null,labelHeight:null,reserveSpace:null,tickLength:null,alignTicksWithAxis:null,tickDecimals:null,tickSize:null,minTickSize:null},yaxis:{autoscaleMargin:.02,position:"left"},xaxes:[],yaxes:[],series:{points:{show:false,radius:3,lineWidth:2,fill:true,fillColor:"#ffffff",symbol:"circle"},lines:{lineWidth:2,fill:false,fillColor:null,steps:false},bars:{show:false,lineWidth:2,barWidth:1,fill:true,fillColor:null,align:"left",horizontal:false,zero:true},shadowSize:3,highlightColor:null},grid:{show:true,aboveData:false,color:"#545454",backgroundColor:null,borderColor:null,tickColor:null,margin:0,labelMargin:5,axisMargin:8,borderWidth:2,minBorderMargin:null,markings:null,markingsColor:"#f4f4f4",markingsLineWidth:2,clickable:false,hoverable:false,autoHighlight:true,mouseActiveRadius:10},interaction:{redrawOverlayInterval:1e3/60},hooks:{}},surface=null,overlay=null,eventHolder=null,ctx=null,octx=null,xaxes=[],yaxes=[],plotOffset={left:0,right:0,top:0,bottom:0},plotWidth=0,plotHeight=0,hooks={processOptions:[],processRawData:[],processDatapoints:[],processOffset:[],drawBackground:[],drawSeries:[],draw:[],bindEvents:[],drawOverlay:[],shutdown:[]},plot=this;plot.setData=setData;plot.setupGrid=setupGrid;plot.draw=draw;plot.getPlaceholder=function(){return placeholder};plot.getCanvas=function(){return surface.element};plot.getPlotOffset=function(){return plotOffset};plot.width=function(){return plotWidth};plot.height=function(){return plotHeight};plot.offset=function(){var o=eventHolder.offset();o.left+=plotOffset.left;o.top+=plotOffset.top;return o};plot.getData=function(){return series};plot.getAxes=function(){var res={},i;$.each(xaxes.concat(yaxes),function(_,axis){if(axis)res[axis.direction+(axis.n!=1?axis.n:"")+"axis"]=axis});return res};plot.getXAxes=function(){return xaxes};plot.getYAxes=function(){return yaxes};plot.c2p=canvasToAxisCoords;plot.p2c=axisToCanvasCoords;plot.getOptions=function(){return options};plot.highlight=highlight;plot.unhighlight=unhighlight;plot.triggerRedrawOverlay=triggerRedrawOverlay;plot.pointOffset=function(point){return{left:parseInt(xaxes[axisNumber(point,"x")-1].p2c(+point.x)+plotOffset.left,10),top:parseInt(yaxes[axisNumber(point,"y")-1].p2c(+point.y)+plotOffset.top,10)}};plot.shutdown=shutdown;plot.destroy=function(){shutdown();placeholder.removeData("plot").empty();series=[];options=null;surface=null;overlay=null;eventHolder=null;ctx=null;octx=null;xaxes=[];yaxes=[];hooks=null;highlights=[];plot=null};plot.resize=function(){var width=placeholder.width(),height=placeholder.height();surface.resize(width,height);overlay.resize(width,height)};plot.hooks=hooks;initPlugins(plot);parseOptions(options_);setupCanvases();setData(data_);setupGrid();draw();bindEvents();function executeHooks(hook,args){args=[plot].concat(args);for(var i=0;i<hook.length;++i)hook[i].apply(this,args)}function initPlugins(){var classes={Canvas:Canvas};for(var i=0;i<plugins.length;++i){var p=plugins[i];p.init(plot,classes);if(p.options)$.extend(true,options,p.options)}}function parseOptions(opts){$.extend(true,options,opts);if(opts&&opts.colors){options.colors=opts.colors}if(options.xaxis.color==null)options.xaxis.color=$.color.parse(options.grid.color).scale("a",.22).toString();if(options.yaxis.color==null)options.yaxis.color=$.color.parse(options.grid.color).scale("a",.22).toString();if(options.xaxis.tickColor==null)options.xaxis.tickColor=options.grid.tickColor||options.xaxis.color;if(options.yaxis.tickColor==null)options.yaxis.tickColor=options.grid.tickColor||options.yaxis.color;if(options.grid.borderColor==null)options.grid.borderColor=options.grid.color;if(options.grid.tickColor==null)options.grid.tickColor=$.color.parse(options.grid.color).scale("a",.22).toString();var i,axisOptions,axisCount,fontSize=placeholder.css("font-size"),fontSizeDefault=fontSize?+fontSize.replace("px",""):13,fontDefaults={style:placeholder.css("font-style"),size:Math.round(.8*fontSizeDefault),variant:placeholder.css("font-variant"),weight:placeholder.css("font-weight"),family:placeholder.css("font-family")};axisCount=options.xaxes.length||1;for(i=0;i<axisCount;++i){axisOptions=options.xaxes[i];if(axisOptions&&!axisOptions.tickColor){axisOptions.tickColor=axisOptions.color}axisOptions=$.extend(true,{},options.xaxis,axisOptions);options.xaxes[i]=axisOptions;if(axisOptions.font){axisOptions.font=$.extend({},fontDefaults,axisOptions.font);if(!axisOptions.font.color){axisOptions.font.color=axisOptions.color}if(!axisOptions.font.lineHeight){axisOptions.font.lineHeight=Math.round(axisOptions.font.size*1.15)}}}axisCount=options.yaxes.length||1;for(i=0;i<axisCount;++i){axisOptions=options.yaxes[i];if(axisOptions&&!axisOptions.tickColor){axisOptions.tickColor=axisOptions.color}axisOptions=$.extend(true,{},options.yaxis,axisOptions);options.yaxes[i]=axisOptions;if(axisOptions.font){axisOptions.font=$.extend({},fontDefaults,axisOptions.font);if(!axisOptions.font.color){axisOptions.font.color=axisOptions.color}if(!axisOptions.font.lineHeight){axisOptions.font.lineHeight=Math.round(axisOptions.font.size*1.15)}}}if(options.xaxis.noTicks&&options.xaxis.ticks==null)options.xaxis.ticks=options.xaxis.noTicks;if(options.yaxis.noTicks&&options.yaxis.ticks==null)options.yaxis.ticks=options.yaxis.noTicks;if(options.x2axis){options.xaxes[1]=$.extend(true,{},options.xaxis,options.x2axis);options.xaxes[1].position="top";if(options.x2axis.min==null){options.xaxes[1].min=null}if(options.x2axis.max==null){options.xaxes[1].max=null}}if(options.y2axis){options.yaxes[1]=$.extend(true,{},options.yaxis,options.y2axis);options.yaxes[1].position="right";if(options.y2axis.min==null){options.yaxes[1].min=null}if(options.y2axis.max==null){options.yaxes[1].max=null}}if(options.grid.coloredAreas)options.grid.markings=options.grid.coloredAreas;if(options.grid.coloredAreasColor)options.grid.markingsColor=options.grid.coloredAreasColor;if(options.lines)$.extend(true,options.series.lines,options.lines);if(options.points)$.extend(true,options.series.points,options.points);if(options.bars)$.extend(true,options.series.bars,options.bars);if(options.shadowSize!=null)options.series.shadowSize=options.shadowSize;if(options.highlightColor!=null)options.series.highlightColor=options.highlightColor;for(i=0;i<options.xaxes.length;++i)getOrCreateAxis(xaxes,i+1).options=options.xaxes[i];for(i=0;i<options.yaxes.length;++i)getOrCreateAxis(yaxes,i+1).options=options.yaxes[i];for(var n in hooks)if(options.hooks[n]&&options.hooks[n].length)hooks[n]=hooks[n].concat(options.hooks[n]);executeHooks(hooks.processOptions,[options])}function setData(d){series=parseData(d);fillInSeriesOptions();processData()}function parseData(d){var res=[];for(var i=0;i<d.length;++i){var s=$.extend(true,{},options.series);if(d[i].data!=null){s.data=d[i].data;delete d[i].data;$.extend(true,s,d[i]);d[i].data=s.data}else s.data=d[i];res.push(s)}return res}function axisNumber(obj,coord){var a=obj[coord+"axis"];if(typeof a=="object")a=a.n;if(typeof a!="number")a=1;return a}function allAxes(){return $.grep(xaxes.concat(yaxes),function(a){return a})}function canvasToAxisCoords(pos){var res={},i,axis;for(i=0;i<xaxes.length;++i){axis=xaxes[i];if(axis&&axis.used)res["x"+axis.n]=axis.c2p(pos.left)}for(i=0;i<yaxes.length;++i){axis=yaxes[i];if(axis&&axis.used)res["y"+axis.n]=axis.c2p(pos.top)}if(res.x1!==undefined)res.x=res.x1;if(res.y1!==undefined)res.y=res.y1;return res}function axisToCanvasCoords(pos){var res={},i,axis,key;for(i=0;i<xaxes.length;++i){axis=xaxes[i];if(axis&&axis.used){key="x"+axis.n;if(pos[key]==null&&axis.n==1)key="x";if(pos[key]!=null){res.left=axis.p2c(pos[key]);break}}}for(i=0;i<yaxes.length;++i){axis=yaxes[i];if(axis&&axis.used){key="y"+axis.n;if(pos[key]==null&&axis.n==1)key="y";if(pos[key]!=null){res.top=axis.p2c(pos[key]);break}}}return res}function getOrCreateAxis(axes,number){if(!axes[number-1])axes[number-1]={n:number,direction:axes==xaxes?"x":"y",options:$.extend(true,{},axes==xaxes?options.xaxis:options.yaxis)};return axes[number-1]}function fillInSeriesOptions(){var neededColors=series.length,maxIndex=-1,i;for(i=0;i<series.length;++i){var sc=series[i].color;if(sc!=null){neededColors--;if(typeof sc=="number"&&sc>maxIndex){maxIndex=sc}}}if(neededColors<=maxIndex){neededColors=maxIndex+1}var c,colors=[],colorPool=options.colors,colorPoolSize=colorPool.length,variation=0;for(i=0;i<neededColors;i++){c=$.color.parse(colorPool[i%colorPoolSize]||"#666");if(i%colorPoolSize==0&&i){if(variation>=0){if(variation<.5){variation=-variation-.2}else variation=0}else variation=-variation}colors[i]=c.scale("rgb",1+variation)}var colori=0,s;for(i=0;i<series.length;++i){s=series[i];if(s.color==null){s.color=colors[colori].toString();++colori}else if(typeof s.color=="number")s.color=colors[s.color].toString();if(s.lines.show==null){var v,show=true;for(v in s)if(s[v]&&s[v].show){show=false;break}if(show)s.lines.show=true}if(s.lines.zero==null){s.lines.zero=!!s.lines.fill}s.xaxis=getOrCreateAxis(xaxes,axisNumber(s,"x"));s.yaxis=getOrCreateAxis(yaxes,axisNumber(s,"y"))}}function processData(){var topSentry=Number.POSITIVE_INFINITY,bottomSentry=Number.NEGATIVE_INFINITY,fakeInfinity=Number.MAX_VALUE,i,j,k,m,length,s,points,ps,x,y,axis,val,f,p,data,format;function updateAxis(axis,min,max){if(min<axis.datamin&&min!=-fakeInfinity)axis.datamin=min;if(max>axis.datamax&&max!=fakeInfinity)axis.datamax=max}$.each(allAxes(),function(_,axis){axis.datamin=topSentry;axis.datamax=bottomSentry;axis.used=false});for(i=0;i<series.length;++i){s=series[i];s.datapoints={points:[]};executeHooks(hooks.processRawData,[s,s.data,s.datapoints])}for(i=0;i<series.length;++i){s=series[i];data=s.data;format=s.datapoints.format;if(!format){format=[];format.push({x:true,number:true,required:true});format.push({y:true,number:true,required:true});if(s.bars.show||s.lines.show&&s.lines.fill){var autoscale=!!(s.bars.show&&s.bars.zero||s.lines.show&&s.lines.zero);format.push({y:true,number:true,required:false,defaultValue:0,autoscale:autoscale});if(s.bars.horizontal){delete format[format.length-1].y;format[format.length-1].x=true}}s.datapoints.format=format}if(s.datapoints.pointsize!=null)continue;s.datapoints.pointsize=format.length;ps=s.datapoints.pointsize;points=s.datapoints.points;var insertSteps=s.lines.show&&s.lines.steps;s.xaxis.used=s.yaxis.used=true;for(j=k=0;j<data.length;++j,k+=ps){p=data[j];var nullify=p==null;if(!nullify){for(m=0;m<ps;++m){val=p[m];f=format[m];if(f){if(f.number&&val!=null){val=+val;if(isNaN(val))val=null;else if(val==Infinity)val=fakeInfinity;else if(val==-Infinity)val=-fakeInfinity}if(val==null){if(f.required)nullify=true;if(f.defaultValue!=null)val=f.defaultValue}}points[k+m]=val}}if(nullify){for(m=0;m<ps;++m){val=points[k+m];if(val!=null){f=format[m];if(f.autoscale!==false){if(f.x){updateAxis(s.xaxis,val,val)}if(f.y){updateAxis(s.yaxis,val,val)}}}points[k+m]=null}}else{if(insertSteps&&k>0&&points[k-ps]!=null&&points[k-ps]!=points[k]&&points[k-ps+1]!=points[k+1]){for(m=0;m<ps;++m)points[k+ps+m]=points[k+m];points[k+1]=points[k-ps+1];k+=ps}}}}for(i=0;i<series.length;++i){s=series[i];executeHooks(hooks.processDatapoints,[s,s.datapoints])}for(i=0;i<series.length;++i){s=series[i];points=s.datapoints.points;ps=s.datapoints.pointsize;format=s.datapoints.format;var xmin=topSentry,ymin=topSentry,xmax=bottomSentry,ymax=bottomSentry;for(j=0;j<points.length;j+=ps){if(points[j]==null)continue;for(m=0;m<ps;++m){val=points[j+m];f=format[m];if(!f||f.autoscale===false||val==fakeInfinity||val==-fakeInfinity)continue;if(f.x){if(val<xmin)xmin=val;if(val>xmax)xmax=val}if(f.y){if(val<ymin)ymin=val;if(val>ymax)ymax=val}}}if(s.bars.show){var delta;switch(s.bars.align){case"left":delta=0;break;case"right":delta=-s.bars.barWidth;break;default:delta=-s.bars.barWidth/2}if(s.bars.horizontal){ymin+=delta;ymax+=delta+s.bars.barWidth}else{xmin+=delta;xmax+=delta+s.bars.barWidth}}updateAxis(s.xaxis,xmin,xmax);updateAxis(s.yaxis,ymin,ymax)}$.each(allAxes(),function(_,axis){if(axis.datamin==topSentry)axis.datamin=null;if(axis.datamax==bottomSentry)axis.datamax=null})}function setupCanvases(){placeholder.css("padding",0).children().filter(function(){return!$(this).hasClass("flot-overlay")&&!$(this).hasClass("flot-base")}).remove();if(placeholder.css("position")=="static")placeholder.css("position","relative");surface=new Canvas("flot-base",placeholder);overlay=new Canvas("flot-overlay",placeholder);ctx=surface.context;octx=overlay.context;eventHolder=$(overlay.element).unbind();var existing=placeholder.data("plot");if(existing){existing.shutdown();overlay.clear()}placeholder.data("plot",plot)}function bindEvents(){if(options.grid.hoverable){eventHolder.mousemove(onMouseMove);eventHolder.bind("mouseleave",onMouseLeave)}if(options.grid.clickable)eventHolder.click(onClick);executeHooks(hooks.bindEvents,[eventHolder])}function shutdown(){if(redrawTimeout)clearTimeout(redrawTimeout);eventHolder.unbind("mousemove",onMouseMove);eventHolder.unbind("mouseleave",onMouseLeave);eventHolder.unbind("click",onClick);executeHooks(hooks.shutdown,[eventHolder])}function setTransformationHelpers(axis){function identity(x){return x}var s,m,t=axis.options.transform||identity,it=axis.options.inverseTransform;if(axis.direction=="x"){s=axis.scale=plotWidth/Math.abs(t(axis.max)-t(axis.min));m=Math.min(t(axis.max),t(axis.min))}else{s=axis.scale=plotHeight/Math.abs(t(axis.max)-t(axis.min));s=-s;m=Math.max(t(axis.max),t(axis.min))}if(t==identity)axis.p2c=function(p){return(p-m)*s};else axis.p2c=function(p){return(t(p)-m)*s};if(!it)axis.c2p=function(c){return m+c/s};else axis.c2p=function(c){return it(m+c/s)}}function measureTickLabels(axis){var opts=axis.options,ticks=axis.ticks||[],labelWidth=opts.labelWidth||0,labelHeight=opts.labelHeight||0,maxWidth=labelWidth||(axis.direction=="x"?Math.floor(surface.width/(ticks.length||1)):null),legacyStyles=axis.direction+"Axis "+axis.direction+axis.n+"Axis",layer="flot-"+axis.direction+"-axis flot-"+axis.direction+axis.n+"-axis "+legacyStyles,font=opts.font||"flot-tick-label tickLabel";for(var i=0;i<ticks.length;++i){var t=ticks[i];if(!t.label)continue;var info=surface.getTextInfo(layer,t.label,font,null,maxWidth);labelWidth=Math.max(labelWidth,info.width);labelHeight=Math.max(labelHeight,info.height)}axis.labelWidth=opts.labelWidth||labelWidth;axis.labelHeight=opts.labelHeight||labelHeight}function allocateAxisBoxFirstPhase(axis){var lw=axis.labelWidth,lh=axis.labelHeight,pos=axis.options.position,isXAxis=axis.direction==="x",tickLength=axis.options.tickLength,axisMargin=options.grid.axisMargin,padding=options.grid.labelMargin,innermost=true,outermost=true,first=true,found=false;$.each(isXAxis?xaxes:yaxes,function(i,a){if(a&&(a.show||a.reserveSpace)){if(a===axis){found=true}else if(a.options.position===pos){if(found){outermost=false}else{innermost=false}}if(!found){first=false}}});if(outermost){axisMargin=0}if(tickLength==null){tickLength=first?"full":5}if(!isNaN(+tickLength))padding+=+tickLength;if(isXAxis){lh+=padding;if(pos=="bottom"){plotOffset.bottom+=lh+axisMargin;axis.box={top:surface.height-plotOffset.bottom,height:lh}}else{axis.box={top:plotOffset.top+axisMargin,height:lh};plotOffset.top+=lh+axisMargin}}else{lw+=padding;if(pos=="left"){axis.box={left:plotOffset.left+axisMargin,width:lw};plotOffset.left+=lw+axisMargin}else{plotOffset.right+=lw+axisMargin;axis.box={left:surface.width-plotOffset.right,width:lw}}}axis.position=pos;axis.tickLength=tickLength;axis.box.padding=padding;axis.innermost=innermost}function allocateAxisBoxSecondPhase(axis){if(axis.direction=="x"){axis.box.left=plotOffset.left-axis.labelWidth/2;axis.box.width=surface.width-plotOffset.left-plotOffset.right+axis.labelWidth}else{axis.box.top=plotOffset.top-axis.labelHeight/2;axis.box.height=surface.height-plotOffset.bottom-plotOffset.top+axis.labelHeight}}function adjustLayoutForThingsStickingOut(){var minMargin=options.grid.minBorderMargin,axis,i;if(minMargin==null){minMargin=0;for(i=0;i<series.length;++i)minMargin=Math.max(minMargin,2*(series[i].points.radius+series[i].points.lineWidth/2))}var margins={left:minMargin,right:minMargin,top:minMargin,bottom:minMargin};$.each(allAxes(),function(_,axis){if(axis.reserveSpace&&axis.ticks&&axis.ticks.length){if(axis.direction==="x"){margins.left=Math.max(margins.left,axis.labelWidth/2);margins.right=Math.max(margins.right,axis.labelWidth/2)}else{margins.bottom=Math.max(margins.bottom,axis.labelHeight/2);margins.top=Math.max(margins.top,axis.labelHeight/2)}}});plotOffset.left=Math.ceil(Math.max(margins.left,plotOffset.left));plotOffset.right=Math.ceil(Math.max(margins.right,plotOffset.right));plotOffset.top=Math.ceil(Math.max(margins.top,plotOffset.top));plotOffset.bottom=Math.ceil(Math.max(margins.bottom,plotOffset.bottom))}function setupGrid(){var i,axes=allAxes(),showGrid=options.grid.show;for(var a in plotOffset){var margin=options.grid.margin||0;plotOffset[a]=typeof margin=="number"?margin:margin[a]||0}executeHooks(hooks.processOffset,[plotOffset]);for(var a in plotOffset){if(typeof options.grid.borderWidth=="object"){plotOffset[a]+=showGrid?options.grid.borderWidth[a]:0}else{plotOffset[a]+=showGrid?options.grid.borderWidth:0}}$.each(axes,function(_,axis){var axisOpts=axis.options;axis.show=axisOpts.show==null?axis.used:axisOpts.show;axis.reserveSpace=axisOpts.reserveSpace==null?axis.show:axisOpts.reserveSpace;setRange(axis)});if(showGrid){var allocatedAxes=$.grep(axes,function(axis){return axis.show||axis.reserveSpace});$.each(allocatedAxes,function(_,axis){setupTickGeneration(axis);setTicks(axis);snapRangeToTicks(axis,axis.ticks);measureTickLabels(axis)});for(i=allocatedAxes.length-1;i>=0;--i)allocateAxisBoxFirstPhase(allocatedAxes[i]);adjustLayoutForThingsStickingOut();$.each(allocatedAxes,function(_,axis){allocateAxisBoxSecondPhase(axis)})}plotWidth=surface.width-plotOffset.left-plotOffset.right;plotHeight=surface.height-plotOffset.bottom-plotOffset.top;$.each(axes,function(_,axis){setTransformationHelpers(axis)});if(showGrid){drawAxisLabels()}insertLegend()}function setRange(axis){var opts=axis.options,min=+(opts.min!=null?opts.min:axis.datamin),max=+(opts.max!=null?opts.max:axis.datamax),delta=max-min;if(delta==0){var widen=max==0?1:.01;if(opts.min==null)min-=widen;if(opts.max==null||opts.min!=null)max+=widen}else{var margin=opts.autoscaleMargin;if(margin!=null){if(opts.min==null){min-=delta*margin;if(min<0&&axis.datamin!=null&&axis.datamin>=0)min=0}if(opts.max==null){max+=delta*margin;if(max>0&&axis.datamax!=null&&axis.datamax<=0)max=0}}}axis.min=min;axis.max=max}function setupTickGeneration(axis){var opts=axis.options;var noTicks;if(typeof opts.ticks=="number"&&opts.ticks>0)noTicks=opts.ticks;else noTicks=.3*Math.sqrt(axis.direction=="x"?surface.width:surface.height);var delta=(axis.max-axis.min)/noTicks,dec=-Math.floor(Math.log(delta)/Math.LN10),maxDec=opts.tickDecimals;if(maxDec!=null&&dec>maxDec){dec=maxDec}var magn=Math.pow(10,-dec),norm=delta/magn,size;if(norm<1.5){size=1}else if(norm<3){size=2;if(norm>2.25&&(maxDec==null||dec+1<=maxDec)){size=2.5;++dec}}else if(norm<7.5){size=5}else{size=10}size*=magn;if(opts.minTickSize!=null&&size<opts.minTickSize){size=opts.minTickSize}axis.delta=delta;axis.tickDecimals=Math.max(0,maxDec!=null?maxDec:dec);axis.tickSize=opts.tickSize||size;if(opts.mode=="time"&&!axis.tickGenerator){throw new Error("Time mode requires the flot.time plugin.")}if(!axis.tickGenerator){axis.tickGenerator=function(axis){var ticks=[],start=floorInBase(axis.min,axis.tickSize),i=0,v=Number.NaN,prev;do{prev=v;v=start+i*axis.tickSize;ticks.push(v);++i}while(v<axis.max&&v!=prev);return ticks};axis.tickFormatter=function(value,axis){var factor=axis.tickDecimals?Math.pow(10,axis.tickDecimals):1;var formatted=""+Math.round(value*factor)/factor;if(axis.tickDecimals!=null){var decimal=formatted.indexOf(".");var precision=decimal==-1?0:formatted.length-decimal-1;if(precision<axis.tickDecimals){return(precision?formatted:formatted+".")+(""+factor).substr(1,axis.tickDecimals-precision)}}return formatted}}if($.isFunction(opts.tickFormatter))axis.tickFormatter=function(v,axis){return""+opts.tickFormatter(v,axis)};if(opts.alignTicksWithAxis!=null){var otherAxis=(axis.direction=="x"?xaxes:yaxes)[opts.alignTicksWithAxis-1];if(otherAxis&&otherAxis.used&&otherAxis!=axis){var niceTicks=axis.tickGenerator(axis);if(niceTicks.length>0){if(opts.min==null)axis.min=Math.min(axis.min,niceTicks[0]);if(opts.max==null&&niceTicks.length>1)axis.max=Math.max(axis.max,niceTicks[niceTicks.length-1])}axis.tickGenerator=function(axis){var ticks=[],v,i;for(i=0;i<otherAxis.ticks.length;++i){v=(otherAxis.ticks[i].v-otherAxis.min)/(otherAxis.max-otherAxis.min);v=axis.min+v*(axis.max-axis.min);ticks.push(v)}return ticks};if(!axis.mode&&opts.tickDecimals==null){var extraDec=Math.max(0,-Math.floor(Math.log(axis.delta)/Math.LN10)+1),ts=axis.tickGenerator(axis);if(!(ts.length>1&&/\..*0$/.test((ts[1]-ts[0]).toFixed(extraDec))))axis.tickDecimals=extraDec}}}}function setTicks(axis){var oticks=axis.options.ticks,ticks=[];if(oticks==null||typeof oticks=="number"&&oticks>0)ticks=axis.tickGenerator(axis);else if(oticks){if($.isFunction(oticks))ticks=oticks(axis);else ticks=oticks}var i,v;axis.ticks=[];for(i=0;i<ticks.length;++i){var label=null;var t=ticks[i];if(typeof t=="object"){v=+t[0];if(t.length>1)label=t[1]}else v=+t;if(label==null)label=axis.tickFormatter(v,axis);if(!isNaN(v))axis.ticks.push({v:v,label:label})}}function snapRangeToTicks(axis,ticks){if(axis.options.autoscaleMargin&&ticks.length>0){if(axis.options.min==null)axis.min=Math.min(axis.min,ticks[0].v);if(axis.options.max==null&&ticks.length>1)axis.max=Math.max(axis.max,ticks[ticks.length-1].v)}}function draw(){surface.clear();executeHooks(hooks.drawBackground,[ctx]);var grid=options.grid;if(grid.show&&grid.backgroundColor)drawBackground();if(grid.show&&!grid.aboveData){drawGrid()}for(var i=0;i<series.length;++i){executeHooks(hooks.drawSeries,[ctx,series[i]]);drawSeries(series[i])}executeHooks(hooks.draw,[ctx]);if(grid.show&&grid.aboveData){drawGrid()}surface.render();triggerRedrawOverlay()}function extractRange(ranges,coord){var axis,from,to,key,axes=allAxes();for(var i=0;i<axes.length;++i){axis=axes[i];if(axis.direction==coord){key=coord+axis.n+"axis";if(!ranges[key]&&axis.n==1)key=coord+"axis";if(ranges[key]){from=ranges[key].from;to=ranges[key].to;break}}}if(!ranges[key]){axis=coord=="x"?xaxes[0]:yaxes[0];from=ranges[coord+"1"];to=ranges[coord+"2"]}if(from!=null&&to!=null&&from>to){var tmp=from;from=to;to=tmp}return{from:from,to:to,axis:axis}}function drawBackground(){ctx.save();ctx.translate(plotOffset.left,plotOffset.top);ctx.fillStyle=getColorOrGradient(options.grid.backgroundColor,plotHeight,0,"rgba(255, 255, 255, 0)");ctx.fillRect(0,0,plotWidth,plotHeight);ctx.restore()}function drawGrid(){var i,axes,bw,bc;ctx.save();ctx.translate(plotOffset.left,plotOffset.top);var markings=options.grid.markings;if(markings){if($.isFunction(markings)){axes=plot.getAxes();axes.xmin=axes.xaxis.min;axes.xmax=axes.xaxis.max;axes.ymin=axes.yaxis.min;axes.ymax=axes.yaxis.max;markings=markings(axes)}for(i=0;i<markings.length;++i){var m=markings[i],xrange=extractRange(m,"x"),yrange=extractRange(m,"y");if(xrange.from==null)xrange.from=xrange.axis.min;if(xrange.to==null)xrange.to=xrange.axis.max;
