public DimensionReductionFitness(
CudaContext context,
IDimensionAccuracy accuracyFunc,
int popSize,
int genLength
)
{
this.accuracyFunc = accuracyFunc;
this.popSize = popSize;
this.context = context;
deviceVectorSizes = new CudaDeviceVariable <int>(popSize);
fitnessKernel = context.LoadKernel(
"kernels/dimensionsReductions.ptx",
"fitnessFunction"
);
fitnessKernel.GridDimensions = 1;
fitnessKernel.BlockDimensions = popSize;
Alpha = 0.7f;
sizeAndIndecesKernel = context.LoadKernel("kernels/Common.ptx", "countVectorsIndeces");
sizeAndIndecesKernel.SetConstantVariable("genLength", genLength);
sizeAndIndecesKernel.GridDimensions = 1;
sizeAndIndecesKernel.BlockDimensions = popSize;
populationIndeces = new CudaDeviceVariable <int>(genLength * popSize);
}
public Layer(FileLayer fl, ref CudaContext ctx)
{
this.ctx = ctx;
type = fl.type;
size = fl.size;
data = new float[fl.size.Mul];
bias = new float[fl.size.Mul];
error = new float[fl.size.Mul];
forward = ctx.LoadKernel("kernel.ptx", "Forward");
forward.GridDimensions = new dim3(size.x, size.y, size.z);
forward.BlockDimensions = new dim3(fl.prevSize.x, fl.prevSize.y, fl.prevSize.z);
back = ctx.LoadKernel("kernel.ptx", "Backprop");
back.GridDimensions = new dim3(size.x, size.y, size.z);
back.BlockDimensions = new dim3(fl.prevSize.x, fl.prevSize.y, fl.prevSize.z);
clear = ctx.LoadKernel("kernel.ptx", "Clear");
clear.GridDimensions = new dim3(size.x, size.y, size.z);
activate = ctx.LoadKernel("kernel.ptx", "Activate");
activate.GridDimensions = new dim3(size.x, size.y, size.z);
}
protected void InitContext()
{
var size = ParticlesCount * DimensionsCount;
var threadsNum = 32;
var blocksNum = ParticlesCount / threadsNum;
Ctx = new CudaContext(0);
UpdateVelocity = Ctx.LoadKernel("update_velocity_kernel.ptx", "updateVelocityKernel");
UpdateVelocity.GridDimensions = blocksNum;
UpdateVelocity.BlockDimensions = threadsNum;
Transpose = Ctx.LoadKernel(KernelFile, "transposeKernel");
Transpose.GridDimensions = blocksNum;
Transpose.BlockDimensions = threadsNum;
HostPositions = Random.RandomVector(size, -5.0, 5.0);
HostVelocities = Random.RandomVector(size, -2.0, 2.0);
HostPersonalBests = (double[])HostPositions.Clone();
HostPersonalBestValues = Enumerable.Repeat(double.MaxValue, ParticlesCount).ToArray();
HostNeighbors = new int[ParticlesCount * 2];
for (var i = 0; i < ParticlesCount * 2; i += 2)
{
int left, right;
if (i == 0)
{
left = ParticlesCount - 1;
}
else
{
left = i - 1;
}
if (i == ParticlesCount - 1)
{
right = 0;
}
else
{
right = i + 1;
}
HostNeighbors[i] = left;
HostNeighbors[i + 1] = right;
}
DevicePositions = HostPositions;
DeviceVelocities = HostVelocities;
DevicePersonalBests = HostPersonalBests;
DevicePersonalBestValues = HostPersonalBestValues;
DeviceNeighbors = HostNeighbors;
Init();
}
public VectorReductionAccuracy(CudaContext context, DeviceDataSet <int> teaching, DeviceDataSet <int> test, int popSize)
{
this.teaching = teaching;
this.test = test;
this.popSize = popSize;
this.context = context;
calculatedNeabours = new CudaDeviceVariable <int>(teaching.length * test.length);
deviceAccuracy = new CudaDeviceVariable <float>(popSize);
Profiler.Start("calculate neabours");
Neabours.CalculateNeabours(context, teaching, test, calculatedNeabours, ThreadsPerBlock);
Profiler.Stop("calculate neabours");
accuracyKernel = context.LoadKernel("kernels/VectorReduction.ptx", "calculateAccuracy");
dim3 gridDimension = new dim3()
{
