private void initGLAndCuda()
{
//Create render target control
m_renderControl = new OpenTK.GLControl(GraphicsMode.Default, 1, 0, GraphicsContextFlags.Default);
m_renderControl.Dock = DockStyle.Fill;
m_renderControl.BackColor = Color.White;
m_renderControl.BorderStyle = BorderStyle.FixedSingle;
m_renderControl.KeyDown += new KeyEventHandler(m_renderControl_KeyDown);
m_renderControl.MouseMove += new MouseEventHandler(m_renderControl_MouseMove);
m_renderControl.MouseDown += new MouseEventHandler(m_renderControl_MouseDown);
m_renderControl.SizeChanged += new EventHandler(m_renderControl_SizeChanged);
panel1.Controls.Add(m_renderControl);
Console.WriteLine(" OpenGL device is Available");
int deviceID = CudaContext.GetMaxGflopsDeviceId();
ctx = CudaContext.CreateOpenGLContext(deviceID, CUCtxFlags.BlockingSync);
string console = string.Format("CUDA device [{0}] has {1} Multi-Processors", ctx.GetDeviceName(), ctx.GetDeviceInfo().MultiProcessorCount);
Console.WriteLine(console);
CUmodule module = ctx.LoadModulePTX("kernel.ptx");
addForces_k = new CudaKernel("addForces_k", module, ctx);
advectVelocity_k = new CudaKernel("advectVelocity_k", module, ctx);
diffuseProject_k = new CudaKernel("diffuseProject_k", module, ctx);
updateVelocity_k = new CudaKernel("updateVelocity_k", module, ctx);
advectParticles_k = new CudaKernel("advectParticles_OGL", module, ctx);
hvfield = new cData[DS];
dvfield = new CudaPitchedDeviceVariable<cData>(DIM, DIM);
tPitch = dvfield.Pitch;
dvfield.CopyToDevice(hvfield);
vxfield = new CudaDeviceVariable<cData>(DS);
vyfield = new CudaDeviceVariable<cData>(DS);
// Create particle array
particles = new cData[DS];
initParticles(particles, DIM, DIM);
// TODO: update kernels to use the new unpadded memory layout for perf
// rather than the old FFTW-compatible layout
planr2c = new CudaFFTPlan2D(DIM, DIM, cufftType.R2C, Compatibility.FFTWPadding);
planc2r = new CudaFFTPlan2D(DIM, DIM, cufftType.C2R, Compatibility.FFTWPadding);
GL.GenBuffers(1, out vbo);
GL.BindBuffer(BufferTarget.ArrayBuffer, vbo);
GL.BufferData<cData>(BufferTarget.ArrayBuffer, new IntPtr(cData.SizeOf * DS), particles, BufferUsageHint.DynamicDraw);
int bsize;
GL.GetBufferParameter(BufferTarget.ArrayBuffer, BufferParameterName.BufferSize, out bsize);
if (bsize != DS * cData.SizeOf)
throw new Exception("Sizes don't match.");
GL.BindBuffer(BufferTarget.ArrayBuffer, 0);
cuda_vbo_resource = new CudaGraphicsInteropResourceCollection();
cuda_vbo_resource.Add(new CudaOpenGLBufferInteropResource(vbo, CUGraphicsRegisterFlags.None));
texref = new CudaTextureArray2D(advectVelocity_k, "texref", CUAddressMode.Wrap, CUFilterMode.Linear, 0, CUArrayFormat.Float, DIM, DIM, CudaArray2DNumChannels.Two);
stopwatch = new CudaStopWatch(CUEventFlags.Default);
reshape();
isInit = true;
display();
}
public GPU_Functionality(int deviceID = 0)
{
ctx = new CudaContext(deviceID);
version = ctx.GetDeviceComputeCapability();
Trace.WriteLine($"cuda compute capability {version.Major}.{version.Minor}");
CUmodule collision_module = ctx.LoadModulePTX("collision_kernels.ptx");
kNarrowPhase = new CudaKernel("kNarrowPhase_new", collision_module, ctx);
kFindClosestFace = new CudaKernel("kFindClosestFace", collision_module, ctx);
kCollisionResponseForce = new CudaKernel("kCollisionResponseForce", collision_module, ctx);
dim3 block = new dim3(block_size, 1, 1);
kNarrowPhase.BlockDimensions = block;
kFindClosestFace.BlockDimensions = block;
kCollisionResponseForce.BlockDimensions = block;
// cz
CUmodule module_cz_kernels = ctx.LoadModulePTX("cz_kernels.ptx");
kczCZForce = new CudaKernel("kczCZForce", module_cz_kernels, ctx);
kczCZForce.BlockDimensions = block;
// elem
CUmodule module_elem_kernels = ctx.LoadModulePTX("elem_kernels.ptx");
kelElementElasticityForce = new CudaKernel("kelElementElasticityForce", module_elem_kernels, ctx);
kelElementElasticityForce.BlockDimensions = block;
}
private void Form1_Load(object sender, EventArgs e)
{
ctx = new PrimaryContext();
ctx.SetCurrent();
modPRelu = ctx.LoadModulePTX("PRelu.ptx");
modDeBayer = ctx.LoadModulePTX("DeBayer.ptx");
modColor = ctx.LoadModulePTX("ImageColorProcessing.ptx");
createBayerKernel = new CreateBayerWithNoiseKernel(ctx, modDeBayer);
deBayerGreenKernel = new DeBayerGreenKernel(modDeBayer, ctx);
deBayerRedBlueKernel = new DeBayerRedBlueKernel(modDeBayer, ctx);
setupCurandKernel = new SetupCurandKernel(ctx, modDeBayer);
highlightRecoveryKernel = new HighlightRecoveryKernel(modColor, ctx);
camToXYZKernel = new ConvertCamToXYZKernel(modColor, ctx);
convertRGBTosRGBKernel = new ConvertRGBTosRGBKernel(modColor, ctx);
//constant variable is set for the entire module!
