jcuda.jcublas.JCublas.cublasSetVectorNative java code examples

/**
 * Wrapper for CUBLAS function.<br />
 * <br />
 * cublasStatus<br />
 * cublasSetVector (int n, int elemSize, const void *x, int incx,
 *                  void *y, int incy)<br />
 *<br />
 * copies n elements from a vector x in CPU memory space to a vector y
 * in GPU memory space. Elements in both vectors are assumed to have a
 * size of elemSize bytes. Storage spacing between consecutive elements
 * is incx for the source vector x and incy for the destination vector
 * y. In general, y points to an object, or part of an object, allocated
 * via cublasAlloc(). Column major format for two-dimensional matrices
 * is assumed throughout CUBLAS. Therefore, if the increment for a vector
 * is equal to 1, this access a column vector while using an increment
 * equal to the leading dimension of the respective matrix accesses a
 * row vector.<br />
 *<br />
 * Return Values<br />
 * -------------<br />
 * CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library not been initialized<br />
 * CUBLAS_STATUS_INVALID_VALUE    if incx, incy, or elemSize <= 0<br />
 * CUBLAS_STATUS_MAPPING_ERROR    if an error occurred accessing GPU memory<br />
 * CUBLAS_STATUS_SUCCESS          if the operation completed successfully<br />
 */
public static int cublasSetVector(int n, int elemSize, Pointer x, int incx, Pointer y, int incy)
{
 return checkResult(cublasSetVectorNative(n, elemSize, x, incx, y, incy));
}

/**
 * Wrapper for CUBLAS function.<br />
 * <br />
 * cublasStatus<br />
 * cublasSetVector (int n, int elemSize, const void *x, int incx,
 *                  void *y, int incy)<br />
 *<br />
 * copies n elements from a vector x in CPU memory space to a vector y
 * in GPU memory space. Elements in both vectors are assumed to have a
 * size of elemSize bytes. Storage spacing between consecutive elements
 * is incx for the source vector x and incy for the destination vector
 * y. In general, y points to an object, or part of an object, allocated
 * via cublasAlloc(). Column major format for two-dimensional matrices
 * is assumed throughout CUBLAS. Therefore, if the increment for a vector
 * is equal to 1, this access a column vector while using an increment
 * equal to the leading dimension of the respective matrix accesses a
 * row vector.<br />
 *<br />
 * Return Values<br />
 * -------------<br />
 * CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library not been initialized<br />
 * CUBLAS_STATUS_INVALID_VALUE    if incx, incy, or elemSize <= 0<br />
 * CUBLAS_STATUS_MAPPING_ERROR    if an error occurred accessing GPU memory<br />
 * CUBLAS_STATUS_SUCCESS          if the operation completed successfully<br />
 */
public static int cublasSetVector(int n, int elemSize, Pointer x, int incx, Pointer y, int incy)
{
  return checkResult(cublasSetVectorNative(n, elemSize, x, incx, y, incy));
}

/**
 * Wrapper for CUBLAS function.<br />
 * <br />
 * cublasStatus<br />
 * cublasSetVector (int n, int elemSize, const void *x, int incx,
 *                  void *y, int incy)<br />
 *<br />
 * copies n elements from a vector x in CPU memory space to a vector y
 * in GPU memory space. Elements in both vectors are assumed to have a
 * size of elemSize bytes. Storage spacing between consecutive elements
 * is incx for the source vector x and incy for the destination vector
 * y. In general, y points to an object, or part of an object, allocated
 * via cublasAlloc(). Column major format for two-dimensional matrices
 * is assumed throughout CUBLAS. Therefore, if the increment for a vector
 * is equal to 1, this access a column vector while using an increment
 * equal to the leading dimension of the respective matrix accesses a
 * row vector.<br />
 *<br />
 * Return Values<br />
 * -------------<br />
 * CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library not been initialized<br />
 * CUBLAS_STATUS_INVALID_VALUE    if incx, incy, or elemSize <= 0<br />
 * CUBLAS_STATUS_MAPPING_ERROR    if an error occurred accessing GPU memory<br />
 * CUBLAS_STATUS_SUCCESS          if the operation completed successfully<br />
 */
public static int cublasSetVector(int n, int elemSize, Pointer x, int incx, Pointer y, int incy)
{
  return checkResult(cublasSetVectorNative(n, elemSize, x, incx, y, incy));
}

