modules/gles3/performance/es3pBufferDataUploadTests.cpp

/*-------------------------------------------------------------------------
 * drawElements Quality Program OpenGL ES 3.0 Module
 * -------------------------------------------------
 *
 * Copyright 2014 The Android Open Source Project
 *
 * 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.
 *
 *//*!
 * \file
 * \brief Buffer data upload performance tests.
 *//*--------------------------------------------------------------------*/

#include "es3pBufferDataUploadTests.hpp"
#include "glsCalibration.hpp"
#include "tcuTestLog.hpp"
#include "tcuVectorUtil.hpp"
#include "tcuSurface.hpp"
#include "tcuCPUWarmup.hpp"
#include "tcuRenderTarget.hpp"
#include "gluRenderContext.hpp"
#include "gluShaderProgram.hpp"
#include "gluStrUtil.hpp"
#include "gluPixelTransfer.hpp"
#include "gluObjectWrapper.hpp"
#include "glwFunctions.hpp"
#include "glwEnums.hpp"
#include "deClock.h"
#include "deMath.h"
#include "deStringUtil.hpp"
#include "deRandom.hpp"
#include "deMemory.h"
#include "deThread.h"
#include "deMeta.hpp"

#include <algorithm>
#include <iomanip>
#include <limits>

namespace deqp
{
namespace gles3
{
namespace Performance
{
namespace
{

using gls::theilSenSiegelLinearRegression;
using gls::LineParametersWithConfidence;
using de::meta::EnableIf;
using de::meta::Not;

static const char* const s_dummyVertexShader =		"#version 300 es\n"
													"in highp vec4 a_position;\n"
													"void main (void)\n"
													"{\n"
													"	gl_Position = a_position;\n"
													"}\n";

static const char* const s_dummyFragnentShader =	"#version 300 es\n"
													"layout(location = 0) out mediump vec4 dEQP_FragColor;\n"
													"void main (void)\n"
													"{\n"
													"	dEQP_FragColor = vec4(1.0, 0.0, 0.0, 1.0);\n"
													"}\n";

static const char* const s_colorVertexShader =		"#version 300 es\n"
													"in highp vec4 a_position;\n"
													"in highp vec4 a_color;\n"
													"out highp vec4 v_color;\n"
													"void main (void)\n"
													"{\n"
													"	gl_Position = a_position;\n"
													"	v_color = a_color;\n"
													"}\n";

static const char* const s_colorFragmentShader =	"#version 300 es\n"
													"layout(location = 0) out mediump vec4 dEQP_FragColor;\n"
													"in mediump vec4 v_color;\n"
													"void main (void)\n"
													"{\n"
													"	dEQP_FragColor = v_color;\n"
													"}\n";

struct SingleOperationDuration
{
	deUint64 totalDuration;
	deUint64 fitResponseDuration; // used for fitting
};

struct MapBufferRangeDuration
{
	deUint64 mapDuration;
	deUint64 unmapDuration;
	deUint64 writeDuration;
	deUint64 allocDuration;
	deUint64 totalDuration;

	deUint64 fitResponseDuration;
};

struct MapBufferRangeDurationNoAlloc
{
	deUint64 mapDuration;
	deUint64 unmapDuration;
	deUint64 writeDuration;
	deUint64 totalDuration;

	deUint64 fitResponseDuration;
};

struct MapBufferRangeFlushDuration
{
	deUint64 mapDuration;
	deUint64 unmapDuration;
	deUint64 writeDuration;
	deUint64 flushDuration;
	deUint64 allocDuration;
	deUint64 totalDuration;

	deUint64 fitResponseDuration;
};

struct MapBufferRangeFlushDurationNoAlloc
{
	deUint64 mapDuration;
	deUint64 unmapDuration;
	deUint64 writeDuration;
	deUint64 flushDuration;
	deUint64 totalDuration;

	deUint64 fitResponseDuration;
};

struct RenderReadDuration
{
	deUint64 renderDuration;
	deUint64 readDuration;
	deUint64 renderReadDuration;
	deUint64 totalDuration;

	deUint64 fitResponseDuration;
};

struct UnrelatedUploadRenderReadDuration
{
	deUint64 renderDuration;
	deUint64 readDuration;
	deUint64 renderReadDuration;
	deUint64 totalDuration;

	deUint64 fitResponseDuration;
};

struct UploadRenderReadDuration
{
	deUint64 uploadDuration;
	deUint64 renderDuration;
	deUint64 readDuration;
	deUint64 totalDuration;
	deUint64 renderReadDuration;

	deUint64 fitResponseDuration;
};

struct UploadRenderReadDurationWithUnrelatedUploadSize
{
	deUint64 uploadDuration;
	deUint64 renderDuration;
	deUint64 readDuration;
	deUint64 totalDuration;
	deUint64 renderReadDuration;

	deUint64 fitResponseDuration;
};

struct RenderUploadRenderReadDuration
{
	deUint64 firstRenderDuration;
	deUint64 uploadDuration;
	deUint64 secondRenderDuration;
	deUint64 readDuration;
	deUint64 totalDuration;
	deUint64 renderReadDuration;

	deUint64 fitResponseDuration;
};

template <typename SampleT>
struct UploadSampleResult
{
	typedef SampleT SampleType;

	int			bufferSize;
	int			allocatedSize;
	int			writtenSize;
	SampleType	duration;
};

template <typename SampleT>
struct RenderSampleResult
{
	typedef SampleT SampleType;

	int			uploadedDataSize;
	int			renderDataSize;
	int			unrelatedDataSize;
	int			numVertices;
	SampleT		duration;
};

struct SingleOperationStatistics
{
	float minTime;
	float maxTime;
	float medianTime;
	float min2DecileTime;		// !< minimum value in the 2nd decile
	float max9DecileTime;		// !< maximum value in the 9th decile
};

struct SingleCallStatistics
{
	SingleOperationStatistics	result;

	float						medianRate;
	float						maxDiffTime;
	float						maxDiff9DecileTime;
	float						medianDiffTime;

	float						maxRelDiffTime;
	float						max9DecileRelDiffTime;
	float						medianRelDiffTime;
};

struct MapCallStatistics
{
	SingleOperationStatistics	map;
	SingleOperationStatistics	unmap;
	SingleOperationStatistics	write;
	SingleOperationStatistics	alloc;
	SingleOperationStatistics	result;

	float						medianRate;
	float						maxDiffTime;
	float						maxDiff9DecileTime;
	float						medianDiffTime;

	float						maxRelDiffTime;
	float						max9DecileRelDiffTime;
	float						medianRelDiffTime;
};

struct MapFlushCallStatistics
{
	SingleOperationStatistics	map;
	SingleOperationStatistics	unmap;
	SingleOperationStatistics	write;
	SingleOperationStatistics	flush;
	SingleOperationStatistics	alloc;
	SingleOperationStatistics	result;

	float						medianRate;
	float						maxDiffTime;
	float						maxDiff9DecileTime;
	float						medianDiffTime;

	float						maxRelDiffTime;
	float						max9DecileRelDiffTime;
	float						medianRelDiffTime;
};

struct RenderReadStatistics
{
	SingleOperationStatistics	render;
	SingleOperationStatistics	read;
	SingleOperationStatistics	result;
	SingleOperationStatistics	total;

	float						medianRate;
	float						maxDiffTime;
	float						maxDiff9DecileTime;
	float						medianDiffTime;

	float						maxRelDiffTime;
	float						max9DecileRelDiffTime;
	float						medianRelDiffTime;
};

struct UploadRenderReadStatistics
{
	SingleOperationStatistics	upload;
	SingleOperationStatistics	render;
	SingleOperationStatistics	read;
	SingleOperationStatistics	result;
	SingleOperationStatistics	total;

	float						medianRate;
	float						maxDiffTime;
	float						maxDiff9DecileTime;
	float						medianDiffTime;

	float						maxRelDiffTime;
	float						max9DecileRelDiffTime;
	float						medianRelDiffTime;
};

struct RenderUploadRenderReadStatistics
{
	SingleOperationStatistics	firstRender;
	SingleOperationStatistics	upload;
	SingleOperationStatistics	secondRender;
	SingleOperationStatistics	read;
	SingleOperationStatistics	result;
	SingleOperationStatistics	total;

	float						medianRate;
	float						maxDiffTime;
	float						maxDiff9DecileTime;
	float						medianDiffTime;

	float						maxRelDiffTime;
	float						max9DecileRelDiffTime;
	float						medianRelDiffTime;
};

template <typename T>
struct SampleTypeTraits
{
};

template <>
struct SampleTypeTraits<SingleOperationDuration>
{
	typedef SingleCallStatistics StatsType;

	enum { HAS_MAP_STATS		= 0	};
	enum { HAS_UNMAP_STATS		= 0	};
	enum { HAS_WRITE_STATS		= 0	};
	enum { HAS_FLUSH_STATS		= 0	};
	enum { HAS_ALLOC_STATS		= 0	};
	enum { LOG_CONTRIBUTIONS	= 0	};
};

template <>
struct SampleTypeTraits<MapBufferRangeDuration>
{
	typedef MapCallStatistics StatsType;

	enum { HAS_MAP_STATS		= 1	};
	enum { HAS_UNMAP_STATS		= 1	};
	enum { HAS_WRITE_STATS		= 1	};
	enum { HAS_FLUSH_STATS		= 0	};
	enum { HAS_ALLOC_STATS		= 1	};
	enum { LOG_CONTRIBUTIONS	= 1	};
};

template <>
struct SampleTypeTraits<MapBufferRangeDurationNoAlloc>
{
	typedef MapCallStatistics StatsType;

	enum { HAS_MAP_STATS		= 1	};
	enum { HAS_UNMAP_STATS		= 1	};
	enum { HAS_WRITE_STATS		= 1	};
	enum { HAS_FLUSH_STATS		= 0	};
	enum { HAS_ALLOC_STATS		= 0	};
	enum { LOG_CONTRIBUTIONS	= 1	};
};

template <>
struct SampleTypeTraits<MapBufferRangeFlushDuration>
{
	typedef MapFlushCallStatistics StatsType;

	enum { HAS_MAP_STATS		= 1	};
	enum { HAS_UNMAP_STATS		= 1	};
	enum { HAS_WRITE_STATS		= 1	};
	enum { HAS_FLUSH_STATS		= 1	};
	enum { HAS_ALLOC_STATS		= 1	};
	enum { LOG_CONTRIBUTIONS	= 1	};
};

template <>
struct SampleTypeTraits<MapBufferRangeFlushDurationNoAlloc>
{
	typedef MapFlushCallStatistics StatsType;

	enum { HAS_MAP_STATS		= 1	};
	enum { HAS_UNMAP_STATS		= 1	};
	enum { HAS_WRITE_STATS		= 1	};
	enum { HAS_FLUSH_STATS		= 1	};
	enum { HAS_ALLOC_STATS		= 0	};
	enum { LOG_CONTRIBUTIONS	= 1	};
};

template <>
struct SampleTypeTraits<RenderReadDuration>
{
	typedef RenderReadStatistics StatsType;

	enum { HAS_RENDER_STATS			= 1	};
	enum { HAS_READ_STATS			= 1	};
	enum { HAS_UPLOAD_STATS			= 0	};
	enum { HAS_TOTAL_STATS			= 1	};
	enum { HAS_FIRST_RENDER_STATS	= 0	};
	enum { HAS_SECOND_RENDER_STATS	= 0	};

	enum { LOG_CONTRIBUTIONS	= 1	};
};

template <>
struct SampleTypeTraits<UnrelatedUploadRenderReadDuration>
{
	typedef RenderReadStatistics StatsType;

	enum { HAS_RENDER_STATS			= 1	};
	enum { HAS_READ_STATS			= 1	};
	enum { HAS_UPLOAD_STATS			= 0	};
	enum { HAS_TOTAL_STATS			= 1	};
	enum { HAS_FIRST_RENDER_STATS	= 0	};
	enum { HAS_SECOND_RENDER_STATS	= 0	};

	enum { LOG_CONTRIBUTIONS	= 1	};
};

template <>
struct SampleTypeTraits<UploadRenderReadDuration>
{
	typedef UploadRenderReadStatistics StatsType;

	enum { HAS_RENDER_STATS			= 1	};
	enum { HAS_READ_STATS			= 1	};
	enum { HAS_UPLOAD_STATS			= 1	};
	enum { HAS_TOTAL_STATS			= 1	};
	enum { HAS_FIRST_RENDER_STATS	= 0	};
	enum { HAS_SECOND_RENDER_STATS	= 0	};

	enum { LOG_CONTRIBUTIONS			= 1	};
	enum { LOG_UNRELATED_UPLOAD_SIZE	= 0 };
};

template <>
struct SampleTypeTraits<UploadRenderReadDurationWithUnrelatedUploadSize>
{
	typedef UploadRenderReadStatistics StatsType;

	enum { HAS_RENDER_STATS			= 1	};
	enum { HAS_READ_STATS			= 1	};
	enum { HAS_UPLOAD_STATS			= 1	};
	enum { HAS_TOTAL_STATS			= 1	};
	enum { HAS_FIRST_RENDER_STATS	= 0	};
	enum { HAS_SECOND_RENDER_STATS	= 0	};

	enum { LOG_CONTRIBUTIONS			= 1	};
	enum { LOG_UNRELATED_UPLOAD_SIZE	= 1 };
};

template <>
struct SampleTypeTraits<RenderUploadRenderReadDuration>
{
	typedef RenderUploadRenderReadStatistics StatsType;

	enum { HAS_RENDER_STATS			= 0	};
	enum { HAS_READ_STATS			= 1	};
	enum { HAS_UPLOAD_STATS			= 1	};
	enum { HAS_TOTAL_STATS			= 1	};
	enum { HAS_FIRST_RENDER_STATS	= 1	};
	enum { HAS_SECOND_RENDER_STATS	= 1	};

	enum { LOG_CONTRIBUTIONS			= 1	};
	enum { LOG_UNRELATED_UPLOAD_SIZE	= 1 };
};

struct UploadSampleAnalyzeResult
{
	float transferRateMedian;
	float transferRateAtRange;
	float transferRateAtInfinity;
};

struct RenderSampleAnalyzeResult
{
	float renderRateMedian;
	float renderRateAtRange;
	float renderRateAtInfinity;
};

class UnmapFailureError : public std::exception
{
public:
	UnmapFailureError (void) : std::exception() {}
};

static std::string getHumanReadableByteSize (int numBytes)
{
	std::ostringstream buf;

	if (numBytes < 1024)
		buf << numBytes << " byte(s)";
	else if (numBytes < 1024 * 1024)
		buf << de::floatToString((float)numBytes/1024.0f, 1) << " KiB";
	else
		buf << de::floatToString((float)numBytes/1024.0f/1024.0f, 1) << " MiB";

	return buf.str();
}

static deUint64 medianTimeMemcpy (void* dst, const void* src, int numBytes)
{
	// Time used by memcpy is assumed to be asymptotically linear

	// With large numBytes, the probability of context switch or other random
	// event is high. Apply memcpy in parts and report how much time would
	// memcpy have used with the median transfer rate.