x = (uint)(test.length / ThreadsPerBlock + 1),
y = (uint)popSize,
z = 1
};
accuracyKernel.GridDimensions = gridDimension;
accuracyKernel.BlockDimensions = ThreadsPerBlock;
accuracyKernel.SetConstantVariable("testVectorsCount", test.length);
accuracyKernel.SetConstantVariable("teachingVectorsCount", teaching.length);
accuracyKernel.SetConstantVariable("attributeCount", teaching.attributeCount);
accuracyKernel.SetConstantVariable("genLength", teaching.length);
K = 3;
CountToPass = 2;
}
public static void CalculateNeabours <T>
(CudaContext context,
DeviceDataSet <T> teaching,
DeviceDataSet <T> test,
CudaDeviceVariable <int> calculatedNeabours,
int threadsPerBlock
) where T : struct
{
var kernel = context.LoadKernel("kernels/VectorReduction.ptx", "calculateNearestNeabours");
kernel.GridDimensions = test.length / threadsPerBlock + 1;
kernel.BlockDimensions = threadsPerBlock;
kernel.SetConstantVariable("testVectorsCount", test.length);
kernel.SetConstantVariable("teachingVectorsCount", teaching.length);
kernel.SetConstantVariable("attributeCount", teaching.attributeCount);
using (var deviceDistanceMemory =
new CudaDeviceVariable <float>(teaching.length * test.length))
{
kernel.Run(
teaching.vectors.DevicePointer,
test.vectors.DevicePointer,
deviceDistanceMemory.DevicePointer,
calculatedNeabours.DevicePointer
);
Thrust.sort_by_key_multiple(deviceDistanceMemory, calculatedNeabours, teaching.length, test.length);
}
}
// Testing managed CUDA call
private static void RunCudaWithAKernel()
{
// C# Cuda code to call kernel
int N = 50000;
int deviceID = 0;
CudaContext ctx = new CudaContext(deviceID);
CudaKernel kernel = ctx.LoadKernel("kernel_x64.ptx", "VecAdd");
int numOfThreads = 256;
kernel.GridDimensions = (N + numOfThreads - 1) / numOfThreads;
kernel.BlockDimensions = numOfThreads;
// allocate memory in host (not gpu)
var h_A = InitWithData(N, numOfThreads * 4);
var h_B = InitWithData(N, numOfThreads);
// Allocate vectors in device memory and copy from host to device.
CudaDeviceVariable <float> d_A = h_A;
CudaDeviceVariable <float> d_B = h_B;
CudaDeviceVariable <float> d_C = new CudaDeviceVariable <float>(N);
//Invoke kernel
kernel.Run(d_A.DevicePointer, d_B.DevicePointer, d_C.DevicePointer, N);
Console.WriteLine("kernel has runeth");
//Copy from memory of device to host.
float[] h_C = d_C;
}
private void generateKernels(string forwardName, string backName, string clrName, string activeName, dim3 kernelSize)
{
forward = ctx.LoadKernel("kernel.ptx", forwardName);
forward.GridDimensions = new dim3(size.x, size.y, size.z);
forward.BlockDimensions = kernelSize;
back = ctx.LoadKernel("kernel.ptx", backName);
back.GridDimensions = new dim3(size.x, size.y, size.z);
back.BlockDimensions = kernelSize;
clear = ctx.LoadKernel("kernel.ptx", activeName);
clear.GridDimensions = new dim3(size.x, size.y, size.z);
activate = ctx.LoadKernel("kernel.ptx", activeName);
activate.GridDimensions = new dim3(size.x, size.y, size.z);
}
//Test CUDA kernel for complex multiplication
public void test(int N)
{
CudaContext ctx = new CudaContext();
CudaKernel kernel = ctx.LoadKernel("kernel.ptx", "ComplexMultCUDA");
kernel.GridDimensions = N;
kernel.BlockDimensions = 1;
double2[] a = new double2[N];
double2[] b = new double2[N];
double2[] c = new double2[N];
for (int i = 0; i < N; i++)
{
a[i].x = 1;
a[i].y = 3;
b[i].x = 2;
b[i].y = 2;
}
CudaDeviceVariable <double2> d_a = null;
CudaDeviceVariable <double2> d_b = null;
try
{
d_a = a;
d_b = b;