/**
 * Wrapper for CUBLAS function.<br />
 * <br />
 * cublasStatus<br />
 * cublasSetVector (int n, int elemSize, const void *x, int incx,
 *                  void *y, int incy)<br />
 *<br />
 * copies n elements from a vector x in CPU memory space to a vector y
 * in GPU memory space. Elements in both vectors are assumed to have a
 * size of elemSize bytes. Storage spacing between consecutive elements
 * is incx for the source vector x and incy for the destination vector
 * y. In general, y points to an object, or part of an object, allocated
 * via cublasAlloc(). Column major format for two-dimensional matrices
 * is assumed throughout CUBLAS. Therefore, if the increment for a vector
 * is equal to 1, this access a column vector while using an increment
 * equal to the leading dimension of the respective matrix accesses a
 * row vector.<br />
 *<br />
 * Return Values<br />
 * -------------<br />
 * CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library not been initialized<br />
 * CUBLAS_STATUS_INVALID_VALUE    if incx, incy, or elemSize <= 0<br />
 * CUBLAS_STATUS_MAPPING_ERROR    if an error occurred accessing GPU memory<br />
 * CUBLAS_STATUS_SUCCESS          if the operation completed successfully<br />
 */
public static int cublasSetVector(int n, int elemSize, Pointer x, int incx, Pointer y, int incy)
{
  return checkResult(cublasSetVectorNative(n, elemSize, x, incx, y, incy));
}

/**
 * Wrapper for CUBLAS function.<br />
 * <br />
 * cublasStatus<br />
 * cublasSetVector (int n, int elemSize, const void *x, int incx,
 *                  void *y, int incy)<br />
 *<br />
 * copies n elements from a vector x in CPU memory space to a vector y
 * in GPU memory space. Elements in both vectors are assumed to have a
 * size of elemSize bytes. Storage spacing between consecutive elements
 * is incx for the source vector x and incy for the destination vector
 * y. In general, y points to an object, or part of an object, allocated
 * via cublasAlloc(). Column major format for two-dimensional matrices
 * is assumed throughout CUBLAS. Therefore, if the increment for a vector
 * is equal to 1, this access a column vector while using an increment
 * equal to the leading dimension of the respective matrix accesses a
 * row vector.<br />
 *<br />
 * Return Values<br />
 * -------------<br />
 * CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library not been initialized<br />
 * CUBLAS_STATUS_INVALID_VALUE    if incx, incy, or elemSize <= 0<br />
 * CUBLAS_STATUS_MAPPING_ERROR    if an error occurred accessing GPU memory<br />
 * CUBLAS_STATUS_SUCCESS          if the operation completed successfully<br />
 */
public static int cublasSetVector(int n, int elemSize, Pointer x, int incx, Pointer y, int incy)
{
  return checkResult(cublasSetVectorNative(n, elemSize, x, incx, y, incy));
}

/**
 * Wrapper for CUBLAS function.<br />
 * <br />
 * cublasStatus<br />
 * cublasSetVector (int n, int elemSize, const void *x, int incx,
 *                  void *y, int incy)<br />
 *<br />
 * copies n elements from a vector x in CPU memory space to a vector y
 * in GPU memory space. Elements in both vectors are assumed to have a
 * size of elemSize bytes. Storage spacing between consecutive elements
 * is incx for the source vector x and incy for the destination vector
 * y. In general, y points to an object, or part of an object, allocated
 * via cublasAlloc(). Column major format for two-dimensional matrices
 * is assumed throughout CUBLAS. Therefore, if the increment for a vector
 * is equal to 1, this access a column vector while using an increment
 * equal to the leading dimension of the respective matrix accesses a
 * row vector.<br />
 *<br />
 * Return Values<br />
 * -------------<br />
 * CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library not been initialized<br />
 * CUBLAS_STATUS_INVALID_VALUE    if incx, incy, or elemSize <= 0<br />
 * CUBLAS_STATUS_MAPPING_ERROR    if an error occurred accessing GPU memory<br />
 * CUBLAS_STATUS_SUCCESS          if the operation completed successfully<br />
 */
public static int cublasSetVector(int n, int elemSize, Pointer x, int incx, Pointer y, int incy)
{
  return checkResult(cublasSetVectorNative(n, elemSize, x, incx, y, incy));
}

/**
 * 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#cublasSetVector(int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetVector (int n, cuComplex x[], int offsetx, int incx, Pointer y, int incy)
{
  ByteBuffer byteBufferx = ByteBuffer.allocateDirect(x.length * 4 * 2);
  byteBufferx.order(ByteOrder.nativeOrder());
  FloatBuffer floatBufferx = byteBufferx.asFloatBuffer();
  int indexx = offsetx;
  for (int i=0; i<n; i++, indexx+=incx)
  {
    floatBufferx.put(indexx*2+0, x[indexx].x);
    floatBufferx.put(indexx*2+1, x[indexx].y);
  }
  return checkResult(cublasSetVectorNative(n, 8, Pointer.to(floatBufferx).withByteOffset(offsetx * 4 * 2), incx, y, incy));
}