createBayerKernel.BayerPattern = new BayerColor[] { BayerColor.Red, BayerColor.Green, BayerColor.Green, BayerColor.Blue };
//If you do not have CUDNN, set the last parameter to false (use NPP instead)
denoiseAndDemoisaic = new DenoiseAndDemoisaic(TileSize, ctx, modPRelu, true);
CuRandStates = new CudaDeviceVariable <byte>(TileSize * TileSize * 48); //one state has the size of 48 bytes
setupCurandKernel.RunSafe(CuRandStates, TileSize * TileSize);
tile = new NPPImage_32fC3(TileSize, TileSize);
cmb_IsoValue.SelectedIndex = 0;
}
public GpuMathOperations()
{
this.cuBlas = new CudaBlas();
this.cudaContext = new CudaContext();
this.cuModule = cudaContext.LoadModulePTX(kernalFile);
this.maxThreadPerBlockDim = (int)Math.Sqrt(this.cudaContext.GetDeviceInfo().MaxThreadsPerBlock);
}
static void InitKernels()
{
cntxt = new CudaContext();
CUmodule cumodule = cntxt.LoadModulePTX(@"C:\work\Sobel\CudaTest\x64\Debug\kernel.ptx");
matrixSumCude = new CudaKernel("_Z15matrixSumKernelPdPKdiii", cumodule, cntxt);
}
public void JITcompile()
{
tic();
foreach (var sc in sourceCodes)
{
if (sc.ptx == null)
{
sc.mod = ctx.LoadModulePTX(sc.cubin, null, null); // no jit, just upload
}
else
{
sc.mod = ctx.LoadModulePTX(sc.ptx); //jit
}
}
SampleCollector.Collect("JIT", toc());
}
public GrabCutGMM()
{
ctx = new CudaContext(CudaContext.GetMaxGflopsDeviceId(), false);
//Load Kernel image from resources
string resName;
if (IntPtr.Size == 8)
{
resName = "GrabCutGMM_x64.ptx";
}
else
{
resName = "GrabCutGMM.ptx";
}
string resNamespace = "GrabCutNPP";
string resource = resNamespace + "." + resName;
Stream stream = Assembly.GetExecutingAssembly().GetManifestResourceStream(resource);
if (stream == null)
{
throw new ArgumentException("Kernel not found in resources.");
}
byte[] kernel = new byte[stream.Length];
int bytesToRead = (int)stream.Length;
while (bytesToRead > 0)
{
bytesToRead -= stream.Read(kernel, (int)stream.Position, bytesToRead);
}
CUmodule module = ctx.LoadModulePTX(kernel);
GMMReductionKernelCreateGmmFlags = new CudaKernel("_Z18GMMReductionKernelILi4ELb1EEviPfiPK6uchar4iPhiiiPj", module, ctx);
GMMReductionKernelNoCreateGmmFlags = new CudaKernel("_Z18GMMReductionKernelILi4ELb0EEviPfiPK6uchar4iPhiiiPj", module, ctx);
GMMFinalizeKernelInvertSigma = new CudaKernel("_Z17GMMFinalizeKernelILi4ELb1EEvPfS0_ii", module, ctx);
GMMFinalizeKernelNoInvertSigma = new CudaKernel("_Z17GMMFinalizeKernelILi4ELb0EEvPfS0_ii", module, ctx);
GMMcommonTerm = new CudaKernel("_Z13GMMcommonTermiPfi", module, ctx);
DataTermKernel = new CudaKernel("_Z14DataTermKernelPiiiPKfiPK6uchar4iPKhiii", module, ctx);
GMMAssignKernel = new CudaKernel("_Z15GMMAssignKerneliPKfiPK6uchar4iPhiii", module, ctx);
GMMFindSplit = new CudaKernel("_Z12GMMFindSplitP10GMMSplit_tiPfi", module, ctx);
GMMDoSplit = new CudaKernel("_Z10GMMDoSplitPK10GMMSplit_tiPfiPK6uchar4iPhiii", module, ctx);
MeanEdgeStrengthReductionKernel = new CudaKernel("_Z31MeanEdgeStrengthReductionKerneliiPf", module, ctx);
MeanEdgeStrengthFinalKernel = new CudaKernel("_Z27MeanEdgeStrengthFinalKernelPfi", module, ctx);
EdgeCuesKernel = new CudaKernel("_Z14EdgeCuesKernelfPKfPiS1_S1_S1_S1_S1_S1_S1_iiii", module, ctx);
SegmentationChangedKernel = new CudaKernel("_Z25SegmentationChangedKernelPiPhS0_iii", module, ctx);
downscaleKernel1 = new CudaKernel("_Z18downscaleKernelBoxI6uchar4EvPT_iiiPKS1_iii", module, ctx);
downscaleKernel2 = new CudaKernel("_Z18downscaleKernelMaxIhEvPT_iiiPKS0_iii", module, ctx);
upsampleAlphaKernel = new CudaKernel("_Z19upsampleAlphaKernelPhS_iiii", module, ctx);
GMMFinalizeKernelInvertSigma.SetConstantVariable("det_indices", det_indices);
GMMFinalizeKernelInvertSigma.SetConstantVariable("inv_indices", inv_indices);
GMMFinalizeKernelNoInvertSigma.SetConstantVariable("det_indices", det_indices);
GMMFinalizeKernelNoInvertSigma.SetConstantVariable("inv_indices", inv_indices);
}
private void InitializeCUDA()
{
context = new CudaContext(CudaContext.GetMaxGflopsDevice(), graphicsDevice.ComPointer, CUCtxFlags.SchedAuto, CudaContext.DirectXVersion.D3D11);
module = context.LoadModulePTX(@"Kernels\kernel.ptx");
kernelPositionWeightNoiseCube = new CudaKernel("position_weight_noise_cube", module, context);
kernelNormalAmbient = new CudaKernel("normal_ambient", module, context);
kernelMarchingCubesCases = new CudaKernel("marching_cubes_cases", module, context);
kernelMarchingCubesVertices = new CudaKernel("marching_cubes_vertices", module, context);
kernelPositionWeightNoiseCubeWarp = new CudaKernel("position_weight_noise_cube_warp", module, context);
kernelPositionWeightFormula = new CudaKernel("position_weight_formula", module, context);
prefixScan = new CUDAPrefixScan(module, context);
}
public OpticalFlow(int width, int height, CudaContext ctx)
{
CUmodule mod = ctx.LoadModulePTX("opticalFlow.ptx");
warpingKernel = new WarpingKernel(ctx, mod);
createFlowFieldFromTiles = new CreateFlowFieldFromTiles(ctx, mod);
computeDerivativesKernel = new ComputeDerivativesKernel(ctx, mod);
lukasKanade = new LukasKanadeKernel(ctx, mod);
d_tmp = new NPPImage_32fC1(width, height);
d_Ix = new NPPImage_32fC1(width, height);
d_Iy = new NPPImage_32fC1(width, height);
d_Iz = new NPPImage_32fC1(width, height);
d_flow = new NPPImage_32fC2(width, height);
buffer = new CudaDeviceVariable <byte>(d_tmp.MeanStdDevGetBufferHostSize() * 3);
mean = new CudaDeviceVariable <double>(1);
std = new CudaDeviceVariable <double>(1);
d_filterX = new float[] { -0.25f, 0.25f, -0.25f, 0.25f };
d_filterY = new float[] { -0.25f, -0.25f, 0.25f, 0.25f };
d_filterT = new float[] { 0.25f, 0.25f, 0.25f, 0.25f };