	// Less than 1MiB, no need to do anything special
	if (numBytes < 1048576)
	{
		deUint64 startTime;
		deUint64 endTime;

		deYield();

		startTime = deGetMicroseconds();
		deMemcpy(dst, src, numBytes);
		endTime = deGetMicroseconds();

		return endTime - startTime;
	}
	else
	{
		// Do memcpy in multiple parts

		const int	numSections		= 5;
		const int	sectionAlign	= 16;

		int			sectionStarts[numSections+1];
		int			sectionLens[numSections];
		deUint64	sectionTimes[numSections];
		deUint64	medianTime;
		deUint64	bestTime		= 0;

		for (int sectionNdx = 0; sectionNdx < numSections; ++sectionNdx)
			sectionStarts[sectionNdx] = deAlign32((numBytes * sectionNdx / numSections), sectionAlign);
		sectionStarts[numSections] = numBytes;

		for (int sectionNdx = 0; sectionNdx < numSections; ++sectionNdx)
			sectionLens[sectionNdx] = sectionStarts[sectionNdx+1] - sectionStarts[sectionNdx];

		// Memcpy is usually called after mapbuffer range which may take
		// a lot of time. To prevent power management from kicking in during
		// copy, warm up more.
		{
			deYield();
			tcu::warmupCPU();
			deYield();
		}

		for (int sectionNdx = 0; sectionNdx < numSections; ++sectionNdx)
		{
			deUint64 startTime;
			deUint64 endTime;

			startTime = deGetMicroseconds();
			deMemcpy((deUint8*)dst + sectionStarts[sectionNdx], (const deUint8*)src + sectionStarts[sectionNdx], sectionLens[sectionNdx]);
			endTime = deGetMicroseconds();

			sectionTimes[sectionNdx] = endTime - startTime;

			if (!bestTime || sectionTimes[sectionNdx] < bestTime)
				bestTime = sectionTimes[sectionNdx];

			// Detect if write takes 50% longer than it should, and warm up if that happened
			if (sectionNdx != numSections-1 && (float)sectionTimes[sectionNdx] > 1.5f * (float)bestTime)
			{
				deYield();
				tcu::warmupCPU();
				deYield();
			}
		}

		std::sort(sectionTimes, sectionTimes + numSections);

		if ((numSections % 2) == 0)
			medianTime = (sectionTimes[numSections / 2 - 1] + sectionTimes[numSections / 2]) / 2;
		else
			medianTime = sectionTimes[numSections / 2];

		return medianTime*numSections;
	}
}

static float dummyCalculation (float initial, int workSize)
{
	float	a = initial;
	int		b = 123;

	for (int ndx = 0; ndx < workSize; ++ndx)
	{
		a = deFloatCos(a + (float)b);
		b = (b + 63) % 107 + de::abs((int)(a*10.0f));
	}

	return a + (float)b;
}

static void busyWait (int microseconds)
{
	const deUint64	maxSingleWaitTime	= 1000; // 1ms
	const deUint64	endTime				= deGetMicroseconds() + microseconds;
	float			dummy				= *tcu::warmupCPUInternal::g_dummy.m_v;
	int				workSize			= 500;

	// exponentially increase work, cap to 1ms
	while (deGetMicroseconds() < endTime)
	{
		const deUint64	startTime		= deGetMicroseconds();
		deUint64		totalTime;

		dummy = dummyCalculation(dummy, workSize);

		totalTime = deGetMicroseconds() - startTime;

		if (totalTime >= maxSingleWaitTime)
			break;
		else
			workSize *= 2;
	}

	// "wait"
	while (deGetMicroseconds() < endTime)
		dummy = dummyCalculation(dummy, workSize);

	*tcu::warmupCPUInternal::g_dummy.m_v = dummy;
}

// Sample from given values using linear interpolation at a given position as if values were laid to range [0, 1]
template <typename T>
static float linearSample (const std::vector<T>& values, float position)
{
	DE_ASSERT(position >= 0.0f);
	DE_ASSERT(position <= 1.0f);

	const float	floatNdx			= (float)(values.size() - 1) * position;
	const int	lowerNdx			= (int)deFloatFloor(floatNdx);
	const int	higherNdx			= lowerNdx + 1;
	const float	interpolationFactor = floatNdx - (float)lowerNdx;

	DE_ASSERT(lowerNdx >= 0 && lowerNdx < (int)values.size());
	DE_ASSERT(higherNdx >= 0 && higherNdx < (int)values.size());
	DE_ASSERT(interpolationFactor >= 0 && interpolationFactor < 1.0f);

	return tcu::mix((float)values[lowerNdx], (float)values[higherNdx], interpolationFactor);
}

template <typename T>
SingleOperationStatistics calculateSingleOperationStatistics (const std::vector<T>& samples, deUint64 T::SampleType::*target)
{
	SingleOperationStatistics	stats;
	std::vector<deUint64>		values(samples.size());

	for (int ndx = 0; ndx < (int)samples.size(); ++ndx)
		values[ndx] = samples[ndx].duration.*target;

	std::sort(values.begin(), values.end());

	stats.minTime			= (float)values.front();
	stats.maxTime			= (float)values.back();
	stats.medianTime		= linearSample(values, 0.5f);
	stats.min2DecileTime	= linearSample(values, 0.1f);
	stats.max9DecileTime	= linearSample(values, 0.9f);

	return stats;
}

template <typename StatisticsType, typename SampleType>
void calculateBasicStatistics (StatisticsType& stats, const LineParametersWithConfidence& fit, const std::vector<SampleType>& samples, int SampleType::*predictor)
{
	std::vector<deUint64> values(samples.size());

	for (int ndx = 0; ndx < (int)samples.size(); ++ndx)
		values[ndx] = samples[ndx].duration.fitResponseDuration;

	// median rate
	{
		std::vector<float> processingRates(samples.size());

		for (int ndx = 0; ndx < (int)samples.size(); ++ndx)
		{
			const float timeInSeconds = (float)values[ndx] / 1000.0f / 1000.0f;
			processingRates[ndx] = (float)(samples[ndx].*predictor) / timeInSeconds;
		}

		std::sort(processingRates.begin(), processingRates.end());

		stats.medianRate = linearSample(processingRates, 0.5f);
	}

	// results compared to the approximation
	{
		std::vector<float> timeDiffs(samples.size());

		for (int ndx = 0; ndx < (int)samples.size(); ++ndx)
		{
			const float prediction	= (float)(samples[ndx].*predictor) * fit.coefficient + fit.offset;
			const float actual		= (float)values[ndx];
			timeDiffs[ndx] = actual - prediction;
		}
		std::sort(timeDiffs.begin(), timeDiffs.end());

		stats.maxDiffTime			= timeDiffs.back();
		stats.maxDiff9DecileTime	= linearSample(timeDiffs, 0.9f);
		stats.medianDiffTime		= linearSample(timeDiffs, 0.5f);
	}

	// relative comparison to the approximation
	{
		std::vector<float> relativeDiffs(samples.size());

		for (int ndx = 0; ndx < (int)samples.size(); ++ndx)
		{
			const float prediction	= (float)(samples[ndx].*predictor) * fit.coefficient + fit.offset;
			const float actual		= (float)values[ndx];

			// Ignore cases where we predict negative times, or if
			// ratio would be (nearly) infinite: ignore if predicted
			// time is less than 1 microsecond
			if (prediction < 1.0f)
				relativeDiffs[ndx] = 0.0f;
			else
				relativeDiffs[ndx] = (actual - prediction) / prediction;
		}
		std::sort(relativeDiffs.begin(), relativeDiffs.end());

		stats.maxRelDiffTime		= relativeDiffs.back();
		stats.max9DecileRelDiffTime	= linearSample(relativeDiffs, 0.9f);
		stats.medianRelDiffTime		= linearSample(relativeDiffs, 0.5f);
	}

	// values calculated using sorted timings

	std::sort(values.begin(), values.end());

	stats.result.minTime = (float)values.front();
	stats.result.maxTime = (float)values.back();
	stats.result.medianTime = linearSample(values, 0.5f);
	stats.result.min2DecileTime = linearSample(values, 0.1f);
	stats.result.max9DecileTime = linearSample(values, 0.9f);
}

template <typename StatisticsType, typename SampleType>
void calculateBasicTransferStatistics (StatisticsType& stats, const LineParametersWithConfidence& fit, const std::vector<SampleType>& samples)
{
	calculateBasicStatistics(stats, fit, samples, &SampleType::writtenSize);
}

template <typename StatisticsType, typename SampleType>
void calculateBasicRenderStatistics (StatisticsType& stats, const LineParametersWithConfidence& fit, const std::vector<SampleType>& samples)
{
	calculateBasicStatistics(stats, fit, samples, &SampleType::renderDataSize);
}

static SingleCallStatistics calculateSampleStatistics (const LineParametersWithConfidence& fit, const std::vector<UploadSampleResult<SingleOperationDuration> >& samples)
{
	SingleCallStatistics stats;

	calculateBasicTransferStatistics(stats, fit, samples);

	return stats;
}

static MapCallStatistics calculateSampleStatistics (const LineParametersWithConfidence& fit, const std::vector<UploadSampleResult<MapBufferRangeDuration> >& samples)
{
	MapCallStatistics stats;

	calculateBasicTransferStatistics(stats, fit, samples);

	stats.map	= calculateSingleOperationStatistics(samples, &MapBufferRangeDuration::mapDuration);
	stats.unmap	= calculateSingleOperationStatistics(samples, &MapBufferRangeDuration::unmapDuration);
	stats.write	= calculateSingleOperationStatistics(samples, &MapBufferRangeDuration::writeDuration);
	stats.alloc	= calculateSingleOperationStatistics(samples, &MapBufferRangeDuration::allocDuration);

	return stats;
}

static MapFlushCallStatistics calculateSampleStatistics (const LineParametersWithConfidence& fit, const std::vector<UploadSampleResult<MapBufferRangeFlushDuration> >& samples)
{
	MapFlushCallStatistics stats;

	calculateBasicTransferStatistics(stats, fit, samples);

	stats.map	= calculateSingleOperationStatistics(samples, &MapBufferRangeFlushDuration::mapDuration);
	stats.unmap	= calculateSingleOperationStatistics(samples, &MapBufferRangeFlushDuration::unmapDuration);
	stats.write	= calculateSingleOperationStatistics(samples, &MapBufferRangeFlushDuration::writeDuration);
	stats.flush	= calculateSingleOperationStatistics(samples, &MapBufferRangeFlushDuration::flushDuration);
	stats.alloc	= calculateSingleOperationStatistics(samples, &MapBufferRangeFlushDuration::allocDuration);

	return stats;
}

static MapCallStatistics calculateSampleStatistics (const LineParametersWithConfidence& fit, const std::vector<UploadSampleResult<MapBufferRangeDurationNoAlloc> >& samples)
{
	MapCallStatistics stats;

	calculateBasicTransferStatistics(stats, fit, samples);

	stats.map	= calculateSingleOperationStatistics(samples, &MapBufferRangeDurationNoAlloc::mapDuration);
	stats.unmap	= calculateSingleOperationStatistics(samples, &MapBufferRangeDurationNoAlloc::unmapDuration);
	stats.write	= calculateSingleOperationStatistics(samples, &MapBufferRangeDurationNoAlloc::writeDuration);

	return stats;
}

static MapFlushCallStatistics calculateSampleStatistics (const LineParametersWithConfidence& fit, const std::vector<UploadSampleResult<MapBufferRangeFlushDurationNoAlloc> >& samples)
{
	MapFlushCallStatistics stats;

	calculateBasicTransferStatistics(stats, fit, samples);

	stats.map	= calculateSingleOperationStatistics(samples, &MapBufferRangeFlushDurationNoAlloc::mapDuration);
	stats.unmap	= calculateSingleOperationStatistics(samples, &MapBufferRangeFlushDurationNoAlloc::unmapDuration);
	stats.write	= calculateSingleOperationStatistics(samples, &MapBufferRangeFlushDurationNoAlloc::writeDuration);
	stats.flush	= calculateSingleOperationStatistics(samples, &MapBufferRangeFlushDurationNoAlloc::flushDuration);

	return stats;
}

static RenderReadStatistics calculateSampleStatistics (const LineParametersWithConfidence& fit, const std::vector<RenderSampleResult<RenderReadDuration> >& samples)
{
	RenderReadStatistics stats;

	calculateBasicRenderStatistics(stats, fit, samples);

	stats.render	= calculateSingleOperationStatistics(samples, &RenderReadDuration::renderDuration);
	stats.read		= calculateSingleOperationStatistics(samples, &RenderReadDuration::readDuration);
	stats.total		= calculateSingleOperationStatistics(samples, &RenderReadDuration::totalDuration);

	return stats;
}

static RenderReadStatistics calculateSampleStatistics (const LineParametersWithConfidence& fit, const std::vector<RenderSampleResult<UnrelatedUploadRenderReadDuration> >& samples)
{
	RenderReadStatistics stats;

	calculateBasicRenderStatistics(stats, fit, samples);

	stats.render	= calculateSingleOperationStatistics(samples, &UnrelatedUploadRenderReadDuration::renderDuration);
	stats.read		= calculateSingleOperationStatistics(samples, &UnrelatedUploadRenderReadDuration::readDuration);
	stats.total		= calculateSingleOperationStatistics(samples, &UnrelatedUploadRenderReadDuration::totalDuration);

