public static void cudaTranspose(ref MathNet.Numerics.LinearAlgebra.Double.DenseMatrix dm)
{
GPGPU gpu = CudafyHost.GetDevice(eGPUType.Cuda);
GPGPUBLAS blas = GPGPUBLAS.Create(gpu);
int cols = dm.ColumnCount, rows = dm.RowCount;
int restRows = rows - cols;
//double[] a = dm.Storage.ToColumnMajorArray();
double[] a = dm.SubMatrix(0, cols, 0, cols).Storage.ToColumnMajorArray();
double[] b = dm.SubMatrix(cols, restRows, 0, cols).Storage.ToColumnMajorArray();
dm = null;
double[] a_d = gpu.CopyToDevice <double>(a);
a = null;
double[] c_d = gpu.Allocate <double>(cols * cols);
double[] x_d = gpu.CopyToDevice <double>(new double[] { 1 });
blas.GEMV(cols, cols, 1, c_d, x_d, 0, x_d, Cudafy.Maths.BLAS.Types.cublasOperation.T);
a = new double[cols * rows];
gpu.CopyFromDevice <double>(c_d, 0, a, 0, cols * cols);
gpu.FreeAll();
a_d = gpu.CopyToDevice <double>(b);
b = null;
c_d = gpu.Allocate <double>(restRows * cols);
x_d = gpu.CopyToDevice <double>(new double[] { 1 });
blas.GEMV(restRows, cols, 1, c_d, x_d, 0, x_d, Cudafy.Maths.BLAS.Types.cublasOperation.T);
gpu.CopyFromDevice <double>(c_d, 0, a, cols * cols, restRows * cols);
gpu.FreeAll();
dm = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(cols, rows, a);
}
private float cuda_malloc_test(int size, bool up)
{
int[] a = new int[size];
int[] dev_a = _gpu.Allocate <int>(size);
_gpu.StartTimer();
for (int i = 0; i < 100; i++)
{
if (up)
{
_gpu.CopyToDevice(a, dev_a);
}
else
{
_gpu.CopyFromDevice(dev_a, a);
}
}
float elapsedTime = _gpu.StopTimer();
_gpu.FreeAll();
GC.Collect();
return(elapsedTime);
}
public static void ExecDoSomeMath(int start, int end)
{
InitGPU();
NN = end - start;
double[] result = new double[NN];
//Allocate GPU
int[] dev_start = _gpu.Allocate <int>(start);
int[] dev_end = _gpu.Allocate <int>(end);
double[] dev_result = _gpu.Allocate <double>(result);
_gpu.CopyToDevice(new int[1] {
start
}, dev_start);
_gpu.CopyToDevice(new int[1] {
end
}, dev_end);
_gpu.Launch(128, 1).DoSomeMath(dev_start, dev_end, dev_result);
_gpu.CopyFromDevice(dev_result, result);
_gpu.FreeAll();
foreach (var r in result)
{
Console.WriteLine(r);
}
}
//GPU PFAC Carving - using PFAC for searching Bytes
public void LaunchPFACCarving(int gpuCore)
{
lock (gpuThreadLock[GPUid])
{
gpu.SetCurrentContext();
gpu.LaunchAsync(gpuOperatingCores, blockSize, gpuCore, "PFACAnalyse", dev_buffer[gpuCore], initialState, dev_lookup, dev_targetEndLength, dev_resultCount[gpuCore], dev_foundCount[gpuCore], dev_foundID[gpuCore], dev_foundLoc[gpuCore]);
gpu.SynchronizeStream(gpuCore);
}
gpu.CopyFromDevice(dev_resultCount[gpuCore], resultCount[gpuCore]);
for (int i = 0; i < resultCount[gpuCore].Length; i++)
{
if (resultCount[gpuCore][i] > 0)
{
gpu.CopyFromDevice(dev_foundID[gpuCore], foundID[gpuCore]);
gpu.CopyFromDevice(dev_foundLoc[gpuCore], foundLoc[gpuCore]);
break;
}
}
//gpu.Synchronize();
FreeBuffers(gpuCore);
}
public void Test_SingleThreadOneGPU_1()
{
_gpu1.SetCurrentContext();
_gpuuintBufferIn0 = _gpu1.CopyToDevice(_uintBufferIn0);
_gpu1.CopyFromDevice(_gpuuintBufferIn0, _uintBufferOut0);
Assert.IsTrue(Compare(_uintBufferIn0, _uintBufferOut0));
ClearOutputsAndGPU(1);
}
public void Test_SingleThreadCopy()
{
_gpuuintBufferIn1 = _gpu.CopyToDevice(_uintBufferIn1);
_gpu.CopyFromDevice(_gpuuintBufferIn1, _uintBufferOut1);
Assert.IsTrue(Compare(_uintBufferIn1, _uintBufferOut1));
ClearOutputs();
_gpu.FreeAll();
}
//[Test]
public void TestCGSolver()
{
Stopwatch sw = new Stopwatch();
float one = 1.0f;
float zero = 0.0f;
_hiMatrixMN = new float[N * N];
_hoVectorN = new float[N];
CreateDiagonalMatrix(_hiMatrixMN, N, 6);
_hiVectorN = new float[N];
_hiVectorN2 = new float[N];
FillBuffer(_hiVectorN2, 6);
_diMatrixMN = _gpu.CopyToDevice(_hiMatrixMN);
_diVectorN = _gpu.Allocate(_hiVectorN);
_diVectorN2 = _gpu.CopyToDevice(_hiVectorN2);
_diPerRow = _gpu.Allocate <int>(N);
_diVectorP = _gpu.Allocate <float>(N);
_diVectorAX = _gpu.Allocate <float>(N);
int nnz = _sparse.NNZ(N, N, _diMatrixMN, _diPerRow);
_diCSRVals = _gpu.Allocate <float>(nnz);
_diCSRCols = _gpu.Allocate <int>(nnz);
_diCSRRows = _gpu.Allocate <int>(N + 1);
_sparse.Dense2CSR(N, N, _diMatrixMN, _diPerRow, _diCSRVals, _diCSRRows, _diCSRCols);
sw.Start();
SolveResult result = _solver.CG(N, nnz, _diCSRVals, _diCSRRows, _diCSRCols, _diVectorN, _diVectorN2, _diVectorP, _diVectorAX, 0.01f, 1000);
long time = sw.ElapsedMilliseconds;
_sparse.CSRMV(N, N, nnz, ref one, _diCSRVals, _diCSRRows, _diCSRCols, _diVectorN, ref zero, _diVectorN2);
_gpu.CopyFromDevice(_diVectorN2, _hoVectorN);
float maxError = 0.0f;
for (int i = 0; i < N; i++)
{
float error = Math.Abs(_hoVectorN[i] - _hiVectorN2[i]);
if (error > maxError)
{
maxError = error;
}
}
Console.WriteLine("Time : {0} ms", time);
Console.WriteLine("Iterate Count : {0}", result.IterateCount);
Console.WriteLine("Residual : {0}", result.LastError);
Console.WriteLine("max error : {0}", maxError);
_gpu.FreeAll();
}
public static int MA(int[,] A, int[,] B, int[,] C, GPGPU gpu, int maxTheadBlockSize, int Size)
{
// allocate the memory on the GPU
int[,] GPU_A = gpu.Allocate<int>(A);
int[,] GPU_B = gpu.Allocate<int>(B);
int[,] GPU_C = gpu.Allocate<int>(C);
// copy the arrays 'a' and 'b' to the GPU
gpu.CopyToDevice(A, GPU_A);
gpu.CopyToDevice(B, GPU_B);
dim3 threadsPerBlock;
// find the number of threads and blocks
if (Size < maxTheadBlockSize)
{
threadsPerBlock = new dim3(Size, Size);
}
else
{
threadsPerBlock = new dim3(maxTheadBlockSize, maxTheadBlockSize);
}
dim3 block = new dim3(Size, Size);
// launch GPU_MA
gpu.Launch(block, threadsPerBlock, "GPU_MA", GPU_A, GPU_B, GPU_C, Size);
// copy the array 'c' back from the GPU to the CPU
gpu.CopyFromDevice(GPU_C, C);
gpu.Free(GPU_A);
gpu.Free(GPU_B);
gpu.Free(GPU_C);
return 1;
}
public static void Execute()
{
CudafyModule km = CudafyTranslator.Cudafy(typeof(ParamsStruct), typeof(ImpliedVolatile));
_gpu = CudafyHost.GetDevice(CudafyModes.Target);
_gpu.LoadModule(km);
ParamsStruct[] host_par = new ParamsStruct[1];
ParamsStruct[] result = new ParamsStruct[1];
host_par[0].OP = 96.95;
host_par[0].Price = 1332.24;
host_par[0].Strike = 1235;
host_par[0].TD = 31;
host_par[0].R = 0.0001355;
host_par[0].Q = 0.0166;
host_par[0].N = 100;// 1000;
host_par[0].kind = 1;
ParamsStruct[] dev_par = _gpu.CopyToDevice(host_par);
float[] PA = _gpu.Allocate<float>(1001);
_gpu.Launch(1,1, "impliedVolatile", dev_par, PA);
_gpu.CopyFromDevice(dev_par, 0, result, 0, 1);
Console.WriteLine("I={0}, B={1}", result[0].i, result[0].B);
//Console.ReadKey();
}
static Bitmap Render(GPGPU gpu, int frameNum)
{
uint[,] deviceImage = gpu.Allocate <uint>(width, height);
float[] pX1_gpu = gpu.CopyToDevice <float>(pX1);
float[] pY1_gpu = gpu.CopyToDevice <float>(pY1);
float[] pZ1_gpu = gpu.CopyToDevice <float>(pZ1);
float[] colorPosition_gpu = gpu.CopyToDevice <float>(colorPosition);
float[] currentTime_gpu = gpu.CopyToDevice <float>(currentTime);
dim3 threadsPerBlock = new dim3(8, 8);
dim3 numBlocks = new dim3(width / threadsPerBlock.x, height / threadsPerBlock.y);
gpu.Launch(numBlocks, threadsPerBlock).renderKernel(deviceImage, pX1_gpu, pY1_gpu, pZ1_gpu, colorPosition_gpu, currentTime_gpu);
uint[,] finalImage = new uint[width, height];
gpu.CopyFromDevice <uint>(deviceImage, finalImage);