}
public ShiftCollection(int aFrameCount, int aMaxTileCountX, int aMaxTileCountY, int aReferenceIndex, TrackingStrategy aStrategy, int aBlockSize, CudaContext ctx)
{
strategy = aStrategy;
referenceIndex = aReferenceIndex;
frameCount = aFrameCount;
if (aBlockSize >= aFrameCount)
{
blockSize = aFrameCount - 1;
}
else
{
blockSize = aBlockSize;
}
blas = new CudaBlas(PointerMode.Device, AtomicsMode.Allowed);
one = 1.0f;
zero = 0.0f;
shiftPairs = new List <ShiftPair>();
int shiftCount = GetShiftCount();
FillShiftPairs();
FillIndexTable();
if (shiftPairs.Count != shiftCount)
{
throw new Exception("Ooups, something went wrong with my math...");
}
shifts = new List <NPPImage_32fC2>(shiftCount);
int[] shiftPitches_h = new int[shiftCount];
CUdeviceptr[] ptrList = new CUdeviceptr[shiftCount];
for (int i = 0; i < shiftCount; i++)
{
NPPImage_32fC2 devVar = new NPPImage_32fC2(aMaxTileCountX, aMaxTileCountY);
shifts.Add(devVar);
shiftPitches_h[i] = devVar.Pitch;
ptrList[i] = devVar.DevicePointer;
}
shiftPitches = shiftPitches_h;
AllShifts_d = new CudaDeviceVariable <float2>(aMaxTileCountX * aMaxTileCountY * shiftCount);
shiftsOneToOne_d = new CudaDeviceVariable <float2>(aMaxTileCountX * aMaxTileCountY * (frameCount - 1));
shifts_d = ptrList;
status = new CudaDeviceVariable <int>(aMaxTileCountX * aMaxTileCountY);
infoInverse = new CudaDeviceVariable <int>(aMaxTileCountX * aMaxTileCountY);
shiftMatrixArray = new CudaDeviceVariable <CUdeviceptr>(aMaxTileCountX * aMaxTileCountY);
shiftMatrixSafeArray = new CudaDeviceVariable <CUdeviceptr>(aMaxTileCountX * aMaxTileCountY);
matrixSquareArray = new CudaDeviceVariable <CUdeviceptr>(aMaxTileCountX * aMaxTileCountY);
matrixInvertedArray = new CudaDeviceVariable <CUdeviceptr>(aMaxTileCountX * aMaxTileCountY);
solvedMatrixArray = new CudaDeviceVariable <CUdeviceptr>(aMaxTileCountX * aMaxTileCountY);
shiftOneToOneArray = new CudaDeviceVariable <CUdeviceptr>(aMaxTileCountX * aMaxTileCountY);
shiftMeasuredArray = new CudaDeviceVariable <CUdeviceptr>(aMaxTileCountX * aMaxTileCountY);
shiftOptimArray = new CudaDeviceVariable <CUdeviceptr>(aMaxTileCountX * aMaxTileCountY);
shiftMatrices = new CudaDeviceVariable <float>(aMaxTileCountX * aMaxTileCountY * shiftCount * (frameCount - 1));
shiftSafeMatrices = new CudaDeviceVariable <float>(aMaxTileCountX * aMaxTileCountY * shiftCount * (frameCount - 1));
matricesSquared = new CudaDeviceVariable <float>(aMaxTileCountX * aMaxTileCountY * (frameCount - 1) * (frameCount - 1));
matricesInverted = new CudaDeviceVariable <float>(aMaxTileCountX * aMaxTileCountY * (frameCount - 1) * (frameCount - 1));
solvedMatrices = new CudaDeviceVariable <float>(aMaxTileCountX * aMaxTileCountY * shiftCount * (frameCount - 1));
shiftsOneToOne = new CudaDeviceVariable <float2>(aMaxTileCountX * aMaxTileCountY * (frameCount - 1));
pivotArray = new CudaDeviceVariable <int>(aMaxTileCountX * aMaxTileCountY * (frameCount - 1));
shiftsMeasured = new CudaDeviceVariable <float2>(aMaxTileCountX * aMaxTileCountY * shiftCount);
shiftsOptim = new CudaDeviceVariable <float2>(aMaxTileCountX * aMaxTileCountY * shiftCount);
buffer = new CudaDeviceVariable <byte>(status.SumGetBufferSize());
statusSum = new CudaDeviceVariable <int>(1);
CUmodule mod = ctx.LoadModulePTX("ShiftMinimizerKernels.ptx");
concatenateShifts = new concatenateShiftsKernel(ctx, mod);
separateShifts = new separateShiftsKernel(ctx, mod);
getOptimalShifts = new getOptimalShiftsKernel(ctx, mod);
copyShiftMatrixKernel = new copyShiftMatrixKernel(ctx, mod);
setPointers = new setPointersKernel(ctx, mod);
checkForOutliers = new checkForOutliersKernel(ctx, mod);
transposeShifts = new transposeShiftsKernel(ctx, mod);
setPointers.RunSafe(shiftMatrixArray, shiftMatrixSafeArray, matrixSquareArray, matrixInvertedArray, solvedMatrixArray,
shiftOneToOneArray, shiftMeasuredArray, shiftOptimArray, shiftMatrices, shiftSafeMatrices, matricesSquared,
matricesInverted, solvedMatrices, shiftsOneToOne, shiftsMeasured, shiftsOptim, aMaxTileCountX * aMaxTileCountY, frameCount, shiftCount);
Reset();
}
static void Main(string[] args)
{
//Read CL arguments
for (int i = 0; i < args.Length; i++)
{
if (args[i] == "-d")
{
deviceID = int.Parse(args[++i]);
}
if (args[i] == "-lr")
{
learning_rate = double.Parse(args[++i], System.Globalization.NumberStyles.AllowDecimalPoint, CultureInfo.InvariantCulture);
}
if (args[i] == "-iso")
{
ISO = args[++i];
}
if (args[i] == "-t")
{
crosscheck = true;
}
if (args[i] == "-w")
{
warmStart = int.Parse(args[++i]);
Console.WriteLine("Start with epoch " + warmStart);
}
if (args[i] == "-s")
{
saveImages = true;
}
}
Console.WriteLine("Using device ID: " + deviceID);
Console.WriteLine("Learning rate: " + learning_rate);
//Init Cuda stuff
ctx = new PrimaryContext(deviceID);
ctx.SetCurrent();
Console.WriteLine("Context created");
CUmodule modPatch = ctx.LoadModulePTX("PatchProcessing.ptx");
Console.WriteLine("modPatch loaded");
CUmodule modBorder = ctx.LoadModulePTX("BorderTreatment.ptx");
Console.WriteLine("modBorder loaded");
CUmodule modError = ctx.LoadModulePTX("ErrorComputation.ptx");
Console.WriteLine("modError loaded");
CUmodule modPRelu = ctx.LoadModulePTX("PRelu.ptx");
Console.WriteLine("modPRelu loaded");
CUmodule modDeBayer = ctx.LoadModulePTX("DeBayer.ptx");
Console.WriteLine("all modules loaded");
deBayerGreenKernel = new DeBayerGreenKernel(modDeBayer, ctx);
deBayerRedBlueKernel = new DeBayerRedBlueKernel(modDeBayer, ctx);
//Both deBayer kernels are load from the same module: setting the constant variable for bayer pattern one is enough...