	return stats;
}

static UploadRenderReadStatistics calculateSampleStatistics (const LineParametersWithConfidence& fit, const std::vector<RenderSampleResult<UploadRenderReadDuration> >& samples)
{
	UploadRenderReadStatistics stats;

	calculateBasicRenderStatistics(stats, fit, samples);

	stats.upload	= calculateSingleOperationStatistics(samples, &UploadRenderReadDuration::uploadDuration);
	stats.render	= calculateSingleOperationStatistics(samples, &UploadRenderReadDuration::renderDuration);
	stats.read		= calculateSingleOperationStatistics(samples, &UploadRenderReadDuration::readDuration);
	stats.total		= calculateSingleOperationStatistics(samples, &UploadRenderReadDuration::totalDuration);

	return stats;
}

static UploadRenderReadStatistics calculateSampleStatistics (const LineParametersWithConfidence& fit, const std::vector<RenderSampleResult<UploadRenderReadDurationWithUnrelatedUploadSize> >& samples)
{
	UploadRenderReadStatistics stats;

	calculateBasicRenderStatistics(stats, fit, samples);

	stats.upload	= calculateSingleOperationStatistics(samples, &UploadRenderReadDurationWithUnrelatedUploadSize::uploadDuration);
	stats.render	= calculateSingleOperationStatistics(samples, &UploadRenderReadDurationWithUnrelatedUploadSize::renderDuration);
	stats.read		= calculateSingleOperationStatistics(samples, &UploadRenderReadDurationWithUnrelatedUploadSize::readDuration);
	stats.total		= calculateSingleOperationStatistics(samples, &UploadRenderReadDurationWithUnrelatedUploadSize::totalDuration);

	return stats;
}

static RenderUploadRenderReadStatistics calculateSampleStatistics (const LineParametersWithConfidence& fit, const std::vector<RenderSampleResult<RenderUploadRenderReadDuration> >& samples)
{
	RenderUploadRenderReadStatistics stats;

	calculateBasicRenderStatistics(stats, fit, samples);

	stats.firstRender	= calculateSingleOperationStatistics(samples, &RenderUploadRenderReadDuration::firstRenderDuration);
	stats.upload		= calculateSingleOperationStatistics(samples, &RenderUploadRenderReadDuration::uploadDuration);
	stats.secondRender	= calculateSingleOperationStatistics(samples, &RenderUploadRenderReadDuration::secondRenderDuration);
	stats.read			= calculateSingleOperationStatistics(samples, &RenderUploadRenderReadDuration::readDuration);
	stats.total			= calculateSingleOperationStatistics(samples, &RenderUploadRenderReadDuration::totalDuration);

	return stats;
}

template <typename DurationType>
static LineParametersWithConfidence fitLineToSamples (const std::vector<UploadSampleResult<DurationType> >& samples, int beginNdx, int endNdx, int step, deUint64 DurationType::*target = &DurationType::fitResponseDuration)
{
	std::vector<tcu::Vec2> samplePoints;

	for (int sampleNdx = beginNdx; sampleNdx < endNdx; sampleNdx += step)
	{
		tcu::Vec2 point;

		point.x() = (float)(samples[sampleNdx].writtenSize);
		point.y() = (float)(samples[sampleNdx].duration.*target);

		samplePoints.push_back(point);
	}

	return theilSenSiegelLinearRegression(samplePoints, 0.6f);
}

template <typename DurationType>
static LineParametersWithConfidence fitLineToSamples (const std::vector<RenderSampleResult<DurationType> >& samples, int beginNdx, int endNdx, int step, deUint64 DurationType::*target = &DurationType::fitResponseDuration)
{
	std::vector<tcu::Vec2> samplePoints;

	for (int sampleNdx = beginNdx; sampleNdx < endNdx; sampleNdx += step)
	{
		tcu::Vec2 point;

		point.x() = (float)(samples[sampleNdx].renderDataSize);
		point.y() = (float)(samples[sampleNdx].duration.*target);

		samplePoints.push_back(point);
	}

	return theilSenSiegelLinearRegression(samplePoints, 0.6f);
}

template <typename T>
static LineParametersWithConfidence fitLineToSamples (const std::vector<T>& samples, int beginNdx, int endNdx, deUint64 T::SampleType::*target = &T::SampleType::fitResponseDuration)
{
	return fitLineToSamples(samples, beginNdx, endNdx, 1, target);
}

template <typename T>
static LineParametersWithConfidence fitLineToSamples (const std::vector<T>& samples, deUint64 T::SampleType::*target = &T::SampleType::fitResponseDuration)
{
	return fitLineToSamples(samples, 0, (int)samples.size(), target);
}

static float getAreaBetweenLines (float xmin, float xmax, float lineAOffset, float lineACoefficient, float lineBOffset, float lineBCoefficient)
{
	const float lineAMin		= lineAOffset + lineACoefficient * xmin;
	const float lineAMax		= lineAOffset + lineACoefficient * xmax;
	const float lineBMin		= lineBOffset + lineBCoefficient * xmin;
	const float lineBMax		= lineBOffset + lineBCoefficient * xmax;
	const bool	aOverBAtBegin	= (lineAMin > lineBMin);
	const bool	aOverBAtEnd		= (lineAMax > lineBMax);

	if (aOverBAtBegin == aOverBAtEnd)
	{
		// lines do not intersect

		const float midpoint	= (xmin + xmax) / 2.0f;
		const float width		= (xmax - xmin);

		const float lineAHeight	= lineAOffset + lineACoefficient * midpoint;
		const float lineBHeight	= lineBOffset + lineBCoefficient * midpoint;

		return width * de::abs(lineAHeight - lineBHeight);
	}
	else
	{

		// lines intersect

		const float approachCoeffient	= de::abs(lineACoefficient - lineBCoefficient);
		const float epsilon				= 0.0001f;
		const float leftHeight			= de::abs(lineAMin - lineBMin);
		const float rightHeight			= de::abs(lineAMax - lineBMax);

		if (approachCoeffient < epsilon)
			return 0.0f;

		return (0.5f * leftHeight * (leftHeight / approachCoeffient)) + (0.5f * rightHeight * (rightHeight / approachCoeffient));
	}
}

template <typename T>
static float calculateSampleFitLinearity (const std::vector<T>& samples, int T::*predictor)
{
	// Compare the fitted line of first half of the samples to the fitted line of
	// the second half of the samples. Calculate a AABB that fully contains every
	// sample's x component and both fit lines in this range. Calculate the ratio
	// of the area between the lines and the AABB.

	const float				epsilon				= 1.e-6f;
	const int				midPoint			= (int)samples.size() / 2;
	const LineParametersWithConfidence	startApproximation	= fitLineToSamples(samples, 0, midPoint, &T::SampleType::fitResponseDuration);
	const LineParametersWithConfidence	endApproximation	= fitLineToSamples(samples, midPoint, (int)samples.size(), &T::SampleType::fitResponseDuration);

	const float				aabbMinX			= (float)(samples.front().*predictor);
	const float				aabbMinY			= de::min(startApproximation.offset + startApproximation.coefficient*aabbMinX, endApproximation.offset + endApproximation.coefficient*aabbMinX);
	const float				aabbMaxX			= (float)(samples.back().*predictor);
	const float				aabbMaxY			= de::max(startApproximation.offset + startApproximation.coefficient*aabbMaxX, endApproximation.offset + endApproximation.coefficient*aabbMaxX);

	const float				aabbArea			= (aabbMaxX - aabbMinX) * (aabbMaxY - aabbMinY);
	const float				areaBetweenLines	= getAreaBetweenLines(aabbMinX, aabbMaxX, startApproximation.offset, startApproximation.coefficient, endApproximation.offset, endApproximation.coefficient);
	const float				errorAreaRatio		= (aabbArea < epsilon) ? (1.0f) : (areaBetweenLines / aabbArea);

	return de::clamp(1.0f - errorAreaRatio, 0.0f, 1.0f);
}

template <typename DurationType>
static float calculateSampleFitLinearity (const std::vector<UploadSampleResult<DurationType> >& samples)
{
	return calculateSampleFitLinearity(samples, &UploadSampleResult<DurationType>::writtenSize);
}

template <typename DurationType>
static float calculateSampleFitLinearity (const std::vector<RenderSampleResult<DurationType> >& samples)
{
	return calculateSampleFitLinearity(samples, &RenderSampleResult<DurationType>::renderDataSize);
}

template <typename T>
static float calculateSampleTemporalStability (const std::vector<T>& samples, int T::*predictor)
{
	// Samples are sampled in the following order: 1) even samples (in random order) 2) odd samples (in random order)
	// Compare the fitted line of even samples to the fitted line of the odd samples. Calculate a AABB that fully
	// contains every sample's x component and both fit lines in this range. Calculate the ratio of the area between
	// the lines and the AABB.

	const float				epsilon				= 1.e-6f;
	const LineParametersWithConfidence	evenApproximation	= fitLineToSamples(samples, 0, (int)samples.size(), 2, &T::SampleType::fitResponseDuration);
	const LineParametersWithConfidence	oddApproximation	= fitLineToSamples(samples, 1, (int)samples.size(), 2, &T::SampleType::fitResponseDuration);

	const float				aabbMinX			= (float)(samples.front().*predictor);
	const float				aabbMinY			= de::min(evenApproximation.offset + evenApproximation.coefficient*aabbMinX, oddApproximation.offset + oddApproximation.coefficient*aabbMinX);
	const float				aabbMaxX			= (float)(samples.back().*predictor);
	const float				aabbMaxY			= de::max(evenApproximation.offset + evenApproximation.coefficient*aabbMaxX, oddApproximation.offset + oddApproximation.coefficient*aabbMaxX);

	const float				aabbArea			= (aabbMaxX - aabbMinX) * (aabbMaxY - aabbMinY);
	const float				areaBetweenLines	= getAreaBetweenLines(aabbMinX, aabbMaxX, evenApproximation.offset, evenApproximation.coefficient, oddApproximation.offset, oddApproximation.coefficient);
	const float				errorAreaRatio		= (aabbArea < epsilon) ? (1.0f) : (areaBetweenLines / aabbArea);

	return de::clamp(1.0f - errorAreaRatio, 0.0f, 1.0f);
}

template <typename DurationType>
static float calculateSampleTemporalStability (const std::vector<UploadSampleResult<DurationType> >& samples)
{
	return calculateSampleTemporalStability(samples, &UploadSampleResult<DurationType>::writtenSize);
}

template <typename DurationType>
static float calculateSampleTemporalStability (const std::vector<RenderSampleResult<DurationType> >& samples)
{
	return calculateSampleTemporalStability(samples, &RenderSampleResult<DurationType>::renderDataSize);
}

template <typename DurationType>
static void bucketizeSamplesUniformly (const std::vector<UploadSampleResult<DurationType> >& samples, std::vector<UploadSampleResult<DurationType> >* buckets, int numBuckets, int& minBufferSize, int& maxBufferSize)
{
	minBufferSize = 0;
	maxBufferSize = 0;

	for (int sampleNdx = 0; sampleNdx < (int)samples.size(); ++sampleNdx)
	{
		DE_ASSERT(samples[sampleNdx].allocatedSize != 0);

		if (!minBufferSize || samples[sampleNdx].allocatedSize < minBufferSize)
			minBufferSize = samples[sampleNdx].allocatedSize;
		if (!maxBufferSize || samples[sampleNdx].allocatedSize > maxBufferSize)
			maxBufferSize = samples[sampleNdx].allocatedSize;
	}

	for (int sampleNdx = 0; sampleNdx < (int)samples.size(); ++sampleNdx)
	{
		const float bucketNdxFloat	= (float)(samples[sampleNdx].allocatedSize - minBufferSize) / (float)(maxBufferSize - minBufferSize) * (float)numBuckets;
		const int bucketNdx			= de::clamp((int)deFloatFloor(bucketNdxFloat), 0, numBuckets-1);

		buckets[bucketNdx].push_back(samples[sampleNdx]);
	}
}

template <typename SampleType>
static typename EnableIf<void, SampleTypeTraits<SampleType>::HAS_MAP_STATS>::Type logMapRangeStats (tcu::TestLog& log, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	log	<< tcu::TestLog::Float("MapRangeMin", "MapRange: Min time", "us", QP_KEY_TAG_TIME, stats.map.minTime)
		<< tcu::TestLog::Float("MapRangeMax", "MapRange: Max time", "us", QP_KEY_TAG_TIME, stats.map.maxTime)
		<< tcu::TestLog::Float("MapRangeMin90", "MapRange: 90%-Min time", "us", QP_KEY_TAG_TIME, stats.map.min2DecileTime)
		<< tcu::TestLog::Float("MapRangeMax90", "MapRange: 90%-Max time", "us", QP_KEY_TAG_TIME, stats.map.max9DecileTime)
		<< tcu::TestLog::Float("MapRangeMedian", "MapRange: Median time", "us", QP_KEY_TAG_TIME, stats.map.medianTime);
}

template <typename SampleType>
static typename EnableIf<void, SampleTypeTraits<SampleType>::HAS_UNMAP_STATS>::Type logUnmapStats (tcu::TestLog& log, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	log	<< tcu::TestLog::Float("UnmapMin", "Unmap: Min time", "us", QP_KEY_TAG_TIME, stats.unmap.minTime)
		<< tcu::TestLog::Float("UnmapMax", "Unmap: Max time", "us", QP_KEY_TAG_TIME, stats.unmap.maxTime)
		<< tcu::TestLog::Float("UnmapMin90", "Unmap: 90%-Min time", "us", QP_KEY_TAG_TIME, stats.unmap.min2DecileTime)
		<< tcu::TestLog::Float("UnmapMax90", "Unmap: 90%-Max time", "us", QP_KEY_TAG_TIME, stats.unmap.max9DecileTime)
		<< tcu::TestLog::Float("UnmapMedian", "Unmap: Median time", "us", QP_KEY_TAG_TIME, stats.unmap.medianTime);
}

