fractorium/Source/EmberCL/RendererCL.h
2023-12-05 11:15:38 +00:00

275 lines
12 KiB
C++

#pragma once
#include "EmberCLPch.h"
#include "OpenCLWrapper.h"
#include "DEOpenCLKernelCreator.h"
#include "FinalAccumOpenCLKernelCreator.h"
#include "RendererClDevice.h"
/// <summary>
/// RendererCLBase and RendererCL classes.
/// </summary>
namespace EmberCLns
{
/// <summary>
/// Serves only as an interface for OpenCL specific rendering functions.
/// </summary>
class EMBERCL_API RendererCLBase
{
public:
virtual ~RendererCLBase() { }
virtual bool ReadFinal(v4F* pixels) { return false; }
virtual bool ClearFinal() { return false; }
virtual bool AnyNvidia() const noexcept { return false; }
bool OptAffine() const noexcept { return m_OptAffine; }
void OptAffine(bool optAffine) noexcept { m_OptAffine = optAffine; }
std::function<void(void)> m_CompileBegun;
protected:
bool m_OptAffine = false;
};
/// <summary>
/// RendererCL is a derivation of the basic CPU renderer which
/// overrides various functions to render on the GPU using OpenCL.
/// This supports multi-GPU rendering and is done in the following manner:
/// -When rendering a single image, the iterations will be split between devices in sub batches.
/// -When animating, a renderer for each device will be created by the calling code,
/// and the frames will each be rendered by a single device as available.
/// The synchronization across devices is done through a single atomic counter.
/// Since this class derives from EmberReport and also contains an
/// OpenCLWrapper member which also derives from EmberReport, the
/// reporting functions are overridden to aggregate the errors from
/// both sources.
/// Template argument T expected to be float or double.
/// Template argument bucketT must always be float.
/// </summary>
template <typename T, typename bucketT>
class EMBERCL_API RendererCL : public Renderer<T, bucketT>, public RendererCLBase
{
using EmberNs::Renderer<T, bucketT>::RendererBase::Abort;
using EmberNs::Renderer<T, bucketT>::RendererBase::EarlyClip;
using EmberNs::Renderer<T, bucketT>::RendererBase::EnterResize;
using EmberNs::Renderer<T, bucketT>::RendererBase::LeaveResize;
using EmberNs::Renderer<T, bucketT>::RendererBase::FinalRasW;
using EmberNs::Renderer<T, bucketT>::RendererBase::FinalRasH;
using EmberNs::Renderer<T, bucketT>::RendererBase::SuperRasW;
using EmberNs::Renderer<T, bucketT>::RendererBase::SuperRasH;
using EmberNs::Renderer<T, bucketT>::RendererBase::SuperSize;
using EmberNs::Renderer<T, bucketT>::RendererBase::BytesPerChannel;
using EmberNs::Renderer<T, bucketT>::RendererBase::TemporalSamples;
using EmberNs::Renderer<T, bucketT>::RendererBase::ItersPerTemporalSample;
using EmberNs::Renderer<T, bucketT>::RendererBase::FuseCount;
using EmberNs::Renderer<T, bucketT>::RendererBase::DensityFilterOffset;
using EmberNs::Renderer<T, bucketT>::RendererBase::PrepFinalAccumVector;
using EmberNs::Renderer<T, bucketT>::RendererBase::Paused;
using EmberNs::Renderer<T, bucketT>::RendererBase::m_ProgressParameter;
using EmberNs::Renderer<T, bucketT>::RendererBase::m_YAxisUp;
using EmberNs::Renderer<T, bucketT>::RendererBase::m_LockAccum;
using EmberNs::Renderer<T, bucketT>::RendererBase::m_Abort;
using EmberNs::Renderer<T, bucketT>::RendererBase::m_LastIter;
using EmberNs::Renderer<T, bucketT>::RendererBase::m_LastIterPercent;
using EmberNs::Renderer<T, bucketT>::RendererBase::m_Stats;
using EmberNs::Renderer<T, bucketT>::RendererBase::m_Callback;
using EmberNs::Renderer<T, bucketT>::RendererBase::m_Rand;
using EmberNs::Renderer<T, bucketT>::RendererBase::m_RenderTimer;
using EmberNs::Renderer<T, bucketT>::RendererBase::m_IterTimer;
using EmberNs::Renderer<T, bucketT>::RendererBase::m_ProgressTimer;
using EmberNs::Renderer<T, bucketT>::RendererBase::EmberReport::AddToReport;
using EmberNs::Renderer<T, bucketT>::RendererBase::m_ResizeCs;