if(yrange.from==null)yrange.from=yrange.axis.min;if(yrange.to==null)yrange.to=yrange.axis.max;if(xrange.to<xrange.axis.min||xrange.from>xrange.axis.max||yrange.to<yrange.axis.min||yrange.from>yrange.axis.max)continue;xrange.from=Math.max(xrange.from,xrange.axis.min);xrange.to=Math.min(xrange.to,xrange.axis.max);yrange.from=Math.max(yrange.from,yrange.axis.min);yrange.to=Math.min(yrange.to,yrange.axis.max);var xequal=xrange.from===xrange.to,yequal=yrange.from===yrange.to;if(xequal&&yequal){continue}xrange.from=Math.floor(xrange.axis.p2c(xrange.from));xrange.to=Math.floor(xrange.axis.p2c(xrange.to));yrange.from=Math.floor(yrange.axis.p2c(yrange.from));yrange.to=Math.floor(yrange.axis.p2c(yrange.to));if(xequal||yequal){var lineWidth=m.lineWidth||options.grid.markingsLineWidth,subPixel=lineWidth%2?.5:0;ctx.beginPath();ctx.strokeStyle=m.color||options.grid.markingsColor;ctx.lineWidth=lineWidth;if(xequal){ctx.moveTo(xrange.to+subPixel,yrange.from);ctx.lineTo(xrange.to+subPixel,yrange.to)}else{ctx.moveTo(xrange.from,yrange.to+subPixel);ctx.lineTo(xrange.to,yrange.to+subPixel)}ctx.stroke()}else{ctx.fillStyle=m.color||options.grid.markingsColor;ctx.fillRect(xrange.from,yrange.to,xrange.to-xrange.from,yrange.from-yrange.to)}}}axes=allAxes();bw=options.grid.borderWidth;for(var j=0;j<axes.length;++j){var axis=axes[j],box=axis.box,t=axis.tickLength,x,y,xoff,yoff;if(!axis.show||axis.ticks.length==0)continue;ctx.lineWidth=1;if(axis.direction=="x"){x=0;if(t=="full")y=axis.position=="top"?0:plotHeight;else y=box.top-plotOffset.top+(axis.position=="top"?box.height:0)}else{y=0;if(t=="full")x=axis.position=="left"?0:plotWidth;else x=box.left-plotOffset.left+(axis.position=="left"?box.width:0)}if(!axis.innermost){ctx.strokeStyle=axis.options.color;ctx.beginPath();xoff=yoff=0;if(axis.direction=="x")xoff=plotWidth+1;else yoff=plotHeight+1;if(ctx.lineWidth==1){if(axis.direction=="x"){y=Math.floor(y)+.5}else{x=Math.floor(x)+.5}}ctx.moveTo(x,y);ctx.lineTo(x+xoff,y+yoff);ctx.stroke()}ctx.strokeStyle=axis.options.tickColor;ctx.beginPath();for(i=0;i<axis.ticks.length;++i){var v=axis.ticks[i].v;xoff=yoff=0;if(isNaN(v)||v<axis.min||v>axis.max||t=="full"&&(typeof bw=="object"&&bw[axis.position]>0||bw>0)&&(v==axis.min||v==axis.max))continue;if(axis.direction=="x"){x=axis.p2c(v);yoff=t=="full"?-plotHeight:t;if(axis.position=="top")yoff=-yoff}else{y=axis.p2c(v);xoff=t=="full"?-plotWidth:t;if(axis.position=="left")xoff=-xoff}if(ctx.lineWidth==1){if(axis.direction=="x")x=Math.floor(x)+.5;else y=Math.floor(y)+.5}ctx.moveTo(x,y);ctx.lineTo(x+xoff,y+yoff)}ctx.stroke()}if(bw){bc=options.grid.borderColor;if(typeof bw=="object"||typeof bc=="object"){if(typeof bw!=="object"){bw={top:bw,right:bw,bottom:bw,left:bw}}if(typeof bc!=="object"){bc={top:bc,right:bc,bottom:bc,left:bc}}if(bw.top>0){ctx.strokeStyle=bc.top;ctx.lineWidth=bw.top;ctx.beginPath();ctx.moveTo(0-bw.left,0-bw.top/2);ctx.lineTo(plotWidth,0-bw.top/2);ctx.stroke()}if(bw.right>0){ctx.strokeStyle=bc.right;ctx.lineWidth=bw.right;ctx.beginPath();ctx.moveTo(plotWidth+bw.right/2,0-bw.top);ctx.lineTo(plotWidth+bw.right/2,plotHeight);ctx.stroke()}if(bw.bottom>0){ctx.strokeStyle=bc.bottom;ctx.lineWidth=bw.bottom;ctx.beginPath();ctx.moveTo(plotWidth+bw.right,plotHeight+bw.bottom/2);ctx.lineTo(0,plotHeight+bw.bottom/2);ctx.stroke()}if(bw.left>0){ctx.strokeStyle=bc.left;ctx.lineWidth=bw.left;ctx.beginPath();ctx.moveTo(0-bw.left/2,plotHeight+bw.bottom);ctx.lineTo(0-bw.left/2,0);ctx.stroke()}}else{ctx.lineWidth=bw;ctx.strokeStyle=options.grid.borderColor;ctx.strokeRect(-bw/2,-bw/2,plotWidth+bw,plotHeight+bw)}}ctx.restore()}function drawAxisLabels(){$.each(allAxes(),function(_,axis){var box=axis.box,legacyStyles=axis.direction+"Axis "+axis.direction+axis.n+"Axis",layer="flot-"+axis.direction+"-axis flot-"+axis.direction+axis.n+"-axis "+legacyStyles,font=axis.options.font||"flot-tick-label tickLabel",tick,x,y,halign,valign;surface.removeText(layer);if(!axis.show||axis.ticks.length==0)return;for(var i=0;i<axis.ticks.length;++i){tick=axis.ticks[i];if(!tick.label||tick.v<axis.min||tick.v>axis.max)continue;if(axis.direction=="x"){halign="center";x=plotOffset.left+axis.p2c(tick.v);if(axis.position=="bottom"){y=box.top+box.padding}else{y=box.top+box.height-box.padding;valign="bottom"}}else{valign="middle";y=plotOffset.top+axis.p2c(tick.v);if(axis.position=="left"){x=box.left+box.width-box.padding;halign="right"}else{x=box.left+box.padding}}surface.addText(layer,x,y,tick.label,font,null,null,halign,valign)}})}function drawSeries(series){if(series.lines.show)drawSeriesLines(series);if(series.bars.show)drawSeriesBars(series);if(series.points.show)drawSeriesPoints(series)}function drawSeriesLines(series){function plotLine(datapoints,xoffset,yoffset,axisx,axisy){var points=datapoints.points,ps=datapoints.pointsize,prevx=null,prevy=null;ctx.beginPath();for(var i=ps;i<points.length;i+=ps){var x1=points[i-ps],y1=points[i-ps+1],x2=points[i],y2=points[i+1];if(x1==null||x2==null)continue;if(y1<=y2&&y1<axisy.min){if(y2<axisy.min)continue;x1=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.min}else if(y2<=y1&&y2<axisy.min){if(y1<axisy.min)continue;x2=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.min}if(y1>=y2&&y1>axisy.max){if(y2>axisy.max)continue;x1=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.max}else if(y2>=y1&&y2>axisy.max){if(y1>axisy.max)continue;x2=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.max}if(x1<=x2&&x1<axisx.min){if(x2<axisx.min)continue;y1=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.min}else if(x2<=x1&&x2<axisx.min){if(x1<axisx.min)continue;y2=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.min}if(x1>=x2&&x1>axisx.max){if(x2>axisx.max)continue;y1=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.max}else if(x2>=x1&&x2>axisx.max){if(x1>axisx.max)continue;y2=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.max}if(x1!=prevx||y1!=prevy)ctx.moveTo(axisx.p2c(x1)+xoffset,axisy.p2c(y1)+yoffset);prevx=x2;prevy=y2;ctx.lineTo(axisx.p2c(x2)+xoffset,axisy.p2c(y2)+yoffset)}ctx.stroke()}function plotLineArea(datapoints,axisx,axisy){var points=datapoints.points,ps=datapoints.pointsize,bottom=Math.min(Math.max(0,axisy.min),axisy.max),i=0,top,areaOpen=false,ypos=1,segmentStart=0,segmentEnd=0;while(true){if(ps>0&&i>points.length+ps)break;i+=ps;var x1=points[i-ps],y1=points[i-ps+ypos],x2=points[i],y2=points[i+ypos];if(areaOpen){if(ps>0&&x1!=null&&x2==null){segmentEnd=i;ps=-ps;ypos=2;continue}if(ps<0&&i==segmentStart+ps){ctx.fill();areaOpen=false;ps=-ps;ypos=1;i=segmentStart=segmentEnd+ps;continue}}if(x1==null||x2==null)continue;if(x1<=x2&&x1<axisx.min){if(x2<axisx.min)continue;y1=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.min}else if(x2<=x1&&x2<axisx.min){if(x1<axisx.min)continue;y2=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.min}if(x1>=x2&&x1>axisx.max){if(x2>axisx.max)continue;y1=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.max}else if(x2>=x1&&x2>axisx.max){if(x1>axisx.max)continue;y2=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.max}if(!areaOpen){ctx.beginPath();ctx.moveTo(axisx.p2c(x1),axisy.p2c(bottom));areaOpen=true}if(y1>=axisy.max&&y2>=axisy.max){ctx.lineTo(axisx.p2c(x1),axisy.p2c(axisy.max));ctx.lineTo(axisx.p2c(x2),axisy.p2c(axisy.max));continue}else if(y1<=axisy.min&&y2<=axisy.min){ctx.lineTo(axisx.p2c(x1),axisy.p2c(axisy.min));ctx.lineTo(axisx.p2c(x2),axisy.p2c(axisy.min));continue}var x1old=x1,x2old=x2;if(y1<=y2&&y1<axisy.min&&y2>=axisy.min){x1=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.min}else if(y2<=y1&&y2<axisy.min&&y1>=axisy.min){x2=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.min}if(y1>=y2&&y1>axisy.max&&y2<=axisy.max){x1=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.max}else if(y2>=y1&&y2>axisy.max&&y1<=axisy.max){x2=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.max}if(x1!=x1old){ctx.lineTo(axisx.p2c(x1old),axisy.p2c(y1))}ctx.lineTo(axisx.p2c(x1),axisy.p2c(y1));ctx.lineTo(axisx.p2c(x2),axisy.p2c(y2));if(x2!=x2old){ctx.lineTo(axisx.p2c(x2),axisy.p2c(y2));ctx.lineTo(axisx.p2c(x2old),axisy.p2c(y2))}}}ctx.save();ctx.translate(plotOffset.left,plotOffset.top);ctx.lineJoin="round";var lw=series.lines.lineWidth,sw=series.shadowSize;if(lw>0&&sw>0){ctx.lineWidth=sw;ctx.strokeStyle="rgba(0,0,0,0.1)";var angle=Math.PI/18;plotLine(series.datapoints,Math.sin(angle)*(lw/2+sw/2),Math.cos(angle)*(lw/2+sw/2),series.xaxis,series.yaxis);ctx.lineWidth=sw/2;plotLine(series.datapoints,Math.sin(angle)*(lw/2+sw/4),Math.cos(angle)*(lw/2+sw/4),series.xaxis,series.yaxis)}ctx.lineWidth=lw;ctx.strokeStyle=series.color;var fillStyle=getFillStyle(series.lines,series.color,0,plotHeight);if(fillStyle){ctx.fillStyle=fillStyle;plotLineArea(series.datapoints,series.xaxis,series.yaxis)}if(lw>0)plotLine(series.datapoints,0,0,series.xaxis,series.yaxis);ctx.restore()}function drawSeriesPoints(series){function plotPoints(datapoints,radius,fillStyle,offset,shadow,axisx,axisy,symbol){var points=datapoints.points,ps=datapoints.pointsize;for(var i=0;i<points.length;i+=ps){var x=points[i],y=points[i+1];if(x==null||x<axisx.min||x>axisx.max||y<axisy.min||y>axisy.max)continue;ctx.beginPath();x=axisx.p2c(x);y=axisy.p2c(y)+offset;if(symbol=="circle")ctx.arc(x,y,radius,0,shadow?Math.PI:Math.PI*2,false);else symbol(ctx,x,y,radius,shadow);ctx.closePath();if(fillStyle){ctx.fillStyle=fillStyle;ctx.fill()}ctx.stroke()}}ctx.save();ctx.translate(plotOffset.left,plotOffset.top);var lw=series.points.lineWidth,sw=series.shadowSize,radius=series.points.radius,symbol=series.points.symbol;if(lw==0)lw=1e-4;if(lw>0&&sw>0){var w=sw/2;ctx.lineWidth=w;ctx.strokeStyle="rgba(0,0,0,0.1)";plotPoints(series.datapoints,radius,null,w+w/2,true,series.xaxis,series.yaxis,symbol);ctx.strokeStyle="rgba(0,0,0,0.2)";plotPoints(series.datapoints,radius,null,w/2,true,series.xaxis,series.yaxis,symbol)}ctx.lineWidth=lw;ctx.strokeStyle=series.color;plotPoints(series.datapoints,radius,getFillStyle(series.points,series.color),0,false,series.xaxis,series.yaxis,symbol);ctx.restore()}function drawBar(x,y,b,barLeft,barRight,fillStyleCallback,axisx,axisy,c,horizontal,lineWidth){var left,right,bottom,top,drawLeft,drawRight,drawTop,drawBottom,tmp;if(horizontal){drawBottom=drawRight=drawTop=true;drawLeft=false;left=b;right=x;top=y+barLeft;bottom=y+barRight;if(right<left){tmp=right;right=left;left=tmp;drawLeft=true;drawRight=false}}else{drawLeft=drawRight=drawTop=true;drawBottom=false;left=x+barLeft;right=x+barRight;bottom=b;top=y;if(top<bottom){tmp=top;top=bottom;bottom=tmp;drawBottom=true;drawTop=false}}if(right<axisx.min||left>axisx.max||top<axisy.min||bottom>axisy.max)return;if(left<axisx.min){left=axisx.min;drawLeft=false}if(right>axisx.max){right=axisx.max;drawRight=false}if(bottom<axisy.min){bottom=axisy.min;drawBottom=false}if(top>axisy.max){top=axisy.max;drawTop=false}left=axisx.p2c(left);bottom=axisy.p2c(bottom);right=axisx.p2c(right);top=axisy.p2c(top);if(fillStyleCallback){c.fillStyle=fillStyleCallback(bottom,top);c.fillRect(left,top,right-left,bottom-top)}if(lineWidth>0&&(drawLeft||drawRight||drawTop||drawBottom)){c.beginPath();c.moveTo(left,bottom);if(drawLeft)c.lineTo(left,top);else c.moveTo(left,top);if(drawTop)c.lineTo(right,top);else c.moveTo(right,top);if(drawRight)c.lineTo(right,bottom);else c.moveTo(right,bottom);if(drawBottom)c.lineTo(left,bottom);else c.moveTo(left,bottom);c.stroke()}}function drawSeriesBars(series){function plotBars(datapoints,barLeft,barRight,fillStyleCallback,axisx,axisy){var points=datapoints.points,ps=datapoints.pointsize;for(var i=0;i<points.length;i+=ps){if(points[i]==null)continue;drawBar(points[i],points[i+1],points[i+2],barLeft,barRight,fillStyleCallback,axisx,axisy,ctx,series.bars.horizontal,series.bars.lineWidth)}}ctx.save();ctx.translate(plotOffset.left,plotOffset.top);ctx.lineWidth=series.bars.lineWidth;ctx.strokeStyle=series.color;var barLeft;switch(series.bars.align){case"left":barLeft=0;break;case"right":barLeft=-series.bars.barWidth;break;default:barLeft=-series.bars.barWidth/2}var fillStyleCallback=series.bars.fill?function(bottom,top){return 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     <div id="wrap">
      <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">KB 0/#req 4096/#threads 1/http-client-local-manager</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">0.6945447807956394</span></td>
    <td><span class="time">0.6958670445819131</span></td>
    <td><span class="confinterval citime">0.696443534191764</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">1.1729300854895685e-3</span></td>
    <td><span class="confinterval citime">1.2917187645368968e-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">KB 0/#req 4096/#threads 1/http-client-local-manager-no-timeout</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">0.6003688477891095</span></td>
    <td><span class="time">0.6130005049885668</span></td>
    <td><span class="confinterval citime">0.6202969779162886</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">1.13280202324006e-2</span></td>
    <td><span class="confinterval citime">1.263786182686494e-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="b2">KB 0/#req 4096/#threads 1/http-streams-ioref</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">0.6399668437564033</span></td>
    <td><span class="time">0.6465790182401586</span></td>
    <td><span class="confinterval citime">0.6498700256997139</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">5.7264897757030525e-3</span></td>
    <td><span class="confinterval citime">5.752430026336491e-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="b3">KB 0/#req 4096/#threads 1/http-streams-ioref-no-catchany</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"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle3" 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 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">0.5704915041423265</span></td>
    <td><span class="time">0.570886766685908</span></td>
    <td><span class="confinterval citime">0.5711227560022452</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">3.6113212956155534e-4</span></td>
    <td><span class="confinterval citime">4.0874548593929555e-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="b4">KB 0/#req 2048/#threads 2/http-client-local-manager</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">0.48078434008489607</span></td>
    <td><span class="time">0.4824543109336044</span></td>
    <td><span class="confinterval citime">0.4838987492661299</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">6.798699777552591e-17</span></td>
    <td><span class="time">2.3312008927069494e-3</span></td>
    <td><span class="confinterval citime">2.5018405803341696e-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="b5">KB 0/#req 2048/#threads 2/http-client-local-manager-no-timeout</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">0.4059948627739432</span></td>
    <td><span class="time">0.4133531771746779</span></td>
    <td><span class="confinterval citime">0.4180653161116474</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">7.0860464667137895e-3</span></td>
    <td><span class="confinterval citime">8.161664051154769e-3</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">KB 0/#req 2048/#threads 2/http-streams-ioref</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">0.46511857610306717</span></td>
    <td><span class="time">0.466823384352453</span></td>
    <td><span class="confinterval citime">0.4680375411965149</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">1.8323655871898626e-3</span></td>
    <td><span class="confinterval citime">2.1029813422725613e-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="b7">KB 0/#req 2048/#threads 2/http-streams-ioref-no-catchany</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">0.3688056551472146</span></td>
    <td><span class="time">0.38098780766120227</span></td>
    <td><span class="confinterval citime">0.3876993618719092</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">6.798699777552591e-17</span></td>
    <td><span class="time">1.0713009190929388e-2</span></td>
    <td><span class="confinterval citime">1.1624752890697353e-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="b8">KB 0/#req 1024/#threads 4/http-client-local-manager</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">0.3107998484810478</span></td>
    <td><span class="time">0.3141481097994447</span></td>
    <td><span class="confinterval citime">0.31749637111784157</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">2.8613077061659456e-3</span></td>
    <td><span class="time">3.96986736801207e-3</span></td>
    <td><span class="confinterval citime">4.756846012448269e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.16</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b9">KB 0/#req 1024/#threads 4/http-client-local-manager-no-timeout</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">0.2660641422109244</span></td>
    <td><span class="time">0.2668085120781516</span></td>
    <td><span class="confinterval citime">0.2684955611329388</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">1.6154186185492848e-4</span></td>
    <td><span class="time">1.159421947632865e-3</span></td>
    <td><span class="confinterval citime">1.4845513000652439e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.16</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b10">KB 0/#req 1024/#threads 4/http-streams-ioref</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde10" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time10" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle10" 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 olstimelb10">xxx</span></td>
    <td><span class="olstimept10">xxx</span></td>
    <td><span class="confinterval olstimeub10">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb10">xxx</span></td>
    <td><span class="olsr2pt10">xxx</span></td>
    <td><span class="confinterval olsr2ub10">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.3141007918252806</span></td>
    <td><span class="time">0.31854096797985615</span></td>
    <td><span class="confinterval citime">0.32175417110114324</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">1.8197670476842454e-3</span></td>
    <td><span class="time">4.7623089073243864e-3</span></td>
    <td><span class="confinterval citime">6.5920027767347265e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.16000000000000003</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b11">KB 0/#req 1024/#threads 4/http-streams-ioref-no-catchany</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde11" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time11" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle11" 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 olstimelb11">xxx</span></td>
    <td><span class="olstimept11">xxx</span></td>
    <td><span class="confinterval olstimeub11">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb11">xxx</span></td>
    <td><span class="olsr2pt11">xxx</span></td>
    <td><span class="confinterval olsr2ub11">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.2388713571653348</span></td>
    <td><span class="time">0.24310553481319191</span></td>
    <td><span class="confinterval citime">0.2476229396542681</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">2.5012780861497774e-3</span></td>
    <td><span class="time">4.782056331322019e-3</span></td>
    <td><span class="confinterval citime">6.736971341094461e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.15999999999999998</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b12">KB 0/#req 512/#threads 8/http-client-local-manager</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde12" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time12" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle12" 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 olstimelb12">xxx</span></td>
    <td><span class="olstimept12">xxx</span></td>
    <td><span class="confinterval olstimeub12">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb12">xxx</span></td>
    <td><span class="olsr2pt12">xxx</span></td>
    <td><span class="confinterval olsr2ub12">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.45067336857751256</span></td>
    <td><span class="time">0.4528660051574159</span></td>
    <td><span class="confinterval citime">0.4544718620082034</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">2.43770533154772e-3</span></td>
    <td><span class="confinterval citime">2.7814256552466214e-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="b13">KB 0/#req 512/#threads 8/http-client-local-manager-no-timeout</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde13" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time13" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle13" 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 olstimelb13">xxx</span></td>
    <td><span class="olstimept13">xxx</span></td>
    <td><span class="confinterval olstimeub13">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb13">xxx</span></td>
    <td><span class="olsr2pt13">xxx</span></td>
    <td><span class="confinterval olsr2ub13">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.18205588867873862</span></td>
    <td><span class="time">0.18308040475594076</span></td>
    <td><span class="confinterval citime">0.18483978607630608</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">6.223612850806512e-4</span></td>