}
catch (Exception e)
{
Console.WriteLine("{0} Exception caught.", e);
return;
}
kernel.Run(d_a.DevicePointer, d_b.DevicePointer, N);
c = d_b;
Console.WriteLine("C.last()={0}+i{1}", c.Last().x, c.Last().y);
}
public float BaseAccuracy()
{
var baseKernel = context.LoadKernel("kernels/VectorReduction.ptx", "calculateAccuracy");
dim3 gridDimension = new dim3()
{
x = (uint)(test.length / ThreadsPerBlock + 1),
y = (uint)1,
z = 1
};
baseKernel.GridDimensions = gridDimension;
baseKernel.BlockDimensions = ThreadsPerBlock;
baseKernel.SetConstantVariable("testVectorsCount", test.length);
baseKernel.SetConstantVariable("teachingVectorsCount", teaching.length);
baseKernel.SetConstantVariable("attributeCount", teaching.attributeCount);
baseKernel.SetConstantVariable("genLength", teaching.length);
var BaseRMSEKernel = context.LoadKernel("kernels/VectorReduction.ptx", "RMSE");
BaseRMSEKernel.GridDimensions = 1;
BaseRMSEKernel.BlockDimensions = 1;
BaseRMSEKernel.SetConstantVariable("testVectorsCount", test.length);
byte[] gen = new byte[teaching.length];
for (int i = 0; i < gen.Length; i++)
{
gen[i] = 1;
}
using (CudaDeviceVariable <byte> deviceGen = gen)
using (CudaDeviceVariable <float> baseAccuracy = new CudaDeviceVariable <float>(1))
{
accuracyKernel.Run(
test.classes.DevicePointer,
teaching.classes.DevicePointer,
deviceGen.DevicePointer,
calculatedNeabours.DevicePointer,
deviceAccuracy.DevicePointer
);
BaseRMSEKernel.Run(baseAccuracy.DevicePointer);
float[] host = baseAccuracy;
return(host[0]);
}
}
public List <float> hypotesis(List <double> x, List <double> h, int N)
{
//int N = 2000000;
string path = Path.GetDirectoryName(mv.plugins[0].filename);
CudaContext ctx = new CudaContext();
CudaKernel kernel = ctx.LoadKernel(path + "\\kernel.ptx", "ComplexMultCUDA");
kernel.GridDimensions = (int)Math.Ceiling((double)(N + h.Count - 1) / 1024);
kernel.BlockDimensions = 1024;
double[] temp_y = new double[N + h.Count - 1];
double[] temp_h = new double[N + h.Count - 1];
double[] temp_x = new double[N + h.Count - 1];
double2[] temp_x2 = new double2[N + h.Count - 1];
h.ToArray().CopyTo(temp_h, 0);
x.ToArray().CopyTo(temp_x, 0);
CudaDeviceVariable <double> d_x = null;
CudaDeviceVariable <double2> d_X = new CudaDeviceVariable <double2>(N + h.Count - 1);
CudaDeviceVariable <double> d_h = new CudaDeviceVariable <double>(N + h.Count - 1);
CudaDeviceVariable <double2> d_H = new CudaDeviceVariable <double2>(N + h.Count - 1);
CudaDeviceVariable <double> d_y = new CudaDeviceVariable <double>(N + h.Count - 1);
CudaFFTPlan1D planForward = new CudaFFTPlan1D(N + h.Count - 1, cufftType.D2Z, 1);
CudaFFTPlan1D planInverse = new CudaFFTPlan1D(N + h.Count - 1, cufftType.Z2D, 1);
try
{
d_h = temp_h;
planForward.Exec(d_h.DevicePointer, d_H.DevicePointer, TransformDirection.Forward);
}
catch (Exception exp)
{
mainView.log(exp, "CUDA error: Impulse response FFT", this);
return(null);
}
try
{
d_x = temp_x;
planForward.Exec(d_x.DevicePointer, d_X.DevicePointer);
kernel.Run(d_H.DevicePointer, d_X.DevicePointer, N + h.Count - 1);
planInverse.Exec(d_X.DevicePointer, d_y.DevicePointer);
}
catch (Exception exp)
{
mainView.log(exp, "Cuda error: kernel run cuda error", this);
}
temp_y = d_y;
return(Array.ConvertAll <double, float>(temp_y, d => (float)d).ToList().GetRange(500, x.Count));
}
public CountVectorKernel(CudaContext context, int vectorCount, int genLength)
{
this.context = context;
kernel = context.LoadKernel("kernels/Common.ptx", "countVectors");
