jcuda.jcublas.JCublas.cublasSetMatrixNative java code examples

/**
 * Wrapper for CUBLAS function.<br />
 * <br />
 * cublasStatus
 * cublasSetMatrix (int rows, int cols, int elemSize, const void *A,
 *                  int lda, void *B, int ldb)<br />
 *<br />
 * copies a tile of rows x cols elements from a matrix A in CPU memory
 * space to a matrix B in GPU memory space. Each element requires storage
 * of elemSize bytes. Both matrices are assumed to be stored in column
 * major format, with the leading dimension (i.e. number of rows) of
 * source matrix A provided in lda, and the leading dimension of matrix B
 * provided in ldb. In general, B points to an object, or part of an
 * object, that was allocated via cublasAlloc().<br />
 *<br />
 * Return Values<br />
 * -------------<br />
 * CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library has not been initialized<br />
 * CUBLAS_STATUS_INVALID_VALUE    if rows or cols < 0, or elemSize, lda, or
 *                                ldb <= 0<br />
 * CUBLAS_STATUS_MAPPING_ERROR    if error occurred accessing GPU memory<br />
 * CUBLAS_STATUS_SUCCESS          if the operation completed successfully<br />
 */
public static int cublasSetMatrix (int rows, int cols, int elemSize, Pointer A, int lda, Pointer B, int ldb)
{
  return checkResult(cublasSetMatrixNative(rows, elemSize, cols, A, lda, B, ldb));
}

/**
 * Wrapper for CUBLAS function.<br />
 * <br />
 * cublasStatus
 * cublasSetMatrix (int rows, int cols, int elemSize, const void *A,
 *                  int lda, void *B, int ldb)<br />
 *<br />
 * copies a tile of rows x cols elements from a matrix A in CPU memory
 * space to a matrix B in GPU memory space. Each element requires storage
 * of elemSize bytes. Both matrices are assumed to be stored in column
 * major format, with the leading dimension (i.e. number of rows) of
 * source matrix A provided in lda, and the leading dimension of matrix B
 * provided in ldb. In general, B points to an object, or part of an
 * object, that was allocated via cublasAlloc().<br />
 *<br />
 * Return Values<br />
 * -------------<br />
 * CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library has not been initialized<br />
 * CUBLAS_STATUS_INVALID_VALUE    if rows or cols < 0, or elemSize, lda, or
 *                                ldb <= 0<br />
 * CUBLAS_STATUS_MAPPING_ERROR    if error occurred accessing GPU memory<br />
 * CUBLAS_STATUS_SUCCESS          if the operation completed successfully<br />
 */
public static int cublasSetMatrix (int rows, int cols, int elemSize, Pointer A, int lda, Pointer B, int ldb)
{
  return checkResult(cublasSetMatrixNative(rows, elemSize, cols, A, lda, B, ldb));
}

/**
 * Wrapper for CUBLAS function.<br />
 * <br />
 * cublasStatus
 * cublasSetMatrix (int rows, int cols, int elemSize, const void *A,
 *                  int lda, void *B, int ldb)<br />
 *<br />
 * copies a tile of rows x cols elements from a matrix A in CPU memory
 * space to a matrix B in GPU memory space. Each element requires storage
 * of elemSize bytes. Both matrices are assumed to be stored in column
 * major format, with the leading dimension (i.e. number of rows) of
 * source matrix A provided in lda, and the leading dimension of matrix B
 * provided in ldb. In general, B points to an object, or part of an
 * object, that was allocated via cublasAlloc().<br />
 *<br />
 * Return Values<br />
 * -------------<br />
 * CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library has not been initialized<br />
 * CUBLAS_STATUS_INVALID_VALUE    if rows or cols < 0, or elemSize, lda, or
 *                                ldb <= 0<br />
 * CUBLAS_STATUS_MAPPING_ERROR    if error occurred accessing GPU memory<br />
 * CUBLAS_STATUS_SUCCESS          if the operation completed successfully<br />
 */
public static int cublasSetMatrix (int rows, int cols, int elemSize, Pointer A, int lda, Pointer B, int ldb)
{
  return checkResult(cublasSetMatrixNative(rows, elemSize, cols, A, lda, B, ldb));
}