deBayerGreenKernel.BayerPattern = new BayerColor[] { BayerColor.Red, BayerColor.Green, BayerColor.Green, BayerColor.Blue };
prepareDataKernel = new PrepareDataKernel(modPatch, ctx);
restoreImageKernel = new RestoreImageKernel(modPatch, ctx);
Console.WriteLine("kernels loaded");
int countOwn = 468083;
int count5k = 33408;
string fileBase = @"/ssd/data/TrainingsDataNN/";
List <float3> WhiteBalanceFactors = new List <float3>();
FileStream fs1 = new FileStream(fileBase + "FromOwnDataset/WhiteBalancesOwn.txt", FileMode.Open, FileAccess.Read);
FileStream fs2 = new FileStream(fileBase + "From5kDataset/WhiteBalances5k.txt", FileMode.Open, FileAccess.Read);
StreamReader sr1 = new StreamReader(fs1);
StreamReader sr2 = new StreamReader(fs2);
for (int i = 0; i < countOwn; i++)
{
fileRawList.Add(fileBase + "FromOwnDataset/ISO" + ISO + "/img_" + i.ToString("0000000") + ".bin");
fileTrouthList.Add(fileBase + "FromOwnDataset/GroundTruth/img_" + i.ToString("0000000") + ".bin");
string line = sr1.ReadLine();
string[] values = line.Split('\t');
float3 wb = new float3(float.Parse(values[1], System.Globalization.NumberStyles.AllowDecimalPoint, CultureInfo.InvariantCulture),
float.Parse(values[2], System.Globalization.NumberStyles.AllowDecimalPoint, CultureInfo.InvariantCulture),
float.Parse(values[3], System.Globalization.NumberStyles.AllowDecimalPoint, CultureInfo.InvariantCulture));
WhiteBalanceFactors.Add(wb);
}
for (int i = 0; i < count5k; i++)
{
fileRawList.Add(fileBase + "From5kDataset/ISO" + ISO + "/img_" + i.ToString("0000000") + ".bin");
fileTrouthList.Add(fileBase + "From5kDataset/GroundTruth/img_" + i.ToString("0000000") + ".bin");
string line = sr2.ReadLine();
string[] values = line.Split('\t');
float3 wb = new float3(float.Parse(values[1], System.Globalization.NumberStyles.AllowDecimalPoint, CultureInfo.InvariantCulture),
float.Parse(values[2], System.Globalization.NumberStyles.AllowDecimalPoint, CultureInfo.InvariantCulture),
float.Parse(values[3], System.Globalization.NumberStyles.AllowDecimalPoint, CultureInfo.InvariantCulture));
WhiteBalanceFactors.Add(wb);
}
sr2.Close();
sr1.Close();
baOriginal = new float3[countOwn + count5k][];
baRAW = new float[countOwn + count5k][];
Random rand = new Random(0);
//random order for the image patches
for (int i = 0; i < countOwn + count5k - 1; i++)
{
int r = i + (rand.Next() % (countOwn + count5k - i));
string temp = fileRawList[i];
fileRawList[i] = fileRawList[r];
fileRawList[r] = temp;
temp = fileTrouthList[i];
fileTrouthList[i] = fileTrouthList[r];
fileTrouthList[r] = temp;
float3 tempf = WhiteBalanceFactors[i];
WhiteBalanceFactors[i] = WhiteBalanceFactors[r];
WhiteBalanceFactors[r] = tempf;
}
Console.WriteLine("Initialization done!");
int trainingSize = (int)((countOwn + count5k) * 0.9f); //4 patches per file
int testSize = fileRawList.Count - trainingSize;
CudaBlas blas = new CudaBlas(PointerMode.Host);
CudaDNNContext cudnn = new CudaDNNContext();
int patchSize = 31;
int patchSize4 = 66; //Size of an 2x2 patch read from file
int batch = 64;
float normalization = 0.5f;
//define neural network:
StartLayer start = new StartLayer(patchSize, patchSize, 3, batch);
FinalLayer final = new FinalLayer(patchSize, patchSize, 3, batch, FinalLayer.Norm.Mix, ctx, modError);
ConvolutionalLayer conv1 = new ConvolutionalLayer(patchSize, patchSize, 3, patchSize, patchSize, 64, batch, 9, 9, ConvolutionalLayer.Activation.PRelu, blas, cudnn, ctx, modBorder, modPRelu);
ConvolutionalLayer conv2 = new ConvolutionalLayer(patchSize, patchSize, 64, patchSize, patchSize, 64, batch, 5, 5, ConvolutionalLayer.Activation.PRelu, blas, cudnn, ctx, modBorder, modPRelu);
ConvolutionalLayer conv3 = new ConvolutionalLayer(patchSize, patchSize, 64, patchSize, patchSize, 3, batch, 5, 5, ConvolutionalLayer.Activation.None, blas, cudnn, ctx, modBorder, modPRelu);
start.ConnectFollowingLayer(conv1);
conv1.ConnectFollowingLayer(conv2);
conv2.ConnectFollowingLayer(conv3);
conv3.ConnectFollowingLayer(final);
CudaDeviceVariable <float3> imgA = new CudaDeviceVariable <float3>(patchSize4 * patchSize4);
CudaDeviceVariable <float3> imgB = new CudaDeviceVariable <float3>(patchSize4 * patchSize4);
CudaDeviceVariable <float> rawd = new CudaDeviceVariable <float>(patchSize4 * patchSize4);
CudaDeviceVariable <float> inputImgs = new CudaDeviceVariable <float>(patchSize * patchSize * 3 * batch);
CudaDeviceVariable <float> groundTrouth = new CudaDeviceVariable <float>(patchSize * patchSize * 3 * batch);
NPPImage_8uC3 imgU3a = new NPPImage_8uC3(patchSize, patchSize);
NPPImage_8uC3 imgU3b = new NPPImage_8uC3(patchSize, patchSize);
NPPImage_8uC3 imgU3c = new NPPImage_8uC3(patchSize, patchSize);
Bitmap a = new Bitmap(patchSize, patchSize, PixelFormat.Format24bppRgb);
Bitmap b = new Bitmap(patchSize, patchSize, PixelFormat.Format24bppRgb);
Bitmap c = new Bitmap(patchSize, patchSize, PixelFormat.Format24bppRgb);
Random randImageOutput = new Random(0);
Random randForInit = new Random(0);
start.InitRandomWeight(randForInit);
conv1.SetActivation(0.1f);
conv2.SetActivation(0.1f);
int startEpoch = warmStart;
FileStream fs;
//restore network in case of warm start:
if (warmStart > 0)
{
fs = new FileStream("epoch_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + "_" + (warmStart - 1) + ".cnn", FileMode.Open, FileAccess.Read);
start.RestoreValues(fs);
fs.Close();
fs.Dispose();
}
//validate results on validation data set
if (crosscheck)
{
FileStream csvResult = new FileStream("results_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + ".csv", FileMode.Append, FileAccess.Write);
StreamWriter sw = new StreamWriter(csvResult);
sw.WriteLine("L1;L2;Mix;Filename");
for (int i = 0; i < 2000; i += 1)
{
string filename = "epoch_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + "_" + i + ".cnn";