gpu.Free(deviceImage);
gpu.Free(pX1_gpu);
gpu.Free(pY1_gpu);
gpu.Free(pZ1_gpu);
gpu.Free(colorPosition_gpu);
gpu.Free(currentTime_gpu);
GCHandle pixels = GCHandle.Alloc(finalImage, GCHandleType.Pinned);
Bitmap bmp = new Bitmap(width, height, width * sizeof(int), PixelFormat.Format32bppRgb, pixels.AddrOfPinnedObject());
bmp.Save("spring" + frameNum + ".png");
pixels.Free();
return(bmp);
}
public static float[] CallGPU()
{
CudafyModes.Target = eGPUType.OpenCL;
CudafyModes.DeviceId = 0;
CudafyTranslator.Language = eLanguage.OpenCL;
CudafyModule km = CudafyTranslator.Cudafy(ePlatform.Auto, eArchitecture.OpenCL, typeof(GPU));
GPGPU gpu = CudafyHost.GetDevice(eGPUType.OpenCL, 0);
gpu.LoadModule(km);
km.Serialize();
float[] input = Utils.GenerateRandomVector();
float[,,] NN = Utils.GenerateRandomMatrix().AsSingleDimension();
float[] output = new float[Utils.N];
Stopwatch gpuSW = new Stopwatch();
gpuSW.Start();
float[] dev_output = gpu.Allocate <float>(output);
float[] dev_input = gpu.CopyToDevice(input);
float[,,] dev_NN = gpu.CopyToDevice(NN);
gpu.Launch(Utils.GRID_SIZE, Utils.BLOCK_SIZE).CalculateNeuralNetwork(dev_input, dev_NN, dev_output);
gpu.CopyFromDevice(dev_output, output);
gpu.FreeAll();
gpuSW.Stop();
Console.WriteLine("GPU: " + gpuSW.ElapsedMilliseconds);
return(output);
}
public static void MyFirstBlasEmulatorTest()
{
Console.WriteLine("MyTest()");
// Get GPU device
CudafyModes.Target = eGPUType.Emulator;
GPGPU gpu = CudafyHost.GetDevice(CudafyModes.Target);
// Create GPGPUBLAS (CUBLAS Wrapper)
using (GPGPUBLAS blas = GPGPUBLAS.Create(gpu))
{
const int N = 100;
float[] a = new float[N];
float[] b = new float[N];
float[] c = new float[N];
float alpha = -1;
float beta = 0;
float[] device_a = gpu.CopyToDevice(a);
float[] device_b = gpu.CopyToDevice(b);
float[] device_c = gpu.CopyToDevice(c);
int m = 10;
int n = 10;
int k = 10;
cublasOperation Op = cublasOperation.N;
blas.GEMM(m, k, n, alpha, device_a, device_b, beta, device_c, Op);
gpu.CopyFromDevice <float>(device_c, c);
}
}
/// <summary>
/// Вызов и исполнение функции проверки что массив отсортирован
/// </summary>
public static void ExecuteSorted(int direction = 1)
{
CudafyModule km = CudafyTranslator.Cudafy();
GPGPU gpu = CudafyHost.GetDevice();
gpu.LoadModule(km);
int[] devA = gpu.Allocate(_a);
int[] devB = gpu.Allocate(_b);
int[] devC = gpu.Allocate(_c);
int[] devD = gpu.Allocate(D);
gpu.CopyToDevice(_a, devA);
gpu.Launch(1, 1).Split(devA, devB, devC, _middle);
gpu.Launch(_gridSize, _blockSize).Sorted(devA, devB, devC, devD, 0, direction);
gpu.Launch(1, 1).Sorted(devA, devB, devC, devD, 1, direction);
gpu.CopyFromDevice(devD, D);
// free the memory allocated on the GPU
gpu.FreeAll();
}
public static void Execute()
{
CudafyModule km = CudafyModule.TryDeserialize();
if (km == null || !km.TryVerifyChecksums())
{
km = CudafyTranslator.Cudafy(typeof(Generic <ushort, ushort>), typeof(SimpleGeneric));
km.Serialize();
}
GPGPU gpu = CudafyHost.GetDevice(CudafyModes.Target);
gpu.LoadModule(km);
var input = new Generic <ushort, ushort>();
input.A = 187;
int[] devoutput = gpu.Allocate <int>(1);
gpu.Launch(1, 1, "Kernel", input, devoutput);
int output;
gpu.CopyFromDevice(devoutput, out output);
Console.WriteLine("Simple Generic: " + ((output == 1) ? "PASSED" : "FAILED"));
}
public Bitmap Render(Rendering.ExecutionOptions options, Action <string> log)
{
try
{
var result = new Bitmap(options.Width, options.Height);
int width = options.Width;
int height = options.Height;
log("Initializing and copying data to GPU memory");
int[,] iterations = new int[height, width];
var dev_iterations = gpu.CopyToDevice(iterations);
var gridSize = new dim3(height, width);
var blockSize = BlockSize;
var minX = (float)options.MinX;
var maxX = (float)options.MaxX;
var minY = (float)options.MinY;
var maxY = (float)options.MaxY;
var stepX = (maxX - minX) / ((float)width);
var stepY = (maxY - minY) / ((float)height);
log("Launching Mandelbrot calculations");
gpu.Launch(gridSize, blockSize, "CalculateMandelbrot", minX, maxY, stepX, stepY, dev_iterations);
log("Mandelbrot calculations done, fetching results from GPU memory");
gpu.CopyFromDevice(dev_iterations, iterations);
log("Generating the final image");
Rendering.fastDrawBitmap(result, iterations);
return(result);
}
finally
{
gpu.FreeAll();
}
}
public static uint[] Evaluate(ulong[] hands, int numCards)
{
// Translates this class to CUDA C and then compliles
CudafyModule km = CudafyTranslator.Cudafy();//eArchitecture.sm_20);
// Get the first GPU and load the module
GPGPU gpu = CudafyHost.GetDevice(CudafyModes.Target, CudafyModes.DeviceId);
gpu.LoadModule(km);
int blockSize = 256;
int blockx = hands.Length / blockSize;
if (hands.Length % blockSize != 0)
{
blockx++;
}
ulong[] dev_hands = gpu.Allocate <ulong>(hands.Length);
uint[] dev_ranks = gpu.Allocate <uint>(hands.Length);
gpu.CopyToDevice(hands, dev_hands);
gpu.StartTimer();
gpu.Launch(blockx, blockSize).evaluate(dev_hands, numCards, hands.Length, dev_ranks);
var ts = gpu.StopTimer();
uint[] toReturn = new uint[hands.Length];
gpu.CopyFromDevice(dev_ranks, toReturn);
return(toReturn);
}
public static void Execute()
{
CudafyModule km = CudafyTranslator.Cudafy(typeof(ParamsStruct), typeof(ImpliedVolatile));
_gpu = CudafyHost.GetDevice(CudafyModes.Target);
_gpu.LoadModule(km);
ParamsStruct[] host_par = new ParamsStruct[1];
ParamsStruct[] result = new ParamsStruct[1];
host_par[0].OP = 96.95;
host_par[0].Price = 1332.24;
host_par[0].Strike = 1235;
host_par[0].TD = 31;
host_par[0].R = 0.0001355;
host_par[0].Q = 0.0166;
host_par[0].N = 100;// 1000;
host_par[0].kind = 1;
ParamsStruct[] dev_par = _gpu.CopyToDevice(host_par);
float[] PA = _gpu.Allocate <float>(1001);
_gpu.Launch(1, 1, "impliedVolatile", dev_par, PA);
_gpu.CopyFromDevice(dev_par, 0, result, 0, 1);
Console.WriteLine("I={0}, B={1}", result[0].i, result[0].B);
//Console.ReadKey();
}
public void ExeTestKernel()
{
GPGPU gpu = CudafyHost.GetDevice(CudafyModes.Target, 0);
eArchitecture arch = gpu.GetArchitecture();
CudafyModule km = CudafyTranslator.Cudafy(arch);
gpu.LoadModule(km);
int[] host_results = new int[N];
// Either assign a new block of memory to hold results on device
var dev_results = gpu.Allocate <int>(N);
// Or fill your array with values first and then
for (int i = 0; i < N; i++)
{
host_results[i] = i * 3;
}
// Copy array with ints to device
var dev_filled_results = gpu.CopyToDevice(host_results);
// 64*16 = 1024 threads per block (which is max for sm_30)
dim3 threadsPerBlock = new dim3(64, 16);
// 8*8 = 64 blocks per grid , just for show so you get varying numbers
dim3 blocksPerGrid = new dim3(8, 8);
//var threadsPerBlock = 1024; // this will only give you blockDim.x = 1024, .y = 0, .z = 0
//var blocksPerGrid = 1; // just for show
gpu.Launch(blocksPerGrid, threadsPerBlock, "GenerateRipples", dev_results, dev_filled_results);
gpu.CopyFromDevice(dev_results, host_results);
}
public static void Example2(GPGPU gpu)
{
ArrayView view1 = new ArrayView();
ArrayView view2 = new ArrayView();
float[] data = Enumerable.Range(0, 1000).Select(t => (float)t).ToArray();
// Two views of the array, simply applying an offset to the array; could slice instead for example.