VectorCount = vectorCount;
GenLength = genLength;
kernel.GridDimensions = 1;
}
public void LoadKernels()
{
performGeneticAlgorythm = context.LoadKernel("kernels/evolutionary2.ptx", "genetic");
performGeneticAlgorythm.GridDimensions = 1;
performGeneticAlgorythm.BlockDimensions = popSize;
performGeneticAlgorythm.DynamicSharedMemory =
(uint)(sizeof(float) * popSize);
}
public float BaseAccuracy()
{
var kernel = context.LoadKernel
(
"kernels/dimensionsReductions.ptx",
"geneticKnn"
);
kernel.GridDimensions = new dim3()
{
x = (uint)(test.vectors.Size / ThreadsPerBlock) + 1,
y = 1,
z = 1
};
kernel.BlockDimensions = ThreadsPerBlock;
kernel.SetConstantVariable("atributeCount", test.attributeCount);
kernel.SetConstantVariable("teachingVectorsCount", teaching.length);
kernel.SetConstantVariable("testVectorsCount", test.length);
kernel.SetConstantVariable("popSize", 1);
kernel.SetConstantVariable("k", K);
kernel.SetConstantVariable("countToPass", CountToPass);
kernel.DynamicSharedMemory = (uint)(test.attributeCount * sizeof(float));
var vectorSizes = new int[1];
vectorSizes[0] = test.attributeCount;
var indeces = Enumerable.Range(0, test.attributeCount).ToArray();
var acc = new float[] { 0f };
var inCashe = new byte[] { 0 };
using (CudaDeviceVariable <int> deviceIndeces = indeces)
using (CudaDeviceVariable <int> deviceVectorSizesLocal = vectorSizes)
using (CudaDeviceVariable <float> accuracy = acc)
using (var heapMem = new CudaDeviceVariable <HeapData>(K))
using (CudaDeviceVariable <byte> deviceIsInCashe = inCashe)
{
kernel.Run(
test.vectors.DevicePointer,
test.classes.DevicePointer,
teaching.vectors.DevicePointer,
teaching.classes.DevicePointer,
deviceVectorSizesLocal.DevicePointer,
deviceIndeces.DevicePointer,
deviceIsInCashe.DevicePointer,
heapMem.DevicePointer,
accuracy.DevicePointer
);
float[] res = accuracy;
return(res[0] / test.length);
}
}
public Layer(Int3 size, CudaContext ctx)
{
this.size = size;
data = new float[size.Mul];
bias = new float[size.Mul];
error = new float[size.Mul];
clear = ctx.LoadKernel("kernel.ptx", "Clear");
clear.GridDimensions = new dim3(size.x, size.y, size.z);
}
public Layer(Int3 size, Layer prev, ref CudaContext ctx, int type)
{
this.ctx = ctx;
this.type = type;
this.size = size;
data = new float[size.Mul];
bias = new float[size.Mul];
error = new float[size.Mul];
generateWeights(size, prev.size, kernelType.fullyConnected);
forward = ctx.LoadKernel("kernel.ptx", "Forward");
forward.GridDimensions = new dim3(size.x, size.y, size.z);
forward.BlockDimensions = new dim3(prev.size.x, prev.size.y, prev.size.z);
back = ctx.LoadKernel("kernel.ptx", "Backprop");
back.GridDimensions = new dim3(size.x, size.y, size.z);
back.BlockDimensions = new dim3(prev.size.x, prev.size.y, prev.size.z);
clear = ctx.LoadKernel("kernel.ptx", "Clear");
clear.GridDimensions = new dim3(size.x, size.y, size.z);
activate = ctx.LoadKernel("kernel.ptx", "Activate");
activate.GridDimensions = new dim3(size.x, size.y, size.z);
SoftmaxSigma = ctx.LoadKernel("kernel.ptx", "SoftmaxSigma");
SoftmaxSigma.GridDimensions = new dim3(size.x, size.y, size.z);
SoftmaxFinal = ctx.LoadKernel("kernel.ptx", "SoftmaxFinal");
SoftmaxFinal.BlockDimensions = new dim3(size.x, size.y, size.z);
SoftmaxVal = new float[] { 0 };
}
public List <float> CUDA_FIR(List <float> x, List <double> h)
{
CudaContext ctx = new CudaContext();
//alloc data to cuda format
double2[] temp_x = new double2[x.Count + h.Count - 1];