/**
 * Wrapper for CUBLAS function.<br />
 * <br />
 * cublasStatus
 * cublasSetMatrix (int rows, int cols, int elemSize, const void *A,
 *                  int lda, void *B, int ldb)<br />
 *<br />
 * copies a tile of rows x cols elements from a matrix A in CPU memory
 * space to a matrix B in GPU memory space. Each element requires storage
 * of elemSize bytes. Both matrices are assumed to be stored in column
 * major format, with the leading dimension (i.e. number of rows) of
 * source matrix A provided in lda, and the leading dimension of matrix B
 * provided in ldb. In general, B points to an object, or part of an
 * object, that was allocated via cublasAlloc().<br />
 *<br />
 * Return Values<br />
 * -------------<br />
 * CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library has not been initialized<br />
 * CUBLAS_STATUS_INVALID_VALUE    if rows or cols < 0, or elemSize, lda, or
 *                                ldb <= 0<br />
 * CUBLAS_STATUS_MAPPING_ERROR    if error occurred accessing GPU memory<br />
 * CUBLAS_STATUS_SUCCESS          if the operation completed successfully<br />
 */
public static int cublasSetMatrix (int rows, int cols, int elemSize, Pointer A, int lda, Pointer B, int ldb)
{
  return checkResult(cublasSetMatrixNative(rows, elemSize, cols, A, lda, B, ldb));
}

/**
 * Wrapper for CUBLAS function.<br />
 * <br />
 * cublasStatus
 * cublasSetMatrix (int rows, int cols, int elemSize, const void *A,
 *                  int lda, void *B, int ldb)<br />
 *<br />
 * copies a tile of rows x cols elements from a matrix A in CPU memory
 * space to a matrix B in GPU memory space. Each element requires storage
 * of elemSize bytes. Both matrices are assumed to be stored in column
 * major format, with the leading dimension (i.e. number of rows) of
 * source matrix A provided in lda, and the leading dimension of matrix B
 * provided in ldb. In general, B points to an object, or part of an
 * object, that was allocated via cublasAlloc().<br />
 *<br />
 * Return Values<br />
 * -------------<br />
 * CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library has not been initialized<br />
 * CUBLAS_STATUS_INVALID_VALUE    if rows or cols < 0, or elemSize, lda, or
 *                                ldb <= 0<br />
 * CUBLAS_STATUS_MAPPING_ERROR    if error occurred accessing GPU memory<br />
 * CUBLAS_STATUS_SUCCESS          if the operation completed successfully<br />
 */
public static int cublasSetMatrix (int rows, int cols, int elemSize, Pointer A, int lda, Pointer B, int ldb)
{
 return checkResult(cublasSetMatrixNative(rows, elemSize, cols, A, lda, B, ldb));
}

/**
 * Wrapper for CUBLAS function.<br />
 * <br />
 * cublasStatus
 * cublasSetMatrix (int rows, int cols, int elemSize, const void *A,
 *                  int lda, void *B, int ldb)<br />
 *<br />
 * copies a tile of rows x cols elements from a matrix A in CPU memory
 * space to a matrix B in GPU memory space. Each element requires storage
 * of elemSize bytes. Both matrices are assumed to be stored in column
 * major format, with the leading dimension (i.e. number of rows) of
 * source matrix A provided in lda, and the leading dimension of matrix B
 * provided in ldb. In general, B points to an object, or part of an
 * object, that was allocated via cublasAlloc().<br />
 *<br />
 * Return Values<br />
 * -------------<br />
 * CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library has not been initialized<br />
 * CUBLAS_STATUS_INVALID_VALUE    if rows or cols < 0, or elemSize, lda, or
 *                                ldb <= 0<br />
 * CUBLAS_STATUS_MAPPING_ERROR    if error occurred accessing GPU memory<br />
 * CUBLAS_STATUS_SUCCESS          if the operation completed successfully<br />
 */
public static int cublasSetMatrix (int rows, int cols, int elemSize, Pointer A, int lda, Pointer B, int ldb)
{
  return checkResult(cublasSetMatrixNative(rows, elemSize, cols, A, lda, B, ldb));
}