view1.CreateView(data, 100);
view2.CreateView(data, 200);
for (int i = 0; i < 1000; ++i)
{
data[i] = data[i] * 10f;
}
// Should copy the 'large' array to the device only once; this is referenced by each ArrayView instance.
var dev_view1 = DeviceClassHelper.CreateDeviceObject(gpu, view1);
var dev_view2 = DeviceClassHelper.CreateDeviceObject(gpu, view2);
var dev_result = gpu.Allocate <float>(5);
var hostResult = new float[5];
gpu.Launch(1, 1).Test2(dev_view1, dev_view2, dev_result);
gpu.CopyFromDevice(dev_result, hostResult);
bool pass = (hostResult[0] == 1050f && hostResult[1] == 7f);
Console.WriteLine(pass ? "Pass" : "Fail");
}
/// <summary>
/// Вызов и исполнение одной элементарной функции по имени функции
/// </summary>
/// <param name="function"></param>
public static void Execute(string function)
{
Debug.Assert(_indexes1.Last() == _sequencies1.Length);
Debug.Assert(_indexes2.Last() == _sequencies2.Length);
CudafyModule km = CudafyTranslator.Cudafy();
GPGPU gpu = CudafyHost.GetDevice();
gpu.LoadModule(km);
// copy the arrays 'a' and 'b' to the GPU
int[] devIndexes1 = gpu.CopyToDevice(_indexes1);
int[] devIndexes2 = gpu.CopyToDevice(_indexes2);
int[] devSequencies1 = gpu.CopyToDevice(_sequencies1);
int[] devSequencies2 = gpu.CopyToDevice(_sequencies2);
int[,] devMatrix = gpu.Allocate(_matrix);
int rows = _matrix.GetLength(0);
int columns = _matrix.GetLength(1);
dim3 gridSize = Math.Min(15, (int)Math.Pow((double)rows * columns, 0.33333333333));
dim3 blockSize = Math.Min(15, (int)Math.Pow((double)rows * columns, 0.33333333333));
gpu.Launch(gridSize, blockSize, function,
devSequencies1, devIndexes1,
devSequencies2, devIndexes2,
devMatrix);
// copy the array 'c' back from the GPU to the CPU
gpu.CopyFromDevice(devMatrix, _matrix);
// free the memory allocated on the GPU
gpu.FreeAll();
}
/// <summary>
/// Выполнение сортировки слияниями
/// Пример использования:
/// CudafySequencies.SetSequencies(arrayOfArray,arrayOfArray);
/// CudafySequencies.Execute("Compare");
/// var compare = CudafySequencies.GetMartix();
/// CudafyArray.SetArray(Enumerable.Range(0,n).ToArray());
/// CudafyArray.SetCompare(compare);
/// CudafyArray.MergeSort();
/// var indexesOfSorted = CudafyArray.GetArray();
/// </summary>
public static void MergeSort(int direction = 1)
{
CudafyModule km = CudafyTranslator.Cudafy();
GPGPU gpu = CudafyHost.GetDevice();
gpu.LoadModule(km);
int[] devA = gpu.Allocate(_a);
int[] devB = gpu.Allocate(_b);
gpu.CopyToDevice(_a, devA);
for (int i = 0; i < _ceiling; i++)
{
int gridSize = Math.Min(15, (int)Math.Pow((_length >> i) + i, 0.333333333333));
int blockSize = Math.Min(15, (int)Math.Pow((_length >> i) + i, 0.333333333333));
gpu.Launch(gridSize, blockSize)
.MergeLinear(((i & 1) == 0) ? devA : devB, ((i & 1) == 0) ? devB : devA, i, 0,
_length,
direction);
}
gpu.CopyFromDevice(((_ceiling & 1) == 0) ? devA : devB, _a);
// free the memory allocated on the GPU
gpu.FreeAll();
}
public static void primaGPU()
{
CudafyModule modul_kernel = CudafyTranslator.Cudafy();
GPGPU vga = CudafyHost.GetDevice(CudafyModes.Target, CudafyModes.DeviceId);
vga.LoadModule(modul_kernel);
Stopwatch waktu = new Stopwatch();
waktu.Start();
int[] list_cpu = new int[KONSTANTA_THREAD];
int[] list_cpy = new int[KONSTANTA_THREAD];
int[] list = vga.Allocate <int>(KONSTANTA_THREAD);
vga.Launch(KONSTANTA_THREAD, 1).ModulAtomic(list);
vga.CopyFromDevice(list, list_cpy);
vga.FreeAll();
int index = 0;
for (int z = 0; z < list_cpy.Length; z++)
{
if (list_cpy[z] != -1)
{
list_cpu[index] = list_cpy[z];
//Console.WriteLine(list_cpu[index]);
index++;
}
}
waktu.Stop();
TimeSpan ts = waktu.Elapsed;
String total = ts.Seconds.ToString();
Console.WriteLine("Total GPU ------ {0} detik> ", total);
}
protected override int CalculateEnergyDiffParallel(byte *energy, int *energyDiff, int width, int height, bool xDir, out int sMin)
{
if (xDir)
{
int blockSize2 = BlockSize / 2;
int blockCountX1 = (width + BlockSize - 1) / BlockSize;
int blockCountX2 = width / BlockSize == blockSize2 ? blockCountX1 : blockCountX1 + 1;
int blockCountY = (height + blockSize2 - 1) / blockSize2;
int gpuBlockCount = 256;
int gpuThreadCount = 1;
for (int i = 0; i < blockCountY; i++)
{
int yStart = i * blockSize2;
int yStop = Math.Min(yStart + blockSize2, height);
_gpu.Launch(blockCountX1, 1).DecTriangle(_energyGPU, _energyDiffGPU,
BlockSize, blockCountX1, width, height, _initWidth, yStart, yStop);
yStart = Math.Min(yStart + 1, height);
_gpu.Launch(blockCountX2, 1).IncTriangle(_energyGPU, _energyDiffGPU,
BlockSize, blockCountX2, width, height, _initWidth, yStart, yStop);
}
}
_gpu.CopyFromDevice(_energyDiffGPU, energyDiffArray);
return(GetMinIndAndValue(energyDiff, width, height, xDir, out sMin));
}
public static void eksekusi()
{
CudafyModule kernel_modul = CudafyTranslator.Cudafy();
GPGPU vga = CudafyHost.GetDevice(CudafyModes.Target, CudafyModes.DeviceId);
vga.LoadModule(kernel_modul);
Stopwatch waktu = new Stopwatch();
waktu.Start();
int[] array_vga = vga.Allocate <int>(KONSTANTA_THREAD);
int[] array_hasil = new int[KONSTANTA_THREAD];
//long[] matriks1 = vga.Allocate<long>(KONSTANTA_THREAD);
//long[] matriks2 = vga.Allocate<long>(KONSTANTA_THREAD);//new int[KONSTANTA_THREAD];
//long[] matriks3 = vga.Allocate<long>(KONSTANTA_THREAD); //[KONSTANTA_THREAD];
vga.Launch(KONSTANTA_THREAD, 1).fungsiAtomic(array_vga);
vga.CopyFromDevice(array_vga, array_hasil);
vga.FreeAll();
//for(int z = 0; z < array_hasil.Length; z++)
//{
// Console.WriteLine("Hasil Ekstrak----" + array_hasil[z]);
//}
vga.FreeAll();
waktu.Stop();
TimeSpan ts = waktu.Elapsed;
String total = ts.Milliseconds.ToString();
Console.WriteLine("Total VGA ------ > " + total);
}
public static bool TestGpuDoublePrecision(int DeviceId)
{
if (DeviceId > CudafyHost.GetDeviceCount(eGPUType.OpenCL))
{
return(false);
}
try
{
CudafyModes.Target = eGPUType.OpenCL;
CudafyTranslator.Language = eLanguage.OpenCL;
CudafyModule km = CudafyTranslator.Cudafy();
GPGPU gpu = CudafyHost.GetDevice(eGPUType.OpenCL, DeviceId);
gpu.LoadModule(km);
double c;
double[] dev_c = gpu.Allocate <double>();
gpu.Launch().add_double(2.5d, 7.5d, dev_c);
gpu.CopyFromDevice(dev_c, out c);
gpu.Free(dev_c);
return(c == 10.0d);
}
catch
{ return(false); }
}
/// <summary>
/// Приведение матрицы к "каноническому" виду, методом Гаусса-Жордана,
/// то есть к матрице, получаемой в результате эквивалентных преобразований
/// над строками, и у которой выполнено следующее - если i - индекс первого ненулевого значения в строке, то во всех
/// остальных строках матрицы по индексу i содержится только ноль.