double2[] temp_h = new double2[x.Count + h.Count - 1];
double2[] temp_y = new double2[x.Count + h.Count - 1];
//data copy
for (int i = 0; i < x.Count; i++)
{
temp_x[i].x = x[i];
}
for (int i = 0; i < h.Count; i++)
{
temp_h[i].x = h[i];
}
CudaDeviceVariable <double2> d_x = null;
CudaDeviceVariable <double2> d_h = null;
CudaFFTPlan1D plan1D = new CudaFFTPlan1D(x.Count + h.Count - 1, cufftType.Z2Z, 1);
CudaKernel kernel = ctx.LoadKernel("kernel.ptx", "ComplexMultCUDA");
kernel.GridDimensions = (int)Math.Ceiling((double)(x.Count + h.Count - 1) / 1024);
kernel.BlockDimensions = 1024;
try
{
d_x = temp_x;
d_h = temp_h;
}
catch (Exception e)
{
//("{0} Exception caught.", e);
return(null);
}
plan1D.Exec(d_x.DevicePointer, TransformDirection.Forward);
plan1D.Exec(d_h.DevicePointer, TransformDirection.Forward);
kernel.Run(d_h.DevicePointer, d_x.DevicePointer, x.Count + h.Count - 1);
plan1D.Exec(d_x.DevicePointer, TransformDirection.Inverse);
temp_y = d_x;
return(temp_y.Select(data => (float)data.x).ToList().GetRange(h.Count / 2, x.Count));
}
private int[] ApplyRest(CudaContext context, CudaDataSet <int> data)
{
int vectorsCount = data.Vectors.GetLength(0);
int attributeCount = data.Vectors.GetLength(1);
var kernel = context.LoadKernel("kernels/drop3.ptx", "findNeighbours");
kernel.GridDimensions = data.Vectors.GetLength(0) / ThreadsPerBlock + 1;
kernel.BlockDimensions = ThreadsPerBlock;
using (CudaDeviceVariable <int> d_classes = data.Classes)
using (CudaDeviceVariable <float> vectors = data.Vectors.Raw)
using (var heapMemory = new CudaDeviceVariable <HeapData>(data.Vectors.GetLength(0) * CasheSize))
using (var nearestEnemyDistances = new CudaDeviceVariable <float>(data.Vectors.GetLength(0)))
{
kernel.Run(
vectors.DevicePointer,
data.Vectors.GetLength(0),
data.Vectors.GetLength(1),
CasheSize,
d_classes.DevicePointer,
heapMemory.DevicePointer,
nearestEnemyDistances.DevicePointer
);
float[] hostNearestEnemy = nearestEnemyDistances;
float[][] hostVectors = data.Vectors.To2d();
var Neighbors = new FlattArray <HeapData>(heapMemory, CasheSize);
var nearestNeighbors = new int[vectorsCount][];
for (int i = 0; i < vectorsCount; i++)
{
nearestNeighbors[i] = new int[CasheSize];
for (int j = 0; j < CasheSize; j++)
{
nearestNeighbors[i][j] = Neighbors[i, j].label;
}
}
HostDataset host = data.ToHostDataSet();
SortDataDesc(host, nearestNeighbors, hostNearestEnemy);
return(proccesData(context, host, nearestNeighbors));
}
}
public VectorReductionFitness(CudaContext context, IVectorReductionAccuracy accuracyCalc, int popSize, int teachingCount)
{
this.teachingCount = teachingCount;
this.accuracyCalc = accuracyCalc;
this.popSize = popSize;
this.context = context;
countVectorsKernel = new CountVectorKernel(context, popSize, teachingCount);
vectorSizes = new CudaDeviceVariable <int>(popSize);
fitnessKernel = context.LoadKernel("kernels/VectorReduction.ptx", "fitnessFunction");
Alpha = 0.7f;
fitnessKernel.BlockDimensions = popSize;
fitnessKernel.GridDimensions = 1;
}
public NeighborFinder(CudaContext context, FlattArray <float> vectors, int countToFind)
{
heap = new Data[countToFind];
this.vectorCount = vectors.GetLength(0);
this.attrCount = vectors.GetLength(1);
this.context = context;
kernel = context.LoadKernel("kernels/drop3.ptx", "calculateDistances");
kernel.GridDimensions = vectors.GetLength(0) / 256 + 1;
kernel.BlockDimensions = 256;
results = new float[vectors.GetLength(0)];