template <typename SampleType>
static typename EnableIf<void, SampleTypeTraits<SampleType>::HAS_WRITE_STATS>::Type logWriteStats (tcu::TestLog& log, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	log	<< tcu::TestLog::Float("WriteMin", "Write: Min time", "us", QP_KEY_TAG_TIME, stats.write.minTime)
		<< tcu::TestLog::Float("WriteMax", "Write: Max time", "us", QP_KEY_TAG_TIME, stats.write.maxTime)
		<< tcu::TestLog::Float("WriteMin90", "Write: 90%-Min time", "us", QP_KEY_TAG_TIME, stats.write.min2DecileTime)
		<< tcu::TestLog::Float("WriteMax90", "Write: 90%-Max time", "us", QP_KEY_TAG_TIME, stats.write.max9DecileTime)
		<< tcu::TestLog::Float("WriteMedian", "Write: Median time", "us", QP_KEY_TAG_TIME, stats.write.medianTime);
}

template <typename SampleType>
static typename EnableIf<void, SampleTypeTraits<SampleType>::HAS_FLUSH_STATS>::Type logFlushStats (tcu::TestLog& log, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	log	<< tcu::TestLog::Float("FlushMin", "Flush: Min time", "us", QP_KEY_TAG_TIME, stats.flush.minTime)
		<< tcu::TestLog::Float("FlushMax", "Flush: Max time", "us", QP_KEY_TAG_TIME, stats.flush.maxTime)
		<< tcu::TestLog::Float("FlushMin90", "Flush: 90%-Min time", "us", QP_KEY_TAG_TIME, stats.flush.min2DecileTime)
		<< tcu::TestLog::Float("FlushMax90", "Flush: 90%-Max time", "us", QP_KEY_TAG_TIME, stats.flush.max9DecileTime)
		<< tcu::TestLog::Float("FlushMedian", "Flush: Median time", "us", QP_KEY_TAG_TIME, stats.flush.medianTime);
}

template <typename SampleType>
static typename EnableIf<void, SampleTypeTraits<SampleType>::HAS_ALLOC_STATS>::Type logAllocStats (tcu::TestLog& log, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	log	<< tcu::TestLog::Float("AllocMin", "Alloc: Min time", "us", QP_KEY_TAG_TIME, stats.alloc.minTime)
		<< tcu::TestLog::Float("AllocMax", "Alloc: Max time", "us", QP_KEY_TAG_TIME, stats.alloc.maxTime)
		<< tcu::TestLog::Float("AllocMin90", "Alloc: 90%-Min time", "us", QP_KEY_TAG_TIME, stats.alloc.min2DecileTime)
		<< tcu::TestLog::Float("AllocMax90", "Alloc: 90%-Max time", "us", QP_KEY_TAG_TIME, stats.alloc.max9DecileTime)
		<< tcu::TestLog::Float("AllocMedian", "Alloc: Median time", "us", QP_KEY_TAG_TIME, stats.alloc.medianTime);
}

template <typename SampleType>
static typename EnableIf<void, Not<SampleTypeTraits<SampleType>::HAS_MAP_STATS>::Value>::Type logMapRangeStats (tcu::TestLog& log, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	DE_UNREF(log);
	DE_UNREF(stats);
}

template <typename SampleType>
static typename EnableIf<void, Not<SampleTypeTraits<SampleType>::HAS_UNMAP_STATS>::Value>::Type logUnmapStats (tcu::TestLog& log, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	DE_UNREF(log);
	DE_UNREF(stats);
}

template <typename SampleType>
static typename EnableIf<void, Not<SampleTypeTraits<SampleType>::HAS_WRITE_STATS>::Value>::Type logWriteStats (tcu::TestLog& log, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	DE_UNREF(log);
	DE_UNREF(stats);
}

template <typename SampleType>
static typename EnableIf<void, Not<SampleTypeTraits<SampleType>::HAS_FLUSH_STATS>::Value>::Type logFlushStats (tcu::TestLog& log, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	DE_UNREF(log);
	DE_UNREF(stats);
}

template <typename SampleType>
static typename EnableIf<void, Not<SampleTypeTraits<SampleType>::HAS_ALLOC_STATS>::Value>::Type logAllocStats (tcu::TestLog& log, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	DE_UNREF(log);
	DE_UNREF(stats);
}

template <typename SampleType>
static typename EnableIf<void, SampleTypeTraits<SampleType>::HAS_MAP_STATS>::Type logMapContribution (tcu::TestLog& log, const std::vector<UploadSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	const LineParametersWithConfidence contributionFitting = fitLineToSamples(samples, &SampleType::mapDuration);
	log	<< tcu::TestLog::Float("MapConstantCost", "Map: Approximated contant cost", "us", QP_KEY_TAG_TIME, contributionFitting.offset)
		<< tcu::TestLog::Float("MapLinearCost", "Map: Approximated linear cost", "us / MB", QP_KEY_TAG_TIME, contributionFitting.coefficient * 1024.0f * 1024.0f)
		<< tcu::TestLog::Float("MapMedianCost", "Map: Median cost", "us", QP_KEY_TAG_TIME, stats.map.medianTime);
}

template <typename SampleType>
static typename EnableIf<void, SampleTypeTraits<SampleType>::HAS_UNMAP_STATS>::Type logUnmapContribution (tcu::TestLog& log, const std::vector<UploadSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	const LineParametersWithConfidence contributionFitting = fitLineToSamples(samples, &SampleType::unmapDuration);
	log	<< tcu::TestLog::Float("UnmapConstantCost", "Unmap: Approximated contant cost", "us", QP_KEY_TAG_TIME, contributionFitting.offset)
		<< tcu::TestLog::Float("UnmapLinearCost", "Unmap: Approximated linear cost", "us / MB", QP_KEY_TAG_TIME, contributionFitting.coefficient * 1024.0f * 1024.0f)
		<< tcu::TestLog::Float("UnmapMedianCost", "Unmap: Median cost", "us", QP_KEY_TAG_TIME, stats.unmap.medianTime);
}

template <typename SampleType>
static typename EnableIf<void, SampleTypeTraits<SampleType>::HAS_WRITE_STATS>::Type logWriteContribution (tcu::TestLog& log, const std::vector<UploadSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	const LineParametersWithConfidence contributionFitting = fitLineToSamples(samples, &SampleType::writeDuration);
	log	<< tcu::TestLog::Float("WriteConstantCost", "Write: Approximated contant cost", "us", QP_KEY_TAG_TIME, contributionFitting.offset)
		<< tcu::TestLog::Float("WriteLinearCost", "Write: Approximated linear cost", "us / MB", QP_KEY_TAG_TIME, contributionFitting.coefficient * 1024.0f * 1024.0f)
		<< tcu::TestLog::Float("WriteMedianCost", "Write: Median cost", "us", QP_KEY_TAG_TIME, stats.write.medianTime);
}

template <typename SampleType>
static typename EnableIf<void, SampleTypeTraits<SampleType>::HAS_FLUSH_STATS>::Type logFlushContribution (tcu::TestLog& log, const std::vector<UploadSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	const LineParametersWithConfidence contributionFitting = fitLineToSamples(samples, &SampleType::flushDuration);
	log	<< tcu::TestLog::Float("FlushConstantCost", "Flush: Approximated contant cost", "us", QP_KEY_TAG_TIME, contributionFitting.offset)
		<< tcu::TestLog::Float("FlushLinearCost", "Flush: Approximated linear cost", "us / MB", QP_KEY_TAG_TIME, contributionFitting.coefficient * 1024.0f * 1024.0f)
		<< tcu::TestLog::Float("FlushMedianCost", "Flush: Median cost", "us", QP_KEY_TAG_TIME, stats.flush.medianTime);
}

template <typename SampleType>
static typename EnableIf<void, SampleTypeTraits<SampleType>::HAS_ALLOC_STATS>::Type logAllocContribution (tcu::TestLog& log, const std::vector<UploadSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	const LineParametersWithConfidence contributionFitting = fitLineToSamples(samples, &SampleType::allocDuration);
	log	<< tcu::TestLog::Float("AllocConstantCost", "Alloc: Approximated contant cost", "us", QP_KEY_TAG_TIME, contributionFitting.offset)
		<< tcu::TestLog::Float("AllocLinearCost", "Alloc: Approximated linear cost", "us / MB", QP_KEY_TAG_TIME, contributionFitting.coefficient * 1024.0f * 1024.0f)
		<< tcu::TestLog::Float("AllocMedianCost", "Alloc: Median cost", "us", QP_KEY_TAG_TIME, stats.alloc.medianTime);
}

template <typename SampleType>
static typename EnableIf<void, SampleTypeTraits<SampleType>::HAS_RENDER_STATS>::Type logRenderContribution (tcu::TestLog& log, const std::vector<RenderSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	const LineParametersWithConfidence contributionFitting = fitLineToSamples(samples, &SampleType::renderDuration);
	log	<< tcu::TestLog::Float("DrawCallConstantCost", "DrawCall: Approximated contant cost", "us", QP_KEY_TAG_TIME, contributionFitting.offset)
		<< tcu::TestLog::Float("DrawCallLinearCost", "DrawCall: Approximated linear cost", "us / MB", QP_KEY_TAG_TIME, contributionFitting.coefficient * 1024.0f * 1024.0f)
		<< tcu::TestLog::Float("DrawCallMedianCost", "DrawCall: Median cost", "us", QP_KEY_TAG_TIME, stats.render.medianTime);
}

template <typename SampleType>
static typename EnableIf<void, SampleTypeTraits<SampleType>::HAS_READ_STATS>::Type logReadContribution (tcu::TestLog& log, const std::vector<RenderSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	const LineParametersWithConfidence contributionFitting = fitLineToSamples(samples, &SampleType::readDuration);
	log	<< tcu::TestLog::Float("ReadConstantCost", "Read: Approximated contant cost", "us", QP_KEY_TAG_TIME, contributionFitting.offset)
		<< tcu::TestLog::Float("ReadLinearCost", "Read: Approximated linear cost", "us / MB", QP_KEY_TAG_TIME, contributionFitting.coefficient * 1024.0f * 1024.0f)
		<< tcu::TestLog::Float("ReadMedianCost", "Read: Median cost", "us", QP_KEY_TAG_TIME, stats.read.medianTime);
}

template <typename SampleType>
static typename EnableIf<void, SampleTypeTraits<SampleType>::HAS_UPLOAD_STATS>::Type logUploadContribution (tcu::TestLog& log, const std::vector<RenderSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	const LineParametersWithConfidence contributionFitting = fitLineToSamples(samples, &SampleType::uploadDuration);
	log	<< tcu::TestLog::Float("UploadConstantCost", "Upload: Approximated contant cost", "us", QP_KEY_TAG_TIME, contributionFitting.offset)
		<< tcu::TestLog::Float("UploadLinearCost", "Upload: Approximated linear cost", "us / MB", QP_KEY_TAG_TIME, contributionFitting.coefficient * 1024.0f * 1024.0f)
		<< tcu::TestLog::Float("UploadMedianCost", "Upload: Median cost", "us", QP_KEY_TAG_TIME, stats.upload.medianTime);
}

template <typename SampleType>
static typename EnableIf<void, SampleTypeTraits<SampleType>::HAS_TOTAL_STATS>::Type logTotalContribution (tcu::TestLog& log, const std::vector<RenderSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	const LineParametersWithConfidence contributionFitting = fitLineToSamples(samples, &SampleType::totalDuration);
	log	<< tcu::TestLog::Float("TotalConstantCost", "Total: Approximated contant cost", "us", QP_KEY_TAG_TIME, contributionFitting.offset)
		<< tcu::TestLog::Float("TotalLinearCost", "Total: Approximated linear cost", "us / MB", QP_KEY_TAG_TIME, contributionFitting.coefficient * 1024.0f * 1024.0f)
		<< tcu::TestLog::Float("TotalMedianCost", "Total: Median cost", "us", QP_KEY_TAG_TIME, stats.total.medianTime);
}

template <typename SampleType>
static typename EnableIf<void, SampleTypeTraits<SampleType>::HAS_FIRST_RENDER_STATS>::Type logFirstRenderContribution (tcu::TestLog& log, const std::vector<RenderSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	const LineParametersWithConfidence contributionFitting = fitLineToSamples(samples, &SampleType::firstRenderDuration);
	log	<< tcu::TestLog::Float("FirstDrawCallConstantCost", "First DrawCall: Approximated contant cost", "us", QP_KEY_TAG_TIME, contributionFitting.offset)
		<< tcu::TestLog::Float("FirstDrawCallLinearCost", "First DrawCall: Approximated linear cost", "us / MB", QP_KEY_TAG_TIME, contributionFitting.coefficient * 1024.0f * 1024.0f)
		<< tcu::TestLog::Float("FirstDrawCallMedianCost", "First DrawCall: Median cost", "us", QP_KEY_TAG_TIME, stats.firstRender.medianTime);
}

template <typename SampleType>
static typename EnableIf<void, SampleTypeTraits<SampleType>::HAS_SECOND_RENDER_STATS>::Type logSecondRenderContribution (tcu::TestLog& log, const std::vector<RenderSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	const LineParametersWithConfidence contributionFitting = fitLineToSamples(samples, &SampleType::secondRenderDuration);
	log	<< tcu::TestLog::Float("SecondDrawCallConstantCost", "Second DrawCall: Approximated contant cost", "us", QP_KEY_TAG_TIME, contributionFitting.offset)
		<< tcu::TestLog::Float("SecondDrawCallLinearCost", "Second DrawCall: Approximated linear cost", "us / MB", QP_KEY_TAG_TIME, contributionFitting.coefficient * 1024.0f * 1024.0f)
		<< tcu::TestLog::Float("SecondDrawCallMedianCost", "Second DrawCall: Median cost", "us", QP_KEY_TAG_TIME, stats.secondRender.medianTime);
}

template <typename SampleType>
static typename EnableIf<void, Not<SampleTypeTraits<SampleType>::HAS_MAP_STATS>::Value>::Type logMapContribution (tcu::TestLog& log, const std::vector<UploadSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	DE_UNREF(log);
	DE_UNREF(samples);
	DE_UNREF(stats);
}

template <typename SampleType>
static typename EnableIf<void, Not<SampleTypeTraits<SampleType>::HAS_UNMAP_STATS>::Value>::Type logUnmapContribution (tcu::TestLog& log, const std::vector<UploadSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	DE_UNREF(log);
	DE_UNREF(samples);
	DE_UNREF(stats);
}