/// Очевидно, что если индекса первого нулевого значения нет (-1), то вся строка нулевая.
/// Приведение матрицы к каноническому виду используется при решении систем линейных уравнений и при поиске
/// фундаментальной системы решений системы линейных уравнений.
/// В данной реализации используется матрица на полем GF(2), то есть булева матрица.
/// </summary>
/// <param name="function"></param>
public static void ExecuteGaussJordan()
{
CudafyModule km = CudafyTranslator.Cudafy();
GPGPU gpu = CudafyHost.GetDevice();
gpu.LoadModule(km);
int[,] devA = gpu.Allocate(_a);
int[,] devB = gpu.Allocate(_b);
int[] devC = gpu.Allocate(_c);
int[] devD = gpu.Allocate(_d);
int[] devE = gpu.Allocate(E);
gpu.CopyToDevice(_a, devA);
int rows = _a.GetLength(0);
int columns = _a.GetLength(1);
dim3 gridSize = Math.Min(15, (int)Math.Pow(rows * columns, 0.33333333333));
dim3 blockSize = Math.Min(15, (int)Math.Pow(rows * columns, 0.33333333333));
gpu.Launch(gridSize, blockSize, "RepeatZero", devA, devB, devC, devD, devE);
for (int i = 0; i < Math.Min(rows, columns); i++)
{
gpu.Launch(gridSize, blockSize, "IndexOfNonZero", devA, devB, devC, devD, devE);
gpu.CopyFromDevice(devC, _c);
while (i < Math.Min(rows, columns) && _c[i] == -1)
{
i++;
}
if (i >= Math.Min(rows, columns))
{
break;
}
int j = _c[i];
gpu.Launch(gridSize, blockSize, "BooleanGaussJordan", devA, devB, i, j);
int[,] t = devA;
devA = devB;
devB = t;
}
gpu.CopyFromDevice(devA, _a);
// free the memory allocated on the GPU
gpu.FreeAll();
}
public static void GetData()
{
Stopwatch sw = new Stopwatch();
sw.Start();
gpu.CopyFromDevice(dev_units, units);
//gpu.CopyFromDevice(dev_pixelMap, pixelMap);
gpu.CopyFromDevice(dev_camera, camera);
gpu.CopyFromDevice(dev_imageBytes, imageBytes);
//gpu.CopyFromDeviceAsync(dev_imageBytes, 0, imageBytesAddress, 0, imageBytes.Length);
//pinned.Free();
double t2 = sw.ElapsedMilliseconds; // 8-9ms
}
public static void Execute(byte[] bitmap)
{
DateTime dt = DateTime.Now;
CudafyModule km = CudafyModule.TryDeserialize(csFILENAME);
// Check the module exists and matches the .NET modules, else make new
if (km == null || !km.TryVerifyChecksums())
{
Console.WriteLine("There was no cached module available so we make a new one.");
km = CudafyModule.Deserialize(typeof(ray_serialize).Name);
km.Serialize(csFILENAME);
}
GPGPU gpu = CudafyHost.GetGPGPU(CudafyModes.Target, 1);
gpu.LoadModule(km);
Console.WriteLine("Time taken to load module: {0}ms", DateTime.Now.Subtract(dt).Milliseconds);
// capture the start time
gpu.StartTimer();
// allocate memory on the GPU for the bitmap (same size as ptr)
byte[] dev_bitmap = gpu.Allocate(bitmap);
// allocate temp memory, initialize it, copy to constant memory on the GPU
Sphere[] temp_s = new Sphere[SPHERES];
for (int i = 0; i < SPHERES; i++)
{
temp_s[i].r = rnd(1.0f);
temp_s[i].g = rnd(1.0f);
temp_s[i].b = rnd(1.0f);
temp_s[i].x = rnd(1000.0f) - 500;
temp_s[i].y = rnd(1000.0f) - 500;
temp_s[i].z = rnd(1000.0f) - 500;
temp_s[i].radius = rnd(100.0f) + 20;
}
gpu.CopyToConstantMemory(temp_s, s);
// generate a bitmap from our sphere data
dim3 grids = new dim3(DIM / 16, DIM / 16);
dim3 threads = new dim3(16, 16);
gpu.Launch(grids, threads, "kernel", dev_bitmap);
// copy our bitmap back from the GPU for display
gpu.CopyFromDevice(dev_bitmap, bitmap);
// get stop time, and display the timing results
float elapsedTime = gpu.StopTimer();
Console.WriteLine("Time to generate: {0} ms", elapsedTime);
gpu.DeviceFreeAll();
}
public static void Execute(byte[] bitmap)
{
CudafyModule km = CudafyModule.TryDeserialize();
if (km == null || !km.TryVerifyChecksums())
{
km = CudafyTranslator.Cudafy(typeof(SphereOpenCL), typeof(ray_opencl_const));
km.TrySerialize();
}
GPGPU gpu = CudafyHost.GetDevice(CudafyModes.Target, CudafyModes.DeviceId);
gpu.LoadModule(km);
// capture the start time
gpu.StartTimer();
// allocate memory on the GPU for the bitmap (same size as ptr)
byte[] dev_bitmap = gpu.Allocate(bitmap);
// allocate memory for the Sphere dataset
//SphereOpenCL[] s = gpu.Allocate<SphereOpenCL>(SPHERES);
// allocate temp memory, initialize it, copy to constant memory on the GPU
SphereOpenCL[] temp_s = new SphereOpenCL[SPHERES];
for (int i = 0; i < SPHERES; i++)
{
temp_s[i].r = rnd(1.0f);
temp_s[i].g = rnd(1.0f);
temp_s[i].b = rnd(1.0f);
temp_s[i].x = rnd(1000.0f) - 500;
temp_s[i].y = rnd(1000.0f) - 500;
temp_s[i].z = rnd(1000.0f) - 500;
temp_s[i].radius = rnd(100.0f) + 20;
}
//gpu.CopyToDevice(temp_s, s);
gpu.CopyToConstantMemory(temp_s, spheres);
// generate a bitmap from our sphere data
dim3 grids = new dim3(ray_gui.DIM / 16, ray_gui.DIM / 16);
dim3 threads = new dim3(16, 16);
//gpu.Launch(grids, threads).kernel(s, dev_bitmap); // Dynamic
gpu.Launch(grids, threads, ((Action <GThread, byte[]>)thekernel), dev_bitmap); // Strongly typed
// copy our bitmap back from the GPU for display
gpu.CopyFromDevice(dev_bitmap, bitmap);
// get stop time, and display the timing results
float elapsedTime = gpu.StopTimer();
Console.WriteLine("Time to generate: {0} ms", elapsedTime);
gpu.FreeAll();
}
public static float[] prepareAndCalculateFloatData(float[] prevMeasures, float[] actMeasures)
{
if ((prevMeasures != null) && (actMeasures != null))
{
float[] previousMeasuresGPU = gpu.Allocate <float>(prevMeasures);
float[] actualMeasuresGPU = gpu.Allocate <float>(actMeasures);
gpu.CopyToDevice(prevMeasures, previousMeasuresGPU);
gpu.CopyToDevice(actMeasures, actualMeasuresGPU);
gpu.Launch(prevMeasures.Length, 1).calculateDataWithCudafy(previousMeasuresGPU, actualMeasuresGPU);
gpu.CopyFromDevice(previousMeasuresGPU, prevMeasures);
gpu.FreeAll();
return(prevMeasures);
}
else
{
return(null);
}
}
public static void Execute()
{
// Translates this class to CUDA C and then compliles
CudafyModule km = CudafyTranslator.Cudafy();
// Get the first GPU and load the module
GPGPU gpu = CudafyHost.GetDevice(CudafyModes.Target, CudafyModes.DeviceId);
gpu.LoadModule(km);
// Create some arrays on the host
int[] a = new int[N];
int[] b = new int[N];
int[] c = new int[N];
// allocate the memory on the GPU
int[] dev_c = gpu.Allocate <int>(c);
// fill the arrays 'a' and 'b' on the CPU
for (int i = 0; i < N; i++)
{
a[i] = i;
b[i] = 2 * i;
}
// copy the arrays 'a' and 'b' to the GPU
int[] dev_a = gpu.CopyToDevice(a);
int[] dev_b = gpu.CopyToDevice(b);
// Launch 128 blocks of 128 threads each
gpu.Launch(128, 128).add(dev_a, dev_b, dev_c);
// copy the array 'c' back from the GPU to the CPU