try
{
FileStream cnn = new FileStream(filename, FileMode.Open, FileAccess.Read);
start.RestoreValues(cnn);
cnn.Close();
cnn.Dispose();
}
catch (Exception)
{
Console.WriteLine("Skipping: " + i);
continue;
}
double errorL1 = 0;
double errorL2 = 0;
double errorMix = 0;
for (int iter = 0; iter < testSize / batch * 4; iter++)
{
//Prepare batch for training:
for (int ba = 0; ba < batch / 4; ba++)
{
int idx = iter * (batch / 4) + ba + trainingSize;
float3[] original;
float[] raw;
if (baRAW[idx - trainingSize] == null)
{
original = ReadRAWFloat3(fileTrouthList[idx]);
raw = ReadRAWFloat(fileRawList[idx]);
baOriginal[idx - trainingSize] = original;
baRAW[idx - trainingSize] = raw;
}
else
{
original = baOriginal[idx - trainingSize];
raw = baRAW[idx - trainingSize];
}
rawd.CopyToDevice(raw);
imgA.CopyToDevice(original);
deBayerGreenKernel.RunSafe(rawd, imgB, patchSize4, new float3(0, 0, 0), WhiteBalanceFactors[idx]);
deBayerRedBlueKernel.RunSafe(rawd, imgB, patchSize4, new float3(0, 0, 0), WhiteBalanceFactors[idx]);
prepareDataKernel.RunSafe(imgA, imgB, groundTrouth, inputImgs, ba, normalization, WhiteBalanceFactors[idx]);
}
start.SetData(inputImgs);
final.SetGroundTrouth(groundTrouth);
float err = start.InferenceTraining(inputImgs);
errorMix += err;
errorL1 += final.GetError(FinalLayer.Norm.L1);
errorL2 += final.GetError(FinalLayer.Norm.L2);
}
Console.WriteLine("Results for: " + filename);
Console.WriteLine("Mean Error L1: " + errorL1 / testSize * batch / 4);
Console.WriteLine("Mean Error L2: " + errorL2 / testSize * batch / 4);
Console.WriteLine("Mean Error Mix: " + errorMix / testSize * batch / 4);
sw.Write((errorL1 / testSize * batch / 4).ToString().Replace(".", ","));
sw.Write(";");
sw.Write((errorL2 / testSize * batch / 4).ToString().Replace(".", ","));
sw.Write(";");
sw.Write((errorMix / testSize * batch / 4).ToString().Replace(".", ","));
sw.Write(";");
sw.WriteLine(filename);
sw.Flush();
}
sw.Close();
csvResult.Close();
csvResult.Dispose();
}
//or train existing network:
else
{
double error = 0;
double errorEpoch = 0;
for (int epoch = startEpoch; epoch < 2000; epoch++)
{
errorEpoch = 0;
error = 0;
for (int iter = 0; iter < trainingSize / batch * 4; iter++)
{
//Prepare batch for training:
for (int ba = 0; ba < batch / 4; ba++)
{
int idx = iter * (batch / 4) + ba;
float3[] original;
float[] raw;
if (baRAW[idx] == null)
{
original = ReadRAWFloat3(fileTrouthList[idx]);
raw = ReadRAWFloat(fileRawList[idx]);
baOriginal[idx] = original;
baRAW[idx] = raw;
}
else
{
original = baOriginal[idx];
raw = baRAW[idx];
}
rawd.CopyToDevice(raw);
imgA.CopyToDevice(original);
deBayerGreenKernel.RunSafe(rawd, imgB, patchSize4, new float3(0, 0, 0), WhiteBalanceFactors[idx]);
deBayerRedBlueKernel.RunSafe(rawd, imgB, patchSize4, new float3(0, 0, 0), WhiteBalanceFactors[idx]);
prepareDataKernel.RunSafe(imgA, imgB, groundTrouth, inputImgs, ba, normalization, WhiteBalanceFactors[idx]);
}
start.SetData(inputImgs);
final.SetGroundTrouth(groundTrouth);
float err = start.InferenceTraining(inputImgs);
final.BackPropagation(groundTrouth);
start.UpdateWeights(GetLearningRate(epoch * (trainingSize) / batch * 4 + iter));//*0+951342
error += err;
errorEpoch += err;
if ((epoch * trainingSize / batch * 4 + iter) % 1000 == 0 && iter != 0)
{
FileStream status = new FileStream("status_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + ".csv", FileMode.Append, FileAccess.Write);
StreamWriter sw = new StreamWriter(status);
sw.WriteLine((error / 1000.0).ToString().Replace(".", ",") + ";" + GetLearningRate(epoch * trainingSize / batch * 4 + iter).ToString().Replace(".", ","));
sw.Close();
status.Close();
status.Dispose();
error = 0;
}
//if ((epoch * trainingSize / batch * 4 + iter) % 10000 == 0)
//{
// fs = new FileStream("iter_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + "_" + (epoch * trainingSize / batch * 4 + iter) + ".cnn", FileMode.Create, FileAccess.Write);
// start.SaveValues(fs);
// fs.Close();
// fs.Dispose();
// Console.WriteLine("Network saved for iteration " + (epoch * trainingSize / batch * 4 + iter) + "!");
//}
Console.WriteLine("Epoch: " + epoch + " Iteration: " + (epoch * trainingSize / batch * 4 + iter) + ", Error: " + err);
if (saveImages && iter == 0)//(epoch * trainingSize / batch * 4 + iter) % 10000 == 0 &&
{
for (int i = 0; i < 1; i++)
{
int imgidx = randImageOutput.Next(batch);
float3 wb = WhiteBalanceFactors[iter * (batch / 4) + imgidx / 4];
restoreImageKernel.RunSafe(groundTrouth, imgU3a, imgidx, wb.x, wb.y, wb.z, normalization);
restoreImageKernel.RunSafe(inputImgs, imgU3b, imgidx, wb.x, wb.y, wb.z, normalization);
CudaDeviceVariable <float> res = final.GetResult();
restoreImageKernel.RunSafe(res, imgU3c, imgidx, wb.x, wb.y, wb.z, normalization);
imgU3a.CopyToHost(a);
imgU3b.CopyToHost(b);
imgU3c.CopyToHost(c);
a.Save("GroundTrouth_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + "_" + epoch + "_" + imgidx + ".png");// * trainingSize / batch * 4 + iter
b.Save("Input_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + "_" + epoch + "_" + imgidx + ".png");
c.Save("Result_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + "_" + epoch + "_" + imgidx + ".png");
}
}
}
errorEpoch /= trainingSize / batch * 4;
fs = new FileStream("errorEpoch_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + ".csv", FileMode.Append, FileAccess.Write);
StreamWriter sw2 = new StreamWriter(fs);
sw2.WriteLine(errorEpoch.ToString().Replace(".", ","));
sw2.Close();
fs.Close();
fs.Dispose();
fs = new FileStream("epoch_" + learning_rate.ToString(CultureInfo.InvariantCulture) + "_" + ISO + "_" + epoch + ".cnn", FileMode.Create, FileAccess.Write);
start.SaveValues(fs);
fs.Close();
fs.Dispose();
}
}
}
private void InitializeD3D()
{
// Create the D3D object.