deviceVectors = vectors.Raw;
deviceResult = new CudaDeviceVariable <float>(vectors.GetLength(0));
deviceIsInDataSet = new CudaDeviceVariable <byte>(vectors.GetLength(0));
}
CudaDataSet <int> Enn(CudaDataSet <int> data, CudaContext context)
{
var kernel = context.LoadKernel("kernels/kernel.ptx", "enn");
kernel.GridDimensions = data.Vectors.GetLength(0) / ThreadsPerBlock + 1;
kernel.BlockDimensions = ThreadsPerBlock;
using (CudaDeviceVariable <float> vectors = data.Vectors.Raw)
using (CudaDeviceVariable <int> classes = data.Classes)
using (var heapMemory = new CudaDeviceVariable <HeapData>(data.Vectors.GetLength(0) * K))
using (var result = new CudaDeviceVariable <byte>(data.Vectors.GetLength(0)))
{
kernel.Run(
vectors.DevicePointer,
data.Vectors.GetLength(0),
data.Vectors.GetLength(1),
classes.DevicePointer,
K,
EnnCountToPass,
heapMemory.DevicePointer,
result.DevicePointer
);
byte[] hostResult = result;
List <int> indeces = new List <int>();
for (int i = 0; i < hostResult.Length; i++)
{
if (hostResult[i] == 1)
{
indeces.Add(i);
}
}
return(data.Filter(hostResult.createIndexesToStay()));
}
}
public List <float> CUDA_FIR_long(List <float> x, List <double> h)
{
CudaContext ctx = new CudaContext();
string path = Path.GetDirectoryName(mv.plugins[0].filename);
int N = 2000000;
//alloc data to cuda format
double2[][] temp_x = new double2[(int)Math.Ceiling((double)(x.Count + h.Count - 1) / (N + h.Count - 1))][];
double2[] temp_h = new double2[N + h.Count - 1];
double2[][] temp_y = new double2[(int)Math.Ceiling((double)(x.Count + h.Count - 1) / (N + h.Count - 1))][];
//data copy
System.Threading.Tasks.Parallel.For(0, (int)Math.Ceiling((double)(x.Count + h.Count - 1) / (N + h.Count - 1)), j => {
temp_x[j] = new double2[N + h.Count - 1];
temp_y[j] = new double2[N + h.Count - 1];
for (int i = 0; (j * N + i) < x.Count && i < N; i++)
{
temp_x[j][i].x = x[j * N + i];
}
});
for (int i = 0; i < h.Count; i++)
{
temp_h[i].x = h[i];
}
CudaDeviceVariable <double2> d_x = null;
CudaDeviceVariable <double2> d_h = null;
CudaFFTPlan1D plan1D = new CudaFFTPlan1D(N + h.Count - 1, cufftType.Z2Z, 1);
CudaKernel kernel = ctx.LoadKernel(path + "\\kernel.ptx", "ComplexMultCUDA");
kernel.GridDimensions = (int)Math.Ceiling((double)(N + h.Count - 1) / 1024);
kernel.BlockDimensions = 1024;
try
{
d_h = temp_h;
}
catch (Exception e)
{
//("{0} Exception caught.", e);
return(null);
}
plan1D.Exec(d_h.DevicePointer, TransformDirection.Forward);
for (int g = 0; g < (int)Math.Ceiling((double)(x.Count + h.Count - 1) / (N + h.Count - 1)); g++)
{
try
{
d_x = temp_x[g];
}
catch (Exception e)
{
mainView.log(e, "cuda alloc data error", this);
return(null);
}
try
{
plan1D.Exec(d_x.DevicePointer, TransformDirection.Forward);
kernel.Run(d_h.DevicePointer, d_x.DevicePointer, N + h.Count - 1);
plan1D.Exec(d_x.DevicePointer, TransformDirection.Inverse);
}
catch (Exception exp)
{
mainView.log(exp, "kernel run cuda error", this);
}
temp_y[g] = d_x;
//this.Invoke((MethodInvoker)delegate
//{
// progressBar1.Value = (int)(50/ (int)Math.Ceiling((double)(x.Count + h.Count - 1) / (N + h.Count - 1)))*g;
//});
d_x.Dispose();
}
d_h.Dispose();
plan1D.Dispose();
return(OverlapAdd(temp_y, h.Count).GetRange(h.Count / 2, x.Count));
}
protected void InitContext()
{
var size = ParticlesCount * DimensionsCount;
var threadsNum = 32;
var blocksNum = ParticlesCount / threadsNum;
Ctx = new CudaContext(0);
UpdateVelocity = Ctx.LoadKernel("update_velocity_kernel.ptx", "updateVelocityKernel");