gpu.CopyFromDevice(dev_c, c);
// verify that the GPU did the work we requested
bool success = true;
for (int i = 0; i < N; i++)
{
if ((a[i] + b[i]) != c[i])
{
Console.WriteLine("{0} + {1} != {2}", a[i], b[i], c[i]);
success = false;
break;
}
}
if (success)
{
Console.WriteLine("We did it!");
}
// free the memory allocated on the GPU
gpu.FreeAll();
}
public static void Execute()
{
CudafyModule km = CudafyTranslator.Cudafy();
GPGPU gpu = CudafyHost.GetDevice(CudafyModes.Target, CudafyModes.DeviceId);
gpu.LoadModule(km);
int c = 0;
int[] dev_c = gpu.Allocate<int>(); // cudaMalloc one Int32
gpu.Launch().add(2, 7, dev_c); // or gpu.Launch(1, 1, "add", 2, 7, dev_c);
gpu.CopyFromDevice(dev_c, out c);
Console.WriteLine("2 + 7 = {0}", c);
gpu.Launch().sub(2, 7, dev_c);
gpu.CopyFromDevice(dev_c, out c);
Console.WriteLine("2 - 7 = {0}", c);
gpu.Free(dev_c);
}
public static void Execute()
{
_gpu = CudafyHost.GetDevice(eGPUType.Cuda);
CudafyModule km = CudafyTranslator.Cudafy(ePlatform.Auto, _gpu.GetArchitecture(), typeof(TextInsertion));
Console.WriteLine(km.CompilerOutput);
_gpu.LoadModule(km);
int[] data = new int[64];
int[] data_d = _gpu.CopyToDevice(data);
int[] res_d = _gpu.Allocate(data);
int[] res = new int[64];
_gpu.Launch(1, 1, "AHybridMethod", data_d, res_d);
_gpu.CopyFromDevice(data_d, res);
for(int i = 0; i < 64; i++)
if (data[i] != res[i])
{
Console.WriteLine("Failed");
break;
}
}
public static void Execute()
{
_gpu = CudafyHost.GetDevice(eGPUType.Cuda);
CudafyModule km = CudafyTranslator.Cudafy(ePlatform.Auto, _gpu.GetArchitecture(), typeof(SIMDFunctions));
//CudafyModule km = CudafyTranslator.Cudafy(ePlatform.Auto, eArchitecture.sm_12, typeof(SIMDFunctions));
_gpu.LoadModule(km);
int w = 1024;
int h = 1024;
for (int loop = 0; loop < 3; loop++)
{
uint[] a = new uint[w * h];
Fill(a);
uint[] dev_a = _gpu.CopyToDevice(a);
uint[] b = new uint[w * h];
Fill(b);
uint[] dev_b = _gpu.CopyToDevice(b);
uint[] c = new uint[w * h];
uint[] dev_c = _gpu.Allocate(c);
_gpu.StartTimer();
_gpu.Launch(h, w, "SIMDFunctionTest", dev_a, dev_b, dev_c);
_gpu.CopyFromDevice(dev_c, c);
float time = _gpu.StopTimer();
Console.WriteLine("Time: {0}", time);
if (loop == 0)
{
bool passed = true;
GThread thread = new GThread(1, 1, null);
for (int i = 0; i < w * h; i++)
{
uint exp = thread.vadd2(a[i], b[i]);
if (exp != c[i])
passed = false;
}
Console.WriteLine("Test {0}", passed ? "passed. " : "failed!");
}
_gpu.FreeAll();
}
}
public static void Example1(GPGPU gpu, int threads)
{
double[] a = new double[threads];
double[] b = new double[threads];
Random r = new Random();
for (int i = 0; i < threads; i++)
{
a[i] = r.NextDouble();
b[i] = r.NextDouble();
}
double[] gpuarr1 = gpu.CopyToDevice(a);
double[] gpuarr2 = gpu.CopyToDevice(b);
double[] result = new double[threads];
var gpuresult = gpu.Allocate<double>(result);
gpu.Launch(threads, 1).Test2(gpuarr1, gpuarr2, gpuresult);
gpu.CopyFromDevice(gpuresult, result);
gpu.Free(gpuarr1);
gpu.Free(gpuarr2);
gpu.Free(gpuresult);
}
public static int MA(int[] A, int[] B, int[] C, int Size, int Size1d, GPGPU gpu, int max_threadsPerBlock)
{
// allocate the memory on the GPU
int[] GPU_A = gpu.Allocate<int>(A);
int[] GPU_B = gpu.Allocate<int>(B);
int[] GPU_C = gpu.Allocate<int>(C);
// copy the arrays 'a' and 'b' to the GPU
gpu.CopyToDevice(A, GPU_A);
gpu.CopyToDevice(B, GPU_B);
int threadsPerBlock = 0;
int blocksPerGrid = 0;
if (Size1d < max_threadsPerBlock)
{
threadsPerBlock = Size1d;
blocksPerGrid = 1;
}
else
{
threadsPerBlock = max_threadsPerBlock;
blocksPerGrid = (Size1d / max_threadsPerBlock) + 1;
}
// launch GPU_MA
gpu.Launch(threadsPerBlock, blocksPerGrid).GPU_MA(GPU_A, GPU_B, GPU_C, Size, Size1d);
// copy the array 'c' back from the GPU to the CPU
gpu.CopyFromDevice(GPU_C, C);
gpu.Free(GPU_A);
gpu.Free(GPU_B);
gpu.Free(GPU_C);
return 1;
}
private void meanToolStripMenuItem_Click(object sender, EventArgs e)
{
DialogResult dr = new DialogResult();
Form dlg1 = new AnalyzeForm();
dr = dlg1.ShowDialog();
for (int ix = 0; ix < Data.columnChoosen.Length;ix++ )
if(Data.columnChoosen[ix]!=-1)
columnChoosen.Add(Data.variableView[Data.columnChoosen[ix]].nama);
judul = "SLR";
if (dr == DialogResult.OK)
{
if (columnChoosen.Count == 2)
{
int columny = Data.columnChoosen[0];
int columnx = Data.columnChoosen[1];
try
{
CudafyModule km = CudafyTranslator.Cudafy(eArchitecture.sm_20);
_gpu = CudafyHost.GetDevice(eGPUType.Cuda);
_gpu.LoadModule(km);
GPGPUProperties gpprop = _gpu.GetDeviceProperties(false);
var sheet = reoGridControl2.CurrentWorksheet;
// Get the first CUDA device and load our module
int Ny = jumlahthread(columny);
int Nx = jumlahthread(columnx);
int N = new int();
if (Ny > Nx)
N = Ny;
else
N = Nx;
float[] ay = new float[Ny];
float[] by = new float[Ny];
float[] ax = new float[Nx];
float[] bx = new float[Nx];
float[] c = new float[N];
// fill the arrays 'a' and 'b' on the CPU
int jumlahDatay = jumlahdata(columny,Ny);
int jumlahDatax = jumlahdata(columnx,Nx);
ay = InitData(1, columny, Ny, ay, by);
by = InitData(2, columny, Ny, ay, by);
ax = InitData(1, columnx, Nx, ax, bx);
bx = InitData(2, columnx, Nx, ax, bx);
float temp,temp2;
int missingCounty = 0;
int missingCountx = 0;
for (int b = 0; b < Data.variableView[columny].missing.Count; b++)
{
for (int a = 0; a < Ny; a++)
{
float.TryParse(Data.variableView[columny].missing[b], out temp);
if (ay[a] == temp)
{
ay[a] = 0;
missingCounty++;
}
}
}
for (int b = 0; b < Data.variableView[columnx].missing.Count; b++)
{
for (int a = 0; a < Nx; a++)
{
float.TryParse(Data.variableView[columnx].missing[b], out temp);
if (ax[a] == temp)
{
ax[a] = 0;
missingCountx++;
}
}
}
if (Data.variableView[columny].missingRange.Count > 1)
{
for (int a = 0; a < Ny; a++)
{
float.TryParse(Data.variableView[columny].missingRange[0], out temp);
float.TryParse(Data.variableView[columny].missingRange[1], out temp2);
if (ay[a] >= temp && ay[a] <= temp2)
{
ay[a] = 0;
missingCounty++;
}
}
}
if (Data.variableView[columnx].missingRange.Count > 1)
{
for (int a = 0; a < Nx; a++)
{
float.TryParse(Data.variableView[columnx].missingRange[0], out temp);
float.TryParse(Data.variableView[columnx].missingRange[1], out temp2);
if (ax[a] >= temp && ax[a] <= temp2)
{
ax[a] = 0;
missingCounty++;
}
}
}
Debug.WriteLine("y : " + missingCounty + "/nx : " + missingCountx);
float[] dev_a = _gpu.CopyToDevice(ay);
float[] dev_b = _gpu.CopyToDevice(ax);
float[] dev_c = _gpu.Allocate<float>(c);
_gpu.Launch((N + 127) / 128, 128).multiplyVector(dev_a, dev_b, dev_c, N);