d3d = new Direct3DEx();
PresentParameters pp = new PresentParameters();
pp.BackBufferWidth = 512;
pp.BackBufferHeight = 512;
pp.BackBufferFormat = Format.Unknown;
pp.BackBufferCount = 0;
pp.Multisample = MultisampleType.None;
pp.MultisampleQuality = 0;
pp.SwapEffect = SwapEffect.Discard;
pp.DeviceWindowHandle = panel1.Handle;
pp.Windowed = true;
pp.EnableAutoDepthStencil = false;
pp.AutoDepthStencilFormat = Format.Unknown;
pp.PresentationInterval = PresentInterval.Default;
bDeviceFound = false;
CUdevice[] cudaDevices = null;
for (g_iAdapter = 0; g_iAdapter < d3d.AdapterCount; g_iAdapter++)
{
device = new DeviceEx(d3d, d3d.Adapters[g_iAdapter].Adapter, DeviceType.Hardware, panel1.Handle, CreateFlags.HardwareVertexProcessing | CreateFlags.Multithreaded, pp);
try
{
cudaDevices = CudaContext.GetDirectXDevices(device.ComPointer, CUd3dXDeviceList.All, CudaContext.DirectXVersion.D3D9);
bDeviceFound = cudaDevices.Length > 0;
Console.WriteLine("> Display Device #" + d3d.Adapters[g_iAdapter].Adapter
+ ": \"" + d3d.Adapters[g_iAdapter].Details.Description + "\" supports Direct3D9 and CUDA.");
break;
}
catch (CudaException)
{
//No Cuda device found for this Direct3D9 device
Console.WriteLine("> Display Device #" + d3d.Adapters[g_iAdapter].Adapter
+ ": \"" + d3d.Adapters[g_iAdapter].Details.Description + "\" supports Direct3D9 but not CUDA.");
}
}
// we check to make sure we have found a cuda-compatible D3D device to work on
if (!bDeviceFound)
{
Console.WriteLine("No CUDA-compatible Direct3D9 device available");
if (device != null)
{
device.Dispose();
}
Close();
return;
}
ctx = new CudaContext(cudaDevices[0], device.ComPointer, CUCtxFlags.BlockingSync, CudaContext.DirectXVersion.D3D9);
// Set projection matrix
SlimDX.Matrix matProj = SlimDX.Matrix.OrthoOffCenterLH(0, 1, 1, 0, 0, 1);
device.SetTransform(TransformState.Projection, matProj);
// Turn off D3D lighting, since we are providing our own vertex colors
device.SetRenderState(RenderState.Lighting, false);
//Load kernels
CUmodule module = ctx.LoadModulePTX("kernel.ptx");
addForces_k = new CudaKernel("addForces_k", module, ctx);
advectVelocity_k = new CudaKernel("advectVelocity_k", module, ctx);
diffuseProject_k = new CudaKernel("diffuseProject_k", module, ctx);
updateVelocity_k = new CudaKernel("updateVelocity_k", module, ctx);
advectParticles_k = new CudaKernel("advectParticles_k", module, ctx);
}
private bool InitializeD3D()
{
HwndSource hwnd = new HwndSource(0, 0, 0, 0, 0, "null", IntPtr.Zero);
// Create the D3D object.
d3d = new Direct3D();
PresentParameters pp = new PresentParameters();
pp.BackBufferWidth = 512;
pp.BackBufferHeight = 512;
pp.BackBufferFormat = Format.Unknown;
pp.BackBufferCount = 0;
pp.Multisample = MultisampleType.None;
pp.MultisampleQuality = 0;
pp.SwapEffect = SwapEffect.Discard;
pp.DeviceWindowHandle = (IntPtr)0;
pp.Windowed = true;
pp.EnableAutoDepthStencil = false;
pp.AutoDepthStencilFormat = Format.Unknown;
pp.PresentationInterval = PresentInterval.Default;
bDeviceFound = false;
CUdevice[] cudaDevices = null;
for (g_iAdapter = 0; g_iAdapter < d3d.AdapterCount; g_iAdapter++)
{
device = new Device(d3d, d3d.Adapters[g_iAdapter].Adapter, DeviceType.Hardware, hwnd.Handle, CreateFlags.HardwareVertexProcessing | CreateFlags.Multithreaded, pp);
try
{
cudaDevices = CudaContext.GetDirectXDevices(device.ComPointer, CUd3dXDeviceList.All, CudaContext.DirectXVersion.D3D9);
bDeviceFound = cudaDevices.Length > 0;
infoLog.AppendText("> Display Device #" + d3d.Adapters[g_iAdapter].Adapter
+ ": \"" + d3d.Adapters[g_iAdapter].Details.Description + "\" supports Direct3D9 and CUDA.\n");
break;
}
catch (CudaException)
{
//No Cuda device found for this Direct3D9 device
infoLog.AppendText("> Display Device #" + d3d.Adapters[g_iAdapter].Adapter
+ ": \"" + d3d.Adapters[g_iAdapter].Details.Description + "\" supports Direct3D9 but not CUDA.\n");
}
}
// we check to make sure we have found a cuda-compatible D3D device to work on
if (!bDeviceFound)
{
infoLog.AppendText("No CUDA-compatible Direct3D9 device available");
if (device != null)
{
device.Dispose();
}
return(false);
}
ctx = new CudaContext(cudaDevices[0], device.ComPointer, CUCtxFlags.BlockingSync, CudaContext.DirectXVersion.D3D9);
deviceName.Text = "Device name: " + ctx.GetDeviceName();
// Set projection matrix
SlimDX.Matrix matProj = SlimDX.Matrix.OrthoOffCenterLH(0, 1, 1, 0, 0, 1);
device.SetTransform(TransformState.Projection, matProj);
// Turn off D3D lighting, since we are providing our own vertex colors
device.SetRenderState(RenderState.Lighting, false);
//Load kernels
CUmodule module = ctx.LoadModulePTX("kernel.ptx");
addForces_k = new CudaKernel("addForces_k", module, ctx);
advectVelocity_k = new CudaKernel("advectVelocity_k", module, ctx);
diffuseProject_k = new CudaKernel("diffuseProject_k", module, ctx);
updateVelocity_k = new CudaKernel("updateVelocity_k", module, ctx);
advectParticles_k = new CudaKernel("advectParticles_k", module, ctx);
d3dimage.Lock();
Surface surf = device.GetBackBuffer(0, 0);
d3dimage.SetBackBuffer(D3DResourceType.IDirect3DSurface9, surf.ComPointer);
d3dimage.Unlock();
surf.Dispose();
//Setup the "real" frame rate counter.