/**
 * 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#cublasSetVector(int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetVector (int n, cuComplex x[], int offsetx, int incx, Pointer y, int incy)
{
  ByteBuffer byteBufferx = ByteBuffer.allocateDirect(x.length * 4 * 2);
  byteBufferx.order(ByteOrder.nativeOrder());
  FloatBuffer floatBufferx = byteBufferx.asFloatBuffer();
  int indexx = offsetx;
  for (int i=0; i<n; i++, indexx+=incx)
  {
    floatBufferx.put(indexx*2+0, x[indexx].x);
    floatBufferx.put(indexx*2+1, x[indexx].y);
  }
  return checkResult(cublasSetVectorNative(n, 8, Pointer.to(floatBufferx).withByteOffset(offsetx * 4 * 2), incx, y, incy));
}

/**
 * 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#cublasSetVector(int, int, jcuda.Pointer, int, jcuda.Pointer, int)
 */
public static int cublasSetVector (int n, cuComplex x[], int offsetx, int incx, Pointer y, int incy)
{
  ByteBuffer byteBufferx = ByteBuffer.allocateDirect(x.length * 4 * 2);
  byteBufferx.order(ByteOrder.nativeOrder());
  FloatBuffer floatBufferx = byteBufferx.asFloatBuffer();
  int indexx = offsetx;
  for (int i=0; i<n; i++, indexx+=incx)
  {
    floatBufferx.put(indexx*2+0, x[indexx].x);
    floatBufferx.put(indexx*2+1, x[indexx].y);
  }
  return checkResult(cublasSetVectorNative(n, 8, Pointer.to(floatBufferx).withByteOffset(offsetx * 4 * 2), incx, y, incy));
}

/**
 * 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#cublasSetVector(int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetVector (int n, cuComplex x[], int offsetx, int incx, Pointer y, int incy)
{
  ByteBuffer byteBufferx = ByteBuffer.allocateDirect(x.length * 4 * 2);
  byteBufferx.order(ByteOrder.nativeOrder());
  FloatBuffer floatBufferx = byteBufferx.asFloatBuffer();
  int indexx = offsetx;
  for (int i=0; i<n; i++, indexx+=incx)
  {
    floatBufferx.put(indexx*2+0, x[indexx].x);
    floatBufferx.put(indexx*2+1, x[indexx].y);
  }
  return checkResult(cublasSetVectorNative(n, 8, Pointer.to(floatBufferx).withByteOffset(offsetx * 4 * 2), incx, y, incy));
}

/**
 * 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#cublasSetVector(int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetVector (int n, cuComplex x[], int offsetx, int incx, Pointer y, int incy)
{
  ByteBuffer byteBufferx = ByteBuffer.allocateDirect(x.length * 4 * 2);
  byteBufferx.order(ByteOrder.nativeOrder());
  FloatBuffer floatBufferx = byteBufferx.asFloatBuffer();
  int indexx = offsetx;
  for (int i=0; i<n; i++, indexx+=incx)
  {
    floatBufferx.put(indexx*2+0, x[indexx].x);
    floatBufferx.put(indexx*2+1, x[indexx].y);
  }
  return checkResult(cublasSetVectorNative(n, 8, Pointer.to(floatBufferx).withByteOffset(offsetx * 4 * 2), incx, y, incy));
}

/**
 * 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#cublasSetVector(int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetVector (int n, cuComplex x[], int offsetx, int incx, Pointer y, int incy)
{
 ByteBuffer byteBufferx = ByteBuffer.allocateDirect(x.length * 4 * 2);
 byteBufferx.order(ByteOrder.nativeOrder());
 FloatBuffer floatBufferx = byteBufferx.asFloatBuffer();
 int indexx = offsetx;
 for (int i=0; i<n; i++, indexx+=incx)
 {
  floatBufferx.put(indexx*2+0, x[indexx].x);
  floatBufferx.put(indexx*2+1, x[indexx].y);
 }
 return checkResult(cublasSetVectorNative(n, 8, Pointer.to(floatBufferx).withByteOffset(offsetx * 4 * 2), incx, y, incy));
}

/**
 * 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#cublasSetVector(int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetVector (int n, cuDoubleComplex x[], int offsetx, int incx, Pointer y, int incy)
{
  ByteBuffer byteBufferx = ByteBuffer.allocateDirect(x.length * 8 * 2);
  byteBufferx.order(ByteOrder.nativeOrder());
  DoubleBuffer doubleBufferx = byteBufferx.asDoubleBuffer();
  int indexx = offsetx;
  for (int i=0; i<n; i++, indexx+=incx)
  {
    doubleBufferx.put(indexx*2+0, x[indexx].x);
    doubleBufferx.put(indexx*2+1, x[indexx].y);
  }
  return checkResult(cublasSetVectorNative(n, 16, Pointer.to(doubleBufferx).withByteOffset(offsetx * 8 * 2), incx, y, incy));
}