float[] save1 = new float[N];
_gpu.CopyFromDevice(dev_c, save1);
//_gpu.Free(dev_a);
//_gpu.Free(dev_b);
//_gpu.Free(dev_c);
dev_a = _gpu.CopyToDevice(by);
dev_b = _gpu.CopyToDevice(bx);
dev_c = _gpu.Allocate<float>(c);
_gpu.Launch((N + 127) / 128, 128).multiplyVector(dev_a, dev_b, dev_c, N);
float[] save2 = new float[N];
_gpu.CopyFromDevice(dev_c, save2);
//_gpu.Free(dev_a);
//_gpu.Free(dev_b);
//_gpu.Free(dev_c);
dev_a = _gpu.CopyToDevice(save1);
dev_b = _gpu.CopyToDevice(save2);
dev_c = _gpu.Allocate<float>(c);
float sumxy = jumlahan(N,dev_a,dev_b,dev_c,c);
//_gpu.Free(dev_a);
//_gpu.Free(dev_b);
//_gpu.Free(dev_c);
//results.Add(mean);
c = new float[Nx];
dev_a = _gpu.CopyToDevice(ax);
dev_b = _gpu.CopyToDevice(bx);
dev_c = _gpu.Allocate<float>(c);
float sumx = jumlahan(Nx, dev_a, dev_b, dev_c, c);
//_gpu.Free(dev_a);
//_gpu.Free(dev_b);
//_gpu.Free(dev_c);
c = new float[Ny];
dev_a = _gpu.CopyToDevice(ay);
dev_b = _gpu.CopyToDevice(by);
dev_c = _gpu.Allocate<float>(c);
float sumy = jumlahan(Nx, dev_a, dev_b, dev_c, c);
//_gpu.Free(dev_a);
//_gpu.Free(dev_b);
//_gpu.Free(dev_c);
c = new float[N];
dev_a = _gpu.CopyToDevice(ax);
dev_b = _gpu.CopyToDevice(ax);
dev_c = _gpu.Allocate<float>(c);
_gpu.Launch((N + 127) / 128, 128).multiplyVector(dev_a, dev_b, dev_c, Nx);
save1 = new float[N];
_gpu.CopyFromDevice(dev_c, save1);
//_gpu.Free(dev_a);
//_gpu.Free(dev_b);
//_gpu.Free(dev_c);
dev_a = _gpu.CopyToDevice(bx);
dev_b = _gpu.CopyToDevice(bx);
dev_c = _gpu.Allocate<float>(c);
_gpu.Launch((N + 127) / 128, 128).multiplyVector(dev_a, dev_b, dev_c, Nx);
save2 = new float[N];
_gpu.CopyFromDevice(dev_c, save2);
//_gpu.Free(dev_a);
//_gpu.Free(dev_b);
//_gpu.Free(dev_c);
dev_a = _gpu.CopyToDevice(save1);
dev_b = _gpu.CopyToDevice(save2);
dev_c = _gpu.Allocate<float>(c);
float sumxquad = jumlahan(Nx, dev_a, dev_b, dev_c, c);
//_gpu.Free(dev_a);
//_gpu.Free(dev_b);
//_gpu.Free(dev_c);
_gpu.FreeAll();
float jumlahData = new float();
if (jumlahDatax>jumlahDatay)
jumlahData = jumlahDatax;
else
jumlahData = jumlahDatay;
float beta = ((jumlahData * sumxy) - (sumx * sumy)) / ((jumlahData * sumxquad) - (sumx * sumx));
ab[0] = beta.ToString();
float alpha = (sumy / (jumlahDatay - missingCounty)) - beta * (sumx / jumlahDatax - missingCountx);
ab[1] = alpha.ToString();
}
catch (CudafyLanguageException cle)
{
}
catch (CudafyCompileException cce)
{
}
catch (CudafyHostException che)
{
Console.Write(che.Message);
}
}
DialogResult dialog = new DialogResult();
Form dialogResult = new ResultSLR();
dialog = dialogResult.ShowDialog();
// Console.ReadLine();
}
else
dlg1.Close();
}
public float computeSum2(float[] array)
{
try
{
// This 'smart' method will Cudafy all members with the Cudafy attribute in the calling type (i.e. Program)
CudafyModule km = CudafyTranslator.Cudafy(eArchitecture.sm_20);
// If cudafying will not work for you (CUDA SDK + VS not set up right) then comment out above and
// uncomment below. Remember to also comment out the Structs and 3D arrays region below.
// CUDA 5.5 SDK must be installed and cl.exe (VC++ compiler) must be in path.
//CudafyModule km = CudafyModule.Deserialize(typeof(Program).Name);
//var options = NvccCompilerOptions.Createx64(eArchitecture.sm_12);
//km.CompilerOptionsList.Add(options);
_gpu = CudafyHost.GetDevice(eGPUType.Cuda);
_gpu.LoadModule(km);
GPGPUProperties gpprop = _gpu.GetDeviceProperties(false);
var sheet = reoGridControl2.CurrentWorksheet;
// Get the first CUDA device and load our module
int N = sheet.RowCount / 2;
float[] a = new float[N];
float[] b = new float[N];
float[] c = new float[N];
// fill the arrays 'a' and 'b' on the CPU
int jumlahData = 0;
for (int i = 0; i < N; i++)
{
if (array[i] != null && array[i].ToString() != "")
{
a[i] = array[i];
jumlahData++;
}
if (array[i + N] != null && array[i + N].ToString() != "")
{
b[i] = array[i + N];
jumlahData++;
}
}
// float temp, temp2;
// int missingCount = 0;
//for (int bx = 0; bx < Data.variableView[column].missing.Count; bx++)
//{
// for (int ax = 0; ax < N; ax++)
// {
// float.TryParse(Data.variableView[column].missing[bx], out temp);
// if (a[ax] == temp)
// {
// a[ax] = 0;
// missingCount++;
// }
// }
//}
//if (Data.variableView[column].missingRange.Count > 1)
//{
// for (int ax = 0; ax < N; ax++)
// {
// float.TryParse(Data.variableView[column].missingRange[0], out temp);
// float.TryParse(Data.variableView[column].missingRange[1], out temp2);
// if (a[ax] >= temp && a[ax] <= temp2)
// {
// a[ax] = 0;
// missingCount++;
// }
// }
// }
// Debug.WriteLine(missingCount);
//float meanSequential = 0;
//for (int i = 0; i < N; i++)
// meanSequential += a[i] + b[i];
//meanSequential = meanSequential / (jumlahData - missingCount); ;
float[] dev_a = _gpu.CopyToDevice(a);
float[] dev_b = _gpu.CopyToDevice(b);
float[] dev_c = _gpu.Allocate<float>(c);
bool first = true;
int N_awal = N;
while (N > 1)
{
if (!first)
{
a = new float[N];
b = new float[N];
// c = new int[N];
float[] baru = new float[N];
for (int i = 0; i < (c.Count() - N); i++)
baru[i] = c[N + i];
dev_a = _gpu.CopyToDevice(c.Take(N).ToArray());
dev_b = _gpu.CopyToDevice(baru);
c = new float[N];
dev_c = _gpu.Allocate<float>(c);
}
float[] d = new float[N];
_gpu.CopyFromDevice(dev_a, d);
// _gpu.Launch(N, 1).addVector(dev_a, dev_b, dev_c, N);
_gpu.Launch((N + 127) / 128, 128).addVector(dev_a, dev_b, dev_c, N);
_gpu.CopyFromDevice(dev_c, c);
_gpu.Free(dev_a);
_gpu.Free(dev_b);
_gpu.Free(dev_c);
if (N % 2 == 0)
N = N / 2;
else
N = (N + 1) / 2;
first = false;
}
// Debug.WriteLine("mean-nya adalah " + (c[0] + c[1]) / (jumlahData - missingCount) + " mean dari sequensial adalah " + meanSequential);
// results.Add((c[0] + c[1]) / (jumlahData - missingCount));
//for (int i = 0; i < N; i++)
// Debug.Assert(a[i] + b[i] == c[i]);
_gpu.FreeAll();
//return (c[0] + c[1]) / (jumlahData - missingCount);
return (c[0] + c[1]);
}
catch (CudafyLanguageException cle)
{
}
catch (CudafyCompileException cce)
{
}
catch (CudafyHostException che)
{
Console.Write(che.Message);
}
return 0;
}
public void computeStdv(int column)
{
try
{
// This 'smart' method will Cudafy all members with the Cudafy attribute in the calling type (i.e. Program)
CudafyModule km = CudafyTranslator.Cudafy(eArchitecture.sm_20);
// If cudafying will not work for you (CUDA SDK + VS not set up right) then comment out above and
// uncomment below. Remember to also comment out the Structs and 3D arrays region below.