    <td><span class="time">1.7265915159426412e-3</span></td>
    <td><span class="confinterval citime">2.4597240027326296e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.1388888888888887</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b14">KB 0/#req 512/#threads 8/http-streams-ioref</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde14" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time14" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle14" 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 olstimelb14">xxx</span></td>
    <td><span class="olstimept14">xxx</span></td>
    <td><span class="confinterval olstimeub14">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb14">xxx</span></td>
    <td><span class="olsr2pt14">xxx</span></td>
    <td><span class="confinterval olsr2ub14">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.22822302941026015</span></td>
    <td><span class="time">0.2324300266992941</span></td>
    <td><span class="confinterval citime">0.23619514810671283</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">3.013075473335044e-3</span></td>
    <td><span class="time">4.99244653242614e-3</span></td>
    <td><span class="confinterval citime">7.267206023084981e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.1388888888888888</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b15">KB 0/#req 512/#threads 8/http-streams-ioref-no-catchany</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde15" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time15" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle15" 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 olstimelb15">xxx</span></td>
    <td><span class="olstimept15">xxx</span></td>
    <td><span class="confinterval olstimeub15">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb15">xxx</span></td>
    <td><span class="olsr2pt15">xxx</span></td>
    <td><span class="confinterval olsr2ub15">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.1641972665833877</span></td>
    <td><span class="time">0.16495018836427688</span></td>
    <td><span class="confinterval citime">0.16578670053862912</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">8.01987764059204e-4</span></td>
    <td><span class="time">1.1272710875031484e-3</span></td>
    <td><span class="confinterval citime">1.5009546974905094e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.12244897959183675</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b16">KB 0/#req 256/#threads 16/http-client-local-manager</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde16" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time16" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle16" 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 olstimelb16">xxx</span></td>
    <td><span class="olstimept16">xxx</span></td>
    <td><span class="confinterval olstimeub16">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb16">xxx</span></td>
    <td><span class="olsr2pt16">xxx</span></td>
    <td><span class="confinterval olsr2ub16">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.4411739388232982</span></td>
    <td><span class="time">0.4460839398080177</span></td>
    <td><span class="confinterval citime">0.4486804589266249</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">6.798699777552591e-17</span></td>
    <td><span class="time">4.273327736862047e-3</span></td>
    <td><span class="confinterval citime">4.497303036251693e-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="b17">KB 0/#req 256/#threads 16/http-client-local-manager-no-timeout</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde17" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time17" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle17" 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 olstimelb17">xxx</span></td>
    <td><span class="olstimept17">xxx</span></td>
    <td><span class="confinterval olstimeub17">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb17">xxx</span></td>
    <td><span class="olsr2pt17">xxx</span></td>
    <td><span class="confinterval olsr2ub17">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.1514758553240861</span></td>
    <td><span class="time">0.152424454650673</span></td>
    <td><span class="confinterval citime">0.15378640923659026</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">1.0061379230180835e-3</span></td>
    <td><span class="time">1.5656422073784954e-3</span></td>
    <td><span class="confinterval citime">2.137765994317149e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.12244897959183673</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b18">KB 0/#req 256/#threads 16/http-streams-ioref</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde18" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time18" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle18" 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 olstimelb18">xxx</span></td>
    <td><span class="olstimept18">xxx</span></td>
    <td><span class="confinterval olstimeub18">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb18">xxx</span></td>
    <td><span class="olsr2pt18">xxx</span></td>
    <td><span class="confinterval olsr2ub18">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.1701893100511124</span></td>
    <td><span class="time">0.17335940088903934</span></td>
    <td><span class="confinterval citime">0.17726367737303414</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">3.0135908523595692e-3</span></td>
    <td><span class="time">4.735149566777617e-3</span></td>
    <td><span class="confinterval citime">7.161798953214419e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.12244897959183673</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b19">KB 0/#req 256/#threads 16/http-streams-ioref-no-catchany</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde19" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time19" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle19" 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 olstimelb19">xxx</span></td>
    <td><span class="olstimept19">xxx</span></td>
    <td><span class="confinterval olstimeub19">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb19">xxx</span></td>
    <td><span class="olsr2pt19">xxx</span></td>
    <td><span class="confinterval olsr2ub19">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.14901515947159225</span></td>
    <td><span class="time">0.15037634195874408</span></td>
    <td><span class="confinterval citime">0.15284955102667588</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">1.6476039667725454e-3</span></td>
    <td><span class="time">2.364748827040983e-3</span></td>
    <td><span class="confinterval citime">3.040608029280516e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.12244897959183672</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b20">KB 0/#req 64/#threads 64/http-client-local-manager</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde20" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time20" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle20" 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 olstimelb20">xxx</span></td>
    <td><span class="olstimept20">xxx</span></td>
    <td><span class="confinterval olstimeub20">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb20">xxx</span></td>
    <td><span class="olsr2pt20">xxx</span></td>
    <td><span class="confinterval olsr2ub20">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.4228685793277273</span></td>
    <td><span class="time">0.42489491319261635</span></td>
    <td><span class="confinterval citime">0.42806366511886645</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">2.7795806094655473e-3</span></td>
    <td><span class="confinterval citime">2.999384179977384e-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="b21">KB 0/#req 64/#threads 64/http-client-local-manager-no-timeout</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde21" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time21" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle21" 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 olstimelb21">xxx</span></td>
    <td><span class="olstimept21">xxx</span></td>
    <td><span class="confinterval olstimeub21">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb21">xxx</span></td>
    <td><span class="olsr2pt21">xxx</span></td>
    <td><span class="confinterval olsr2ub21">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.14590952573864402</span></td>
    <td><span class="time">0.14736837479506665</span></td>
    <td><span class="confinterval citime">0.14915428594662936</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">1.1272884245153005e-3</span></td>
    <td><span class="time">2.2538296916626904e-3</span></td>
    <td><span class="confinterval citime">3.3747182402558105e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.12244897959183675</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b22">KB 0/#req 64/#threads 64/http-streams-ioref</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde22" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time22" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle22" 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 olstimelb22">xxx</span></td>
    <td><span class="olstimept22">xxx</span></td>
    <td><span class="confinterval olstimeub22">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb22">xxx</span></td>
    <td><span class="olsr2pt22">xxx</span></td>
    <td><span class="confinterval olsr2ub22">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.15874909669698914</span></td>
    <td><span class="time">0.16415553382335415</span></td>
    <td><span class="confinterval citime">0.17105501418518518</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">5.36069433701367e-3</span></td>
    <td><span class="time">8.590003243564827e-3</span></td>
    <td><span class="confinterval citime">1.1197163736210774e-2</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.12622108945334687</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b23">KB 0/#req 64/#threads 64/http-streams-ioref-no-catchany</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde23" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time23" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle23" 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 olstimelb23">xxx</span></td>
    <td><span class="olstimept23">xxx</span></td>
    <td><span class="confinterval olstimeub23">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb23">xxx</span></td>
    <td><span class="olsr2pt23">xxx</span></td>
    <td><span class="confinterval olsr2ub23">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.18103666915166214</span></td>
    <td><span class="time">0.1881811242050511</span></td>
    <td><span class="confinterval citime">0.20082180187014975</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">1.7456661012178556e-3</span></td>
    <td><span class="time">1.2918851747345916e-2</span></td>
    <td><span class="confinterval citime">1.753634208988458e-2</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.1513180032174908</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]];
    for (var i = 0; i < times.length; i++)
      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 },
              xaxis: { tickFormatter: iterFormatter() },
              yaxis: { tickFormatter: $.unitFormatter(scale) } });
    $.addTooltip("#time" + number,
		 function(iters,secs) {
		   return ($.renderTime(secs / scale) + ' / ' +
			   iters.toLocaleString() + ' iters');
		 });
    if (0) {
      var cyclepairs = new Array(cycles.length);
      for (var i = 0; i < cycles.length; i++)
	cyclepairs[i] = [cycles[i],i];
      $.plot($("#cycle" + number),
	     [{ label: name + " cycles",
		data: cyclepairs }],
	     { points: { show: true },
	       grid: { borderColor: "#777", hoverable: true },
	       xaxis: { tickFormatter:
			function(cycles,axis) { return cycles + ' cycles'; }},
	       yaxis: { ticks: false },
	     });
      $.addTooltip("#cycles" + number, function(x,y) { return x + ' cycles'; });
    }
  };
  var reports = [{"reportAnalysis":{"anMean":{"estUpperBound":0.696443534191764,"estLowerBound":0.6945447807956394,"estPoint":0.6958670445819131,"estConfidenceLevel":0.95},"anRegress":[{"regRSquare":{"estUpperBound":1,"estLowerBound":0.9999604191135036,"estPoint":0.9999878711799278,"estConfidenceLevel":0.95},"regResponder":"time","regCoeffs":{"y":{"estUpperBound":1.037872768938863e-2,"estLowerBound":-2.6845955639157693e-2,"estPoint":-7.711121055761313e-3,"estConfidenceLevel":0.95},"iters":{"estUpperBound":null,"estLowerBound":0.6910858431365331,"estPoint":0.6983740319265045,"estConfidenceLevel":0.95}}}],"anStdDev":{"estUpperBound":1.2917187645368968e-3,"estLowerBound":0.0,"estPoint":1.1729300854895685e-3,"estConfidenceLevel":0.95},"anOutlierVar":{"ovFraction":0.1875,"ovDesc":"moderate","ovEffect":"Moderate"},"anOverhead":3.8294241382531885e-6},"reportKDEs":[{"kdeValues":[0.6943210485427126,0.6943421885981073,0.6943633286535019,0.6943844687088966,0.6944056087642912,0.6944267488196859,0.6944478888750806,0.6944690289304752,0.6944901689858699,0.6945113090412646,0.6945324490966592,0.6945535891520538,0.6945747292074484,0.6945958692628431,0.6946170093182378,0.6946381493736324,0.6946592894290271,0.6946804294844218,0.6947015695398164,0.6947227095952111,0.6947438496506058,0.6947649897060004,0.6947861297613951,0.6948072698167898,0.6948284098721844,0.6948495499275791,0.6948706899829737,0.6948918300383684,0.6949129700937631,0.6949341101491577,0.6949552502045524,0.6949763902599471,0.6949975303153416,0.6950186703707363,0.6950398104261309,0.6950609504815256,0.6950820905369203,0.6951032305923149,0.6951243706477096,0.6951455107031043,0.6951666507584989,0.6951877908138936,0.6952089308692883,0.6952300709246829,0.6952512109800776,0.6952723510354722,0.6952934910908669,0.6953146311462616,0.6953357712016562,0.6953569112570509,0.6953780513124456,0.6953991913678402,0.6954203314232349,0.6954414714786294,0.6954626115340241,0.6954837515894188,0.6955048916448134,0.6955260317002081,0.6955471717556028,0.6955683118109974,0.6955894518663921,0.6956105919217868,0.6956317319771814,0.6956528720325761,0.6956740120879708,0.6956951521433654,0.6957162921987601,0.6957374322541547,0.6957585723095494,0.6957797123649441,0.6958008524203387,0.6958219924757334,0.6958431325311281,0.6958642725865227,0.6958854126419174,0.6959065526973119,0.6959276927527066,0.6959488328081013,0.6959699728634959,0.6959911129188906,0.6960122529742853,0.6960333930296799,0.6960545330850746,0.6960756731404693,0.6960968131958639,0.6961179532512586,0.6961390933066532,0.6961602333620479,0.6961813734174426,0.6962025134728372,0.6962236535282319,0.6962447935836266,0.6962659336390212,0.6962870736944159,0.6963082137498106,0.6963293538052052,0.6963504938605998,0.6963716339159944,0.6963927739713891,0.6964139140267838,0.6964350540821784,0.6964561941375731,0.6964773341929678,0.6964984742483624,0.6965196143037571,0.6965407543591517,0.6965618944145464,0.6965830344699411,0.6966041745253357,0.6966253145807304,0.6966464546361251,0.6966675946915197,0.6966887347469144,0.6967098748023091,0.6967310148577037,0.6967521549130984,0.696773294968493,0.6967944350238876,0.6968155750792823,0.6968367151346769,0.6968578551900716,0.6968789952454663,0.6969001353008609,0.6969212753562556,0.6969424154116503,0.6969635554670449,0.6969846955224396,0.6970058355778342],"kdeType":"time","kdePDF":[316.42386013892394,316.4366912054438,316.4623970104474,316.50106474239936,316.55282479577505,316.61785030640624,316.6963565339615,316.7886000932821,316.89487803671705,317.01552679001867,317.1509209447614,317.3014719106554,317.4676264315064,317.6498649689626,317.84869995854774,318.06467394284056,318.2983575870004,318.55034758216345,318.82126444254476,319.1117502023813,319.4224660191181,319.7540896895086,320.10731308553534,320.4828395172791,320.881381030067,321.3036556434108,321.75038453940465,322.2222892083953,322.7200885598479,323.24449600643476,323.79621652943894,324.3759437336251,324.9843568997511,325.622118042908,326.28986898485664,326.98822844849326,327.7177891825214,328.47911512432,329.272738608906,330.09915763175803,330.958833173134,331.8521865913462,332.77959709228173,333.7413992822506,334.73788081103186,335.76928011174374,336.83578424392283,337.93752684591516,339.0745862024156,340.24698343267545,341.4546808046061,342.6975801796675,343.97552159310897,345.2882819737704,346.6355740073147,348.01704514637896,349.43227677078124,350.8807835005232,352.3620126639653,353.875343923144,355.4200890578298,356.9954919095129,358.600728486126,360.23490722790416,361.8970694343995,363.5861898522661,365.301177423051,367.04087618982715,368.80406636114134,370.5894655303499,372.39573004807204,374.22145654510024,376.0651836027783,377.92539356748875,379.80051450557454,381.68892229467554,383.5889428471649,385.49885446104537,387.41689029339585,389.34124095116033,391.27005719382595,393.2014527422645,395.1335071877992,397.06426899531215,398.9917585940221,400.91397154935663,402.8288818091769,404.73444501744535,406.6286018882944,408.5092816333146,410.3744054347854,412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0/#req 4096/#threads 1/http-client-local-manager","reportOutliers":{"highSevere":0,"highMild":0,"lowMild":0,"samplesSeen":3,"lowSevere":0},"reportMeasured":[[0.6895788359688595,1.7641240000000025,1582403352,1,null,null,null,null,null,null,null],[1.3925504139624536,3.550123000000003,3195593166,2,null,null,null,null,null,null,null],[2.0836362570989877,5.322861,4781483941,3,null,null,null,null,null,null,null],[2.787130328011699,7.109632000000005,6395838139,4,null,null,null,null,null,null,null]]},{"reportAnalysis":{"anMean":{"estUpperBound":0.6202969779162886,"estLowerBound":0.6003688477891095,"estPoint":0.6130005049885668,"estConfidenceLevel":0.95},"anRegress":[{"regRSquare":{"estUpperBound":1,"estLowerBound":0.9954911357236221,"estPoint":0.9985897218775696,"estConfidenceLevel":0.95},"regResponder":"time","regCoeffs":{"y":{"estUpperBound":9.603523160330658e-2,"estLowerBound":-0.26267111068591714,"estPoint":-1.093162759206998e-2,"estConfidenceLevel":0.95},"iters":{"estUpperBound":0.6879265229217716,"estLowerBound":0.5683577421586974,"estPoint":0.6183831170317711,"estConfidenceLevel":0.95}}}],"anStdDev":{"estUpperBound":1.263786182686494e-2,"estLowerBound":0.0,"estPoint":1.13280202324006e-2,"estConfidenceLevel":0.95},"anOutlierVar":{"ovFraction":0.1875,"ovDesc":"moderate","ovEffect":"Moderate"},"anOverhead":3.8294241382531885e-6},"reportKDEs":[{"kdeValues":[0.598179905910793,0.5983867350646497,0.5985935642185063,0.598800393372363,0.5990072225262196,0.5992140516800764,0.5994208808339331,0.5996277099877897,0.5998345391416464,0.600041368295503,0.6002481974493598,0.6004550266032165,0.6006618557570731,0.6008686849109298,0.6010755140647864,0.6012823432186432,0.6014891723724999,0.6016960015263565,0.6019028306802132,0.6021096598340698,0.6023164889879266,0.6025233181417833,0.6027301472956399,0.6029369764494966,0.6031438056033532,0.60335063475721,0.6035574639110667,0.6037642930649233,0.60397112221878,0.6041779513726366,0.6043847805264934,0.6045916096803501,0.6047984388342067,0.6050052679880634,0.60521209714192,0.6054189262957768,0.6056257554496335,0.6058325846034901,0.6060394137573468,0.6062462429112034,0.6064530720650602,0.6066599012189169,0.6068667303727735,0.6070735595266302,0.6072803886804868,0.6074872178343436,0.6076940469882003,0.6079008761420569,0.6081077052959136,0.6083145344497702,0.608521363603627,0.6087281927574837,0.6089350219113403,0.609141851065197,0.6093486802190536,0.6095555093729104,0.6097623385267671,0.6099691676806237,0.6101759968344804,0.610382825988337,0.6105896551421938,0.6107964842960505,0.6110033134499071,0.6112101426037638,0.6114169717576204,0.6116238009114772,0.6118306300653338,0.6120374592191905,0.6122442883730472,0.6124511175269038,0.6126579466807606,0.6128647758346172,0.6130716049884739,0.6132784341423306,0.6134852632961872,0.613692092450044,0.6138989216039006,0.6141057507577573,0.614312579911614,0.6145194090654706,0.6147262382193274,0.614933067373184,0.6151398965270407,0.6153467256808974,0.615553554834754,0.6157603839886108,0.6159672131424674,0.6161740422963241,0.6163808714501808,0.6165877006040374,0.6167945297578942,0.6170013589117508,0.6172081880656075,0.6174150172194642,0.6176218463733208,0.6178286755271776,0.6180355046810342,0.6182423338348909,0.6184491629887476,0.6186559921426042,0.618862821296461,0.6190696504503176,0.6192764796041743,0.619483308758031,0.6196901379118877,0.6198969670657444,0.620103796219601,0.6203106253734577,0.6205174545273144,0.620724283681171,0.6209311128350278,0.6211379419888844,0.6213447711427411,0.6215516002965978,0.6217584294504545,0.6219652586043112,0.6221720877581678,0.6223789169120245,0.6225857460658812,0.6227925752197379,0.6229994043735946,0.6232062335274512,0.6234130626813079,0.6236198918351646,0.6238267209890213,0.624033550142878,0.6242403792967346,0.6244472084505913],"kdeType":"time","kdePDF":[33.1791306996091,33.18024085015909,33.182465024380825,33.1858109545985,33.19029020453984,33.19591812773715,33.202713812247254,33.21070001184836,33.219903063910415,33.23035279417376,33.24208240870795,33.25512837335952,33.26953028103251,33.28533070718089,33.30257505392474,33.32131138323488,33.3415902396613,33.36346446311028,33.38698899220308,33.41222065877596,33.43921797410547,33.46804090746675,33.49875065765342,33.53140941810754,33.56608013632559,33.60282626822224,33.641711528147376,33.6827996352637,33.726154057002006,33.771837750318824,33.81991290148689,33.87044066515256,33.92348090339569,33.97909192552707,34.037330229356,34.09825024465592,34.16190407954959,34.228341270526485,34.29760853679508,34.36974953965983,34.44480464759883,34.52281070770188,34.60380082411136,34.687804144088275,34.774845652305686,34.86494597394838,34.95812118717441,35.0543826454681,35.153736810388175,35.256185095185984,35.36172371974078,35.470343577227936,35.582030112906104,35.69676321537639,35.814517120635315,35.93526032920866,36.058955536621085,36.18555957742058,36.31502338294362,36.44729195297082,36.58230434138883,36.71999365593792,36.86028707209114,37.00310586107387,37.148365431999984,37.29597538806414,37.4458395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  reports.map(mangulate);