template <typename SampleType>
static typename EnableIf<void, Not<SampleTypeTraits<SampleType>::HAS_WRITE_STATS>::Value>::Type logWriteContribution (tcu::TestLog& log, const std::vector<UploadSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	DE_UNREF(log);
	DE_UNREF(samples);
	DE_UNREF(stats);
}

template <typename SampleType>
static typename EnableIf<void, Not<SampleTypeTraits<SampleType>::HAS_FLUSH_STATS>::Value>::Type logFlushContribution (tcu::TestLog& log, const std::vector<UploadSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	DE_UNREF(log);
	DE_UNREF(samples);
	DE_UNREF(stats);
}

template <typename SampleType>
static typename EnableIf<void, Not<SampleTypeTraits<SampleType>::HAS_ALLOC_STATS>::Value>::Type logAllocContribution (tcu::TestLog& log, const std::vector<UploadSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	DE_UNREF(log);
	DE_UNREF(samples);
	DE_UNREF(stats);
}

template <typename SampleType>
static typename EnableIf<void, Not<SampleTypeTraits<SampleType>::HAS_RENDER_STATS>::Value>::Type logRenderContribution (tcu::TestLog& log, const std::vector<RenderSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	DE_UNREF(log);
	DE_UNREF(samples);
	DE_UNREF(stats);
}

template <typename SampleType>
static typename EnableIf<void, Not<SampleTypeTraits<SampleType>::HAS_READ_STATS>::Value>::Type logReadContribution (tcu::TestLog& log, const std::vector<RenderSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	DE_UNREF(log);
	DE_UNREF(samples);
	DE_UNREF(stats);
}

template <typename SampleType>
static typename EnableIf<void, Not<SampleTypeTraits<SampleType>::HAS_UPLOAD_STATS>::Value>::Type logUploadContribution (tcu::TestLog& log, const std::vector<RenderSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	DE_UNREF(log);
	DE_UNREF(samples);
	DE_UNREF(stats);
}

template <typename SampleType>
static typename EnableIf<void, Not<SampleTypeTraits<SampleType>::HAS_TOTAL_STATS>::Value>::Type logTotalContribution (tcu::TestLog& log, const std::vector<RenderSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	DE_UNREF(log);
	DE_UNREF(samples);
	DE_UNREF(stats);
}

template <typename SampleType>
static typename EnableIf<void, Not<SampleTypeTraits<SampleType>::HAS_FIRST_RENDER_STATS>::Value>::Type logFirstRenderContribution (tcu::TestLog& log, const std::vector<RenderSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	DE_UNREF(log);
	DE_UNREF(samples);
	DE_UNREF(stats);
}

template <typename SampleType>
static typename EnableIf<void, Not<SampleTypeTraits<SampleType>::HAS_SECOND_RENDER_STATS>::Value>::Type logSecondRenderContribution (tcu::TestLog& log, const std::vector<RenderSampleResult<SampleType> >& samples, const typename SampleTypeTraits<SampleType>::StatsType& stats)
{
	DE_UNREF(log);
	DE_UNREF(samples);
	DE_UNREF(stats);
}

void logSampleList (tcu::TestLog& log, const LineParametersWithConfidence& theilSenFitting, const std::vector<UploadSampleResult<SingleOperationDuration> >& samples)
{
	log << tcu::TestLog::SampleList("Samples", "Samples")
		<< tcu::TestLog::SampleInfo
		<< tcu::TestLog::ValueInfo("WrittenSize",		"Written size",			"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("BufferSize",		"Buffer size",			"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("UploadTime",		"Upload time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("FitResidual",		"Fit residual",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::EndSampleInfo;

	for (int sampleNdx = 0; sampleNdx < (int)samples.size(); ++sampleNdx)
	{
		const float fitResidual = (float)samples[sampleNdx].duration.fitResponseDuration - (theilSenFitting.offset + theilSenFitting.coefficient * (float)samples[sampleNdx].writtenSize);
		log	<< tcu::TestLog::Sample
			<< samples[sampleNdx].writtenSize
			<< samples[sampleNdx].bufferSize
			<< (int)samples[sampleNdx].duration.totalDuration
			<< fitResidual
			<< tcu::TestLog::EndSample;
	}

	log << tcu::TestLog::EndSampleList;
}

void logSampleList (tcu::TestLog& log, const LineParametersWithConfidence& theilSenFitting, const std::vector<UploadSampleResult<MapBufferRangeDuration> >& samples)
{
	log << tcu::TestLog::SampleList("Samples", "Samples")
		<< tcu::TestLog::SampleInfo
		<< tcu::TestLog::ValueInfo("WrittenSize",		"Written size",			"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("BufferSize",		"Buffer size",			"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("TotalTime",			"Total time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("AllocTime",			"Alloc time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("MapTime",			"Map time",				"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("UnmapTime",			"Unmap time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("WriteTime",			"Write time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("FitResidual",		"Fit residual",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::EndSampleInfo;

	for (int sampleNdx = 0; sampleNdx < (int)samples.size(); ++sampleNdx)
	{
		const float fitResidual = (float)samples[sampleNdx].duration.fitResponseDuration - (theilSenFitting.offset + theilSenFitting.coefficient * (float)samples[sampleNdx].writtenSize);
		log	<< tcu::TestLog::Sample
			<< samples[sampleNdx].writtenSize
			<< samples[sampleNdx].bufferSize
			<< (int)samples[sampleNdx].duration.totalDuration
			<< (int)samples[sampleNdx].duration.allocDuration
			<< (int)samples[sampleNdx].duration.mapDuration
			<< (int)samples[sampleNdx].duration.unmapDuration
			<< (int)samples[sampleNdx].duration.writeDuration
			<< fitResidual
			<< tcu::TestLog::EndSample;
	}

	log << tcu::TestLog::EndSampleList;
}

void logSampleList (tcu::TestLog& log, const LineParametersWithConfidence& theilSenFitting, const std::vector<UploadSampleResult<MapBufferRangeDurationNoAlloc> >& samples)
{
	log << tcu::TestLog::SampleList("Samples", "Samples")
		<< tcu::TestLog::SampleInfo
		<< tcu::TestLog::ValueInfo("WrittenSize",		"Written size",			"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("BufferSize",		"Buffer size",			"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("TotalTime",			"Total time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("MapTime",			"Map time",				"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("UnmapTime",			"Unmap time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("WriteTime",			"Write time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("FitResidual",		"Fit residual",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::EndSampleInfo;

	for (int sampleNdx = 0; sampleNdx < (int)samples.size(); ++sampleNdx)
	{
		const float fitResidual = (float)samples[sampleNdx].duration.fitResponseDuration - (theilSenFitting.offset + theilSenFitting.coefficient * (float)samples[sampleNdx].writtenSize);
		log	<< tcu::TestLog::Sample
			<< samples[sampleNdx].writtenSize
			<< samples[sampleNdx].bufferSize
			<< (int)samples[sampleNdx].duration.totalDuration
			<< (int)samples[sampleNdx].duration.mapDuration
			<< (int)samples[sampleNdx].duration.unmapDuration
			<< (int)samples[sampleNdx].duration.writeDuration
			<< fitResidual
			<< tcu::TestLog::EndSample;
	}

	log << tcu::TestLog::EndSampleList;
}

void logSampleList (tcu::TestLog& log, const LineParametersWithConfidence& theilSenFitting, const std::vector<UploadSampleResult<MapBufferRangeFlushDuration> >& samples)
{
	log << tcu::TestLog::SampleList("Samples", "Samples")
		<< tcu::TestLog::SampleInfo
		<< tcu::TestLog::ValueInfo("WrittenSize",		"Written size",			"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("BufferSize",		"Buffer size",			"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("TotalTime",			"Total time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("AllocTime",			"Alloc time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("MapTime",			"Map time",				"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("UnmapTime",			"Unmap time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("WriteTime",			"Write time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("FlushTime",			"Flush time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("FitResidual",		"Fit residual",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::EndSampleInfo;

	for (int sampleNdx = 0; sampleNdx < (int)samples.size(); ++sampleNdx)
	{
		const float fitResidual = (float)samples[sampleNdx].duration.fitResponseDuration - (theilSenFitting.offset + theilSenFitting.coefficient * (float)samples[sampleNdx].writtenSize);
		log	<< tcu::TestLog::Sample
			<< samples[sampleNdx].writtenSize
			<< samples[sampleNdx].bufferSize
			<< (int)samples[sampleNdx].duration.totalDuration
			<< (int)samples[sampleNdx].duration.allocDuration
			<< (int)samples[sampleNdx].duration.mapDuration
			<< (int)samples[sampleNdx].duration.unmapDuration
			<< (int)samples[sampleNdx].duration.writeDuration
			<< (int)samples[sampleNdx].duration.flushDuration
			<< fitResidual
			<< tcu::TestLog::EndSample;
	}

	log << tcu::TestLog::EndSampleList;
}

void logSampleList (tcu::TestLog& log, const LineParametersWithConfidence& theilSenFitting, const std::vector<UploadSampleResult<MapBufferRangeFlushDurationNoAlloc> >& samples)
{
	log << tcu::TestLog::SampleList("Samples", "Samples")
		<< tcu::TestLog::SampleInfo
		<< tcu::TestLog::ValueInfo("WrittenSize",		"Written size",			"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("BufferSize",		"Buffer size",			"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("TotalTime",			"Total time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("MapTime",			"Map time",				"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("UnmapTime",			"Unmap time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("WriteTime",			"Write time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("FlushTime",			"Flush time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("FitResidual",		"Fit residual",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::EndSampleInfo;

	for (int sampleNdx = 0; sampleNdx < (int)samples.size(); ++sampleNdx)
	{
		const float fitResidual = (float)samples[sampleNdx].duration.fitResponseDuration - (theilSenFitting.offset + theilSenFitting.coefficient * (float)samples[sampleNdx].writtenSize);
		log	<< tcu::TestLog::Sample
			<< samples[sampleNdx].writtenSize
			<< samples[sampleNdx].bufferSize
			<< (int)samples[sampleNdx].duration.totalDuration
			<< (int)samples[sampleNdx].duration.mapDuration
			<< (int)samples[sampleNdx].duration.unmapDuration
			<< (int)samples[sampleNdx].duration.writeDuration
			<< (int)samples[sampleNdx].duration.flushDuration
			<< fitResidual
			<< tcu::TestLog::EndSample;
	}

	log << tcu::TestLog::EndSampleList;
}

void logSampleList (tcu::TestLog& log, const LineParametersWithConfidence& theilSenFitting, const std::vector<RenderSampleResult<RenderReadDuration> >& samples)
{
	log << tcu::TestLog::SampleList("Samples", "Samples")
		<< tcu::TestLog::SampleInfo
		<< tcu::TestLog::ValueInfo("DataSize",			"Data processed",		"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("VertexCount",		"Number of vertices",	"vertices",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("TotalTime",			"Total time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("DrawCallTime",		"Draw call time",		"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("ReadTime",			"ReadPixels time",		"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("FitResidual",		"Fit residual",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::EndSampleInfo;

	for (int sampleNdx = 0; sampleNdx < (int)samples.size(); ++sampleNdx)
	{
		const float fitResidual = (float)samples[sampleNdx].duration.fitResponseDuration - (theilSenFitting.offset + theilSenFitting.coefficient * (float)samples[sampleNdx].renderDataSize);
		log	<< tcu::TestLog::Sample
			<< samples[sampleNdx].renderDataSize
			<< samples[sampleNdx].numVertices
			<< (int)samples[sampleNdx].duration.renderReadDuration
			<< (int)samples[sampleNdx].duration.renderDuration
			<< (int)samples[sampleNdx].duration.readDuration
			<< fitResidual
			<< tcu::TestLog::EndSample;
	}

	log << tcu::TestLog::EndSampleList;
}

void logSampleList (tcu::TestLog& log, const LineParametersWithConfidence& theilSenFitting, const std::vector<RenderSampleResult<UnrelatedUploadRenderReadDuration> >& samples)
{
	log << tcu::TestLog::SampleList("Samples", "Samples")
		<< tcu::TestLog::SampleInfo
		<< tcu::TestLog::ValueInfo("DataSize",				"Data processed",			"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("VertexCount",			"Number of vertices",		"vertices",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("UnrelatedUploadSize",	"Unrelated upload size",	"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("TotalTime",				"Total time",				"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("DrawCallTime",			"Draw call time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("ReadTime",				"ReadPixels time",			"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("FitResidual",			"Fit residual",				"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::EndSampleInfo;

	for (int sampleNdx = 0; sampleNdx < (int)samples.size(); ++sampleNdx)
	{
		const float fitResidual = (float)samples[sampleNdx].duration.fitResponseDuration - (theilSenFitting.offset + theilSenFitting.coefficient * (float)samples[sampleNdx].renderDataSize);
		log	<< tcu::TestLog::Sample
			<< samples[sampleNdx].renderDataSize
			<< samples[sampleNdx].numVertices
			<< samples[sampleNdx].unrelatedDataSize
			<< (int)samples[sampleNdx].duration.renderReadDuration
			<< (int)samples[sampleNdx].duration.renderDuration
			<< (int)samples[sampleNdx].duration.readDuration
			<< fitResidual
			<< tcu::TestLog::EndSample;
	}

	log << tcu::TestLog::EndSampleList;
}

void logSampleList (tcu::TestLog& log, const LineParametersWithConfidence& theilSenFitting, const std::vector<RenderSampleResult<UploadRenderReadDuration> >& samples)
{
	log << tcu::TestLog::SampleList("Samples", "Samples")
		<< tcu::TestLog::SampleInfo
		<< tcu::TestLog::ValueInfo("DataSize",			"Data processed",					"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("UploadSize",		"Data uploaded",					"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("VertexCount",		"Number of vertices",				"vertices",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("DrawReadTime",		"Draw call and ReadPixels time",	"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("TotalTime",			"Total time",						"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("Upload time",		"Upload time",						"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("DrawCallTime",		"Draw call time",					"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("ReadTime",			"ReadPixels time",					"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("FitResidual",		"Fit residual",						"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::EndSampleInfo;