using EmberNs::Renderer<T, bucketT>::RendererBase::m_ProcessAction;
using EmberNs::Renderer<T, bucketT>::m_RotMat;
using EmberNs::Renderer<T, bucketT>::m_Ember;
using EmberNs::Renderer<T, bucketT>::m_Csa;
using EmberNs::Renderer<T, bucketT>::m_CurvesSet;
using EmberNs::Renderer<T, bucketT>::CenterX;
using EmberNs::Renderer<T, bucketT>::CenterY;
using EmberNs::Renderer<T, bucketT>::K1;
using EmberNs::Renderer<T, bucketT>::K2;
using EmberNs::Renderer<T, bucketT>::Supersample;
using EmberNs::Renderer<T, bucketT>::HighlightPower;
using EmberNs::Renderer<T, bucketT>::HistBuckets;
using EmberNs::Renderer<T, bucketT>::AccumulatorBuckets;
using EmberNs::Renderer<T, bucketT>::GetDensityFilter;
using EmberNs::Renderer<T, bucketT>::GetSpatialFilter;
using EmberNs::Renderer<T, bucketT>::CoordMap;
using EmberNs::Renderer<T, bucketT>::XformDistributions;
using EmberNs::Renderer<T, bucketT>::XformDistributionsSize;
using EmberNs::Renderer<T, bucketT>::m_Dmap;
using EmberNs::Renderer<T, bucketT>::m_DensityFilter;
using EmberNs::Renderer<T, bucketT>::m_SpatialFilter;
public:
RendererCL(const vector<pair<size_t, size_t>>& devices, bool shared = false, GLuint outputTexID = 0);
RendererCL(const RendererCL<T, bucketT>& renderer) = delete;
RendererCL<T, bucketT>& operator = (const RendererCL<T, bucketT>& renderer) = delete;
virtual ~RendererCL() = default;
//Non-virtual member functions for OpenCL specific tasks.
bool Init(const vector<pair<size_t, size_t>>& devices, bool shared, GLuint outputTexID);
bool SetOutputTexture(GLuint outputTexID);
//Iters per kernel/block/grid.
inline size_t IterCountPerKernel() const noexcept;
inline size_t IterCountPerBlock() const noexcept;
inline size_t IterCountPerGrid() const noexcept;
//Kernels per block.
inline size_t IterBlockKernelWidth() const noexcept;
inline size_t IterBlockKernelHeight() const noexcept;
inline size_t IterBlockKernelCount() const noexcept;
//Kernels per grid.
inline size_t IterGridKernelWidth() const noexcept;
inline size_t IterGridKernelHeight() const noexcept;
inline size_t IterGridKernelCount() const noexcept;
//Blocks per grid.
inline size_t IterGridBlockWidth() const noexcept;
inline size_t IterGridBlockHeight() const noexcept;
inline size_t IterGridBlockCount() const noexcept;
//Allow for changing the number of blocks in each dimension of the grid.
void IterBlocksWide(size_t w) noexcept;
void IterBlocksHigh(size_t h) noexcept;
bool ReadHist(size_t device);
bool ReadAccum();
bool ReadPoints(size_t device, vector<PointCL<T>>& vec);
bool ClearHist();
bool ClearHist(size_t device);
bool ClearAccum();
bool WritePoints(size_t device, vector<PointCL<T>>& vec);
#ifdef TEST_CL
bool WriteRandomPoints(size_t device);
#endif
void InitStateVec();
void SubBatchPercentPerThread(float f);
float SubBatchPercentPerThread() const;
const string& IterKernel() const;
const string& DEKernel() const;
const string& FinalAccumKernel() const;
//Access to underlying OpenCL structures. Use cautiously.
const vector<unique_ptr<RendererClDevice>>& Devices() const;
//Virtual functions overridden from RendererCLBase.
virtual bool ReadFinal(v4F* pixels);
virtual bool ClearFinal();
//Public virtual functions overridden from Renderer or RendererBase.
size_t MemoryAvailable() override;
bool Ok() const override;
size_t SubBatchSize() const override;
size_t ThreadCount() const override;
bool CreateDEFilter(bool& newAlloc) override;
bool CreateSpatialFilter(bool& newAlloc) override;
eRendererType RendererType() const override;
bool Shared() const override;
void ClearErrorReport() noexcept override;
string ErrorReportString() override;
vector<string> ErrorReport() override;
bool RandVec(vector<QTIsaac<ISAAC_SIZE, ISAAC_INT>>& randVec) override;
bool AnyNvidia() const noexcept override;
#ifndef TEST_CL
protected:
#endif
//Protected virtual functions overridden from Renderer.