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$(document).ready(function () {
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   </div>
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     <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>
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i=0,position;position=positions[i];i++){if(position.x==x&&position.y==y){position.active=true;return}}position={active:true,rendered:false,element:positions.length?info.element.clone():info.element,x:x,y:y};positions.push(position);position.element.css({top:Math.round(y),left:Math.round(x),"text-align":halign})};Canvas.prototype.removeText=function(layer,x,y,text,font,angle){if(text==null){var layerCache=this._textCache[layer];if(layerCache!=null){for(var styleKey in layerCache){if(hasOwnProperty.call(layerCache,styleKey)){var styleCache=layerCache[styleKey];for(var key in styleCache){if(hasOwnProperty.call(styleCache,key)){var positions=styleCache[key].positions;for(var i=0,position;position=positions[i];i++){position.active=false}}}}}}}else{var positions=this.getTextInfo(layer,text,font,angle).positions;for(var i=0,position;position=positions[i];i++){if(position.x==x&&position.y==y){position.active=false}}}};function Plot(placeholder,data_,options_,plugins){var series=[],options={colors:["#edc240","#afd8f8","#cb4b4b","#4da74d","#9440ed"],legend:{show:true,noColumns:1,labelFormatter:null,labelBoxBorderColor:"#ccc",container:null,position:"ne",margin:5,backgroundColor:null,backgroundOpacity:.85,sorted:null},xaxis:{show:null,position:"bottom",mode:null,font:null,color:null,tickColor:null,transform:null,inverseTransform:null,min:null,max:null,autoscaleMargin:null,ticks:null,tickFormatter:null,labelWidth:null,labelHeight:null,reserveSpace:null,tickLength:null,alignTicksWithAxis:null,tickDecimals:null,tickSize:null,minTickSize:null},yaxis:{autoscaleMargin:.02,position:"left"},xaxes:[],yaxes:[],series:{points:{show:false,radius:3,lineWidth:2,fill:true,fillColor:"#ffffff",symbol:"circle"},lines:{lineWidth:2,fill:false,fillColor:null,steps:false},bars:{show:false,lineWidth:2,barWidth:1,fill:true,fillColor:null,align:"left",horizontal:false,zero:true},shadowSize:3,highlightColor:null},grid:{show:true,aboveData:false,color:"#545454",backgroundColor:null,borderColor:null,tickColor:null,margin:0,labelMargin:5,axisMargin:8,borderWidth:2,minBorderMargin:null,markings:null,markingsColor:"#f4f4f4",markingsLineWidth:2,clickable:false,hoverable:false,autoHighlight:true,mouseActiveRadius:10},interaction:{redrawOverlayInterval:1e3/60},hooks:{}},surface=null,overlay=null,eventHolder=null,ctx=null,octx=null,xaxes=[],yaxes=[],plotOffset={left:0,right:0,top:0,bottom:0},plotWidth=0,plotHeight=0,hooks={processOptions:[],processRawData:[],processDatapoints:[],processOffset:[],drawBackground:[],drawSeries:[],draw:[],bindEvents:[],drawOverlay:[],shutdown:[]},plot=this;plot.setData=setData;plot.setupGrid=setupGrid;plot.draw=draw;plot.getPlaceholder=function(){return placeholder};plot.getCanvas=function(){return surface.element};plot.getPlotOffset=function(){return plotOffset};plot.width=function(){return plotWidth};plot.height=function(){return plotHeight};plot.offset=function(){var o=eventHolder.offset();o.left+=plotOffset.left;o.top+=plotOffset.top;return o};plot.getData=function(){return series};plot.getAxes=function(){var res={},i;$.each(xaxes.concat(yaxes),function(_,axis){if(axis)res[axis.direction+(axis.n!=1?axis.n:"")+"axis"]=axis});return res};plot.getXAxes=function(){return xaxes};plot.getYAxes=function(){return yaxes};plot.c2p=canvasToAxisCoords;plot.p2c=axisToCanvasCoords;plot.getOptions=function(){return options};plot.highlight=highlight;plot.unhighlight=unhighlight;plot.triggerRedrawOverlay=triggerRedrawOverlay;plot.pointOffset=function(point){return{left:parseInt(xaxes[axisNumber(point,"x")-1].p2c(+point.x)+plotOffset.left,10),top:parseInt(yaxes[axisNumber(point,"y")-1].p2c(+point.y)+plotOffset.top,10)}};plot.shutdown=shutdown;plot.destroy=function(){shutdown();placeholder.removeData("plot").empty();series=[];options=null;surface=null;overlay=null;eventHolder=null;ctx=null;octx=null;xaxes=[];yaxes=[];hooks=null;highlights=[];plot=null};plot.resize=function(){var width=placeholder.width(),height=placeholder.height();surface.resize(width,height);overlay.resize(width,height)};plot.hooks=hooks;initPlugins(plot);parseOptions(options_);setupCanvases();setData(data_);setupGrid();draw();bindEvents();function executeHooks(hook,args){args=[plot].concat(args);for(var i=0;i<hook.length;++i)hook[i].apply(this,args)}function initPlugins(){var classes={Canvas:Canvas};for(var i=0;i<plugins.length;++i){var p=plugins[i];p.init(plot,classes);if(p.options)$.extend(true,options,p.options)}}function parseOptions(opts){$.extend(true,options,opts);if(opts&&opts.colors){options.colors=opts.colors}if(options.xaxis.color==null)options.xaxis.color=$.color.parse(options.grid.color).scale("a",.22).toString();if(options.yaxis.color==null)options.yaxis.color=$.color.parse(options.grid.color).scale("a",.22).toString();if(options.xaxis.tickColor==null)options.xaxis.tickColor=options.grid.tickColor||options.xaxis.color;if(options.yaxis.tickColor==null)options.yaxis.tickColor=options.grid.tickColor||options.yaxis.color;if(options.grid.borderColor==null)options.grid.borderColor=options.grid.color;if(options.grid.tickColor==null)options.grid.tickColor=$.color.parse(options.grid.color).scale("a",.22).toString();var i,axisOptions,axisCount,fontSize=placeholder.css("font-size"),fontSizeDefault=fontSize?+fontSize.replace("px",""):13,fontDefaults={style:placeholder.css("font-style"),size:Math.round(.8*fontSizeDefault),variant:placeholder.css("font-variant"),weight:placeholder.css("font-weight"),family:placeholder.css("font-family")};axisCount=options.xaxes.length||1;for(i=0;i<axisCount;++i){axisOptions=options.xaxes[i];if(axisOptions&&!axisOptions.tickColor){axisOptions.tickColor=axisOptions.color}axisOptions=$.extend(true,{},options.xaxis,axisOptions);options.xaxes[i]=axisOptions;if(axisOptions.font){axisOptions.font=$.extend({},fontDefaults,axisOptions.font);if(!axisOptions.font.color){axisOptions.font.color=axisOptions.color}if(!axisOptions.font.lineHeight){axisOptions.font.lineHeight=Math.round(axisOptions.font.size*1.15)}}}axisCount=options.yaxes.length||1;for(i=0;i<axisCount;++i){axisOptions=options.yaxes[i];if(axisOptions&&!axisOptions.tickColor){axisOptions.tickColor=axisOptions.color}axisOptions=$.extend(true,{},options.yaxis,axisOptions);options.yaxes[i]=axisOptions;if(axisOptions.font){axisOptions.font=$.extend({},fontDefaults,axisOptions.font);if(!axisOptions.font.color){axisOptions.font.color=axisOptions.color}if(!axisOptions.font.lineHeight){axisOptions.font.lineHeight=Math.round(axisOptions.font.size*1.15)}}}if(options.xaxis.noTicks&&options.xaxis.ticks==null)options.xaxis.ticks=options.xaxis.noTicks;if(options.yaxis.noTicks&&options.yaxis.ticks==null)options.yaxis.ticks=options.yaxis.noTicks;if(options.x2axis){options.xaxes[1]=$.extend(true,{},options.xaxis,options.x2axis);options.xaxes[1].position="top";if(options.x2axis.min==null){options.xaxes[1].min=null}if(options.x2axis.max==null){options.xaxes[1].max=null}}if(options.y2axis){options.yaxes[1]=$.extend(true,{},options.yaxis,options.y2axis);options.yaxes[1].position="right";if(options.y2axis.min==null){options.yaxes[1].min=null}if(options.y2axis.max==null){options.yaxes[1].max=null}}if(options.grid.coloredAreas)options.grid.markings=options.grid.coloredAreas;if(options.grid.coloredAreasColor)options.grid.markingsColor=options.grid.coloredAreasColor;if(options.lines)$.extend(true,options.series.lines,options.lines);if(options.points)$.extend(true,options.series.points,options.points);if(options.bars)$.extend(true,options.series.bars,options.bars);if(options.shadowSize!=null)options.series.shadowSize=options.shadowSize;if(options.highlightColor!=null)options.series.highlightColor=options.highlightColor;for(i=0;i<options.xaxes.length;++i)getOrCreateAxis(xaxes,i+1).options=options.xaxes[i];for(i=0;i<options.yaxes.length;++i)getOrCreateAxis(yaxes,i+1).options=options.yaxes[i];for(var n in hooks)if(options.hooks[n]&&options.hooks[n].length)hooks[n]=hooks[n].concat(options.hooks[n]);executeHooks(hooks.processOptions,[options])}function setData(d){series=parseData(d);fillInSeriesOptions();processData()}function parseData(d){var res=[];for(var i=0;i<d.length;++i){var s=$.extend(true,{},options.series);if(d[i].data!=null){s.data=d[i].data;delete d[i].data;$.extend(true,s,d[i]);d[i].data=s.data}else s.data=d[i];res.push(s)}return res}function axisNumber(obj,coord){var a=obj[coord+"axis"];if(typeof a=="object")a=a.n;if(typeof a!="number")a=1;return a}function allAxes(){return $.grep(xaxes.concat(yaxes),function(a){return a})}function canvasToAxisCoords(pos){var res={},i,axis;for(i=0;i<xaxes.length;++i){axis=xaxes[i];if(axis&&axis.used)res["x"+axis.n]=axis.c2p(pos.left)}for(i=0;i<yaxes.length;++i){axis=yaxes[i];if(axis&&axis.used)res["y"+axis.n]=axis.c2p(pos.top)}if(res.x1!==undefined)res.x=res.x1;if(res.y1!==undefined)res.y=res.y1;return res}function axisToCanvasCoords(pos){var res={},i,axis,key;for(i=0;i<xaxes.length;++i){axis=xaxes[i];if(axis&&axis.used){key="x"+axis.n;if(pos[key]==null&&axis.n==1)key="x";if(pos[key]!=null){res.left=axis.p2c(pos[key]);break}}}for(i=0;i<yaxes.length;++i){axis=yaxes[i];if(axis&&axis.used){key="y"+axis.n;if(pos[key]==null&&axis.n==1)key="y";if(pos[key]!=null){res.top=axis.p2c(pos[key]);break}}}return res}function getOrCreateAxis(axes,number){if(!axes[number-1])axes[number-1]={n:number,direction:axes==xaxes?"x":"y",options:$.extend(true,{},axes==xaxes?options.xaxis:options.yaxis)};return axes[number-1]}function fillInSeriesOptions(){var neededColors=series.length,maxIndex=-1,i;for(i=0;i<series.length;++i){var sc=series[i].color;if(sc!=null){neededColors--;if(typeof sc=="number"&&sc>maxIndex){maxIndex=sc}}}if(neededColors<=maxIndex){neededColors=maxIndex+1}var c,colors=[],colorPool=options.colors,colorPoolSize=colorPool.length,variation=0;for(i=0;i<neededColors;i++){c=$.color.parse(colorPool[i%colorPoolSize]||"#666");if(i%colorPoolSize==0&&i){if(variation>=0){if(variation<.5){variation=-variation-.2}else variation=0}else variation=-variation}colors[i]=c.scale("rgb",1+variation)}var colori=0,s;for(i=0;i<series.length;++i){s=series[i];if(s.color==null){s.color=colors[colori].toString();++colori}else if(typeof s.color=="number")s.color=colors[s.color].toString();if(s.lines.show==null){var v,show=true;for(v in s)if(s[v]&&s[v].show){show=false;break}if(show)s.lines.show=true}if(s.lines.zero==null){s.lines.zero=!!s.lines.fill}s.xaxis=getOrCreateAxis(xaxes,axisNumber(s,"x"));s.yaxis=getOrCreateAxis(yaxes,axisNumber(s,"y"))}}function processData(){var topSentry=Number.POSITIVE_INFINITY,bottomSentry=Number.NEGATIVE_INFINITY,fakeInfinity=Number.MAX_VALUE,i,j,k,m,length,s,points,ps,x,y,axis,val,f,p,data,format;function updateAxis(axis,min,max){if(min<axis.datamin&&min!=-fakeInfinity)axis.datamin=min;if(max>axis.datamax&&max!=fakeInfinity)axis.datamax=max}$.each(allAxes(),function(_,axis){axis.datamin=topSentry;axis.datamax=bottomSentry;axis.used=false});for(i=0;i<series.length;++i){s=series[i];s.datapoints={points:[]};executeHooks(hooks.processRawData,[s,s.data,s.datapoints])}for(i=0;i<series.length;++i){s=series[i];data=s.data;format=s.datapoints.format;if(!format){format=[];format.push({x:true,number:true,required:true});format.push({y:true,number:true,required:true});if(s.bars.show||s.lines.show&&s.lines.fill){var autoscale=!!(s.bars.show&&s.bars.zero||s.lines.show&&s.lines.zero);format.push({y:true,number:true,required:false,defaultValue:0,autoscale:autoscale});if(s.bars.horizontal){delete format[format.length-1].y;format[format.length-1].x=true}}s.datapoints.format=format}if(s.datapoints.pointsize!=null)continue;s.datapoints.pointsize=format.length;ps=s.datapoints.pointsize;points=s.datapoints.points;var insertSteps=s.lines.show&&s.lines.steps;s.xaxis.used=s.yaxis.used=true;for(j=k=0;j<data.length;++j,k+=ps){p=data[j];var nullify=p==null;if(!nullify){for(m=0;m<ps;++m){val=p[m];f=format[m];if(f){if(f.number&&val!=null){val=+val;if(isNaN(val))val=null;else if(val==Infinity)val=fakeInfinity;else if(val==-Infinity)val=-fakeInfinity}if(val==null){if(f.required)nullify=true;if(f.defaultValue!=null)val=f.defaultValue}}points[k+m]=val}}if(nullify){for(m=0;m<ps;++m){val=points[k+m];if(val!=null){f=format[m];if(f.autoscale!==false){if(f.x){updateAxis(s.xaxis,val,val)}if(f.y){updateAxis(s.yaxis,val,val)}}}points[k+m]=null}}else{if(insertSteps&&k>0&&points[k-ps]!=null&&points[k-ps]!=points[k]&&points[k-ps+1]!=points[k+1]){for(m=0;m<ps;++m)points[k+ps+m]=points[k+m];points[k+1]=points[k-ps+1];k+=ps}}}}for(i=0;i<series.length;++i){s=series[i];executeHooks(hooks.processDatapoints,[s,s.datapoints])}for(i=0;i<series.length;++i){s=series[i];points=s.datapoints.points;ps=s.datapoints.pointsize;format=s.datapoints.format;var xmin=topSentry,ymin=topSentry,xmax=bottomSentry,ymax=bottomSentry;for(j=0;j<points.length;j+=ps){if(points[j]==null)continue;for(m=0;m<ps;++m){val=points[j+m];f=format[m];if(!f||f.autoscale===false||val==fakeInfinity||val==-fakeInfinity)continue;if(f.x){if(val<xmin)xmin=val;if(val>xmax)xmax=val}if(f.y){if(val<ymin)ymin=val;if(val>ymax)ymax=val}}}if(s.bars.show){var delta;switch(s.bars.align){case"left":delta=0;break;case"right":delta=-s.bars.barWidth;break;default:delta=-s.bars.barWidth/2}if(s.bars.horizontal){ymin+=delta;ymax+=delta+s.bars.barWidth}else{xmin+=delta;xmax+=delta+s.bars.barWidth}}updateAxis(s.xaxis,xmin,xmax);updateAxis(s.yaxis,ymin,ymax)}$.each(allAxes(),function(_,axis){if(axis.datamin==topSentry)axis.datamin=null;if(axis.datamax==bottomSentry)axis.datamax=null})}function setupCanvases(){placeholder.css("padding",0).children().filter(function(){return!$(this).hasClass("flot-overlay")&&!$(this).hasClass("flot-base")}).remove();if(placeholder.css("position")=="static")placeholder.css("position","relative");surface=new Canvas("flot-base",placeholder);overlay=new Canvas("flot-overlay",placeholder);ctx=surface.context;octx=overlay.context;eventHolder=$(overlay.element).unbind();var existing=placeholder.data("plot");if(existing){existing.shutdown();overlay.clear()}placeholder.data("plot",plot)}function bindEvents(){if(options.grid.hoverable){eventHolder.mousemove(onMouseMove);eventHolder.bind("mouseleave",onMouseLeave)}if(options.grid.clickable)eventHolder.click(onClick);executeHooks(hooks.bindEvents,[eventHolder])}function shutdown(){if(redrawTimeout)clearTimeout(redrawTimeout);eventHolder.unbind("mousemove",onMouseMove);eventHolder.unbind("mouseleave",onMouseLeave);eventHolder.unbind("click",onClick);executeHooks(hooks.shutdown,[eventHolder])}function setTransformationHelpers(axis){function identity(x){return x}var s,m,t=axis.options.transform||identity,it=axis.options.inverseTransform;if(axis.direction=="x"){s=axis.scale=plotWidth/Math.abs(t(axis.max)-t(axis.min));m=Math.min(t(axis.max),t(axis.min))}else{s=axis.scale=plotHeight/Math.abs(t(axis.max)-t(axis.min));s=-s;m=Math.max(t(axis.max),t(axis.min))}if(t==identity)axis.p2c=function(p){return(p-m)*s};else axis.p2c=function(p){return(t(p)-m)*s};if(!it)axis.c2p=function(c){return m+c/s};else axis.c2p=function(c){return it(m+c/s)}}function measureTickLabels(axis){var opts=axis.options,ticks=axis.ticks||[],labelWidth=opts.labelWidth||0,labelHeight=opts.labelHeight||0,maxWidth=labelWidth||(axis.direction=="x"?Math.floor(surface.width/(ticks.length||1)):null),legacyStyles=axis.direction+"Axis "+axis.direction+axis.n+"Axis",layer="flot-"+axis.direction+"-axis flot-"+axis.direction+axis.n+"-axis "+legacyStyles,font=opts.font||"flot-tick-label tickLabel";for(var i=0;i<ticks.length;++i){var t=ticks[i];if(!t.label)continue;var info=surface.getTextInfo(layer,t.label,font,null,maxWidth);labelWidth=Math.max(labelWidth,info.width);labelHeight=Math.max(labelHeight,info.height)}axis.labelWidth=opts.labelWidth||labelWidth;axis.labelHeight=opts.labelHeight||labelHeight}function allocateAxisBoxFirstPhase(axis){var lw=axis.labelWidth,lh=axis.labelHeight,pos=axis.options.position,isXAxis=axis.direction==="x",tickLength=axis.options.tickLength,axisMargin=options.grid.axisMargin,padding=options.grid.labelMargin,innermost=true,outermost=true,first=true,found=false;$.each(isXAxis?xaxes:yaxes,function(i,a){if(a&&(a.show||a.reserveSpace)){if(a===axis){found=true}else if(a.options.position===pos){if(found){outermost=false}else{innermost=false}}if(!found){first=false}}});if(outermost){axisMargin=0}if(tickLength==null){tickLength=first?"full":5}if(!isNaN(+tickLength))padding+=+tickLength;if(isXAxis){lh+=padding;if(pos=="bottom"){plotOffset.bottom+=lh+axisMargin;axis.box={top:surface.height-plotOffset.bottom,height:lh}}else{axis.box={top:plotOffset.top+axisMargin,height:lh};plotOffset.top+=lh+axisMargin}}else{lw+=padding;if(pos=="left"){axis.box={left:plotOffset.left+axisMargin,width:lw};plotOffset.left+=lw+axisMargin}else{plotOffset.right+=lw+axisMargin;axis.box={left:surface.width-plotOffset.right,width:lw}}}axis.position=pos;axis.tickLength=tickLength;axis.box.padding=padding;axis.innermost=innermost}function allocateAxisBoxSecondPhase(axis){if(axis.direction=="x"){axis.box.left=plotOffset.left-axis.labelWidth/2;axis.box.width=surface.width-plotOffset.left-plotOffset.right+axis.labelWidth}else{axis.box.top=plotOffset.top-axis.labelHeight/2;axis.box.height=surface.height-plotOffset.bottom-plotOffset.top+axis.labelHeight}}function adjustLayoutForThingsStickingOut(){var minMargin=options.grid.minBorderMargin,axis,i;if(minMargin==null){minMargin=0;for(i=0;i<series.length;++i)minMargin=Math.max(minMargin,2*(series[i].points.radius+series[i].points.lineWidth/2))}var margins={left:minMargin,right:minMargin,top:minMargin,bottom:minMargin};$.each(allAxes(),function(_,axis){if(axis.reserveSpace&&axis.ticks&&axis.ticks.length){if(axis.direction==="x"){margins.left=Math.max(margins.left,axis.labelWidth/2);margins.right=Math.max(margins.right,axis.labelWidth/2)}else{margins.bottom=Math.max(margins.bottom,axis.labelHeight/2);margins.top=Math.max(margins.top,axis.labelHeight/2)}}});plotOffset.left=Math.ceil(Math.max(margins.left,plotOffset.left));plotOffset.right=Math.ceil(Math.max(margins.right,plotOffset.right));plotOffset.top=Math.ceil(Math.max(margins.top,plotOffset.top));plotOffset.bottom=Math.ceil(Math.max(margins.bottom,plotOffset.bottom))}function setupGrid(){var i,axes=allAxes(),showGrid=options.grid.show;for(var a in plotOffset){var margin=options.grid.margin||0;plotOffset[a]=typeof margin=="number"?margin:margin[a]||0}executeHooks(hooks.processOffset,[plotOffset]);for(var a in plotOffset){if(typeof options.grid.borderWidth=="object"){plotOffset[a]+=showGrid?options.grid.borderWidth[a]:0}else{plotOffset[a]+=showGrid?options.grid.borderWidth:0}}$.each(axes,function(_,axis){var axisOpts=axis.options;axis.show=axisOpts.show==null?axis.used:axisOpts.show;axis.reserveSpace=axisOpts.reserveSpace==null?axis.show:axisOpts.reserveSpace;setRange(axis)});if(showGrid){var allocatedAxes=$.grep(axes,function(axis){return axis.show||axis.reserveSpace});$.each(allocatedAxes,function(_,axis){setupTickGeneration(axis);setTicks(axis);snapRangeToTicks(axis,axis.ticks);measureTickLabels(axis)});for(i=allocatedAxes.length-1;i>=0;--i)allocateAxisBoxFirstPhase(allocatedAxes[i]);adjustLayoutForThingsStickingOut();$.each(allocatedAxes,function(_,axis){allocateAxisBoxSecondPhase(axis)})}plotWidth=surface.width-plotOffset.left-plotOffset.right;plotHeight=surface.height-plotOffset.bottom-plotOffset.top;$.each(axes,function(_,axis){setTransformationHelpers(axis)});if(showGrid){drawAxisLabels()}insertLegend()}function setRange(axis){var opts=axis.options,min=+(opts.min!=null?opts.min:axis.datamin),max=+(opts.max!=null?opts.max:axis.datamax),delta=max-min;if(delta==0){var widen=max==0?1:.01;if(opts.min==null)min-=widen;if(opts.max==null||opts.min!=null)max+=widen}else{var margin=opts.autoscaleMargin;if(margin!=null){if(opts.min==null){min-=delta*margin;if(min<0&&axis.datamin!=null&&axis.datamin>=0)min=0}if(opts.max==null){max+=delta*margin;if(max>0&&axis.datamax!=null&&axis.datamax<=0)max=0}}}axis.min=min;axis.max=max}function setupTickGeneration(axis){var opts=axis.options;var noTicks;if(typeof opts.ticks=="number"&&opts.ticks>0)noTicks=opts.ticks;else noTicks=.3*Math.sqrt(axis.direction=="x"?surface.width:surface.height);var delta=(axis.max-axis.min)/noTicks,dec=-Math.floor(Math.log(delta)/Math.LN10),maxDec=opts.tickDecimals;if(maxDec!=null&&dec>maxDec){dec=maxDec}var magn=Math.pow(10,-dec),norm=delta/magn,size;if(norm<1.5){size=1}else if(norm<3){size=2;if(norm>2.25&&(maxDec==null||dec+1<=maxDec)){size=2.5;++dec}}else if(norm<7.5){size=5}else{size=10}size*=magn;if(opts.minTickSize!=null&&size<opts.minTickSize){size=opts.minTickSize}axis.delta=delta;axis.tickDecimals=Math.max(0,maxDec!=null?maxDec:dec);axis.tickSize=opts.tickSize||size;if(opts.mode=="time"&&!axis.tickGenerator){throw new Error("Time mode requires the flot.time plugin.")}if(!axis.tickGenerator){axis.tickGenerator=function(axis){var ticks=[],start=floorInBase(axis.min,axis.tickSize),i=0,v=Number.NaN,prev;do{prev=v;v=start+i*axis.tickSize;ticks.push(v);++i}while(v<axis.max&&v!=prev);return ticks};axis.tickFormatter=function(value,axis){var factor=axis.tickDecimals?Math.pow(10,axis.tickDecimals):1;var formatted=""+Math.round(value*factor)/factor;if(axis.tickDecimals!=null){var decimal=formatted.indexOf(".");var precision=decimal==-1?0:formatted.length-decimal-1;if(precision<axis.tickDecimals){return(precision?formatted:formatted+".")+(""+factor).substr(1,axis.tickDecimals-precision)}}return formatted}}if($.isFunction(opts.tickFormatter))axis.tickFormatter=function(v,axis){return""+opts.tickFormatter(v,axis)};if(opts.alignTicksWithAxis!=null){var otherAxis=(axis.direction=="x"?xaxes:yaxes)[opts.alignTicksWithAxis-1];if(otherAxis&&otherAxis.used&&otherAxis!=axis){var niceTicks=axis.tickGenerator(axis);if(niceTicks.length>0){if(opts.min==null)axis.min=Math.min(axis.min,niceTicks[0]);if(opts.max==null&&niceTicks.length>1)axis.max=Math.max(axis.max,niceTicks[niceTicks.length-1])}axis.tickGenerator=function(axis){var ticks=[],v,i;for(i=0;i<otherAxis.ticks.length;++i){v=(otherAxis.ticks[i].v-otherAxis.min)/(otherAxis.max-otherAxis.min);v=axis.min+v*(axis.max-axis.min);ticks.push(v)}return ticks};if(!axis.mode&&opts.tickDecimals==null){var extraDec=Math.max(0,-Math.floor(Math.log(axis.delta)/Math.LN10)+1),ts=axis.tickGenerator(axis);if(!(ts.length>1&&/\..*0$/.test((ts[1]-ts[0]).toFixed(extraDec))))axis.tickDecimals=extraDec}}}}function setTicks(axis){var oticks=axis.options.ticks,ticks=[];if(oticks==null||typeof oticks=="number"&&oticks>0)ticks=axis.tickGenerator(axis);else if(oticks){if($.isFunction(oticks))ticks=oticks(axis);else ticks=oticks}var i,v;axis.ticks=[];for(i=0;i<ticks.length;++i){var label=null;var t=ticks[i];if(typeof t=="object"){v=+t[0];if(t.length>1)label=t[1]}else v=+t;if(label==null)label=axis.tickFormatter(v,axis);if(!isNaN(v))axis.ticks.push({v:v,label:label})}}function snapRangeToTicks(axis,ticks){if(axis.options.autoscaleMargin&&ticks.length>0){if(axis.options.min==null)axis.min=Math.min(axis.min,ticks[0].v);if(axis.options.max==null&&ticks.length>1)axis.max=Math.max(axis.max,ticks[ticks.length-1].v)}}function draw(){surface.clear();executeHooks(hooks.drawBackground,[ctx]);var grid=options.grid;if(grid.show&&grid.backgroundColor)drawBackground();if(grid.show&&!grid.aboveData){drawGrid()}for(var i=0;i<series.length;++i){executeHooks(hooks.drawSeries,[ctx,series[i]]);drawSeries(series[i])}executeHooks(hooks.draw,[ctx]);if(grid.show&&grid.aboveData){drawGrid()}surface.render();triggerRedrawOverlay()}function extractRange(ranges,coord){var axis,from,to,key,axes=allAxes();for(var i=0;i<axes.length;++i){axis=axes[i];if(axis.direction==coord){key=coord+axis.n+"axis";if(!ranges[key]&&axis.n==1)key=coord+"axis";if(ranges[key]){from=ranges[key].from;to=ranges[key].to;break}}}if(!ranges[key]){axis=coord=="x"?xaxes[0]:yaxes[0];from=ranges[coord+"1"];to=ranges[coord+"2"]}if(from!=null&&to!=null&&from>to){var tmp=from;from=to;to=tmp}return{from:from,to:to,axis:axis}}function drawBackground(){ctx.save();ctx.translate(plotOffset.left,plotOffset.top);ctx.fillStyle=getColorOrGradient(options.grid.backgroundColor,plotHeight,0,"rgba(255, 255, 255, 0)");ctx.fillRect(0,0,plotWidth,plotHeight);ctx.restore()}function drawGrid(){var i,axes,bw,bc;ctx.save();ctx.translate(plotOffset.left,plotOffset.top);var markings=options.grid.markings;if(markings){if($.isFunction(markings)){axes=plot.getAxes();axes.xmin=axes.xaxis.min;axes.xmax=axes.xaxis.max;axes.ymin=axes.yaxis.min;axes.ymax=axes.yaxis.max;markings=markings(axes)}for(i=0;i<markings.length;++i){var m=markings[i],xrange=extractRange(m,"x"),yrange=extractRange(m,"y");if(xrange.from==null)xrange.from=xrange.axis.min;if(xrange.to==null)xrange.to=xrange.axis.max;
if(yrange.from==null)yrange.from=yrange.axis.min;if(yrange.to==null)yrange.to=yrange.axis.max;if(xrange.to<xrange.axis.min||xrange.from>xrange.axis.max||yrange.to<yrange.axis.min||yrange.from>yrange.axis.max)continue;xrange.from=Math.max(xrange.from,xrange.axis.min);xrange.to=Math.min(xrange.to,xrange.axis.max);yrange.from=Math.max(yrange.from,yrange.axis.min);yrange.to=Math.min(yrange.to,yrange.axis.max);var xequal=xrange.from===xrange.to,yequal=yrange.from===yrange.to;if(xequal&&yequal){continue}xrange.from=Math.floor(xrange.axis.p2c(xrange.from));xrange.to=Math.floor(xrange.axis.p2c(xrange.to));yrange.from=Math.floor(yrange.axis.p2c(yrange.from));yrange.to=Math.floor(yrange.axis.p2c(yrange.to));if(xequal||yequal){var lineWidth=m.lineWidth||options.grid.markingsLineWidth,subPixel=lineWidth%2?.5:0;ctx.beginPath();ctx.strokeStyle=m.color||options.grid.markingsColor;ctx.lineWidth=lineWidth;if(xequal){ctx.moveTo(xrange.to+subPixel,yrange.from);ctx.lineTo(xrange.to+subPixel,yrange.to)}else{ctx.moveTo(xrange.from,yrange.to+subPixel);ctx.lineTo(xrange.to,yrange.to+subPixel)}ctx.stroke()}else{ctx.fillStyle=m.color||options.grid.markingsColor;ctx.fillRect(xrange.from,yrange.to,xrange.to-xrange.from,yrange.from-yrange.to)}}}axes=allAxes();bw=options.grid.borderWidth;for(var j=0;j<axes.length;++j){var axis=axes[j],box=axis.box,t=axis.tickLength,x,y,xoff,yoff;if(!axis.show||axis.ticks.length==0)continue;ctx.lineWidth=1;if(axis.direction=="x"){x=0;if(t=="full")y=axis.position=="top"?0:plotHeight;else y=box.top-plotOffset.top+(axis.position=="top"?box.height:0)}else{y=0;if(t=="full")x=axis.position=="left"?0:plotWidth;else x=box.left-plotOffset.left+(axis.position=="left"?box.width:0)}if(!axis.innermost){ctx.strokeStyle=axis.options.color;ctx.beginPath();xoff=yoff=0;if(axis.direction=="x")xoff=plotWidth+1;else yoff=plotHeight+1;if(ctx.lineWidth==1){if(axis.direction=="x"){y=Math.floor(y)+.5}else{x=Math.floor(x)+.5}}ctx.moveTo(x,y);ctx.lineTo(x+xoff,y+yoff);ctx.stroke()}ctx.strokeStyle=axis.options.tickColor;ctx.beginPath();for(i=0;i<axis.ticks.length;++i){var v=axis.ticks[i].v;xoff=yoff=0;if(isNaN(v)||v<axis.min||v>axis.max||t=="full"&&(typeof bw=="object"&&bw[axis.position]>0||bw>0)&&(v==axis.min||v==axis.max))continue;if(axis.direction=="x"){x=axis.p2c(v);yoff=t=="full"?-plotHeight:t;if(axis.position=="top")yoff=-yoff}else{y=axis.p2c(v);xoff=t=="full"?-plotWidth:t;if(axis.position=="left")xoff=-xoff}if(ctx.lineWidth==1){if(axis.direction=="x")x=Math.floor(x)+.5;else y=Math.floor(y)+.5}ctx.moveTo(x,y);ctx.lineTo(x+xoff,y+yoff)}ctx.stroke()}if(bw){bc=options.grid.borderColor;if(typeof bw=="object"||typeof bc=="object"){if(typeof bw!=="object"){bw={top:bw,right:bw,bottom:bw,left:bw}}if(typeof bc!=="object"){bc={top:bc,right:bc,bottom:bc,left:bc}}if(bw.top>0){ctx.strokeStyle=bc.top;ctx.lineWidth=bw.top;ctx.beginPath();ctx.moveTo(0-bw.left,0-bw.top/2);ctx.lineTo(plotWidth,0-bw.top/2);ctx.stroke()}if(bw.right>0){ctx.strokeStyle=bc.right;ctx.lineWidth=bw.right;ctx.beginPath();ctx.moveTo(plotWidth+bw.right/2,0-bw.top);ctx.lineTo(plotWidth+bw.right/2,plotHeight);ctx.stroke()}if(bw.bottom>0){ctx.strokeStyle=bc.bottom;ctx.lineWidth=bw.bottom;ctx.beginPath();ctx.moveTo(plotWidth+bw.right,plotHeight+bw.bottom/2);ctx.lineTo(0,plotHeight+bw.bottom/2);ctx.stroke()}if(bw.left>0){ctx.strokeStyle=bc.left;ctx.lineWidth=bw.left;ctx.beginPath();ctx.moveTo(0-bw.left/2,plotHeight+bw.bottom);ctx.lineTo(0-bw.left/2,0);ctx.stroke()}}else{ctx.lineWidth=bw;ctx.strokeStyle=options.grid.borderColor;ctx.strokeRect(-bw/2,-bw/2,plotWidth+bw,plotHeight+bw)}}ctx.restore()}function drawAxisLabels(){$.each(allAxes(),function(_,axis){var box=axis.box,legacyStyles=axis.direction+"Axis "+axis.direction+axis.n+"Axis",layer="flot-"+axis.direction+"-axis flot-"+axis.direction+axis.n+"-axis "+legacyStyles,font=axis.options.font||"flot-tick-label tickLabel",tick,x,y,halign,valign;surface.removeText(layer);if(!axis.show||axis.ticks.length==0)return;for(var i=0;i<axis.ticks.length;++i){tick=axis.ticks[i];if(!tick.label||tick.v<axis.min||tick.v>axis.max)continue;if(axis.direction=="x"){halign="center";x=plotOffset.left+axis.p2c(tick.v);if(axis.position=="bottom"){y=box.top+box.padding}else{y=box.top+box.height-box.padding;valign="bottom"}}else{valign="middle";y=plotOffset.top+axis.p2c(tick.v);if(axis.position=="left"){x=box.left+box.width-box.padding;halign="right"}else{x=box.left+box.padding}}surface.addText(layer,x,y,tick.label,font,null,null,halign,valign)}})}function drawSeries(series){if(series.lines.show)drawSeriesLines(series);if(series.bars.show)drawSeriesBars(series);if(series.points.show)drawSeriesPoints(series)}function drawSeriesLines(series){function plotLine(datapoints,xoffset,yoffset,axisx,axisy){var points=datapoints.points,ps=datapoints.pointsize,prevx=null,prevy=null;ctx.beginPath();for(var i=ps;i<points.length;i+=ps){var x1=points[i-ps],y1=points[i-ps+1],x2=points[i],y2=points[i+1];if(x1==null||x2==null)continue;if(y1<=y2&&y1<axisy.min){if(y2<axisy.min)continue;x1=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.min}else if(y2<=y1&&y2<axisy.min){if(y1<axisy.min)continue;x2=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.min}if(y1>=y2&&y1>axisy.max){if(y2>axisy.max)continue;x1=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.max}else if(y2>=y1&&y2>axisy.max){if(y1>axisy.max)continue;x2=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.max}if(x1<=x2&&x1<axisx.min){if(x2<axisx.min)continue;y1=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.min}else if(x2<=x1&&x2<axisx.min){if(x1<axisx.min)continue;y2=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.min}if(x1>=x2&&x1>axisx.max){if(x2>axisx.max)continue;y1=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.max}else if(x2>=x1&&x2>axisx.max){if(x1>axisx.max)continue;y2=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.max}if(x1!