/**
 * 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#cublasSetVector(int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetVector (int n, cuDoubleComplex x[], int offsetx, int incx, Pointer y, int incy)
{
  ByteBuffer byteBufferx = ByteBuffer.allocateDirect(x.length * 8 * 2);
  byteBufferx.order(ByteOrder.nativeOrder());
  DoubleBuffer doubleBufferx = byteBufferx.asDoubleBuffer();
  int indexx = offsetx;
  for (int i=0; i<n; i++, indexx+=incx)
  {
    doubleBufferx.put(indexx*2+0, x[indexx].x);
    doubleBufferx.put(indexx*2+1, x[indexx].y);
  }
  return checkResult(cublasSetVectorNative(n, 16, Pointer.to(doubleBufferx).withByteOffset(offsetx * 8 * 2), incx, y, incy));
}

/**
 * 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#cublasSetVector(int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetVector (int n, cuDoubleComplex x[], int offsetx, int incx, Pointer y, int incy)
{
  ByteBuffer byteBufferx = ByteBuffer.allocateDirect(x.length * 8 * 2);
  byteBufferx.order(ByteOrder.nativeOrder());
  DoubleBuffer doubleBufferx = byteBufferx.asDoubleBuffer();
  int indexx = offsetx;
  for (int i=0; i<n; i++, indexx+=incx)
  {
    doubleBufferx.put(indexx*2+0, x[indexx].x);
    doubleBufferx.put(indexx*2+1, x[indexx].y);
  }
  return checkResult(cublasSetVectorNative(n, 16, Pointer.to(doubleBufferx).withByteOffset(offsetx * 8 * 2), incx, y, incy));
}

/**
 * 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#cublasSetVector(int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetVector (int n, cuDoubleComplex x[], int offsetx, int incx, Pointer y, int incy)
{
 ByteBuffer byteBufferx = ByteBuffer.allocateDirect(x.length * 8 * 2);
 byteBufferx.order(ByteOrder.nativeOrder());
 DoubleBuffer doubleBufferx = byteBufferx.asDoubleBuffer();
 int indexx = offsetx;
 for (int i=0; i<n; i++, indexx+=incx)
 {
  doubleBufferx.put(indexx*2+0, x[indexx].x);
  doubleBufferx.put(indexx*2+1, x[indexx].y);
 }
 return checkResult(cublasSetVectorNative(n, 16, Pointer.to(doubleBufferx).withByteOffset(offsetx * 8 * 2), incx, y, incy));
}

/**
 * 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#cublasSetVector(int, int, jcuda.Pointer, int, jcuda.Pointer, int)
 */
public static int cublasSetVector (int n, cuDoubleComplex x[], int offsetx, int incx, Pointer y, int incy)
{
  ByteBuffer byteBufferx = ByteBuffer.allocateDirect(x.length * 8 * 2);
  byteBufferx.order(ByteOrder.nativeOrder());
  DoubleBuffer doubleBufferx = byteBufferx.asDoubleBuffer();
  int indexx = offsetx;
  for (int i=0; i<n; i++, indexx+=incx)
  {
    doubleBufferx.put(indexx*2+0, x[indexx].x);
    doubleBufferx.put(indexx*2+1, x[indexx].y);
  }
  return checkResult(cublasSetVectorNative(n, 16, Pointer.to(doubleBufferx).withByteOffset(offsetx * 8 * 2), incx, y, incy));
}

/**
 * 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#cublasSetVector(int, int, Pointer, int, Pointer, int)
 */
public static int cublasSetVector (int n, cuDoubleComplex x[], int offsetx, int incx, Pointer y, int incy)
{
  ByteBuffer byteBufferx = ByteBuffer.allocateDirect(x.length * 8 * 2);
  byteBufferx.order(ByteOrder.nativeOrder());
  DoubleBuffer doubleBufferx = byteBufferx.asDoubleBuffer();
  int indexx = offsetx;
  for (int i=0; i<n; i++, indexx+=incx)
  {
    doubleBufferx.put(indexx*2+0, x[indexx].x);
    doubleBufferx.put(indexx*2+1, x[indexx].y);
  }
  return checkResult(cublasSetVectorNative(n, 16, Pointer.to(doubleBufferx).withByteOffset(offsetx * 8 * 2), incx, y, incy));
}

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How to use cublasSetVectorNativemethodin jcuda.jcublas.JCublas

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

How to use
cublasSetVectorNative
method
in
jcuda.jcublas.JCublas