UpdateVelocity.GridDimensions = blocksNum;
UpdateVelocity.BlockDimensions = threadsNum;
Transpose = Ctx.LoadKernel(KernelFile, "transposeKernel");
Transpose.GridDimensions = blocksNum;
Transpose.BlockDimensions = threadsNum;
HostPositions = Random.RandomVector(size, -5.0, 5.0);
HostVelocities = Random.RandomVector(size, -2.0, 2.0);
HostPersonalBests = (double[]) HostPositions.Clone();
HostPersonalBestValues = Enumerable.Repeat(double.MaxValue,ParticlesCount).ToArray();
HostNeighbors = new int[ParticlesCount * 2];
for (var i = 0; i < ParticlesCount*2; i += 2)
{
int left, right;
if (i == 0)
left = ParticlesCount - 1;
else
left = i - 1;
if (i == ParticlesCount - 1)
right = 0;
else
right = i + 1;
HostNeighbors[i] = left;
HostNeighbors[i + 1] = right;
}
DevicePositions = HostPositions;
DeviceVelocities = HostVelocities;
DevicePersonalBests = HostPersonalBests;
DevicePersonalBestValues = HostPersonalBestValues;
DeviceNeighbors = HostNeighbors;
Init();
}
public List <float> hypotesis_long(List <double> x, List <double> h, int N)
{
//int N = 2000000;
string path = Path.GetDirectoryName(mv.plugins[0].filename);
CudaContext ctx = new CudaContext();
CudaKernel kernel = ctx.LoadKernel(path + "\\kernel.ptx", "ComplexMultCUDA");
kernel.GridDimensions = (int)Math.Ceiling((double)(N + h.Count - 1) / 1024);
kernel.BlockDimensions = 1024;
int blocks = (int)Math.Ceiling((double)(x.Count + h.Count - 1) / (N + h.Count - 1));
double[][] temp_y = new double[blocks][];
double[] temp_h = new double[N + h.Count - 1];
double[] temp_x = new double[N + h.Count - 1];
h.ToArray().CopyTo(temp_h, 0);
CudaDeviceVariable <double> d_x = null;
CudaDeviceVariable <double2> d_X = new CudaDeviceVariable <double2>(N + h.Count - 1);
CudaDeviceVariable <double> d_h = new CudaDeviceVariable <double>(N + h.Count - 1);
CudaDeviceVariable <double2> d_H = new CudaDeviceVariable <double2>(N + h.Count - 1);
//CudaDeviceVariable<double> d_y = new CudaDeviceVariable<double>(N + h.Count - 1);
CudaFFTPlan1D planForward = new CudaFFTPlan1D(N + h.Count - 1, cufftType.D2Z, 1);
CudaFFTPlan1D planInverse = new CudaFFTPlan1D(N + h.Count - 1, cufftType.Z2D, 1);
try
{
d_h = temp_h;
planForward.Exec(d_h.DevicePointer, d_H.DevicePointer, TransformDirection.Forward);
}
catch (Exception exp)
{
mainView.log(exp, "CUDA error: Impulse response FFT", this);
return(null);
}
for (int g = 0; g < blocks; g++)
{
int P = N;
if (x.Count - N * g < N)
{
P = x.Count - N * g;
}
x.GetRange(N * g, P).ToArray().CopyTo(temp_x, 0);
try
{
d_x = temp_x;
planForward.Exec(d_x.DevicePointer, d_X.DevicePointer);
kernel.Run(d_H.DevicePointer, d_X.DevicePointer, N + h.Count - 1);
planInverse.Exec(d_X.DevicePointer, d_x.DevicePointer);
}
catch (Exception exp)
{
mainView.log(exp, "Cuda error: kernel run cuda error", this);
}
temp_y[g] = d_x;
}
return(OverlapAdd(temp_y, h.Count).GetRange(h.Count / 2, x.Count));
}
public List <float> hypotesis_long_save(List <double> xx, List <double> h, int N)
{
int n = (int)Math.Ceiling((double)(xx.Count() + 0.000000000001) / N);
double[] temp_data = new double[n * (N + h.Count - 1) - (n - 1) * (h.Count - 1)];
xx.CopyTo(temp_data, h.Count - 1);
List <double> x = temp_data.ToList();
//int N = 2000000;
string path = Path.GetDirectoryName(mv.plugins[0].filename);
CudaContext ctx = new CudaContext();
CudaKernel kernel = ctx.LoadKernel(path + "\\kernel.ptx", "ComplexMultCUDA");
kernel.GridDimensions = (int)Math.Ceiling((double)(N + h.Count - 1) / 1024);