	for (int sampleNdx = 0; sampleNdx < (int)samples.size(); ++sampleNdx)
	{
		const float fitResidual = (float)samples[sampleNdx].duration.fitResponseDuration - (theilSenFitting.offset + theilSenFitting.coefficient * (float)samples[sampleNdx].renderDataSize);
		log	<< tcu::TestLog::Sample
			<< samples[sampleNdx].renderDataSize
			<< samples[sampleNdx].uploadedDataSize
			<< samples[sampleNdx].numVertices
			<< (int)samples[sampleNdx].duration.renderReadDuration
			<< (int)samples[sampleNdx].duration.totalDuration
			<< (int)samples[sampleNdx].duration.uploadDuration
			<< (int)samples[sampleNdx].duration.renderDuration
			<< (int)samples[sampleNdx].duration.readDuration
			<< fitResidual
			<< tcu::TestLog::EndSample;
	}

	log << tcu::TestLog::EndSampleList;
}

void logSampleList (tcu::TestLog& log, const LineParametersWithConfidence& theilSenFitting, const std::vector<RenderSampleResult<UploadRenderReadDurationWithUnrelatedUploadSize> >& samples)
{
	log << tcu::TestLog::SampleList("Samples", "Samples")
		<< tcu::TestLog::SampleInfo
		<< tcu::TestLog::ValueInfo("DataSize",				"Data processed",					"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("UploadSize",			"Data uploaded",					"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("VertexCount",			"Number of vertices",				"vertices",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("UnrelatedUploadSize",	"Unrelated upload size",			"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("DrawReadTime",			"Draw call and ReadPixels time",	"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("TotalTime",				"Total time",						"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("Upload time",			"Upload time",						"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("DrawCallTime",			"Draw call time",					"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("ReadTime",				"ReadPixels time",					"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("FitResidual",			"Fit residual",						"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::EndSampleInfo;

	for (int sampleNdx = 0; sampleNdx < (int)samples.size(); ++sampleNdx)
	{
		const float fitResidual = (float)samples[sampleNdx].duration.fitResponseDuration - (theilSenFitting.offset + theilSenFitting.coefficient * (float)samples[sampleNdx].renderDataSize);
		log	<< tcu::TestLog::Sample
			<< samples[sampleNdx].renderDataSize
			<< samples[sampleNdx].uploadedDataSize
			<< samples[sampleNdx].numVertices
			<< samples[sampleNdx].unrelatedDataSize
			<< (int)samples[sampleNdx].duration.renderReadDuration
			<< (int)samples[sampleNdx].duration.totalDuration
			<< (int)samples[sampleNdx].duration.uploadDuration
			<< (int)samples[sampleNdx].duration.renderDuration
			<< (int)samples[sampleNdx].duration.readDuration
			<< fitResidual
			<< tcu::TestLog::EndSample;
	}

	log << tcu::TestLog::EndSampleList;
}

void logSampleList (tcu::TestLog& log, const LineParametersWithConfidence& theilSenFitting, const std::vector<RenderSampleResult<RenderUploadRenderReadDuration> >& samples)
{
	log << tcu::TestLog::SampleList("Samples", "Samples")
		<< tcu::TestLog::SampleInfo
		<< tcu::TestLog::ValueInfo("DataSize",				"Data processed",						"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("UploadSize",			"Data uploaded",						"bytes",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("VertexCount",			"Number of vertices",					"vertices",	QP_SAMPLE_VALUE_TAG_PREDICTOR)
		<< tcu::TestLog::ValueInfo("DrawReadTime",			"Second draw call and ReadPixels time",	"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("TotalTime",				"Total time",							"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("FirstDrawCallTime",		"First draw call time",					"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("Upload time",			"Upload time",							"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("SecondDrawCallTime",	"Second draw call time",				"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("ReadTime",				"ReadPixels time",						"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::ValueInfo("FitResidual",			"Fit residual",							"us",		QP_SAMPLE_VALUE_TAG_RESPONSE)
		<< tcu::TestLog::EndSampleInfo;

	for (int sampleNdx = 0; sampleNdx < (int)samples.size(); ++sampleNdx)
	{
		const float fitResidual = (float)samples[sampleNdx].duration.fitResponseDuration - (theilSenFitting.offset + theilSenFitting.coefficient * (float)samples[sampleNdx].renderDataSize);
		log	<< tcu::TestLog::Sample
			<< samples[sampleNdx].renderDataSize
			<< samples[sampleNdx].uploadedDataSize
			<< samples[sampleNdx].numVertices
			<< (int)samples[sampleNdx].duration.renderReadDuration
			<< (int)samples[sampleNdx].duration.totalDuration
			<< (int)samples[sampleNdx].duration.firstRenderDuration
			<< (int)samples[sampleNdx].duration.uploadDuration
			<< (int)samples[sampleNdx].duration.secondRenderDuration
			<< (int)samples[sampleNdx].duration.readDuration
			<< fitResidual
			<< tcu::TestLog::EndSample;
	}

	log << tcu::TestLog::EndSampleList;
}

template <typename SampleType>
static UploadSampleAnalyzeResult analyzeSampleResults (tcu::TestLog& log, const std::vector<UploadSampleResult<SampleType> >& samples, bool logBucketPerformance)
{
	// Assume data is linear with some outliers, fit a line
	const LineParametersWithConfidence						theilSenFitting						= fitLineToSamples(samples);
	const typename SampleTypeTraits<SampleType>::StatsType	resultStats							= calculateSampleStatistics(theilSenFitting, samples);
	float													approximatedTransferRate;
	float													approximatedTransferRateNoConstant;

	// Output raw samples
	{
		const tcu::ScopedLogSection	section(log, "Samples", "Samples");
		logSampleList(log, theilSenFitting, samples);
	}

	// Calculate results for different ranges
	if (logBucketPerformance)
	{
		const int										numBuckets				= 4;
		int												minBufferSize			= 0;
		int												maxBufferSize			= 0;
		std::vector<UploadSampleResult<SampleType> >	buckets[numBuckets];

		bucketizeSamplesUniformly(samples, &buckets[0], numBuckets, minBufferSize, maxBufferSize);

		for (int bucketNdx = 0; bucketNdx < numBuckets; ++bucketNdx)
		{
			if (buckets[bucketNdx].empty())
				continue;

			// Print a nice result summary

			const int												bucketRangeMin	= minBufferSize + (int)(((float) bucketNdx    / (float)numBuckets) * (float)(maxBufferSize - minBufferSize));
			const int												bucketRangeMax	= minBufferSize + (int)(((float)(bucketNdx+1) / (float)numBuckets) * (float)(maxBufferSize - minBufferSize));
			const typename SampleTypeTraits<SampleType>::StatsType	stats			= calculateSampleStatistics(theilSenFitting, buckets[bucketNdx]);
			const tcu::ScopedLogSection								section			(log, "BufferSizeRange", std::string("Transfer performance with buffer size in range [").append(getHumanReadableByteSize(bucketRangeMin).append(", ").append(getHumanReadableByteSize(bucketRangeMax).append("]"))));

			logMapRangeStats<SampleType>(log, stats);
			logUnmapStats<SampleType>(log, stats);
			logWriteStats<SampleType>(log, stats);
			logFlushStats<SampleType>(log, stats);
			logAllocStats<SampleType>(log, stats);

			log	<< tcu::TestLog::Float("Min", "Total: Min time", "us", QP_KEY_TAG_TIME, stats.result.minTime)
				<< tcu::TestLog::Float("Max", "Total: Max time", "us", QP_KEY_TAG_TIME, stats.result.maxTime)
				<< tcu::TestLog::Float("Min90", "Total: 90%-Min time", "us", QP_KEY_TAG_TIME, stats.result.min2DecileTime)
				<< tcu::TestLog::Float("Max90", "Total: 90%-Max time", "us", QP_KEY_TAG_TIME, stats.result.max9DecileTime)
				<< tcu::TestLog::Float("Median", "Total: Median time", "us", QP_KEY_TAG_TIME, stats.result.medianTime)
				<< tcu::TestLog::Float("MedianTransfer", "Median transfer rate", "MB / s", QP_KEY_TAG_PERFORMANCE, stats.medianRate / 1024.0f / 1024.0f)
				<< tcu::TestLog::Float("MaxDiff", "Max difference to approximated", "us", QP_KEY_TAG_TIME, stats.maxDiffTime)
				<< tcu::TestLog::Float("Max90Diff", "90%-Max difference to approximated", "us", QP_KEY_TAG_TIME, stats.maxDiff9DecileTime)
				<< tcu::TestLog::Float("MedianDiff", "Median difference to approximated", "us", QP_KEY_TAG_TIME, stats.medianDiffTime)
				<< tcu::TestLog::Float("MaxRelDiff", "Max relative difference to approximated", "%", QP_KEY_TAG_NONE, stats.maxRelDiffTime * 100.0f)
				<< tcu::TestLog::Float("Max90RelDiff", "90%-Max relative difference to approximated", "%", QP_KEY_TAG_NONE, stats.max9DecileRelDiffTime * 100.0f)
				<< tcu::TestLog::Float("MedianRelDiff", "Median relative difference to approximated", "%", QP_KEY_TAG_NONE, stats.medianRelDiffTime * 100.0f);
		}
	}

	// Contributions
	if (SampleTypeTraits<SampleType>::LOG_CONTRIBUTIONS)
	{
		const tcu::ScopedLogSection	section(log, "Contribution", "Contributions");

		logMapContribution(log, samples, resultStats);
		logUnmapContribution(log, samples, resultStats);
		logWriteContribution(log, samples, resultStats);
		logFlushContribution(log, samples, resultStats);
		logAllocContribution(log, samples, resultStats);
	}

	// Print results
	{
		const tcu::ScopedLogSection	section(log, "Results", "Results");

		const int	medianBufferSize					= (samples.front().bufferSize + samples.back().bufferSize) / 2;
		const float	approximatedTransferTime			= (theilSenFitting.offset + theilSenFitting.coefficient * (float)medianBufferSize) / 1000.0f / 1000.0f;
		const float	approximatedTransferTimeNoConstant	= (theilSenFitting.coefficient * (float)medianBufferSize) / 1000.0f / 1000.0f;
		const float	sampleLinearity						= calculateSampleFitLinearity(samples);
		const float	sampleTemporalStability				= calculateSampleTemporalStability(samples);

		approximatedTransferRateNoConstant				= (float)medianBufferSize / approximatedTransferTimeNoConstant;
		approximatedTransferRate						= (float)medianBufferSize / approximatedTransferTime;

		log	<< tcu::TestLog::Float("ResultLinearity", "Sample linearity", "%", QP_KEY_TAG_QUALITY, sampleLinearity * 100.0f)
			<< tcu::TestLog::Float("SampleTemporalStability", "Sample temporal stability", "%", QP_KEY_TAG_QUALITY, sampleTemporalStability * 100.0f)
			<< tcu::TestLog::Float("ApproximatedConstantCost", "Approximated contant cost", "us", QP_KEY_TAG_TIME, theilSenFitting.offset)
			<< tcu::TestLog::Float("ApproximatedConstantCostConfidence60Lower", "Approximated contant cost 60% confidence lower limit", "us", QP_KEY_TAG_TIME, theilSenFitting.offsetConfidenceLower)
			<< tcu::TestLog::Float("ApproximatedConstantCostConfidence60Upper", "Approximated contant cost 60% confidence upper limit", "us", QP_KEY_TAG_TIME, theilSenFitting.offsetConfidenceUpper)
			<< tcu::TestLog::Float("ApproximatedLinearCost", "Approximated linear cost", "us / MB", QP_KEY_TAG_TIME, theilSenFitting.coefficient * 1024.0f * 1024.0f)
			<< tcu::TestLog::Float("ApproximatedLinearCostConfidence60Lower", "Approximated linear cost 60% confidence lower limit", "us / MB", QP_KEY_TAG_TIME, theilSenFitting.coefficientConfidenceLower * 1024.0f * 1024.0f)
			<< tcu::TestLog::Float("ApproximatedLinearCostConfidence60Upper", "Approximated linear cost 60% confidence upper limit", "us / MB", QP_KEY_TAG_TIME, theilSenFitting.coefficientConfidenceUpper * 1024.0f * 1024.0f)
			<< tcu::TestLog::Float("ApproximatedTransferRate", "Approximated transfer rate", "MB / s", QP_KEY_TAG_PERFORMANCE, approximatedTransferRate / 1024.0f / 1024.0f)
			<< tcu::TestLog::Float("ApproximatedTransferRateNoConstant", "Approximated transfer rate without constant cost", "MB / s", QP_KEY_TAG_PERFORMANCE, approximatedTransferRateNoConstant / 1024.0f / 1024.0f)
			<< tcu::TestLog::Float("SampleMedianTime", "Median sample time", "us", QP_KEY_TAG_TIME, resultStats.result.medianTime)
			<< tcu::TestLog::Float("SampleMedianTransfer", "Median transfer rate", "MB / s", QP_KEY_TAG_PERFORMANCE, resultStats.medianRate / 1024.0f / 1024.0f);
	}

	// return approximated transfer rate
	{
		UploadSampleAnalyzeResult result;

		result.transferRateMedian = resultStats.medianRate;
		result.transferRateAtRange = approximatedTransferRate;
		result.transferRateAtInfinity = approximatedTransferRateNoConstant;

		return result;
	}
}

template <typename SampleType>
static RenderSampleAnalyzeResult analyzeSampleResults (tcu::TestLog& log, const std::vector<RenderSampleResult<SampleType> >& samples)
{
	// Assume data is linear with some outliers, fit a line
	const LineParametersWithConfidence						theilSenFitting						= fitLineToSamples(samples);
	const typename SampleTypeTraits<SampleType>::StatsType	resultStats							= calculateSampleStatistics(theilSenFitting, samples);
	float													approximatedProcessingRate;
	float													approximatedProcessingRateNoConstant;

	// output raw samples
	{
		const tcu::ScopedLogSection	section(log, "Samples", "Samples");
		logSampleList(log, theilSenFitting, samples);
	}