=prevx||y1!=prevy)ctx.moveTo(axisx.p2c(x1)+xoffset,axisy.p2c(y1)+yoffset);prevx=x2;prevy=y2;ctx.lineTo(axisx.p2c(x2)+xoffset,axisy.p2c(y2)+yoffset)}ctx.stroke()}function plotLineArea(datapoints,axisx,axisy){var points=datapoints.points,ps=datapoints.pointsize,bottom=Math.min(Math.max(0,axisy.min),axisy.max),i=0,top,areaOpen=false,ypos=1,segmentStart=0,segmentEnd=0;while(true){if(ps>0&&i>points.length+ps)break;i+=ps;var x1=points[i-ps],y1=points[i-ps+ypos],x2=points[i],y2=points[i+ypos];if(areaOpen){if(ps>0&&x1!=null&&x2==null){segmentEnd=i;ps=-ps;ypos=2;continue}if(ps<0&&i==segmentStart+ps){ctx.fill();areaOpen=false;ps=-ps;ypos=1;i=segmentStart=segmentEnd+ps;continue}}if(x1==null||x2==null)continue;if(x1<=x2&&x1<axisx.min){if(x2<axisx.min)continue;y1=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.min}else if(x2<=x1&&x2<axisx.min){if(x1<axisx.min)continue;y2=(axisx.min-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.min}if(x1>=x2&&x1>axisx.max){if(x2>axisx.max)continue;y1=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x1=axisx.max}else if(x2>=x1&&x2>axisx.max){if(x1>axisx.max)continue;y2=(axisx.max-x1)/(x2-x1)*(y2-y1)+y1;x2=axisx.max}if(!areaOpen){ctx.beginPath();ctx.moveTo(axisx.p2c(x1),axisy.p2c(bottom));areaOpen=true}if(y1>=axisy.max&&y2>=axisy.max){ctx.lineTo(axisx.p2c(x1),axisy.p2c(axisy.max));ctx.lineTo(axisx.p2c(x2),axisy.p2c(axisy.max));continue}else if(y1<=axisy.min&&y2<=axisy.min){ctx.lineTo(axisx.p2c(x1),axisy.p2c(axisy.min));ctx.lineTo(axisx.p2c(x2),axisy.p2c(axisy.min));continue}var x1old=x1,x2old=x2;if(y1<=y2&&y1<axisy.min&&y2>=axisy.min){x1=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.min}else if(y2<=y1&&y2<axisy.min&&y1>=axisy.min){x2=(axisy.min-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.min}if(y1>=y2&&y1>axisy.max&&y2<=axisy.max){x1=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y1=axisy.max}else if(y2>=y1&&y2>axisy.max&&y1<=axisy.max){x2=(axisy.max-y1)/(y2-y1)*(x2-x1)+x1;y2=axisy.max}if(x1!=x1old){ctx.lineTo(axisx.p2c(x1old),axisy.p2c(y1))}ctx.lineTo(axisx.p2c(x1),axisy.p2c(y1));ctx.lineTo(axisx.p2c(x2),axisy.p2c(y2));if(x2!=x2old){ctx.lineTo(axisx.p2c(x2),axisy.p2c(y2));ctx.lineTo(axisx.p2c(x2old),axisy.p2c(y2))}}}ctx.save();ctx.translate(plotOffset.left,plotOffset.top);ctx.lineJoin="round";var lw=series.lines.lineWidth,sw=series.shadowSize;if(lw>0&&sw>0){ctx.lineWidth=sw;ctx.strokeStyle="rgba(0,0,0,0.1)";var angle=Math.PI/18;plotLine(series.datapoints,Math.sin(angle)*(lw/2+sw/2),Math.cos(angle)*(lw/2+sw/2),series.xaxis,series.yaxis);ctx.lineWidth=sw/2;plotLine(series.datapoints,Math.sin(angle)*(lw/2+sw/4),Math.cos(angle)*(lw/2+sw/4),series.xaxis,series.yaxis)}ctx.lineWidth=lw;ctx.strokeStyle=series.color;var fillStyle=getFillStyle(series.lines,series.color,0,plotHeight);if(fillStyle){ctx.fillStyle=fillStyle;plotLineArea(series.datapoints,series.xaxis,series.yaxis)}if(lw>0)plotLine(series.datapoints,0,0,series.xaxis,series.yaxis);ctx.restore()}function drawSeriesPoints(series){function plotPoints(datapoints,radius,fillStyle,offset,shadow,axisx,axisy,symbol){var points=datapoints.points,ps=datapoints.pointsize;for(var i=0;i<points.length;i+=ps){var x=points[i],y=points[i+1];if(x==null||x<axisx.min||x>axisx.max||y<axisy.min||y>axisy.max)continue;ctx.beginPath();x=axisx.p2c(x);y=axisy.p2c(y)+offset;if(symbol=="circle")ctx.arc(x,y,radius,0,shadow?Math.PI:Math.PI*2,false);else symbol(ctx,x,y,radius,shadow);ctx.closePath();if(fillStyle){ctx.fillStyle=fillStyle;ctx.fill()}ctx.stroke()}}ctx.save();ctx.translate(plotOffset.left,plotOffset.top);var lw=series.points.lineWidth,sw=series.shadowSize,radius=series.points.radius,symbol=series.points.symbol;if(lw==0)lw=1e-4;if(lw>0&&sw>0){var w=sw/2;ctx.lineWidth=w;ctx.strokeStyle="rgba(0,0,0,0.1)";plotPoints(series.datapoints,radius,null,w+w/2,true,series.xaxis,series.yaxis,symbol);ctx.strokeStyle="rgba(0,0,0,0.2)";plotPoints(series.datapoints,radius,null,w/2,true,series.xaxis,series.yaxis,symbol)}ctx.lineWidth=lw;ctx.strokeStyle=series.color;plotPoints(series.datapoints,radius,getFillStyle(series.points,series.color),0,false,series.xaxis,series.yaxis,symbol);ctx.restore()}function drawBar(x,y,b,barLeft,barRight,fillStyleCallback,axisx,axisy,c,horizontal,lineWidth){var left,right,bottom,top,drawLeft,drawRight,drawTop,drawBottom,tmp;if(horizontal){drawBottom=drawRight=drawTop=true;drawLeft=false;left=b;right=x;top=y+barLeft;bottom=y+barRight;if(right<left){tmp=right;right=left;left=tmp;drawLeft=true;drawRight=false}}else{drawLeft=drawRight=drawTop=true;drawBottom=false;left=x+barLeft;right=x+barRight;bottom=b;top=y;if(top<bottom){tmp=top;top=bottom;bottom=tmp;drawBottom=true;drawTop=false}}if(right<axisx.min||left>axisx.max||top<axisy.min||bottom>axisy.max)return;if(left<axisx.min){left=axisx.min;drawLeft=false}if(right>axisx.max){right=axisx.max;drawRight=false}if(bottom<axisy.min){bottom=axisy.min;drawBottom=false}if(top>axisy.max){top=axisy.max;drawTop=false}left=axisx.p2c(left);bottom=axisy.p2c(bottom);right=axisx.p2c(right);top=axisy.p2c(top);if(fillStyleCallback){c.fillStyle=fillStyleCallback(bottom,top);c.fillRect(left,top,right-left,bottom-top)}if(lineWidth>0&&(drawLeft||drawRight||drawTop||drawBottom)){c.beginPath();c.moveTo(left,bottom);if(drawLeft)c.lineTo(left,top);else 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    <body>
     <div id="wrap">
      <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">KB 0/#req 4096/#threads 1/http-client</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">0.7278861041635064</span></td>
    <td><span class="time">0.7349605701291111</span></td>
    <td><span class="confinterval citime">0.7418426721905705</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">1.3597399555105182e-16</span></td>
    <td><span class="time">1.175709972331994e-2</span></td>
    <td><span class="confinterval citime">1.1920150433322532e-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="b1">KB 0/#req 4096/#threads 1/http-client-global-manager</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">0.7249702998530628</span></td>
    <td><span class="time">0.7268695733312418</span></td>
    <td><span class="confinterval citime">0.7300524192303758</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">1.3597399555105182e-16</span></td>
    <td><span class="time">2.773563224660993e-3</span></td>
    <td><span class="confinterval citime">2.9341629905454475e-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="b2">KB 0/#req 4096/#threads 1/http-streams-mvar</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">0.6892508928292008</span></td>
    <td><span class="time">0.689901797291342</span></td>
    <td><span class="confinterval citime">0.6904024326403211</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">7.706877365883793e-4</span></td>
    <td><span class="confinterval citime">8.67125860496736e-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="b3">KB 0/#req 4096/#threads 1/http-streams-ioref</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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<!--
    <td><div id="cycle3" 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 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">0.64599017291798</span></td>
    <td><span class="time">0.6481612870990044</span></td>
    <td><span class="confinterval citime">0.6493933427334283</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">1.9309218007465552e-3</span></td>
    <td><span class="confinterval citime">2.13398295657369e-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="b4">KB 0/#req 2048/#threads 2/http-client</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">0.49245917830621316</span></td>
    <td><span class="time">0.4950251183501398</span></td>
    <td><span class="confinterval citime">0.4963610676402095</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">2.2278459231758856e-3</span></td>
    <td><span class="confinterval citime">2.313932046736249e-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="b5">KB 0/#req 2048/#threads 2/http-client-global-manager</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">0.509022977829496</span></td>
    <td><span class="time">0.5130217350213897</span></td>
    <td><span class="confinterval citime">0.5154783472975274</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">3.7247963775888376e-3</span></td>
    <td><span class="confinterval citime">4.254977276767909e-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="b6">KB 0/#req 2048/#threads 2/http-streams-mvar</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">0.4688083025689265</span></td>
    <td><span class="time">0.4811350664269132</span></td>
    <td><span class="confinterval citime">0.4874942523388926</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">1.0691439716226438e-2</span></td>
    <td><span class="confinterval citime">1.1014433094324497e-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="b7">KB 0/#req 2048/#threads 2/http-streams-ioref</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">0.46778164317251864</span></td>
    <td><span class="time">0.4691583169620696</span></td>
    <td><span class="confinterval citime">0.46998320184962994</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">1.2606471477497772e-3</span></td>
    <td><span class="confinterval citime">1.4287425356502622e-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="b8">KB 0/#req 1024/#threads 4/http-client</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">0.33170050789139827</span></td>
    <td><span class="time">0.3351992948733948</span></td>
    <td><span class="confinterval citime">0.3411942984024317</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">5.215969454869923e-3</span></td>
    <td><span class="confinterval citime">5.481242507175072e-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="b9">KB 0/#req 1024/#threads 4/http-client-global-manager</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">0.3396187698530993</span></td>
    <td><span class="time">0.3436774306299739</span></td>
    <td><span class="confinterval citime">0.3456674702372741</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">3.5168792082820362e-3</span></td>
    <td><span class="confinterval citime">3.58295696731542e-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="b10">KB 0/#req 1024/#threads 4/http-streams-mvar</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde10" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time10" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle10" 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 olstimelb10">xxx</span></td>
    <td><span class="olstimept10">xxx</span></td>
    <td><span class="confinterval olstimeub10">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb10">xxx</span></td>
    <td><span class="olsr2pt10">xxx</span></td>
    <td><span class="confinterval olsr2ub10">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.3440524057811634</span></td>
    <td><span class="time">0.3482573733745613</span></td>
    <td><span class="confinterval citime">0.3505432298451248</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">3.682926035617218e-3</span></td>
    <td><span class="confinterval citime">3.9592195458260674e-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="b11">KB 0/#req 1024/#threads 4/http-streams-ioref</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde11" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time11" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle11" 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 olstimelb11">xxx</span></td>
    <td><span class="olstimept11">xxx</span></td>
    <td><span class="confinterval olstimeub11">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb11">xxx</span></td>
    <td><span class="olsr2pt11">xxx</span></td>
    <td><span class="confinterval olsr2ub11">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.31465231563055107</span></td>
    <td><span class="time">0.31827060287982617</span></td>
    <td><span class="confinterval citime">0.3211912518952623</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">1.8195493555074604e-3</span></td>
    <td><span class="time">3.85728208467394e-3</span></td>
    <td><span class="confinterval citime">5.228555544954764e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.16000000000000003</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b12">KB 0/#req 512/#threads 8/http-client</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde12" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time12" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle12" 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 olstimelb12">xxx</span></td>
    <td><span class="olstimept12">xxx</span></td>
    <td><span class="confinterval olstimeub12">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb12">xxx</span></td>
    <td><span class="olsr2pt12">xxx</span></td>
    <td><span class="confinterval olsr2ub12">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.44931976214170205</span></td>
    <td><span class="time">0.45545674273838443</span></td>
    <td><span class="confinterval citime">0.4602117834297508</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">7.338049344816491e-3</span></td>
    <td><span class="confinterval citime">8.235972069503944e-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="b13">KB 0/#req 512/#threads 8/http-client-global-manager</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde13" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time13" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle13" 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 olstimelb13">xxx</span></td>
    <td><span class="olstimept13">xxx</span></td>
    <td><span class="confinterval olstimeub13">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb13">xxx</span></td>
    <td><span class="olsr2pt13">xxx</span></td>
    <td><span class="confinterval olsr2ub13">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.44660077141483995</span></td>
    <td><span class="time">0.46207869952483566</span></td>
    <td><span class="confinterval citime">0.46992499400270504</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">1.3408145744684852e-2</span></td>
    <td><span class="confinterval citime">1.359018068681686e-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="b14">KB 0/#req 512/#threads 8/http-streams-mvar</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde14" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time14" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle14" 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 olstimelb14">xxx</span></td>
    <td><span class="olstimept14">xxx</span></td>
    <td><span class="confinterval olstimeub14">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb14">xxx</span></td>
    <td><span class="olsr2pt14">xxx</span></td>
    <td><span class="confinterval olsr2ub14">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.3021286488203603</span></td>
    <td><span class="time">0.303368965841746</span></td>
    <td><span class="confinterval citime">0.3049923863931644</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">7.662070600654713e-4</span></td>
    <td><span class="time">1.5810516202486604e-3</span></td>
    <td><span class="confinterval citime">2.0436212348732613e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.15999999999999998</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b15">KB 0/#req 512/#threads 8/http-streams-ioref</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde15" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time15" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle15" 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 olstimelb15">xxx</span></td>
    <td><span class="olstimept15">xxx</span></td>
    <td><span class="confinterval olstimeub15">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb15">xxx</span></td>
    <td><span class="olsr2pt15">xxx</span></td>
    <td><span class="confinterval olsr2ub15">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.23115200865020027</span></td>
    <td><span class="time">0.23374693782464445</span></td>
    <td><span class="confinterval citime">0.23972049245782942</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">9.315102606164631e-4</span></td>
    <td><span class="time">4.794662106737427e-3</span></td>
    <td><span class="confinterval citime">6.374667950790616e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.1388888888888888</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b16">KB 0/#req 256/#threads 16/http-client</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde16" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time16" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle16" 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 olstimelb16">xxx</span></td>
    <td><span class="olstimept16">xxx</span></td>
    <td><span class="confinterval olstimeub16">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb16">xxx</span></td>
    <td><span class="olsr2pt16">xxx</span></td>
    <td><span class="confinterval olsr2ub16">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.4518893785526694</span></td>
    <td><span class="time">0.46040724603643995</span></td>
    <td><span class="confinterval citime">0.46485789580818154</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">7.3990773193852895e-3</span></td>
    <td><span class="confinterval citime">7.708751531351161e-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="b17">KB 0/#req 256/#threads 16/http-client-global-manager</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde17" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time17" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle17" 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 olstimelb17">xxx</span></td>
    <td><span class="olstimept17">xxx</span></td>
    <td><span class="confinterval olstimeub17">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb17">xxx</span></td>
    <td><span class="olsr2pt17">xxx</span></td>
    <td><span class="confinterval olsr2ub17">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.4777597183932339</span></td>
    <td><span class="time">0.4993615693525623</span></td>
    <td><span class="confinterval citime">0.5126133521832922</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">2.0100753454764534e-2</span></td>
    <td><span class="confinterval citime">2.2952761153692915e-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="b18">KB 0/#req 256/#threads 16/http-streams-mvar</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde18" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time18" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle18" 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 olstimelb18">xxx</span></td>
    <td><span class="olstimept18">xxx</span></td>
    <td><span class="confinterval olstimeub18">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb18">xxx</span></td>
    <td><span class="olsr2pt18">xxx</span></td>
    <td><span class="confinterval olsr2ub18">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.30261689286670185</span></td>
    <td><span class="time">0.3060603326955787</span></td>
    <td><span class="confinterval citime">0.30814255288398446</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">1.4621986558347455e-3</span></td>
    <td><span class="time">3.374675064953174e-3</span></td>
    <td><span class="confinterval citime">4.2536550645712766e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.16000000000000003</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b19">KB 0/#req 256/#threads 16/http-streams-ioref</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde19" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time19" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle19" 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 olstimelb19">xxx</span></td>
    <td><span class="olstimept19">xxx</span></td>
    <td><span class="confinterval olstimeub19">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb19">xxx</span></td>
    <td><span class="olsr2pt19">xxx</span></td>
    <td><span class="confinterval olsr2ub19">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.20521781042672074</span></td>
    <td><span class="time">0.20755966304692594</span></td>
    <td><span class="confinterval citime">0.2099015156671311</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">1.9915456359186645e-3</span></td>
    <td><span class="time">3.1539533371525456e-3</span></td>
    <td><span class="confinterval citime">4.421881896276148e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.13888888888888876</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b20">KB 0/#req 128/#threads 32/http-client</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde20" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time20" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle20" 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 olstimelb20">xxx</span></td>
    <td><span class="olstimept20">xxx</span></td>
    <td><span class="confinterval olstimeub20">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb20">xxx</span></td>
    <td><span class="olsr2pt20">xxx</span></td>
    <td><span class="confinterval olsr2ub20">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.4640902614267797</span></td>
    <td><span class="time">0.47081001647777593</span></td>
    <td><span class="confinterval citime">0.47671194127558386</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">9.592933335062876e-3</span></td>
    <td><span class="confinterval citime">1.022243361225395e-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="b21">KB 0/#req 128/#threads 32/http-client-global-manager</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde21" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time21" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle21" 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 olstimelb21">xxx</span></td>
    <td><span class="olstimept21">xxx</span></td>
    <td><span class="confinterval olstimeub21">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb21">xxx</span></td>
    <td><span class="olsr2pt21">xxx</span></td>
    <td><span class="confinterval olsr2ub21">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.4854867282999256</span></td>
    <td><span class="time">0.4862660362491648</span></td>
    <td><span class="confinterval citime">0.487296871892202</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">9.309159960626634e-4</span></td>
    <td><span class="confinterval citime">1.0450868902726446e-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="b22">KB 0/#req 128/#threads 32/http-streams-mvar</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde22" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time22" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle22" 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 olstimelb22">xxx</span></td>
    <td><span class="olstimept22">xxx</span></td>
    <td><span class="confinterval olstimeub22">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb22">xxx</span></td>
    <td><span class="olsr2pt22">xxx</span></td>
    <td><span class="confinterval olsr2ub22">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.2993873821074808</span></td>
    <td><span class="time">0.30405323132901063</span></td>
    <td><span class="confinterval citime">0.30975850870748667</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">2.8935545244324623e-3</span></td>
    <td><span class="time">6.9515668330322215e-3</span></td>
    <td><span class="confinterval citime">9.08283443924827e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.16</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b23">KB 0/#req 128/#threads 32/http-streams-ioref</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde23" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time23" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle23" 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 olstimelb23">xxx</span></td>
    <td><span class="olstimept23">xxx</span></td>
    <td><span class="confinterval olstimeub23">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb23">xxx</span></td>
    <td><span class="olsr2pt23">xxx</span></td>
    <td><span class="confinterval olsr2ub23">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.21784566980523437</span></td>
    <td><span class="time">0.22140473075141043</span></td>
    <td><span class="confinterval citime">0.22713367915986474</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">2.1387584376880465e-3</span></td>
    <td><span class="time">6.1562130963470985e-3</span></td>
    <td><span class="confinterval citime">8.489568037839509e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.1388888888888888</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b24">KB 0/#req 64/#threads 64/http-client</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde24" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time24" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle24" 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 olstimelb24">xxx</span></td>
    <td><span class="olstimept24">xxx</span></td>
    <td><span class="confinterval olstimeub24">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb24">xxx</span></td>
    <td><span class="olsr2pt24">xxx</span></td>
    <td><span class="confinterval olsr2ub24">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.5083060258673843</span></td>
    <td><span class="time">0.5252610370319238</span></td>
    <td><span class="confinterval citime">0.5407067561735978</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">2.554619941551834e-2</span></td>
    <td><span class="confinterval citime">2.6752770312818506e-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="b25">KB 0/#req 64/#threads 64/http-client-global-manager</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde25" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time25" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle25" 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 olstimelb25">xxx</span></td>
    <td><span class="olstimept25">xxx</span></td>
    <td><span class="confinterval olstimeub25">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb25">xxx</span></td>
    <td><span class="olsr2pt25">xxx</span></td>
    <td><span class="confinterval olsr2ub25">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.49938722140391106</span></td>
    <td><span class="time">0.507851847308052</span></td>
    <td><span class="confinterval citime">0.5130621815742286</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">0.0</span></td>
    <td><span class="time">7.89595782602737e-3</span></td>
    <td><span class="confinterval citime">9.024563673434944e-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="b26">KB 0/#req 64/#threads 64/http-streams-mvar</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde26" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time26" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle26" 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 olstimelb26">xxx</span></td>
    <td><span class="olstimept26">xxx</span></td>
    <td><span class="confinterval olstimeub26">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb26">xxx</span></td>
    <td><span class="olsr2pt26">xxx</span></td>
    <td><span class="confinterval olsr2ub26">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.2858758023381678</span></td>
    <td><span class="time">0.2890920053796858</span></td>
    <td><span class="confinterval citime">0.29454256346537</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">5.690294289269574e-4</span></td>
    <td><span class="time">5.165910875819158e-3</span></td>
    <td><span class="confinterval citime">6.578277070470348e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.16</span>%)
     effect on estimated standard deviation.</p>
 </span>
<h2><a name="b27">KB 0/#req 64/#threads 64/http-streams-ioref</a></h2>
 <table width="100%">
  <tbody>
   <tr>
    <td><div id="kde27" class="kdechart"
             style="width:450px;height:278px;"></div></td>
    <td><div id="time27" class="timechart"
             style="width:450px;height:278px;"></div></td>
<!--
    <td><div id="cycle27" 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 olstimelb27">xxx</span></td>
    <td><span class="olstimept27">xxx</span></td>
    <td><span class="confinterval olstimeub27">xxx</span></td>
   </tr>
   <tr>
    <td>R&#xb2; goodness-of-fit</td>
    <td><span class="confinterval olsr2lb27">xxx</span></td>
    <td><span class="olsr2pt27">xxx</span></td>
    <td><span class="confinterval olsr2ub27">xxx</span></td>
   </tr>
   <tr>
    <td>Mean execution time</td>
    <td><span class="confinterval citime">0.2009714310309274</span></td>
    <td><span class="time">0.20445987888580652</span></td>
    <td><span class="confinterval citime">0.20722290137052152</span></td>
   </tr>
   <tr>
    <td>Standard deviation</td>
    <td><span class="confinterval citime">2.966298410899109e-3</span></td>
    <td><span class="time">4.06436818175516e-3</span></td>
    <td><span class="confinterval citime">4.789994637516271e-3</span></td>
   </tr>
  </tbody>
 </table>