// CUDA 5.5 SDK must be installed and cl.exe (VC++ compiler) must be in path.
//CudafyModule km = CudafyModule.Deserialize(typeof(Program).Name);
//var options = NvccCompilerOptions.Createx64(eArchitecture.sm_12);
//km.CompilerOptionsList.Add(options);
_gpu = CudafyHost.GetDevice(eGPUType.Cuda);
_gpu.LoadModule(km);
GPGPUProperties gpprop = _gpu.GetDeviceProperties(false);
var sheet = reoGridControl2.CurrentWorksheet;
// Get the first CUDA device and load our module
int N = sheet.RowCount / 2; //karena a dan b diisi "data" berdasar ganjil genap
float[] a = new float[N];
float[] b = new float[N];
float[] c = new float[N];
// fill the arrays 'a' and 'b' on the CPU
jumlahData = 0;
for (int i = 0; i < N; i++) //ini buat membagi data ke a b
{
if (sheet[i, column] != null && sheet[i, column].ToString() != "")
{
float.TryParse(sheet[i, column].ToString(), out a[i]);
jumlahData++;
}
if (sheet[i + N, column] != null && sheet[i + N, column].ToString() != "")
{
float.TryParse(sheet[i + N, column].ToString(), out b[i]);
jumlahData++;
}
}
float temp, temp2;
missingCount = 0;
for (int bx = 0; bx < Data.variableView[column].missing.Count; bx++)
{
for (int ax = 0; ax < N; ax++)
{
float.TryParse(Data.variableView[column].missing[bx], out temp);
if (a[ax] == temp)
{
a[ax] = 0;
missingCount++;
}
}
}
if (Data.variableView[column].missingRange.Count > 1)
{
for (int ax = 0; ax < N; ax++)
{
float.TryParse(Data.variableView[column].missingRange[0], out temp);
float.TryParse(Data.variableView[column].missingRange[1], out temp2);
if (a[ax] >= temp && a[ax] <= temp2)
{
a[ax] = 0;
missingCount++;
}
}
}
Debug.WriteLine(missingCount);
/*
float meanSequential = 0;
for (int i = 0; i < N; i++)
meanSequential += a[i] + b[i];
meanSequential = meanSequential / (jumlahData - missingCount); ;
*/
float[] dev_a = _gpu.CopyToDevice(a);
float[] dev_b = _gpu.CopyToDevice(b);
float[] dev_c = _gpu.Allocate<float>(c);
bool first = true;
int N_awal = N;
while (N > 1)
{
if (!first)
{
a = new float[N];
b = new float[N];
// c = new int[N];
float[] baru = new float[N];
for (int i = 0; i < (c.Count() - N); i++)
baru[i] = c[N + i];
dev_a = _gpu.CopyToDevice(c.Take(N).ToArray());
dev_b = _gpu.CopyToDevice(baru);
c = new float[N];
dev_c = _gpu.Allocate<float>(c);
}
float[] d = new float[N];
_gpu.CopyFromDevice(dev_a, d);
// _gpu.Launch(N, 1).addVector(dev_a, dev_b, dev_c, N);
_gpu.Launch((N + 127) / 128, 128).addVector(dev_a, dev_b, dev_c, N);
_gpu.CopyFromDevice(dev_c, c);
_gpu.Free(dev_a);
_gpu.Free(dev_b);
_gpu.Free(dev_c);
if (N % 2 == 0)
N = N / 2;
else
N = (N + 1) / 2;
first = false;
}
float[] mean = new float[1];
mean[0] = (c[0] + c[1]) / (jumlahData - missingCount);
float[] dev_mean = _gpu.CopyToDevice(mean);
// float[] data2 = new float[jumlahData];
// float[] dev_data2 = _gpu.Allocate<float>(data2);
float[] temp4 = new float[jumlahData];
float[] dev_temp4 = _gpu.Allocate<float>(temp4);
float[] data = new float[jumlahData];
for (int i = 0; i < jumlahData; i++) //parse semua data ke array data
{
if (sheet[i, column] != null && sheet[i, column].ToString() != "")
{
float.TryParse(sheet[i, column].ToString(), out data[i]);
}
}
float[] dev_data = _gpu.CopyToDevice(data);
float[] x = new float[jumlahData+1];
float[] dev_x = _gpu.Allocate<float>(x);
_gpu.Launch((jumlahData + 127) / 128, 128).minusMean(dev_mean, dev_data, dev_x, jumlahData, dev_temp4);
_gpu.CopyFromDevice(dev_x, x);
_gpu.Free(dev_mean);
_gpu.Free(dev_data);
_gpu.Free(dev_x);
_gpu.Free(dev_temp4);
// fill the arrays 'a' and 'b' on the CPU
N = ((jumlahData+1) / 2);
float[] isi1 = new float[N];
float[] isi2 = new float[N];
for (int i = 0; i < N; i++) //ini buat membagi data ke a b
{
if (x[i] != null && x.ToString() != "")
{
float.TryParse(x[i].ToString(), out isi1[i]);
}
if (x[i + N] != null && x[i + N].ToString() != "")
{
float.TryParse(x[i + N].ToString(), out isi2[i]);
}
}
float[] isic = new float[N];
float[] dev_isi1 = _gpu.CopyToDevice(isi1);
float[] dev_isi2 = _gpu.CopyToDevice(isi2);
float[] dev_isic = _gpu.Allocate<float>(isic);
bool first1 = true;
while (N > 1)
{
if (!first1)
{
isi1 = new float[N];
isi2 = new float[N];
// c = new int[N];
float[] baru = new float[N];
for (int i = 0; i < (isic.Count() - N); i++)
baru[i] = isic[N + i];
dev_isi1 = _gpu.CopyToDevice(c.Take(N).ToArray());
dev_isi2 = _gpu.CopyToDevice(baru);
c = new float[N];
dev_isic = _gpu.Allocate<float>(isic);
}
float[] isid = new float[N];
_gpu.CopyFromDevice(dev_isi1, isid);
// _gpu.Launch(N, 1).addVector(dev_a, dev_b, dev_c, N);
_gpu.Launch((N + 127) / 128, 128).addVector(dev_isi1, dev_isi2, dev_isic, N);
_gpu.CopyFromDevice(dev_isic, isic);
_gpu.Free(dev_isi1);
_gpu.Free(dev_isi2);
_gpu.Free(dev_isic);
if (N % 2 == 0)
N = N / 2;
else
N = (N + 1) / 2;
first = false;
}
float temp3 = (float)Math.Sqrt((isic[0] + isic[1]) / (jumlahData - 1));
// Debug.WriteLine("mean-nya adalah " + (c[0] + c[1]));
Debug.WriteLine("STDV = " + ((isic[0] + isic[1]) / (jumlahData - 1)));
results.Add(temp3);
//for (int i = 0; i < N; i++)
// Debug.Assert(a[i] + b[i] == c[i]);
_gpu.FreeAll();
}
catch (CudafyLanguageException cle)
{
}
catch (CudafyCompileException cce)
{
}
catch (CudafyHostException che)
{
Console.Write(che.Message);
}
}
private void meanPararelToolStripMenuItem_Click(object sender, EventArgs e)
{
DialogResult dr = new DialogResult();
Form dlg1 = new AnalyzeForm();
dr = dlg1.ShowDialog();
for (int ix = 0; ix < Data.columnChoosen.Length; ix++)
if (Data.columnChoosen[ix] != -1)
columnChoosen.Add(Data.variableView[Data.columnChoosen[ix]].nama);
if (dr == DialogResult.OK)
{
for (int index = 0; index < Data.columnChoosen.Length; index++)
if (Data.columnChoosen[index] != -1)
{
int column = Data.columnChoosen[index];
try
{
// This 'smart' method will Cudafy all members with the Cudafy attribute in the calling type (i.e. Program)
CudafyModule km = CudafyTranslator.Cudafy(eArchitecture.sm_20);
// If cudafying will not work for you (CUDA SDK + VS not set up right) then comment out above and
// uncomment below. Remember to also comment out the Structs and 3D arrays region below.
// CUDA 5.5 SDK must be installed and cl.exe (VC++ compiler) must be in path.