/**
 * Extended wrapper for arrays of cuComplex values. Note that this method
 * only exists for convenience and compatibility with native C code. It
 * is much more efficient to provide a Pointer to a float array containing
 * the complex numbers, where each pair of consecutive numbers in the array
 * describes the real- and imaginary part of one complex number.
 *
 * @see JCublas#cublasSetMatrix(int, int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetMatrix (int rows, int cols, cuComplex A[], int offsetA, int lda, Pointer B, int ldb)
{
 ByteBuffer byteBufferA = ByteBuffer.allocateDirect(A.length * 4 * 2);
 byteBufferA.order(ByteOrder.nativeOrder());
 FloatBuffer floatBufferA = byteBufferA.asFloatBuffer();
 for (int i=0; i<A.length; i++)
 {
  floatBufferA.put(A[i].x);
  floatBufferA.put(A[i].y);
 }
 return checkResult(cublasSetMatrixNative(rows, cols, 8, Pointer.to(floatBufferA).withByteOffset(offsetA * 4 * 2), lda, B, ldb));
}

/**
 * Extended wrapper for arrays of cuComplex values. Note that this method
 * only exists for convenience and compatibility with native C code. It
 * is much more efficient to provide a Pointer to a float array containing
 * the complex numbers, where each pair of consecutive numbers in the array
 * describes the real- and imaginary part of one complex number.
 *
 * @see JCublas#cublasSetMatrix(int, int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetMatrix (int rows, int cols, cuComplex A[], int offsetA, int lda, Pointer B, int ldb)
{
  ByteBuffer byteBufferA = ByteBuffer.allocateDirect(A.length * 4 * 2);
  byteBufferA.order(ByteOrder.nativeOrder());
  FloatBuffer floatBufferA = byteBufferA.asFloatBuffer();
  for (int i=0; i<A.length; i++)
  {
    floatBufferA.put(A[i].x);
    floatBufferA.put(A[i].y);
  }
  return checkResult(cublasSetMatrixNative(rows, cols, 8, Pointer.to(floatBufferA).withByteOffset(offsetA * 4 * 2), lda, B, ldb));
}

/**
 * Extended wrapper for arrays of cuComplex values. Note that this method
 * only exists for convenience and compatibility with native C code. It
 * is much more efficient to provide a Pointer to a float array containing
 * the complex numbers, where each pair of consecutive numbers in the array
 * describes the real- and imaginary part of one complex number.
 *
 * @see jcuda.jcublas.JCublas#cublasSetMatrix(int, int, int, jcuda.Pointer, int, jcuda.Pointer, int)
 */
public static int cublasSetMatrix (int rows, int cols, cuComplex A[], int offsetA, int lda, Pointer B, int ldb)
{
  ByteBuffer byteBufferA = ByteBuffer.allocateDirect(A.length * 4 * 2);
  byteBufferA.order(ByteOrder.nativeOrder());
  FloatBuffer floatBufferA = byteBufferA.asFloatBuffer();
  for (int i=0; i<A.length; i++)
  {
    floatBufferA.put(A[i].x);
    floatBufferA.put(A[i].y);
  }
  return checkResult(cublasSetMatrixNative(rows, cols, 8, Pointer.to(floatBufferA).withByteOffset(offsetA * 4 * 2), lda, B, ldb));
}

/**
 * Extended wrapper for arrays of cuComplex values. Note that this method
 * only exists for convenience and compatibility with native C code. It
 * is much more efficient to provide a Pointer to a float array containing
 * the complex numbers, where each pair of consecutive numbers in the array
 * describes the real- and imaginary part of one complex number.
 *
 * @see JCublas#cublasSetMatrix(int, int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetMatrix (int rows, int cols, cuComplex A[], int offsetA, int lda, Pointer B, int ldb)
{
  ByteBuffer byteBufferA = ByteBuffer.allocateDirect(A.length * 4 * 2);
  byteBufferA.order(ByteOrder.nativeOrder());
  FloatBuffer floatBufferA = byteBufferA.asFloatBuffer();
  for (int i=0; i<A.length; i++)
  {
    floatBufferA.put(A[i].x);
    floatBufferA.put(A[i].y);
  }
  return checkResult(cublasSetMatrixNative(rows, cols, 8, Pointer.to(floatBufferA).withByteOffset(offsetA * 4 * 2), lda, B, ldb));
}