	// Contributions
	if (SampleTypeTraits<SampleType>::LOG_CONTRIBUTIONS)
	{
		const tcu::ScopedLogSection	section(log, "Contribution", "Contributions");

		logFirstRenderContribution(log, samples, resultStats);
		logUploadContribution(log, samples, resultStats);
		logRenderContribution(log, samples, resultStats);
		logSecondRenderContribution(log, samples, resultStats);
		logReadContribution(log, samples, resultStats);
		logTotalContribution(log, samples, resultStats);
	}

	// print results
	{
		const tcu::ScopedLogSection	section(log, "Results", "Results");

		const int	medianDataSize						= (samples.front().renderDataSize + samples.back().renderDataSize) / 2;
		const float	approximatedRenderTime				= (theilSenFitting.offset + theilSenFitting.coefficient * (float)medianDataSize) / 1000.0f / 1000.0f;
		const float	approximatedRenderTimeNoConstant	= (theilSenFitting.coefficient * (float)medianDataSize) / 1000.0f / 1000.0f;
		const float	sampleLinearity						= calculateSampleFitLinearity(samples);
		const float	sampleTemporalStability				= calculateSampleTemporalStability(samples);

		approximatedProcessingRateNoConstant			= (float)medianDataSize / approximatedRenderTimeNoConstant;
		approximatedProcessingRate						= (float)medianDataSize / approximatedRenderTime;

		log	<< tcu::TestLog::Float("ResultLinearity", "Sample linearity", "%", QP_KEY_TAG_QUALITY, sampleLinearity * 100.0f)
			<< tcu::TestLog::Float("SampleTemporalStability", "Sample temporal stability", "%", QP_KEY_TAG_QUALITY, sampleTemporalStability * 100.0f)
			<< tcu::TestLog::Float("ApproximatedConstantCost", "Approximated contant cost", "us", QP_KEY_TAG_TIME, theilSenFitting.offset)
			<< tcu::TestLog::Float("ApproximatedConstantCostConfidence60Lower", "Approximated contant cost 60% confidence lower limit", "us", QP_KEY_TAG_TIME, theilSenFitting.offsetConfidenceLower)
			<< tcu::TestLog::Float("ApproximatedConstantCostConfidence60Upper", "Approximated contant cost 60% confidence upper limit", "us", QP_KEY_TAG_TIME, theilSenFitting.offsetConfidenceUpper)
			<< tcu::TestLog::Float("ApproximatedLinearCost", "Approximated linear cost", "us / MB", QP_KEY_TAG_TIME, theilSenFitting.coefficient * 1024.0f * 1024.0f)
			<< tcu::TestLog::Float("ApproximatedLinearCostConfidence60Lower", "Approximated linear cost 60% confidence lower limit", "us / MB", QP_KEY_TAG_TIME, theilSenFitting.coefficientConfidenceLower * 1024.0f * 1024.0f)
			<< tcu::TestLog::Float("ApproximatedLinearCostConfidence60Upper", "Approximated linear cost 60% confidence upper limit", "us / MB", QP_KEY_TAG_TIME, theilSenFitting.coefficientConfidenceUpper * 1024.0f * 1024.0f)
			<< tcu::TestLog::Float("ApproximatedProcessRate", "Approximated processing rate", "MB / s", QP_KEY_TAG_PERFORMANCE, approximatedProcessingRate / 1024.0f / 1024.0f)
			<< tcu::TestLog::Float("ApproximatedProcessRateNoConstant", "Approximated processing rate without constant cost", "MB / s", QP_KEY_TAG_PERFORMANCE, approximatedProcessingRateNoConstant / 1024.0f / 1024.0f)
			<< tcu::TestLog::Float("SampleMedianTime", "Median sample time", "us", QP_KEY_TAG_TIME, resultStats.result.medianTime)
			<< tcu::TestLog::Float("SampleMedianProcess", "Median processing rate", "MB / s", QP_KEY_TAG_PERFORMANCE, resultStats.medianRate / 1024.0f / 1024.0f);
	}

	// return approximated render rate
	{
		RenderSampleAnalyzeResult result;

		result.renderRateMedian		= resultStats.medianRate;
		result.renderRateAtRange	= approximatedProcessingRate;
		result.renderRateAtInfinity = approximatedProcessingRateNoConstant;

		return result;
	}
	return RenderSampleAnalyzeResult();
}

static void generateTwoPassRandomIterationOrder (std::vector<int>& iterationOrder, int numSamples)
{
	de::Random	rnd			(0xabc);
	const int	midPoint	= (numSamples+1) / 2;		// !< ceil(m_numSamples / 2)

	DE_ASSERT((int)iterationOrder.size() == numSamples);

	// Two "passes" over range, randomize order in both passes
	// This allows to us detect if iterations are not independent
	// (first run and later run samples differ significantly?)

	for (int sampleNdx = 0; sampleNdx < midPoint; ++sampleNdx)
		iterationOrder[sampleNdx] = sampleNdx * 2;
	for (int sampleNdx = midPoint; sampleNdx < numSamples; ++sampleNdx)
		iterationOrder[sampleNdx] = (sampleNdx - midPoint) * 2 + 1;

	for (int ndx = 0; ndx < midPoint; ++ndx)
		std::swap(iterationOrder[ndx], iterationOrder[rnd.getInt(0, midPoint - 1)]);
	for (int ndx = midPoint; ndx < (int)iterationOrder.size(); ++ndx)
		std::swap(iterationOrder[ndx], iterationOrder[rnd.getInt(midPoint, (int)iterationOrder.size()-1)]);
}

template <typename SampleType>
class BasicBufferCase : public TestCase
{
public:

	enum Flags
	{
		FLAG_ALLOCATE_LARGER_BUFFER = 0x01,
	};
							BasicBufferCase		(Context& context, const char* name, const char* desc, int bufferSizeMin, int bufferSizeMax, int numSamples, int flags);
							~BasicBufferCase	(void);

	virtual void			init				(void);
	virtual void			deinit				(void);

protected:
	IterateResult			iterate				(void);

	virtual bool			runSample			(int iteration, UploadSampleResult<SampleType>& sample) = 0;
	virtual void			logAndSetTestResult	(const std::vector<UploadSampleResult<SampleType> >& results) = 0;

	void					disableGLWarmup		(void);
	void					waitGLResults		(void);

	enum
	{
		DUMMY_RENDER_AREA_SIZE = 32
	};

	glu::ShaderProgram*		m_dummyProgram;
	deInt32					m_dummyProgramPosLoc;
	deUint32				m_bufferID;

	const int				m_numSamples;
	const int				m_bufferSizeMin;
	const int				m_bufferSizeMax;
	const bool				m_allocateLargerBuffer;

private:
	int						m_iteration;
	std::vector<int>		m_iterationOrder;
	std::vector<UploadSampleResult<SampleType> > m_results;

	bool					m_useGL;
	int						m_bufferRandomizerTimer;
};

template <typename SampleType>
BasicBufferCase<SampleType>::BasicBufferCase (Context& context, const char* name, const char* desc, int bufferSizeMin, int bufferSizeMax, int numSamples, int flags)
	: TestCase					(context, tcu::NODETYPE_PERFORMANCE, name, desc)
	, m_dummyProgram			(DE_NULL)
	, m_dummyProgramPosLoc		(-1)
	, m_bufferID				(0)
	, m_numSamples				(numSamples)
	, m_bufferSizeMin			(bufferSizeMin)
	, m_bufferSizeMax			(bufferSizeMax)
	, m_allocateLargerBuffer	((flags & FLAG_ALLOCATE_LARGER_BUFFER) != 0)
	, m_iteration				(0)
	, m_iterationOrder			(numSamples)
	, m_results					(numSamples)
	, m_useGL					(true)
	, m_bufferRandomizerTimer	(0)
{
	// "randomize" iteration order. Deterministic, patternless
	generateTwoPassRandomIterationOrder(m_iterationOrder, m_numSamples);

	// choose buffer sizes
	for (int sampleNdx = 0; sampleNdx < m_numSamples; ++sampleNdx)
	{
		const int rawBufferSize			= (int)deFloatFloor((float)bufferSizeMin + (float)(bufferSizeMax - bufferSizeMin) * ((float)(sampleNdx + 1) / (float)m_numSamples));
		const int bufferSize			= deAlign32(rawBufferSize, 16);
		const int allocatedBufferSize	= deAlign32((m_allocateLargerBuffer) ? ((int)((float)bufferSize * 1.5f)) : (bufferSize), 16);

		m_results[sampleNdx].bufferSize		= bufferSize;
		m_results[sampleNdx].allocatedSize	= allocatedBufferSize;
		m_results[sampleNdx].writtenSize	= -1;
	}
}

template <typename SampleType>
BasicBufferCase<SampleType>::~BasicBufferCase (void)
{
	deinit();
}

template <typename SampleType>
void BasicBufferCase<SampleType>::init (void)
{
	const glw::Functions& gl = m_context.getRenderContext().getFunctions();

	if (!m_useGL)
		return;

	// \note Viewport size is not checked, it won't matter if the render target actually is smaller hhan DUMMY_RENDER_AREA_SIZE

	// dummy shader

	m_dummyProgram = new glu::ShaderProgram(m_context.getRenderContext(), glu::ProgramSources() << glu::VertexSource(s_dummyVertexShader) << glu::FragmentSource(s_dummyFragnentShader));
	if (!m_dummyProgram->isOk())
	{
		m_testCtx.getLog() << *m_dummyProgram;
		throw tcu::TestError("failed to build shader program");
	}

	m_dummyProgramPosLoc = gl.getAttribLocation(m_dummyProgram->getProgram(), "a_position");
	if (m_dummyProgramPosLoc == -1)
		throw tcu::TestError("a_position location was -1");
}

template <typename SampleType>
void BasicBufferCase<SampleType>::deinit (void)
{
	if (m_bufferID)
	{
		m_context.getRenderContext().getFunctions().deleteBuffers(1, &m_bufferID);
		m_bufferID = 0;
	}

	delete m_dummyProgram;
	m_dummyProgram = DE_NULL;
}

template <typename SampleType>
TestCase::IterateResult BasicBufferCase<SampleType>::iterate (void)
{
	const glw::Functions&	gl				= m_context.getRenderContext().getFunctions();
	static bool				buffersWarmedUp	= false;

	static const deUint32	usages[] =
	{
		GL_STREAM_DRAW, GL_STREAM_READ, GL_STREAM_COPY,
		GL_STATIC_DRAW, GL_STATIC_READ, GL_STATIC_COPY,
		GL_DYNAMIC_DRAW, GL_DYNAMIC_READ, GL_DYNAMIC_COPY,
	};

	// Allocate some random sized buffers and remove them to
	// make sure the first samples too have some buffers removed
	// just before their allocation. This is only needed by the
	// the first test.

	if (m_useGL && !buffersWarmedUp)
	{
		const int					numRandomBuffers				= 6;
		const int					numRepeats						= 10;
		const int					maxBufferSize					= 16777216;
		const std::vector<deUint8>	zeroData						(maxBufferSize, 0x00);
		de::Random					rnd								(0x1234);
		deUint32					bufferIDs[numRandomBuffers]		= {0};

		gl.useProgram(m_dummyProgram->getProgram());
		gl.viewport(0, 0, DUMMY_RENDER_AREA_SIZE, DUMMY_RENDER_AREA_SIZE);
		gl.enableVertexAttribArray(m_dummyProgramPosLoc);

		for (int ndx = 0; ndx < numRepeats; ++ndx)
		{
			// Create buffer and maybe draw from it
			for (int randomBufferNdx = 0; randomBufferNdx < numRandomBuffers; ++randomBufferNdx)
			{
				const int		randomSize	= deAlign32(rnd.getInt(1, maxBufferSize), 4*4);
				const deUint32	usage		= usages[rnd.getUint32() % (deUint32)DE_LENGTH_OF_ARRAY(usages)];

				gl.genBuffers(1, &bufferIDs[randomBufferNdx]);
				gl.bindBuffer(GL_ARRAY_BUFFER, bufferIDs[randomBufferNdx]);
				gl.bufferData(GL_ARRAY_BUFFER, randomSize, &zeroData[0], usage);

				if (rnd.getBool())
				{
					gl.vertexAttribPointer(m_dummyProgramPosLoc, 4, GL_FLOAT, GL_FALSE, 0, DE_NULL);
					gl.drawArrays(GL_POINTS, 0, 1);
					gl.drawArrays(GL_POINTS, randomSize / (int)sizeof(float[4]) - 1, 1);
				}
			}

			for (int randomBufferNdx = 0; randomBufferNdx < numRandomBuffers; ++randomBufferNdx)
				gl.deleteBuffers(1, &bufferIDs[randomBufferNdx]);

			waitGLResults();
			GLU_EXPECT_NO_ERROR(gl.getError(), "Buffer gen");

			m_testCtx.touchWatchdog();
		}

		buffersWarmedUp = true;
		return CONTINUE;
	}
	else if (m_useGL && m_bufferRandomizerTimer++ % 8 == 0)
	{
		// Do some random buffer operations to every now and then
		// to make sure the previous test iterations won't affect
		// following test runs.

		const int					numRandomBuffers				= 3;
		const int					maxBufferSize					= 16777216;
		const std::vector<deUint8>	zeroData						(maxBufferSize, 0x00);
		de::Random					rnd								(0x1234 + 0xabc * m_bufferRandomizerTimer);

		// BufferData
		{
			deUint32 bufferIDs[numRandomBuffers] = {0};

			for (int randomBufferNdx = 0; randomBufferNdx < numRandomBuffers; ++randomBufferNdx)
			{
				const int		randomSize	= deAlign32(rnd.getInt(1, maxBufferSize), 4*4);
				const deUint32	usage		= usages[rnd.getUint32() % (deUint32)DE_LENGTH_OF_ARRAY(usages)];

				gl.genBuffers(1, &bufferIDs[randomBufferNdx]);
				gl.bindBuffer(GL_ARRAY_BUFFER, bufferIDs[randomBufferNdx]);
				gl.bufferData(GL_ARRAY_BUFFER, randomSize, &zeroData[0], usage);
			}

			for (int randomBufferNdx = 0; randomBufferNdx < numRandomBuffers; ++randomBufferNdx)
				gl.deleteBuffers(1, &bufferIDs[randomBufferNdx]);
		}