 <span class="outliers">
   <p>Outlying measurements have moderate
     (<span class="percent">0.1388888888888888</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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  reports.map(mangulate);

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  var means = $.scaleTimes([0.7349605701291111,0.7268695733312418,0.689901797291342,0.6481612870990044,0.4950251183501398,0.5130217350213897,0.4811350664269132,0.4691583169620696,0.3351992948733948,0.3436774306299739,0.3482573733745613,0.31827060287982617,0.45545674273838443,0.46207869952483566,0.303368965841746,0.23374693782464445,0.46040724603643995,0.4993615693525623,0.3060603326955787,0.20755966304692594,0.47081001647777593,0.4862660362491648,0.30405323132901063,0.22140473075141043,0.5252610370319238,0.507851847308052,0.2890920053796858,0.20445987888580652,]);
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  o.draw();
  $.addTooltip("#overview", function(x,y) { return $.renderTime(x / means[1]); });
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$(document).ready(function () {
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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>

## StreamsVsClient.hs
-- Copyright (c) 2013-2014 PivotCloud, Inc.
--
-- Main
--
-- Please feel free to contact us at licensing@pivotmail.com with any
-- contributions, additions, or other feedback; we would love to hear from
-- you.
--
-- Licensed under the Apache License, Version 2.0 (the "License"); you may
-- not use this file except in compliance with the License. You may obtain a
-- copy of the License at http://www.apache.org/licenses/LICENSE-2.0
--
-- Unless required by applicable law or agreed to in writing, software
-- distributed under the License is distributed on an "AS IS" BASIS, WITHOUT
-- WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the
-- License for the specific language governing permissions and limitations
-- under the License.