/**
 * Extended wrapper for arrays of cuComplex values. Note that this method
 * only exists for convenience and compatibility with native C code. It
 * is much more efficient to provide a Pointer to a float array containing
 * the complex numbers, where each pair of consecutive numbers in the array
 * describes the real- and imaginary part of one complex number.
 *
 * @see JCublas#cublasSetMatrix(int, int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetMatrix (int rows, int cols, cuComplex A[], int offsetA, int lda, Pointer B, int ldb)
{
  ByteBuffer byteBufferA = ByteBuffer.allocateDirect(A.length * 4 * 2);
  byteBufferA.order(ByteOrder.nativeOrder());
  FloatBuffer floatBufferA = byteBufferA.asFloatBuffer();
  for (int i=0; i<A.length; i++)
  {
    floatBufferA.put(A[i].x);
    floatBufferA.put(A[i].y);
  }
  return checkResult(cublasSetMatrixNative(rows, cols, 8, Pointer.to(floatBufferA).withByteOffset(offsetA * 4 * 2), lda, B, ldb));
}

/**
 * Extended wrapper for arrays of cuComplex values. Note that this method
 * only exists for convenience and compatibility with native C code. It
 * is much more efficient to provide a Pointer to a float array containing
 * the complex numbers, where each pair of consecutive numbers in the array
 * describes the real- and imaginary part of one complex number.
 *
 * @see JCublas#cublasSetMatrix(int, int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetMatrix (int rows, int cols, cuComplex A[], int offsetA, int lda, Pointer B, int ldb)
{
  ByteBuffer byteBufferA = ByteBuffer.allocateDirect(A.length * 4 * 2);
  byteBufferA.order(ByteOrder.nativeOrder());
  FloatBuffer floatBufferA = byteBufferA.asFloatBuffer();
  for (int i=0; i<A.length; i++)
  {
    floatBufferA.put(A[i].x);
    floatBufferA.put(A[i].y);
  }
  return checkResult(cublasSetMatrixNative(rows, cols, 8, Pointer.to(floatBufferA).withByteOffset(offsetA * 4 * 2), lda, B, ldb));
}

/**
 * Extended wrapper for arrays of cuDoubleComplex values. Note that this method
 * only exists for convenience and compatibility with native C code. It
 * is much more efficient to provide a Pointer to a double array containing
 * the complex numbers, where each pair of consecutive numbers in the array
 * describes the real- and imaginary part of one complex number.
 *
 * @see JCublas#cublasSetMatrix(int, int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetMatrix (int rows, int cols, cuDoubleComplex A[], int offsetA, int lda, Pointer B, int ldb)
{
  ByteBuffer byteBufferA = ByteBuffer.allocateDirect(A.length * 8 * 2);
  byteBufferA.order(ByteOrder.nativeOrder());
  DoubleBuffer doubleBufferA = byteBufferA.asDoubleBuffer();
  for (int i=0; i<A.length; i++)
  {
    doubleBufferA.put(A[i].x);
    doubleBufferA.put(A[i].y);
  }
  return checkResult(cublasSetMatrixNative(rows, cols, 16, Pointer.to(doubleBufferA).withByteOffset(offsetA * 8 * 2), lda, B, ldb));
}

/**
 * Extended wrapper for arrays of cuDoubleComplex values. Note that this method
 * only exists for convenience and compatibility with native C code. It
 * is much more efficient to provide a Pointer to a double array containing
 * the complex numbers, where each pair of consecutive numbers in the array
 * describes the real- and imaginary part of one complex number.
 *
 * @see JCublas#cublasSetMatrix(int, int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetMatrix (int rows, int cols, cuDoubleComplex A[], int offsetA, int lda, Pointer B, int ldb)
{
 ByteBuffer byteBufferA = ByteBuffer.allocateDirect(A.length * 8 * 2);
 byteBufferA.order(ByteOrder.nativeOrder());
 DoubleBuffer doubleBufferA = byteBufferA.asDoubleBuffer();
 for (int i=0; i<A.length; i++)
 {
  doubleBufferA.put(A[i].x);
  doubleBufferA.put(A[i].y);
 }
 return checkResult(cublasSetMatrixNative(rows, cols, 16, Pointer.to(doubleBufferA).withByteOffset(offsetA * 8 * 2), lda, B, ldb));
}