		GLU_EXPECT_NO_ERROR(gl.getError(), "buffer ops");

		// Do some memory mappings
		{
			deUint32 bufferIDs[numRandomBuffers] = {0};

			for (int randomBufferNdx = 0; randomBufferNdx < numRandomBuffers; ++randomBufferNdx)
			{
				const int		randomSize	= deAlign32(rnd.getInt(1, maxBufferSize), 4*4);
				const deUint32	usage		= usages[rnd.getUint32() % (deUint32)DE_LENGTH_OF_ARRAY(usages)];
				void*			ptr;

				gl.genBuffers(1, &bufferIDs[randomBufferNdx]);
				gl.bindBuffer(GL_ARRAY_BUFFER, bufferIDs[randomBufferNdx]);
				gl.bufferData(GL_ARRAY_BUFFER, randomSize, &zeroData[0], usage);

				gl.vertexAttribPointer(m_dummyProgramPosLoc, 4, GL_FLOAT, GL_FALSE, 0, DE_NULL);
				gl.drawArrays(GL_POINTS, 0, 1);
				gl.drawArrays(GL_POINTS, randomSize / (int)sizeof(float[4]) - 1, 1);

				if (rnd.getBool())
					waitGLResults();

				ptr = gl.mapBufferRange(GL_ARRAY_BUFFER, 0, randomSize, GL_MAP_WRITE_BIT);
				if (ptr)
				{
					medianTimeMemcpy(ptr, &zeroData[0], randomSize);
					gl.unmapBuffer(GL_ARRAY_BUFFER);
				}
			}

			for (int randomBufferNdx = 0; randomBufferNdx < numRandomBuffers; ++randomBufferNdx)
				gl.deleteBuffers(1, &bufferIDs[randomBufferNdx]);

			waitGLResults();
		}

		GLU_EXPECT_NO_ERROR(gl.getError(), "buffer maps");
		return CONTINUE;
	}
	else
	{
		const int	currentIteration	= m_iteration;
		const int	sampleNdx			= m_iterationOrder[currentIteration];
		const bool	sampleRunSuccessful	= runSample(currentIteration, m_results[sampleNdx]);

		GLU_EXPECT_NO_ERROR(gl.getError(), "post runSample()");

		// Retry failed samples
		if (!sampleRunSuccessful)
			return CONTINUE;

		if (++m_iteration >= m_numSamples)
		{
			logAndSetTestResult(m_results);
			return STOP;
		}
		else
			return CONTINUE;
	}
}

template <typename SampleType>
void BasicBufferCase<SampleType>::disableGLWarmup (void)
{
	m_useGL = false;
}

template <typename SampleType>
void BasicBufferCase<SampleType>::waitGLResults (void)
{
	tcu::Surface dummySurface(DUMMY_RENDER_AREA_SIZE, DUMMY_RENDER_AREA_SIZE);
	glu::readPixels(m_context.getRenderContext(), 0, 0, dummySurface.getAccess());
}

template <typename SampleType>
class BasicUploadCase : public BasicBufferCase<SampleType>
{
public:
	enum CaseType
	{
		CASE_NO_BUFFERS = 0,
		CASE_NEW_BUFFER,
		CASE_UNSPECIFIED_BUFFER,
		CASE_SPECIFIED_BUFFER,
		CASE_USED_BUFFER,
		CASE_USED_LARGER_BUFFER,

		CASE_LAST
	};

	enum CaseFlags
	{
		FLAG_DONT_LOG_BUFFER_INFO				= 0x01,
		FLAG_RESULT_BUFFER_UNSPECIFIED_CONTENT	= 0x02,
	};

	enum ResultType
	{
		RESULT_MEDIAN_TRANSFER_RATE = 0,
		RESULT_ASYMPTOTIC_TRANSFER_RATE,
	};

						BasicUploadCase		(Context& context,
											 const char* name,
											 const char* desc,
											 int bufferSizeMin,
											 int bufferSizeMax,
											 int numSamples,
											 deUint32 bufferUsage,
											 CaseType caseType,
											 ResultType resultType,
											 int flags = 0);

						~BasicUploadCase	(void);

	virtual void		init				(void);
	virtual void		deinit				(void);

private:
	bool				runSample			(int iteration, UploadSampleResult<SampleType>& sample);
	void				createBuffer		(int bufferSize, int iteration);
	void				deleteBuffer		(int bufferSize);
	void				useBuffer			(int bufferSize);

	virtual void		testBufferUpload	(UploadSampleResult<SampleType>& result, int writeSize) = 0;
	void				logAndSetTestResult	(const std::vector<UploadSampleResult<SampleType> >& results);

	deUint32			m_dummyBufferID;

protected:
	const CaseType		m_caseType;
	const ResultType	m_resultType;
	const deUint32		m_bufferUsage;
	const bool			m_logBufferInfo;
	const bool			m_bufferUnspecifiedContent;
	std::vector<deUint8> m_zeroData;

	using BasicBufferCase<SampleType>::m_testCtx;
	using BasicBufferCase<SampleType>::m_context;

	using BasicBufferCase<SampleType>::DUMMY_RENDER_AREA_SIZE;
	using BasicBufferCase<SampleType>::m_dummyProgram;
	using BasicBufferCase<SampleType>::m_dummyProgramPosLoc;
	using BasicBufferCase<SampleType>::m_bufferID;
	using BasicBufferCase<SampleType>::m_numSamples;
	using BasicBufferCase<SampleType>::m_bufferSizeMin;
	using BasicBufferCase<SampleType>::m_bufferSizeMax;
	using BasicBufferCase<SampleType>::m_allocateLargerBuffer;
};

template <typename SampleType>
BasicUploadCase<SampleType>::BasicUploadCase (Context& context, const char* name, const char* desc, int bufferSizeMin, int bufferSizeMax, int numSamples, deUint32 bufferUsage, CaseType caseType, ResultType resultType, int flags)
	: BasicBufferCase<SampleType>	(context, name, desc, bufferSizeMin, bufferSizeMax, numSamples, (caseType == CASE_USED_LARGER_BUFFER) ? (BasicBufferCase<SampleType>::FLAG_ALLOCATE_LARGER_BUFFER) : (0))
	, m_dummyBufferID				(0)
	, m_caseType					(caseType)
	, m_resultType					(resultType)
	, m_bufferUsage					(bufferUsage)
	, m_logBufferInfo				((flags & FLAG_DONT_LOG_BUFFER_INFO) == 0)
	, m_bufferUnspecifiedContent	((flags & FLAG_RESULT_BUFFER_UNSPECIFIED_CONTENT) != 0)
	, m_zeroData					()
{
	DE_ASSERT(m_caseType < CASE_LAST);
}

template <typename SampleType>
BasicUploadCase<SampleType>::~BasicUploadCase (void)
{
	deinit();
}

template <typename SampleType>
void BasicUploadCase<SampleType>::init (void)
{
	const glw::Functions& gl = m_context.getRenderContext().getFunctions();

	BasicBufferCase<SampleType>::init();

	// zero buffer as upload source
	m_zeroData.resize(m_bufferSizeMax, 0x00);

	// dummy buffer

	gl.genBuffers(1, &m_dummyBufferID);
	GLU_EXPECT_NO_ERROR(gl.getError(), "Gen buf");

	// log basic info

	m_testCtx.getLog()
		<< tcu::TestLog::Message
		<< "Testing performance with " << m_numSamples << " test samples. Sample order is randomized. All samples at even positions (first = 0) are tested before samples at odd positions.\n"
		<< "Buffer sizes are in range [" << getHumanReadableByteSize(m_bufferSizeMin) << ", " << getHumanReadableByteSize(m_bufferSizeMax) << "]."
		<< tcu::TestLog::EndMessage;

	if (m_logBufferInfo)
	{
		switch (m_caseType)
		{
			case CASE_NO_BUFFERS:
				break;

			case CASE_NEW_BUFFER:
				m_testCtx.getLog() << tcu::TestLog::Message << "Target buffer is generated but not specified (i.e glBufferData() not called)." << tcu::TestLog::EndMessage;
				break;

			case CASE_UNSPECIFIED_BUFFER:
				m_testCtx.getLog() << tcu::TestLog::Message << "Target buffer is allocated with glBufferData(NULL)." << tcu::TestLog::EndMessage;
				break;

			case CASE_SPECIFIED_BUFFER:
				m_testCtx.getLog() << tcu::TestLog::Message << "Target buffer contents are specified prior testing with glBufferData(data)." << tcu::TestLog::EndMessage;
				break;

			case CASE_USED_BUFFER:
				m_testCtx.getLog() << tcu::TestLog::Message << "Target buffer has been used in drawing before testing." << tcu::TestLog::EndMessage;
				break;

			case CASE_USED_LARGER_BUFFER:
				m_testCtx.getLog() << tcu::TestLog::Message << "Target buffer is larger and has been used in drawing before testing." << tcu::TestLog::EndMessage;
				break;

			default:
				DE_ASSERT(false);
				break;
		}
	}

	if (m_resultType == RESULT_MEDIAN_TRANSFER_RATE)
		m_testCtx.getLog() << tcu::TestLog::Message << "Test result is the median transfer rate of the test samples." << tcu::TestLog::EndMessage;
	else if (m_resultType == RESULT_ASYMPTOTIC_TRANSFER_RATE)
		m_testCtx.getLog() << tcu::TestLog::Message << "Test result is the asymptotic transfer rate as the buffer size approaches infinity." << tcu::TestLog::EndMessage;
	else
		DE_ASSERT(false);
}

template <typename SampleType>
void BasicUploadCase<SampleType>::deinit (void)
{
	if (m_dummyBufferID)
	{
		m_context.getRenderContext().getFunctions().deleteBuffers(1, &m_dummyBufferID);
		m_dummyBufferID = 0;
	}

	m_zeroData = std::vector<deUint8>();

	BasicBufferCase<SampleType>::deinit();
}

template <typename SampleType>
bool BasicUploadCase<SampleType>::runSample (int iteration, UploadSampleResult<SampleType>& sample)
{
	const glw::Functions&	gl					= m_context.getRenderContext().getFunctions();
	const int				allocatedBufferSize	= sample.allocatedSize;
	const int				bufferSize			= sample.bufferSize;

	if (m_caseType != CASE_NO_BUFFERS)
		createBuffer(iteration, allocatedBufferSize);

	// warmup CPU before the test to make sure the power management governor
	// keeps us in the "high performance" mode
	{
		deYield();
		tcu::warmupCPU();
		deYield();
	}

	testBufferUpload(sample, bufferSize);
	GLU_EXPECT_NO_ERROR(gl.getError(), "Buffer upload sample");

	if (m_caseType != CASE_NO_BUFFERS)
		deleteBuffer(bufferSize);

	return true;
}

template <typename SampleType>
void BasicUploadCase<SampleType>::createBuffer (int iteration, int bufferSize)
{
	DE_ASSERT(!m_bufferID);
	DE_ASSERT(m_caseType != CASE_NO_BUFFERS);

	const glw::Functions& gl = m_context.getRenderContext().getFunctions();

	// create buffer

	if (m_caseType == CASE_NO_BUFFERS)
		return;

	// create empty buffer

	gl.genBuffers(1, &m_bufferID);
	gl.bindBuffer(GL_ARRAY_BUFFER, m_bufferID);
	GLU_EXPECT_NO_ERROR(gl.getError(), "Buffer gen");

	if (m_caseType == CASE_NEW_BUFFER)
	{
		// upload something else first, this should reduce noise in samples

		de::Random					rng				(0xbadc * iteration);
		const int					sizeDelta		= rng.getInt(0, 2097140);
		const int					dummyUploadSize = deAlign32(1048576 + sizeDelta, 4*4); // Vary buffer size to make sure it is always reallocated
		const std::vector<deUint8>	dummyData		(dummyUploadSize, 0x20);

		gl.bindBuffer(GL_ARRAY_BUFFER, m_dummyBufferID);
		gl.bufferData(GL_ARRAY_BUFFER, dummyUploadSize, &dummyData[0], m_bufferUsage);

		// make sure upload won't interfere with the test
		useBuffer(dummyUploadSize);

		// don't kill the buffer so that the following upload cannot potentially reuse the buffer

		return;
	}

	// specify it

	if (m_caseType == CASE_UNSPECIFIED_BUFFER)
		gl.bufferData(GL_ARRAY_BUFFER, bufferSize, DE_NULL, m_bufferUsage);
	else
	{
		const std::vector<deUint8> dummyData(bufferSize, 0x20);
		gl.bufferData(GL_ARRAY_BUFFER, bufferSize, &dummyData[0], m_bufferUsage);
	}

	if (m_caseType == CASE_UNSPECIFIED_BUFFER || m_caseType == CASE_SPECIFIED_BUFFER)
		return;

	// use it and make sure it is uploaded

	useBuffer(bufferSize);
	DE_ASSERT(m_caseType == CASE_USED_BUFFER || m_caseType == CASE_USED_LARGER_BUFFER);
}

template <typename SampleType>
void BasicUploadCase<SampleType>::deleteBuffer (int bufferSize)
{
	DE_ASSERT(m_bufferID);
	DE_ASSERT(m_caseType != CASE_NO_BUFFERS);

	// render from the buffer to make sure it actually made it to the gpu. This is to
	// make sure that if the upload actually happens later or is happening right now in
	// the background, it will not interfere with further test runs

	// if buffer contains unspecified content, sourcing data from it results in undefined
	// results, possibly including program termination. Specify all data to prevent such
	// case from happening

	const glw::Functions& gl = m_context.getRenderContext().getFunctions();

	gl.bindBuffer(GL_ARRAY_BUFFER, m_bufferID);

	if (m_bufferUnspecifiedContent)
	{
		const std::vector<deUint8> dummyData(bufferSize, 0x20);
		gl.bufferData(GL_ARRAY_BUFFER, bufferSize, &dummyData[0], m_bufferUsage);

		GLU_EXPECT_NO_ERROR(gl.getError(), "re-specify buffer");
	}

	useBuffer(bufferSize);

	gl.deleteBuffers(1, &m_bufferID);
	m_bufferID = 0;
}

template <typename SampleType>
void BasicUploadCase<SampleType>::useBuffer (int bufferSize)
{
	const glw::Functions