bool Alloc(bool histOnly = false) override;
bool ResetBuckets(bool resetHist = true, bool resetAccum = true) override;
eRenderStatus LogScaleDensityFilter(bool forceOutput = false) override;
eRenderStatus GaussianDensityFilter() override;
eRenderStatus AccumulatorToFinalImage(vector<v4F>& pixels, size_t finalOffset) override;
EmberStats Iterate(size_t iterCount, size_t temporalSample) override;
#ifndef TEST_CL
private:
#endif
//Private functions for making and running OpenCL programs.
bool BuildIterProgramForEmber(bool doAccum = true);
bool RunIter(size_t iterCount, size_t temporalSample, size_t& itersRan);
eRenderStatus RunLogScaleFilter();
eRenderStatus RunDensityFilter();
eRenderStatus RunFinalAccum();
bool ClearBuffer(size_t device, const string& bufferName, uint width, uint height, uint elementSize);
bool RunDensityFilterPrivate(size_t kernelIndex, size_t gridW, size_t gridH, size_t blockW, size_t blockH, uint chunkSizeW, uint chunkSizeH, uint colChunkPass, uint rowChunkPass);
int MakeAndGetDensityFilterProgram(size_t ss, uint filterWidth);
int MakeAndGetFinalAccumProgram();
int MakeAndGetGammaCorrectionProgram();
bool CreateHostBuffer();
bool SumDeviceHist();
void FillSeeds();
//Private functions passing data to OpenCL programs.
void ConvertDensityFilter();
void ConvertSpatialFilter();
void ConvertEmber(Ember<T>& ember, EmberCL<T>& emberCL, vector<XformCL<T>>& xformsCL);
void ConvertCarToRas(const CarToRas<T>& carToRas);
std::string ErrorStr(const std::string& loc, const std::string& error, RendererClDevice* dev);
bool m_Init = false;
bool m_Shared = false;
bool m_DoublePrecision = typeid(T) == typeid(double);
float m_SubBatchPercentPerThread = 0.025f;//0.025 * 10,240 gives a default value of 256 iters per thread for the default sub batch size of 10,240 which almost all flames will use.
//It's critical that these numbers never change. They are
//based on the cuburn model of each kernel launch containing
//256 threads. 32 wide by 8 high. Everything done in the OpenCL
//iteraion kernel depends on these dimensions.
size_t m_IterCountPerKernel = 256;
size_t m_IterBlocksWide = 64, m_IterBlockWidth = 32;
size_t m_IterBlocksHigh = 2, m_IterBlockHeight = 8;
size_t m_MaxDEBlockSizeW;
size_t m_MaxDEBlockSizeH;
//Buffer names.
string m_EmberBufferName = "Ember";
string m_XformsBufferName = "Xforms";
string m_ParVarsBufferName = "ParVars";
string m_GlobalSharedBufferName = "GlobalShared";
string m_SeedsBufferName = "Seeds";
string m_DistBufferName = "Dist";
string m_CarToRasBufferName = "CarToRas";
string m_DEFilterParamsBufferName = "DEFilterParams";
string m_SpatialFilterParamsBufferName = "SpatialFilterParams";
string m_DECoefsBufferName = "DECoefs";
string m_DEWidthsBufferName = "DEWidths";
string m_DECoefIndicesBufferName = "DECoefIndices";
string m_SpatialFilterCoefsBufferName = "SpatialFilterCoefs";
string m_CurvesCsaName = "CurvesCsa";
string m_HostBufferName = "Host";
string m_HistBufferName = "Hist";
string m_AccumBufferName = "Accum";
string m_FinalImageName = "Final";
string m_PointsBufferName = "Points";
#ifdef KNL_USE_GLOBAL_CONSEC
string m_ConsecBufferName = "Consec";
#endif
string m_VarStateBufferName = "VarState";
//Kernels.
string m_IterKernel;
cl::ImageFormat m_PaletteFormat;
cl::ImageFormat m_FinalFormat;
cl::Image2D m_Palette;
cl::ImageGL m_AccumImage;
GLuint m_OutputTexID;
EmberCL<T> m_EmberCL;
vector<XformCL<T>> m_XformsCL;
vector<vector<glm::highp_uvec2>> m_Seeds;
CarToRasCL<T> m_CarToRasCL;
DensityFilterCL<bucketT> m_DensityFilterCL;
SpatialFilterCL<bucketT> m_SpatialFilterCL;
IterOpenCLKernelCreator<T> m_IterOpenCLKernelCreator;
DEOpenCLKernelCreator m_DEOpenCLKernelCreator;
FinalAccumOpenCLKernelCreator m_FinalAccumOpenCLKernelCreator;
pair<string, vector<T>> m_Params;
pair<string, vector<T>> m_GlobalShared;
vector<T> m_VarStates;
vector<unique_ptr<RendererClDevice>> m_Devices;
Ember<T> m_LastBuiltEmber;
};
}