-- |
-- Module: Main
-- Description: benchmarks for http-client and http-streams
-- Copyright: Copyright (c) 2013-2014 PivotCloud, Inc.
-- License: Apache License, Version 2.0
-- Maintainer: Lars Kuhtz <lkuhtz@pivotmail.com>
-- Stability: experimental
--
-- Here are the results for best performance when the number of threads
-- is larger than the number of cores:
--
-- > http-client-local-manager ---\                          /--- http-streamsIORef ---\    /--- http-streams-ioref-no-catchany
-- >                               -- http-streams-mvar -----                           ----
-- > http-client-global-manager --/                          \--- http-streams-state --/    \--- http-streams-mvar-no-catchany
--
-- When the number of cores matches the number of concurrent threads the
-- performance of all benchmarks is in the same order with the "no-catchany"
-- variants somewhat better than all others.
--

{-# LANGUAGE RecordWildCards #-}
{-# LANGUAGE FlexibleContexts #-}
{-# LANGUAGE MultiWayIf #-}
{-# LANGUAGE CPP #-}
{-# LANGUAGE LambdaCase #-}
{-# LANGUAGE OverloadedStrings #-}
{-# LANGUAGE ScopedTypeVariables #-}
{-# LANGUAGE UnicodeSyntax #-}

module Main
( main
) where

import Control.Applicative
import Control.Concurrent
import Control.Concurrent.Async
import Control.Exception.Enclosed
import Control.Exception.Lifted
import Control.Monad
import Control.Monad.Except
import Control.Monad.Trans.Control
import Control.Monad.State.Strict

import Criterion.Main

import qualified Data.ByteString as B
import qualified Data.ByteString.Char8 as B8
import qualified Data.ByteString.Lazy as LB
import Data.Either
import Data.IORef
import qualified Data.List as L
import Data.Monoid.Unicode
import Data.String
import qualified Data.Text as T
import Data.Word

import qualified Network.Wai as WAI
import qualified Network.Wai.Handler.Warp as WARP

import qualified Network.HTTP.Types as HTTP
import qualified Network.HTTP.Client as HTTP
import qualified Network.Http.Client as HS

import Prelude.Unicode

import qualified System.IO.Streams as S
import System.IO.Unsafe (unsafePerformIO)
import System.Random (randomIO)

-- -------------------------------------------------------------------------- --
-- Utils

sshow ∷ (Show a, IsString s) ⇒ a → s
sshow = fromString ∘ show

server ∷ Int → IO ()
server port = WARP.run port $ \req respond → do
        body ← WAI.strictRequestBody req
        respond $ WAI.responseLBS HTTP.status200 [] body

-- -------------------------------------------------------------------------- --
-- Benchmark Parameters

data BenchmarkType
    = HttpClientLocalManager
    | HttpClientLocalManagerNoTimeout
    | HttpClientGlobalManager
    | HttpStreamsMVar
    | HttpStreamsMVarNoCatchAny
    | HttpStreamsIORef
    | HttpStreamsIORefNoCatchAny
    | HttpStreamsState
    deriving (Show, Read, Eq, Ord, Bounded, Enum)

benchmarkTypeText
    ∷ IsString s
    ⇒ BenchmarkType
    → s
benchmarkTypeText HttpClientLocalManager = "http-client-local-manager"
benchmarkTypeText HttpClientLocalManagerNoTimeout = "http-client-local-manager-no-timeout"
benchmarkTypeText HttpClientGlobalManager = "http-client-global-manager"
benchmarkTypeText HttpStreamsMVar = "http-streams-mvar"
benchmarkTypeText HttpStreamsMVarNoCatchAny = "http-streams-mvar-no-catchany"
benchmarkTypeText HttpStreamsIORef = "http-streams-ioref"
benchmarkTypeText HttpStreamsIORefNoCatchAny = "http-streams-ioref-no-catchany"
benchmarkTypeText HttpStreamsState = "http-streams-state"

data BenchmarkParams = BenchmarkParams
    { _bparamsRequestSize ∷ ![Int]
        -- ^ The size of the request body. If this @0@ the request is made
        -- as a @GET@ request. Otherwise a @POST@ request is performed.

    , _bparamsTotalRequestsN ∷ ![Int]
        -- ^ The total number of requests made in each benchmark.
        --
        -- Note that he number of requests per thread is computed as @_pbaramsTotalRequestsN
        -- `div` _bparamsThreadsN@. Therefor, with a poor choice of the parameters, the
        -- actually executed number of requests can be off from
        -- @_bparamsTotalRequestsN@ by up to @_bparamsThreadsN@.

    , _bparamsThreadsN ∷ ![Int]
        -- ^ number of independent threads making concurrent requests.

    , _bparamsTypes ∷ ![BenchmarkType]
        -- ^ the set of different benchmark setups that is used.