//The cuda counter only measures cuda runtime, not the overhead to actually
//show the result via DirectX and WPF.
realLastTick = Environment.TickCount;
return(true);
}
public GpuMathOperations()
{
this.cuBlas = new CudaBlas();
this.cudaContext = new CudaContext();
this.cuModule = cudaContext.LoadModulePTX(kernalFile);
this.maxThreadPerBlockDim = (int)Math.Sqrt(this.cudaContext.GetDeviceInfo().MaxThreadsPerBlock);
}
private void InitializeCUDA()
{
context = new CudaContext(CudaContext.GetMaxGflopsDevice(), graphicsDevice.ComPointer, CUCtxFlags.SchedAuto, CudaContext.DirectXVersion.D3D11);
module = context.LoadModulePTX(@"Kernels\kernel.ptx");
kernelPositionWeightNoiseCube = new CudaKernel("position_weight_noise_cube", module, context);
kernelNormalAmbient = new CudaKernel("normal_ambient", module, context);
kernelMarchingCubesCases = new CudaKernel("marching_cubes_cases", module, context);
kernelMarchingCubesVertices = new CudaKernel("marching_cubes_vertices", module, context);
kernelPositionWeightNoiseCubeWarp = new CudaKernel("position_weight_noise_cube_warp", module, context);
kernelPositionWeightFormula = new CudaKernel("position_weight_formula", module, context);
prefixScan = new CUDAPrefixScan(module, context);
}
private bool InitializeD3D()
{
HwndSource hwnd = new HwndSource(0, 0, 0, 0, 0, "null", IntPtr.Zero);
// Create the D3D object.
d3d = new Direct3DEx();
PresentParameters pp = new PresentParameters();
pp.BackBufferWidth = 512;
pp.BackBufferHeight = 512;
pp.BackBufferFormat = Format.Unknown;
pp.BackBufferCount = 0;
pp.Multisample = MultisampleType.None;
pp.MultisampleQuality = 0;
pp.SwapEffect = SwapEffect.Discard;
pp.DeviceWindowHandle = (IntPtr)0;
pp.Windowed = true;
pp.EnableAutoDepthStencil = false;
pp.AutoDepthStencilFormat = Format.Unknown;
pp.PresentationInterval = PresentInterval.Default;
bDeviceFound = false;
CUdevice[] cudaDevices = null;
for (g_iAdapter = 0; g_iAdapter < d3d.AdapterCount; g_iAdapter++)
{
device = new DeviceEx(d3d, d3d.Adapters[g_iAdapter].Adapter, DeviceType.Hardware, hwnd.Handle, CreateFlags.HardwareVertexProcessing | CreateFlags.Multithreaded, pp);
try
{
cudaDevices = CudaContext.GetDirectXDevices(device.ComPointer, CUd3dXDeviceList.All, CudaContext.DirectXVersion.D3D9);
bDeviceFound = cudaDevices.Length > 0;
infoLog.AppendText("> Display Device #" + d3d.Adapters[g_iAdapter].Adapter
+ ": \"" + d3d.Adapters[g_iAdapter].Details.Description + "\" supports Direct3D9 and CUDA.\n");
break;
}
catch (CudaException)
{
//No Cuda device found for this Direct3D9 device
infoLog.AppendText("> Display Device #" + d3d.Adapters[g_iAdapter].Adapter
+ ": \"" + d3d.Adapters[g_iAdapter].Details.Description + "\" supports Direct3D9 but not CUDA.\n");
}
}
// we check to make sure we have found a cuda-compatible D3D device to work on
if (!bDeviceFound)
{
infoLog.AppendText("No CUDA-compatible Direct3D9 device available");
if (device != null)
device.Dispose();
return false;
}
ctx = new CudaContext(cudaDevices[0], device.ComPointer, CUCtxFlags.BlockingSync, CudaContext.DirectXVersion.D3D9);
deviceName.Text = "Device name: " + ctx.GetDeviceName();
// Set projection matrix
SlimDX.Matrix matProj = SlimDX.Matrix.OrthoOffCenterLH(0, 1, 1, 0, 0, 1);
device.SetTransform(TransformState.Projection, matProj);
// Turn off D3D lighting, since we are providing our own vertex colors
device.SetRenderState(RenderState.Lighting, false);
//Load kernels
CUmodule module = ctx.LoadModulePTX("kernel.ptx");
addForces_k = new CudaKernel("addForces_k", module, ctx);
advectVelocity_k = new CudaKernel("advectVelocity_k", module, ctx);
diffuseProject_k = new CudaKernel("diffuseProject_k", module, ctx);
updateVelocity_k = new CudaKernel("updateVelocity_k", module, ctx);
advectParticles_k = new CudaKernel("advectParticles_k", module, ctx);
d3dimage.Lock();
Surface surf = device.GetBackBuffer(0, 0);
d3dimage.SetBackBuffer(D3DResourceType.IDirect3DSurface9, surf.ComPointer);
d3dimage.Unlock();
surf.Dispose();
//Setup the "real" frame rate counter.
//The cuda counter only measures cuda runtime, not the overhead to actually
//show the result via DirectX and WPF.
realLastTick = Environment.TickCount;
return true;
}
private void InitializeD3D()
{
// Create the D3D object.
d3d = new Direct3DEx();
PresentParameters pp = new PresentParameters();
pp.BackBufferWidth = 512;
pp.BackBufferHeight = 512;
pp.BackBufferFormat = Format.Unknown;
pp.BackBufferCount = 0;
pp.Multisample = MultisampleType.None;
pp.MultisampleQuality = 0;
pp.SwapEffect = SwapEffect.Discard;
pp.DeviceWindowHandle = panel1.Handle;
pp.Windowed = true;
pp.EnableAutoDepthStencil = false;
pp.AutoDepthStencilFormat = Format.Unknown;
pp.PresentationInterval = PresentInterval.Default;
bDeviceFound = false;
CUdevice[] cudaDevices = null;
for (g_iAdapter = 0; g_iAdapter < d3d.AdapterCount; g_iAdapter++)
{
device = new DeviceEx(d3d, d3d.Adapters[g_iAdapter].Adapter, DeviceType.Hardware, panel1.Handle, CreateFlags.HardwareVertexProcessing | CreateFlags.Multithreaded, pp);
try
{
cudaDevices = CudaContext.GetDirectXDevices(device.ComPointer, CUd3dXDeviceList.All, CudaContext.DirectXVersion.D3D9);
bDeviceFound = cudaDevices.Length > 0;
Console.WriteLine("> Display Device #" + d3d.Adapters[g_iAdapter].Adapter
+ ": \"" + d3d.Adapters[g_iAdapter].Details.Description + "\" supports Direct3D9 and CUDA.");
break;
}
catch (CudaException)
{
//No Cuda device found for this Direct3D9 device
Console.WriteLine("> Display Device #" + d3d.Adapters[g_iAdapter].Adapter
+ ": \"" + d3d.Adapters[g_iAdapter].Details.Description + "\" supports Direct3D9 but not CUDA.");
}
}
// we check to make sure we have found a cuda-compatible D3D device to work on
if (!bDeviceFound)
{
Console.WriteLine("No CUDA-compatible Direct3D9 device available");
if (device != null)
device.Dispose();
Close();
return;
}
ctx = new CudaContext(cudaDevices[0], device.ComPointer, CUCtxFlags.BlockingSync, CudaContext.DirectXVersion.D3D9);
// Set projection matrix
SlimDX.Matrix matProj = SlimDX.Matrix.OrthoOffCenterLH(0, 1, 1, 0, 0, 1);
device.SetTransform(TransformState.Projection, matProj);