kernel.BlockDimensions = 1024;
int blocks = (int)Math.Ceiling((double)(x.Count + h.Count - 1) / (N + h.Count - 1));
double[][] temp_y = new double[n][];
double[] temp_h = new double[N + h.Count - 1];
double[] temp_x = new double[N + h.Count - 1];
h.ToArray().CopyTo(temp_h, 0);
CudaDeviceVariable <double> d_x = null;
CudaDeviceVariable <double> d_h = new CudaDeviceVariable <double>(N + h.Count - 1);
CudaDeviceVariable <double2> d_H = new CudaDeviceVariable <double2>(N + h.Count - 1);
//CudaDeviceVariable<double> d_y = new CudaDeviceVariable<double>(N + h.Count - 1);
CudaFFTPlan1D planForward = new CudaFFTPlan1D(N + h.Count - 1, cufftType.D2Z, 1);
CudaFFTPlan1D planInverse = new CudaFFTPlan1D(N + h.Count - 1, cufftType.Z2D, 1);
try
{
d_h = temp_h;
planForward.Exec(d_h.DevicePointer, d_H.DevicePointer, TransformDirection.Forward);
}
catch (Exception exp)
{
mainView.log(exp, "CUDA error: Impulse response FFT", this);
return(null);
}
for (int g = 0; g < n; g++)
{
CudaDeviceVariable <double2> d_X = new CudaDeviceVariable <double2>(N + h.Count - 1);
int P = N + h.Count - 1;
//if (x.Count - P * g < P) P = x.Count - P * g;
int L = h.Count - 1;
if (g == 0)
{
L = 0;
}
x.CopyTo(P * g - L * g, temp_x, 0, P);
try
{
d_x = temp_x;
planForward.Exec(d_x.DevicePointer, d_X.DevicePointer);
kernel.Run(d_H.DevicePointer, d_X.DevicePointer, N + h.Count - 1);
planInverse.Exec(d_X.DevicePointer, d_x.DevicePointer);
}
catch (Exception exp)
{
mainView.log(exp, "Cuda error: kernel run cuda error", this);
}
temp_y[g] = d_x;
d_x.Dispose();
d_X.Dispose();
}
planForward.Dispose();
planInverse.Dispose();
d_x.Dispose();
d_h.Dispose();
d_H.Dispose();
ctx.Dispose();
return(OverlapSave(temp_y, h.Count, N + h.Count - 1).GetRange(h.Count / 2, xx.Count));
}
public DimensionReductionAccuracy(
CudaContext context,
DeviceDataSet <int> teaching,
DeviceDataSet <int> test,
int popSize
)
{
this.popSize = popSize;
this.teaching = teaching;
this.test = test;
this.context = context;
accuracyKernel = context.LoadKernel
(
"kernels/dimensionsReductions.ptx",
"geneticKnn"
);
accuracyKernel.GridDimensions = new dim3()
{
x = (uint)(test.vectors.Size / ThreadsPerBlock) + 1,
y = (uint)popSize,
z = 1
};
accuracyKernel.BlockDimensions = ThreadsPerBlock;
K = 3;
CountToPass = 2;
accuracyKernel.SetConstantVariable("atributeCount", test.attributeCount);
accuracyKernel.SetConstantVariable("teachingVectorsCount", teaching.length);
accuracyKernel.SetConstantVariable("testVectorsCount", test.length);
accuracyKernel.SetConstantVariable("popSize", popSize);
accuracyKernel.DynamicSharedMemory = (uint)(test.attributeCount * sizeof(float));
saveCasheKernel = context.LoadKernel(
"kernels/dimensionsReductions.ptx",
"saveToCashe"
);
saveCasheKernel.GridDimensions = (popSize * 32) / ThreadsPerBlock + 1;
saveCasheKernel.BlockDimensions = ThreadsPerBlock;
saveCasheKernel.SetConstantVariable("atributeCount", teaching.attributeCount);
saveCasheKernel.SetConstantVariable("popSize", teaching.attributeCount);
readCasheKernel = context.LoadKernel(
"kernels/dimensionsReductions.ptx",
"readCashe"
);
readCasheKernel.GridDimensions = 1;
readCasheKernel.BlockDimensions = popSize;
readCasheKernel.SetConstantVariable("atributeCount", teaching.attributeCount);
casheTreeRoot = new Node()
{
mutex = 0,
one = (IntPtr)0,
zero = (IntPtr)0,
};
isInCashe = new CudaDeviceVariable <byte>(popSize);
accuracy = new CudaDeviceVariable <float>(popSize);
}