/**
 * Extended wrapper for arrays of cuDoubleComplex values. Note that this method
 * only exists for convenience and compatibility with native C code. It
 * is much more efficient to provide a Pointer to a double array containing
 * the complex numbers, where each pair of consecutive numbers in the array
 * describes the real- and imaginary part of one complex number.
 *
 * @see JCublas#cublasSetMatrix(int, int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetMatrix (int rows, int cols, cuDoubleComplex A[], int offsetA, int lda, Pointer B, int ldb)
{
  ByteBuffer byteBufferA = ByteBuffer.allocateDirect(A.length * 8 * 2);
  byteBufferA.order(ByteOrder.nativeOrder());
  DoubleBuffer doubleBufferA = byteBufferA.asDoubleBuffer();
  for (int i=0; i<A.length; i++)
  {
    doubleBufferA.put(A[i].x);
    doubleBufferA.put(A[i].y);
  }
  return checkResult(cublasSetMatrixNative(rows, cols, 16, Pointer.to(doubleBufferA).withByteOffset(offsetA * 8 * 2), lda, B, ldb));
}

/**
 * Extended wrapper for arrays of cuDoubleComplex values. Note that this method
 * only exists for convenience and compatibility with native C code. It
 * is much more efficient to provide a Pointer to a double array containing
 * the complex numbers, where each pair of consecutive numbers in the array
 * describes the real- and imaginary part of one complex number.
 *
 * @see jcuda.jcublas.JCublas#cublasSetMatrix(int, int, int, jcuda.Pointer, int, jcuda.Pointer, int)
 */
public static int cublasSetMatrix (int rows, int cols, cuDoubleComplex A[], int offsetA, int lda, Pointer B, int ldb)
{
  ByteBuffer byteBufferA = ByteBuffer.allocateDirect(A.length * 8 * 2);
  byteBufferA.order(ByteOrder.nativeOrder());
  DoubleBuffer doubleBufferA = byteBufferA.asDoubleBuffer();
  for (int i=0; i<A.length; i++)
  {
    doubleBufferA.put(A[i].x);
    doubleBufferA.put(A[i].y);
  }
  return checkResult(cublasSetMatrixNative(rows, cols, 16, Pointer.to(doubleBufferA).withByteOffset(offsetA * 8 * 2), lda, B, ldb));
}

/**
 * Extended wrapper for arrays of cuDoubleComplex values. Note that this method
 * only exists for convenience and compatibility with native C code. It
 * is much more efficient to provide a Pointer to a double array containing
 * the complex numbers, where each pair of consecutive numbers in the array
 * describes the real- and imaginary part of one complex number.
 *
 * @see JCublas#cublasSetMatrix(int, int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetMatrix (int rows, int cols, cuDoubleComplex A[], int offsetA, int lda, Pointer B, int ldb)
{
  ByteBuffer byteBufferA = ByteBuffer.allocateDirect(A.length * 8 * 2);
  byteBufferA.order(ByteOrder.nativeOrder());
  DoubleBuffer doubleBufferA = byteBufferA.asDoubleBuffer();
  for (int i=0; i<A.length; i++)
  {
    doubleBufferA.put(A[i].x);
    doubleBufferA.put(A[i].y);
  }
  return checkResult(cublasSetMatrixNative(rows, cols, 16, Pointer.to(doubleBufferA).withByteOffset(offsetA * 8 * 2), lda, B, ldb));
}

/**
 * Extended wrapper for arrays of cuDoubleComplex values. Note that this method
 * only exists for convenience and compatibility with native C code. It
 * is much more efficient to provide a Pointer to a double array containing
 * the complex numbers, where each pair of consecutive numbers in the array
 * describes the real- and imaginary part of one complex number.
 *
 * @see JCublas#cublasSetMatrix(int, int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetMatrix (int rows, int cols, cuDoubleComplex A[], int offsetA, int lda, Pointer B, int ldb)
{
  ByteBuffer byteBufferA = ByteBuffer.allocateDirect(A.length * 8 * 2);
  byteBufferA.order(ByteOrder.nativeOrder());
  DoubleBuffer doubleBufferA = byteBufferA.asDoubleBuffer();
  for (int i=0; i<A.length; i++)
  {
    doubleBufferA.put(A[i].x);
    doubleBufferA.put(A[i].y);
  }
  return checkResult(cublasSetMatrixNative(rows, cols, 16, Pointer.to(doubleBufferA).withByteOffset(offsetA * 8 * 2), lda, B, ldb));
}

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How to use cublasSetMatrixNativemethodin jcuda.jcublas.JCublas

Best Java code snippets using jcuda.jcublas.JCublas.cublasSetMatrixNative (Showing top 18 results out of 315)

How to use
cublasSetMatrixNative
method
in
jcuda.jcublas.JCublas