public static void writeMatrixToFile(MathNet.Numerics.LinearAlgebra.Double.DenseMatrix matrix, string file, System.Windows.Forms.RichTextBox tb1, MainForm frm)
{
System.IO.Stream fileStream = new System.IO.FileStream(file, System.IO.FileMode.Create);
System.IO.BinaryWriter bw = new System.IO.BinaryWriter(fileStream, System.Text.Encoding.Default);
double[] arr = matrix.ToColumnWiseArray();
System.IO.TextWriter tr = new System.IO.StreamWriter(file + ".metainfo");
tr.WriteLine("Column count");
tr.WriteLine(matrix.ColumnCount);
tr.WriteLine("Row count");
tr.WriteLine(matrix.RowCount);
tr.Flush();
tr.Close();
int len = arr.Length;
matrix = null;
int index = 0;
byte[] bytes = new byte[arr.Length * 8];
foreach (double item in arr)
{
BitConverter.GetBytes(item).CopyTo(bytes, index);
if (index / 8 / 500D == Math.Round(index / 8 / 500D))
{
tb1.BeginInvoke(new MainForm.setProgressDel(frm.addVal), new object[] { (int)index / 8, len, "Записано" });
}
index += 8;
}
bw.Write(bytes);
bw.Flush();
tb1.BeginInvoke(new MainForm.setProgressDel(frm.addVal), new object[] { (int)0, len, "" });
bw.Flush();
bw.Close();
fileStream.Close();
}
public static void cudaTransposeAndMultiply(ref MathNet.Numerics.LinearAlgebra.Double.DenseMatrix dm)
{
Cudafy.CudafyModule km = Cudafy.Translator.CudafyTranslator.Cudafy();
km.Serialize();
GPGPU gpu = CudafyHost.GetDevice(eGPUType.Cuda);
int cols = dm.ColumnCount, rows = dm.RowCount;
dm.Storage.ToColumnMajorArray();
double[] a = dm.ToColumnWiseArray();
dm = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(1, 1);
double[] dev_a = gpu.Allocate <double>(a.Length);
GPGPUBLAS blas = GPGPUBLAS.Create(gpu);
double[] a_d = gpu.CopyToDevice <double>(a);
double[] c_d = gpu.Allocate <double>(cols * cols);
gpu.StartTimer();
blas.GEMM(cols, rows, cols, 1, a_d, a_d, 0, c_d, Cudafy.Maths.BLAS.Types.cublasOperation.T);
a = new double[cols * cols];
gpu.CopyFromDevice <double>(c_d, a);
gpu.FreeAll();
dm = new MathNet.Numerics.LinearAlgebra.Double.DenseMatrix(cols, cols, a);
}