// Turn off D3D lighting, since we are providing our own vertex colors
device.SetRenderState(RenderState.Lighting, false);
//Load kernels
CUmodule module = ctx.LoadModulePTX("kernel.ptx");
addForces_k = new CudaKernel("addForces_k", module, ctx);
advectVelocity_k = new CudaKernel("advectVelocity_k", module, ctx);
diffuseProject_k = new CudaKernel("diffuseProject_k", module, ctx);
updateVelocity_k = new CudaKernel("updateVelocity_k", module, ctx);
advectParticles_k = new CudaKernel("advectParticles_k", module, ctx);
}
private void initGLAndCuda()
{
//Create render target control
m_renderControl = new OpenTK.GLControl(GraphicsMode.Default, 1, 0, GraphicsContextFlags.Default);
m_renderControl.Dock = DockStyle.Fill;
m_renderControl.BackColor = Color.White;
m_renderControl.BorderStyle = BorderStyle.FixedSingle;
m_renderControl.KeyDown += new KeyEventHandler(m_renderControl_KeyDown);
m_renderControl.MouseMove += new MouseEventHandler(m_renderControl_MouseMove);
m_renderControl.MouseDown += new MouseEventHandler(m_renderControl_MouseDown);
m_renderControl.SizeChanged += new EventHandler(m_renderControl_SizeChanged);
panel1.Controls.Add(m_renderControl);
Console.WriteLine(" OpenGL device is Available");
int deviceID = CudaContext.GetMaxGflopsDeviceId();
ctx = CudaContext.CreateOpenGLContext(deviceID, CUCtxFlags.BlockingSync);
string console = string.Format("CUDA device [{0}] has {1} Multi-Processors", ctx.GetDeviceName(), ctx.GetDeviceInfo().MultiProcessorCount);
Console.WriteLine(console);
CUmodule module = ctx.LoadModulePTX("kernel.ptx");
addForces_k = new CudaKernel("addForces_k", module, ctx);
advectVelocity_k = new CudaKernel("advectVelocity_k", module, ctx);
diffuseProject_k = new CudaKernel("diffuseProject_k", module, ctx);
updateVelocity_k = new CudaKernel("updateVelocity_k", module, ctx);
advectParticles_k = new CudaKernel("advectParticles_OGL", module, ctx);
hvfield = new cData[DS];
dvfield = new CudaPitchedDeviceVariable <cData>(DIM, DIM);
tPitch = dvfield.Pitch;
dvfield.CopyToDevice(hvfield);
vxfield = new CudaDeviceVariable <cData>(DS);
vyfield = new CudaDeviceVariable <cData>(DS);
// Create particle array
particles = new cData[DS];
initParticles(particles, DIM, DIM);
// TODO: update kernels to use the new unpadded memory layout for perf
// rather than the old FFTW-compatible layout
planr2c = new CudaFFTPlan2D(DIM, DIM, cufftType.R2C, Compatibility.FFTWPadding);
planc2r = new CudaFFTPlan2D(DIM, DIM, cufftType.C2R, Compatibility.FFTWPadding);
GL.GenBuffers(1, out vbo);
GL.BindBuffer(BufferTarget.ArrayBuffer, vbo);
GL.BufferData <cData>(BufferTarget.ArrayBuffer, new IntPtr(cData.SizeOf * DS), particles, BufferUsageHint.DynamicDraw);
int bsize;
GL.GetBufferParameter(BufferTarget.ArrayBuffer, BufferParameterName.BufferSize, out bsize);
if (bsize != DS * cData.SizeOf)
{
throw new Exception("Sizes don't match.");
}
GL.BindBuffer(BufferTarget.ArrayBuffer, 0);
cuda_vbo_resource = new CudaGraphicsInteropResourceCollection();
cuda_vbo_resource.Add(new CudaOpenGLBufferInteropResource(vbo, CUGraphicsRegisterFlags.None));
texref = new CudaTextureArray2D(advectVelocity_k, "texref", CUAddressMode.Wrap, CUFilterMode.Linear, 0, CUArrayFormat.Float, DIM, DIM, CudaArray2DNumChannels.Two);
stopwatch = new CudaStopWatch(CUEventFlags.Default);
reshape();
isInit = true;
display();
}
private static async Task <CudaModule> CompileAsync(
IMethod method,
IEnumerable <ITypeMember> memberRoots,
IEnumerable <IType> typeRoots,
int threadIdParamIndex,
ClrAssembly assembly,
CudaContext context)
{
// Figure out which members we need to compile.
var desc = await CreateContentDescriptionAsync(method, memberRoots, typeRoots, assembly);
// Compile those members to LLVM IR. Use an Itanium name mangling scheme.
var mangler = new ItaniumMangler(assembly.Resolver.TypeEnvironment);
var moduleBuilder = LlvmBackend.Compile(desc, assembly.Resolver.TypeEnvironment);
var module = moduleBuilder.Module;
// Generate type metadata for all type roots.
foreach (var type in typeRoots)
{
moduleBuilder.Metadata.GetMetadata(type, moduleBuilder);
}
// Get the compiled kernel function.
var kernelFuncName = mangler.Mangle(method, true);
var kernelFunc = LLVM.GetNamedFunction(module, kernelFuncName);
if (threadIdParamIndex >= 0)
{
// If we have a thread ID parameter, then we need to generate a thunk
// kernel function that calls our actual kernel function. This thunk's
// responsibility is to determine the thread ID of the kernel.
var thunkKernelName = "kernel";
var thunkTargetType = kernelFunc.TypeOf().GetElementType();
var thunkParamTypes = new List <LLVMTypeRef>(thunkTargetType.GetParamTypes());
if (threadIdParamIndex < thunkParamTypes.Count)
{
thunkParamTypes.RemoveAt(threadIdParamIndex);
}
var thunkKernel = LLVM.AddFunction(
module,
thunkKernelName,
LLVM.FunctionType(
thunkTargetType.GetReturnType(),
thunkParamTypes.ToArray(),
thunkTargetType.IsFunctionVarArg));
using (var builder = new IRBuilder(moduleBuilder.Context))
{
builder.PositionBuilderAtEnd(thunkKernel.AppendBasicBlock("entry"));
var args = new List <LLVMValueRef>(thunkKernel.GetParams());
args.Insert(threadIdParamIndex, ComputeUniqueThreadId(builder, module));
var call = builder.CreateCall(kernelFunc, args.ToArray(), "");
if (call.TypeOf().TypeKind == LLVMTypeKind.LLVMVoidTypeKind)
{
builder.CreateRetVoid();
}
else
{
builder.CreateRet(call);
}
}
kernelFuncName = thunkKernelName;
kernelFunc = thunkKernel;
}
// Mark the compiled kernel as a kernel symbol.
LLVM.AddNamedMetadataOperand(
module,
"nvvm.annotations",
LLVM.MDNode(new LLVMValueRef[]
{
kernelFunc,
MDString("kernel"),
LLVM.ConstInt(LLVM.Int32TypeInContext(LLVM.GetModuleContext(module)), 1, false)
}));
// LLVM.DumpModule(module);
// Compile that LLVM IR down to PTX.
LLVMTargetMachineRef machine;
var ptx = CompileToPtx(module, context.GetDeviceComputeCapability(), out machine);
// Console.WriteLine(System.Text.Encoding.UTF8.GetString(ptx));
// Load the PTX kernel.
return(new CudaModule(assembly, moduleBuilder, machine, context.LoadModulePTX(ptx), kernelFuncName, context));
}