//CudafyModule km = CudafyModule.Deserialize(typeof(Program).Name);
//var options = NvccCompilerOptions.Createx64(eArchitecture.sm_12);
//km.CompilerOptionsList.Add(options);
_gpu = CudafyHost.GetDevice(eGPUType.Cuda);
_gpu.LoadModule(km);
GPGPUProperties gpprop = _gpu.GetDeviceProperties(false);
var sheet = reoGridControl2.CurrentWorksheet;
// Get the first CUDA device and load our module
int N = sheet.RowCount / 2;
float[] a = new float[N];
float[] b = new float[N];
float[] c = new float[N];
// fill the arrays 'a' and 'b' on the CPU
int jumlahData = 0;
for (int i = 0; i < N; i++)
{
if (sheet[i, column] != null && sheet[i, column].ToString() != "")
{
float.TryParse(sheet[i, column].ToString(), out a[i]);
jumlahData++;
}
if (sheet[i + N, column] != null && sheet[i + N, column].ToString() != "")
{
float.TryParse(sheet[i + N, column].ToString(), out b[i]);
jumlahData++;
}
}
float temp, temp2;
int missingCount = 0;
for (int bx = 0; bx < Data.variableView[column].missing.Count; bx++)
{
for (int ax = 0; ax < N; ax++)
{
float.TryParse(Data.variableView[column].missing[bx], out temp);
if (a[ax] == temp)
{
a[ax] = 0;
missingCount++;
}
}
}
if (Data.variableView[column].missingRange.Count > 1)
{
for (int ax = 0; ax < N; ax++)
{
float.TryParse(Data.variableView[column].missingRange[0], out temp);
float.TryParse(Data.variableView[column].missingRange[1], out temp2);
if (a[ax] >= temp && a[ax] <= temp2)
{
a[ax] = 0;
missingCount++;
}
}
}
Debug.WriteLine(missingCount);
float meanSequential = 0;
for (int i = 0; i < N; i++)
meanSequential += a[i] + b[i];
meanSequential = meanSequential / (jumlahData - missingCount); ;
float[] dev_a = _gpu.CopyToDevice(a);
float[] dev_b = _gpu.CopyToDevice(b);
float[] dev_c = _gpu.Allocate<float>(c);
bool first = true;
int N_awal = N;
while (N > 1)
{
if (!first)
{
a = new float[N];
b = new float[N];
// c = new int[N];
float[] baru = new float[N];
for (int i = 0; i < (c.Count() - N); i++)
baru[i] = c[N + i];
dev_a = _gpu.CopyToDevice(c.Take(N).ToArray());
dev_b = _gpu.CopyToDevice(baru);
c = new float[N];
dev_c = _gpu.Allocate<float>(c);
}
float[] d = new float[N];
_gpu.CopyFromDevice(dev_a, d);
// _gpu.Launch(N, 1).addVector(dev_a, dev_b, dev_c, N);
_gpu.Launch((N + 127) / 128, 128).addVector(dev_a, dev_b, dev_c, N);
_gpu.CopyFromDevice(dev_c, c);
_gpu.Free(dev_a);
_gpu.Free(dev_b);
_gpu.Free(dev_c);
if (N % 2 == 0)
N = N / 2;
else
N = (N + 1) / 2;
first = false;
}
Debug.WriteLine("mean-nya adalah " + (c[0] + c[1]) / (jumlahData - missingCount) + " mean dari sequensial adalah " + meanSequential);
results.Add((c[0] + c[1]) / (jumlahData - missingCount));
//for (int i = 0; i < N; i++)
// Debug.Assert(a[i] + b[i] == c[i]);
_gpu.FreeAll();
}
catch (CudafyLanguageException cle)
{
}
catch (CudafyCompileException cce)
{
}
catch (CudafyHostException che)
{
Console.Write(che.Message);
}
}
DialogResult dialog = new DialogResult();
Form dialogResult = new FormResultsMean();
dialog = dialogResult.ShowDialog();
// Console.ReadLine();
}
else
dlg1.Close();
}
public static void Example2(GPGPU gpu)
{
ArrayView view1 = new ArrayView();
ArrayView view2 = new ArrayView();
float[] data = Enumerable.Range(0, 1000).Select(t => (float)t).ToArray();
// Two views of the array, simply applying an offset to the array; could slice instead for example.
view1.CreateView(data, 100);
view2.CreateView(data, 200);
for (int i = 0; i < 1000; ++i) data[i] = data[i] * 10f;
// Should copy the 'large' array to the device only once; this is referenced by each ArrayView instance.
var dev_view1 = DeviceClassHelper.CreateDeviceObject(gpu, view1);
var dev_view2 = DeviceClassHelper.CreateDeviceObject(gpu, view2);
var dev_result = gpu.Allocate<float>(5);
var hostResult = new float[5];
gpu.Launch(1, 1).Test2(dev_view1, dev_view2, dev_result);
gpu.CopyFromDevice(dev_result, hostResult);
bool pass = (hostResult[0] == 1050f && hostResult[1] == 7f);
Console.WriteLine(pass ? "Pass" : "Fail");
}
private void varianceToolStripMenuItem_Click(object sender, EventArgs e)
{
DialogResult dr = new DialogResult();
Form dlg1 = new AnalyzeForm();
dr = dlg1.ShowDialog();
for (int ix = 0; ix < Data.columnChoosen.Length; ix++)
if (Data.columnChoosen[ix] != -1)
columnChoosen.Add(Data.variableView[Data.columnChoosen[ix]].nama);
judul = "Variance";
if (dr == DialogResult.OK)
{
for (int index = 0; index < Data.columnChoosen.Length; index++)
if (Data.columnChoosen[index] != -1)
{
int column = Data.columnChoosen[index];
try
{
CudafyModule km = CudafyTranslator.Cudafy(eArchitecture.sm_20);
_gpu = CudafyHost.GetDevice(eGPUType.Cuda);
_gpu.LoadModule(km);
GPGPUProperties gpprop = _gpu.GetDeviceProperties(false);
var sheet = reoGridControl2.CurrentWorksheet;
// Get the first CUDA device and load our module
int N = jumlahthread(column);
float[] a = new float[N];
float[] b = new float[N];
float[] c = new float[N];
// fill the arrays 'a' and 'b' on the CPU
int jumlahData = jumlahdata(column,N);
a = InitData(1, column, N,a,b);
b = InitData(2, column, N,a,b);
float temp, temp2;
int missingCount = 0;
for (int bx = 0; bx < Data.variableView[column].missing.Count; bx++)
{
for (int ax = 0; ax < N; ax++)
{
float.TryParse(Data.variableView[column].missing[bx], out temp);
if (a[ax] == temp)
{
a[ax] = 0;
missingCount++;
}
}
}
if (Data.variableView[column].missingRange.Count > 1)
{
for (int ax = 0; ax < N; ax++)
{
float.TryParse(Data.variableView[column].missingRange[0], out temp);
float.TryParse(Data.variableView[column].missingRange[1], out temp2);
if (a[ax] >= temp && a[ax] <= temp2)
{
a[ax] = 0;
missingCount++;
}
}
}
Debug.WriteLine(missingCount);
float[] dev_a = _gpu.CopyToDevice(a);
float[] dev_b = _gpu.CopyToDevice(b);
float[] dev_c = _gpu.Allocate<float>(c);
int N1 = N;
float hasil = jumlahan(N, dev_a, dev_b, dev_c, c);
float mean = (hasil / (jumlahData - missingCount));
_gpu.FreeAll();
c = new float[N1];
dev_a = _gpu.CopyToDevice(a);
dev_b = _gpu.CopyToDevice(b);
dev_c = _gpu.Allocate<float>(c);
_gpu.Launch((N1 + 127) / 128, 128).powerVector(dev_a, dev_c, mean, N1);
_gpu.CopyFromDevice(dev_c, c);
_gpu.Free(dev_a);
_gpu.Free(dev_c);
float[] d = new float[N];
dev_c = _gpu.Allocate<float>(d);
_gpu.Launch((N1 + 127) / 128, 128).powerVector(dev_b, dev_c, mean, N1);
_gpu.CopyFromDevice(dev_c, d);
_gpu.Free(dev_b);
_gpu.Free(dev_c);
_gpu.FreeAll();
if (jumlahData%2 != 0)
{
d[N1-1] = 0;
}
float[] f = new float[N1];
hasil = new float();
dev_a = _gpu.CopyToDevice(c);
dev_b = _gpu.CopyToDevice(d);
dev_c = _gpu.Allocate<float>(c);
hasil = jumlahan(N, dev_a, dev_b, dev_c, c);
float variance = (hasil / (jumlahData - missingCount-1));
results.Add(variance);
}
catch (CudafyLanguageException cle)
{
}
catch (CudafyCompileException cce)
{
}
catch (CudafyHostException che)
{
Console.Write(che.Message);
}
}
DialogResult dialog = new DialogResult();
Form dialogResult = new ResultForm();
dialog = dialogResult.ShowDialog();
// Console.ReadLine();
}
else
dlg1.Close();
}