    , _bparamsUrl ∷ !T.Text
        -- ^ The HTTP URL of the request.
    }
    deriving (Show, Read, Eq, Ord)

-- -------------------------------------------------------------------------- --
-- Some predefined benchmark groups

benchmarksAll ∷ [BenchmarkType]
benchmarksAll =
    [ HttpClientLocalManager
    , HttpClientLocalManagerNoTimeout
    , HttpClientGlobalManager
    , HttpStreamsMVar
    , HttpStreamsMVarNoCatchAny
    , HttpStreamsIORef
    , HttpStreamsIORefNoCatchAny
    , HttpStreamsState
    ]

benchmarksHttpClient ∷ [BenchmarkType]
benchmarksHttpClient =
    [ HttpClientLocalManager
    , HttpClientLocalManagerNoTimeout
    , HttpClientGlobalManager
    ]

benchmarksHttpStreams ∷ [BenchmarkType]
benchmarksHttpStreams =
    [ HttpStreamsMVar
    , HttpStreamsMVarNoCatchAny
    , HttpStreamsIORef
    , HttpStreamsIORefNoCatchAny
    , HttpStreamsState
    ]

benchmarksBest ∷ [BenchmarkType]
benchmarksBest =
    [ HttpClientLocalManager
    , HttpStreamsIORef
    ]

-- -------------------------------------------------------------------------- --
-- Main

main ∷ IO ()
main = do
    if withWarp
      then
        race (server 8282) (benchmark params) >>= \case
            Right _ → return ()
            Left e → error $ "HTTP server exited unexpectedly: " ⊕ show e
      else
        benchmark params
  where
    params = BenchmarkParams
        { _bparamsRequestSize = [0]
        , _bparamsTotalRequestsN = [4096]
        , _bparamsThreadsN = [1,2,4,8,16,64]
        -- , _bparamsThreadsN = [128]
        , _bparamsTypes = [HttpClientLocalManager, HttpClientLocalManagerNoTimeout, HttpStreamsIORef, HttpStreamsIORefNoCatchAny]
        -- , _bparamsTypes = [HttpClientGlobalManager, HttpClientLocalManager, HttpStreamsMVar, HttpStreamsIORef]
        -- , _bparamsTypes = [HttpClientGlobalManager, HttpClientLocalManager, HttpStreamsMVar, HttpStreamsIORef, HttpStreamsIORefNoCatchAny]
        , _bparamsUrl = "http://127.0.0.1:8282"
        }
    withWarp = True

-- -------------------------------------------------------------------------- --
-- Execute Benchmarks

benchmark ∷ BenchmarkParams → IO ()
benchmark BenchmarkParams{..} = defaultMain $
    [ createBenchmarkGroup httpClientReq httpStreamsReq _bparamsTypes (reqN `div` threadN) threadN size
    | reqN ← _bparamsTotalRequestsN
    , threadN ← _bparamsThreadsN
    , size ← _bparamsRequestSize
    ]
  where
    -- 1 MB of static random bytes
    body ∷ B.ByteString
    body = unsafePerformIO $ fromString <$> replicateM (1024 * 1024) randomIO
    {-# NOINLINE body #-}

    rawRequest = either (error ∘ show) id ∘ HTTP.parseUrl $ T.unpack _bparamsUrl
    path = HTTP.path rawRequest
    host = HTTP.host rawRequest
    port = fromIntegral $ HTTP.port rawRequest

    httpClientReq size = rawRequest
        { HTTP.requestBody = HTTP.RequestBodyBS $ B.take size body
        , HTTP.method = if size ≡ 0 then "GET" else "POST"
        }

    httpStreamsReq size = (host, port, req, reqBody)
      where
        method = if size ≡ 0 then HS.GET else HS.POST
        reqBody = if size ≡ 0 then Nothing else Just (B.take size body)
    #if MIN_VERSION_http_streams(0,8,0)
        req = HS.buildRequest $ do
            HS.http method path
            HS.setAccept "*/*"
    #else
        req = unsafePerformIO ∘ HS.buildRequest $ do
            HS.http method path
            HS.setAccept "*/*"
        {-# NOINLINE req #-}
    #endif

-- -------------------------------------------------------------------------- --
-- Create Benchmarks

createBenchmark
    ∷ (Int → HTTP.Request)
    → (Int → (B8.ByteString, Word16, HS.Request, Maybe B8.ByteString))
    → BenchmarkType
    → Int
        -- ^ number of requests
    → Int
        -- ^ number of threads
    → Int
        -- ^ size of body
    → Benchmark
createBenchmark httpClientReq httpStreamsReq typ reqN threadN size = case typ of
        HttpClientLocalManager → bcClient benchHttpClient
        HttpClientLocalManagerNoTimeout → bcClient benchHttpClientNoTimeout
        HttpClientGlobalManager → bcClient benchHttpClient2
        HttpStreamsMVar → bcStreams benchHttpStreamsMVar
        HttpStreamsMVarNoCatchAny → bcStreams benchHttpStreamsMVarNoCatchAny
        HttpStreamsIORef → bcStreams benchHttpStreamsIORef
        HttpStreamsIORefNoCatchAny → bcStreams benchHttpStreamsIORefNoCatchAny
        HttpStreamsState → bcStreams benchHttpStreamsState
  where
    bcClient f = bench (benchmarkTypeText typ) ∘ nfIO $ f reqN threadN (httpClientReq size)
    bcStreams f = bench (benchmarkTypeText typ) ∘ nfIO $ f reqN threadN (httpStreamsReq size)

createBenchmarkGroup
    ∷ (Int → HTTP.Request)
    → (Int → (B8.ByteString, Word16, HS.Request, Maybe B8.ByteString))
    → [BenchmarkType]
    → Int
        -- ^ number of requests
    → Int
        -- ^ number of threads
    → Int
        -- ^ size of body
    → Benchmark
createBenchmarkGroup httpClientReq httpStreamsReq typs reqN threadN size =
    bgroup label $ map create typs
  where
    label = L.intercalate "/" ["KB " ⊕ show size, "#req " ⊕ show reqN, "#threads " ⊕ show threadN]
    create typ = createBenchmark httpClientReq httpStreamsReq typ reqN threadN size

-- -------------------------------------------------------------------------- --
-- Tools for Creating Benchmarks

check
    ∷ MonadIO m
    ⇒ (Either T.Text α → Bool)
    → ExceptT T.Text m α
    → m ()
check c x = runExceptT x >>= \r →
    unless (c r) $ error "check failed"

-- | http-streams with connection stored in MVar
--
benchHttpStreamsMVar
    ∷ Int
        -- ^ number requests per thread
    → Int
        -- ^ number of threads
    → (B8.ByteString, Word16, HS.Request, Maybe B8.ByteString)
    → IO ()
benchHttpStreamsMVar n p (host, port, req, body) = void ∘ mapConcurrently run $ replicate p req
  where
    run r = withConnection host port $ \useConnection reset →
        replicateM_ n ∘ check isRight $ httpStreamsRequest useConnection reset body r

-- | http-streams with connection stored in MVar without catchAny
--
benchHttpStreamsMVarNoCatchAny
    ∷ Int
        -- ^ number requests per thread
    → Int
        -- ^ number of threads
    → (B8.ByteString, Word16, HS.Request, Maybe B8.ByteString)
    → IO ()
benchHttpStreamsMVarNoCatchAny n p (host, port, req, body) = void ∘ mapConcurrently run $ replicate p req
  where
    run r = withConnection host port $ \useConnection reset →
        replicateM_ n ∘ check isRight $ httpStreamsRequestNoCatchAny useConnection reset body r

-- | http-streams with connection stored in IORef
--
benchHttpStreamsIORef
    ∷ Int
        -- ^ number requests per thread
    → Int
        -- ^ number of threads
    → (B8.ByteString, Word16, HS.Request, Maybe B8.ByteString)
    → IO ()
benchHttpStreamsIORef n p (host, port, req, body) = void ∘ mapConcurrently run $ replicate p req
  where
    run r = withConnection_ host port $ \useConnection reset →
        replicateM_ n ∘ check isRight $ httpStreamsRequest useConnection reset body r

-- | http-streams with connection stored in IORef without catchAny
--
benchHttpStreamsIORefNoCatchAny
    ∷ Int
        -- ^ number requests per thread
    → Int
        -- ^ number of threads
    → (B8.ByteString, Word16, HS.Request, Maybe B8.ByteString)
    → IO ()
benchHttpStreamsIORefNoCatchAny n p (host, port, req, body) = void ∘ mapConcurrently run $ replicate p req
  where
    run r = withConnection_ host port $ \useConnection reset →
        replicateM_ n ∘ check isRight $ httpStreamsRequestNoCatchAny useConnection reset body r

-- | http-streams with connection in a state monad
--
benchHttpStreamsState
    ∷ Int
        -- ^ number requests per thread
    → Int
        -- ^ number of threads
    → (B8.ByteString, Word16, HS.Request, Maybe B8.ByteString)
    → IO ()
benchHttpStreamsState n p (host, port, req, body) = void ∘ mapConcurrently run $ replicate p req
  where
    run ∷ HS.Request → IO ()
    run r = do
        connection ← newConnectionS host port
        (_,connection') ← flip runStateT connection $
            replicateM_ n ∘ check isRight $ httpStreamsRequestS host port body r
        closeConnectionS connection' -- FIXME make this exception safe

-- | http-client with thread local manager
--
benchHttpClient
    ∷ Int
        -- ^ number requests per thread
    → Int
        -- ^ number of threads
    → HTTP.Request
    → IO ()
benchHttpClient n p = void ∘ mapConcurrently run ∘ replicate p
  where
    run req = HTTP.withManager settings $ \mgr → replicateM_ n ∘ check isRight $
        httpClientRequest mgr req
    settings = HTTP.defaultManagerSettings
        { HTTP.managerResponseTimeout = Just 5000000
        , HTTP.managerConnCount = 1
        }

-- | http-client with thread local manager
--
benchHttpClientNoTimeout
    ∷ Int
        -- ^ number requests per thread
    → Int
        -- ^ number of threads
    → HTTP.Request
    → IO ()
benchHttpClientNoTimeout n p = void ∘ mapConcurrently run ∘ replicate p
  where
    run req = HTTP.withManager settings $ \mgr → replicateM_ n ∘ check isRight $
        httpClientRequest mgr req
    settings = HTTP.defaultManagerSettings
        { HTTP.managerResponseTimeout = Nothing
        , HTTP.managerConnCount = 1
        }

-- | http-client global manager
--
benchHttpClient2
    ∷ Int
        -- ^ number requests per thread
    → Int
        -- ^ number of threads
    → HTTP.Request → IO ()
benchHttpClient2 n p req = HTTP.withManager settings $ \mgr →
    void ∘ mapConcurrently (run mgr) $ replicate p req
  where
    run mgr = replicateM_ n ∘ check isRight ∘ httpClientRequest mgr
    settings = HTTP.defaultManagerSettings
        -- { HTTP.managerResponseTimeout = Just 5000000
        { HTTP.managerResponseTimeout = Nothing
        , HTTP.managerConnCount = p
        }

-- -------------------------------------------------------------------------- --
-- http-client
--

httpClientRequest
    ∷ (MonadIO m, MonadError T.Text m)
    ⇒ HTTP.Manager
        -- ^ connection manager
    → HTTP.Request
        -- ^ request
    → m B.ByteString
httpClientRequest mgr req = do
    r ← liftIO $ HTTP.httpLbs req mgr
    case HTTP.responseStatus r of
        s   | HTTP.statusIsSuccessful s → return ∘ LB.toStrict $ HTTP.responseBody r
            | HTTP.statusIsServerError s → throwError $ "server error: " ⊕ sshow s
            | HTTP.statusIsClientError s → throwError $ "client error: " ⊕ sshow s
            | otherwise → throwError $ "unexpected response status: " ⊕ sshow s

-- -------------------------------------------------------------------------- --
-- http-streams

-- | This is not a production level interface to http-streams. It is only
-- meant to roughly exhibit similar performance as an production grade
-- implementation would have.
--
newtype HttpConnection = HttpConnection
    { httpConnectionVar ∷ MVar HS.Connection
    }

newConnection
    ∷ (MonadIO m, Functor m)
    ⇒ B8.ByteString
        -- ^ host name
    → Word16
        -- ^ port
    → m HttpConnection
newConnection host port = HttpConnection
    <$> liftIO (newMVar =<< HS.openConnection host port)

-- | This will deadlock when the connection had been closed before!
--
closeConnection
    ∷ MonadIO m
    ⇒ HttpConnection
    → m ()
closeConnection conn = liftIO $ do
    HS.closeConnection =<< takeMVar (httpConnectionVar conn)

resetConnection
    ∷ MonadIO m
    ⇒ B8.ByteString
        -- ^ host name
    → Word16
        -- ^ port
    → HttpConnection
    → m ()
resetConnection host port conn = do
    closeConnection conn
    liftIO $ putMVar (httpConnectionVar conn) =<< HS.openConnection host port

-- | It is up to the user to explicitly reset the connection if needed.
--
withConnection
    ∷ ∀ m n o α β . (MonadIO m, MonadBaseControl IO m, MonadIO n, MonadBaseControl IO n, MonadIO o)
    ⇒ B8.ByteString
        -- ^ host name
    → Word16
        -- ^ port
    → (((HS.Connection → n β) → n β) → o () →  m α)
    → m α
withConnection host port inner =
    bracket (newConnection host port) closeConnection $ \conn@(HttpConnection var) → do
        let useConnection = bracket (liftIO $ takeMVar var) (liftIO ∘ putMVar var)
        inner useConnection (resetConnection host port conn)

-- |
--
-- No redirections are performed!
--
httpStreamsRequest
    ∷ (MonadIO m, MonadBaseControl IO m, MonadError T.Text m)
    ⇒ ((HS.Connection → m (HTTP.Status, B.ByteString)) → m (HTTP.Status, B.ByteString))
        -- ^ use connection
    → m ()
        -- ^ reset connection
    → Maybe B.ByteString
    → HS.Request
        -- ^ HTTP request
    → m B.ByteString
httpStreamsRequest useConnection reset body req = do
    -- When an error occurred we don't know if the connection is clean. We
    -- could do a little better, but we take a conservative approach and
    -- always reset the connection.
    --
    -- We wrap this to into a 'catchAny' and reset the connection
    -- when an exception occurs.
    (status, responseBody) ← tryAnyText ∘ useConnection $ \connection →
        liftIO $ do
            b ← bodyStream
            HS.sendRequest connection req b
            HS.receiveResponse connection handler
    case status of
        s   | HTTP.statusIsSuccessful s → return responseBody
            | HTTP.statusIsServerError s → throwError $ "server error: " ⊕ sshow s
            | HTTP.statusIsClientError s → throwError $ "client error: " ⊕ sshow s
            | otherwise → throwError $ "unexpected response status: " ⊕ sshow s
  where

    bodyStream = case body of
        Nothing → return HS.emptyBody
        Just b → HS.inputStreamBody <$> S.fromByteString b

    -- with this there is a significant difference between http-streams-mvar and http-streams-ioref
    tryAnyText f = f `catchAny` \e → reset >> throwError (sshow e)

    -- using this makes the differences between http-streams-mvar and http-streams-ioref almost
    -- disappear.
    -- tryAnyText = id

    handler ∷ HS.Response → S.InputStream B.ByteString → IO (HTTP.Status, B.ByteString)
    handler response istream = do
        responseBody ← HS.concatHandler response istream
        return (HTTP.mkStatus (HS.getStatusCode response) (HS.getStatusMessage response), responseBody)

httpStreamsRequestNoCatchAny
    ∷ (MonadIO m, MonadBaseControl IO m, MonadError T.Text m)
    ⇒ ((HS.Connection → m (HTTP.Status, B.ByteString)) → m (HTTP.Status, B.ByteString))
        -- ^ use connection
    → m ()
        -- ^ reset connection
    → Maybe B.ByteString
    → HS.Request
        -- ^ HTTP request
    → m B.ByteString
httpStreamsRequestNoCatchAny useConnection reset body req = do
    -- When an error occurred we don't know if the connection is clean. We
    -- could do a little better, but we take a conservative approach and
    -- always reset the connection.
    --
    -- We wrap this to into a 'catchAny' and reset the connection
    -- when an exception occurs.
    (status, responseBody) ← tryAnyText ∘ useConnection $ \connection →
        liftIO $ do
            b ← bodyStream
            HS.sendRequest connection req b
            HS.receiveResponse connection handler
    case status of
        s   | HTTP.statusIsSuccessful s → return responseBody
            | HTTP.statusIsServerError s → throwError $ "server error: " ⊕ sshow s
            | HTTP.statusIsClientError s → throwError $ "client error: " ⊕ sshow s
            | otherwise → throwError $ "unexpected response status: " ⊕ sshow s
  where

    bodyStream = case body of
        Nothing → return HS.emptyBody
        Just b → HS.inputStreamBody <$> S.fromByteString b

    -- using this makes the differences between http-streams-mvar and http-streams-ioref almost
    -- disappear.
    tryAnyText = id

    handler ∷ HS.Response → S.InputStream B.ByteString → IO (HTTP.Status, B.ByteString)
    handler response istream = do
        responseBody ← HS.concatHandler response istream
        return (HTTP.mkStatus (HS.getStatusCode response) (HS.getStatusMessage response), responseBody)

-- -------------------------------------------------------------------------- --
-- http-streams with IORef

-- | This is not a production level interface to http-streams. It is only
-- meant to roughly exhibit similar performance as an production grade
-- implementation would have.
--
newtype HttpConnection_ = HttpConnection_
    { httpConnectionVar_ ∷ IORef HS.Connection
    }

newConnection_
    ∷ (MonadIO m, Functor m)
    ⇒ B8.ByteString
        -- ^ host name
    → Word16
        -- ^ port
    → m HttpConnection_
newConnection_ host port = HttpConnection_
    <$> liftIO (newIORef =<< HS.openConnection host port)

-- | This will deadlock when the connection had been closed before!
--
closeConnection_
    ∷ MonadIO m
    ⇒ HttpConnection_
    → m ()
closeConnection_ conn = liftIO $ do
    HS.closeConnection =<< readIORef (httpConnectionVar_ conn)

resetConnection_
    ∷ MonadIO m
    ⇒ B8.ByteString
        -- ^ host name
    → Word16
        -- ^ port
    → HttpConnection_
    → m ()
resetConnection_ host port conn = do
    closeConnection_ conn
    liftIO $ writeIORef (httpConnectionVar_ conn) =<< HS.openConnection host port

-- | It is up to the user to explicitly reset the connection if needed.
--
withConnection_
    ∷ ∀ m n o α β . (MonadIO m, MonadBaseControl IO m, MonadIO n, MonadBaseControl IO n, MonadIO o)
    ⇒ B8.ByteString
        -- ^ host name
    → Word16
        -- ^ port
    → (((HS.Connection → n β) → n β) → o () →  m α)
    → m α
withConnection_ host port inner =
    bracket (newConnection_ host port) closeConnection_ $ \conn@(HttpConnection_ var) → do
        let useConnection f = liftIO (readIORef var) >>= f
        inner useConnection (resetConnection_ host port conn)

-- -------------------------------------------------------------------------- --
-- http-streams with connection in state monad

newConnectionS
    ∷ MonadIO m
    ⇒ B8.ByteString
        -- ^ host name
    → Word16
        -- ^ port
    → m HS.Connection
newConnectionS host port = liftIO $ HS.openConnection host port

-- | This will deadlock when the connection had been closed before!
--
closeConnectionS
    ∷ MonadIO m
    ⇒ HS.Connection
    → m ()
closeConnectionS connection = liftIO $ HS.closeConnection connection

resetConnectionS
    ∷ (MonadIO m, MonadState HS.Connection m)
    ⇒ B8.ByteString
        -- ^ host name
    → Word16
        -- ^ port
    → m ()
resetConnectionS host port = do
    get >>= closeConnectionS
    newConnectionS host port >>= put

-- |
--
-- No redirections are performed!
--
httpStreamsRequestS
    ∷ (MonadIO m, MonadBaseControl IO m, MonadError T.Text m, MonadState HS.Connection m)
    ⇒ B8.ByteString
        -- ^ host name
    → Word16
        -- ^ port
    → Maybe B.ByteString
        -- ^ the request body
    → HS.Request
        -- ^ HTTP request
    → m B.ByteString
httpStreamsRequestS host port body req = do

    connection ← get

    -- When an error occurred we don't know if the connection is clean. We
    -- could do a little better, but we take a conservative approach and
    -- always reset the connection.
    --
    -- We wrap this to into a 'catchAny' and reset the connection
    -- when an exception occurs.
    (status, responseBody) ← tryAnyText ∘ liftIO $ do
        b ← bodyStream
        HS.sendRequest connection req b
        HS.receiveResponse connection handler
    case status of
        s   | HTTP.statusIsSuccessful s → return responseBody
            | HTTP.statusIsServerError s → throwError $ "server error: " ⊕ sshow s
            | HTTP.statusIsClientError s → throwError $ "client error: " ⊕ sshow s
            | otherwise → throwError $ "unexpected response status: " ⊕ sshow s
  where

    bodyStream = case body of
        Nothing → return HS.emptyBody
        Just b → HS.inputStreamBody <$> S.fromByteString b

    tryAnyText f = f `catchAny` \e → do
        resetConnectionS host port
        throwError $ sshow e

    handler ∷ HS.Response → S.InputStream B.ByteString → IO (HTTP.Status, B.ByteString)
    handler response istream = do
        responseBody ← HS.concatHandler response istream
        return (HTTP.mkStatus (HS.getStatusCode response) (HS.getStatusMessage response), responseBody)