Public

If the given result is different to cublasStatus.CUBLAS_STATUS_SUCCESS and
exceptions have been enab

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Obtain the current CUBLAS status by calling cublasGetErrorNative, and store the
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A font provides the

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java.awt.image.Kernel

 KeyStore is responsible for maintaining cryptographic keys and their owners. 
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HashSet is an implementation of a Set. All optional operations (adding and
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java.util.HashSet

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/**
     * <pre>
     * void
     * cublasSsyr2k (char uplo, char trans, int n, int k, float alpha,
     *               const float *A, int lda, const float *B, int ldb,
     *               float beta, float *C, int ldc)
     *
     * performs one of the symmetric rank 2k operations
     *
     *    C = alpha * A * transpose(B) + alpha * B * transpose(A) + beta * C, or
     *    C = alpha * transpose(A) * B + alpha * transpose(B) * A + beta * C.
     *
     * Alpha and beta are single precision scalars. C is an n x n symmetric matrix
     * consisting of single precision elements and stored in either lower or upper
     * storage mode. A and B are matrices consisting of single precision elements
     * with dimension of n x k in the first case, and k x n in the second case.
     *
     * Input
     * -----
     * uplo   specifies whether the symmetric matrix C is stored in upper or lower
     *        storage mode, as follows. If uplo == 'U' or 'u', only the upper
     *        triangular part of the symmetric matrix is to be referenced, and the
     *        elements of the strictly lower triangular part are to be infered from
     *        those in the upper triangular part. If uplo == 'L' or 'l', only the
     *        lower triangular part of the symmetric matrix is to be references,
     *        and the elements of the strictly upper triangular part are to be
     *        infered from those in the lower triangular part.
     * trans  specifies the operation to be performed. If trans == 'N' or 'n',
     *        C = alpha * A * transpose(B) + alpha * B * transpose(A) + beta * C,
     *        If trans == 'T', 't', 'C', or 'c', C = alpha * transpose(A) * B +
     *        alpha * transpose(B) * A + beta * C.
     * n      specifies the number of rows and the number columns of matrix C. If
     *        trans == 'N' or 'n', n specifies the number of rows of matrix A. If
     *        trans == 'T', 't', 'C', or 'c', n specifies the columns of matrix A.
     *        n must be at least zero.
     * k      If trans == 'N' or 'n', k specifies the number of rows of matrix A.
     *        If trans == 'T', 't', 'C', or 'c', k specifies the number of rows of
     *        matrix A. k must be at least zero.
     * alpha  single precision scalar multiplier.
     * A      single precision array of dimensions (lda, ka), where ka is k when
     *        trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n',
     *        the leading n x k part of array A must contain the matrix A,
     *        otherwise the leading k x n part of the array must contain the matrix
     *        A.
     * lda    leading dimension of A. When trans == 'N' or 'n' then lda must be at
     *        least max(1, n). Otherwise lda must be at least max(1,k).
     * B      single precision array of dimensions (lda, kb), where kb is k when
     *        trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n',
     *        the leading n x k part of array B must contain the matrix B,
     *        otherwise the leading k x n part of the array must contain the matrix
     *        B.
     * ldb    leading dimension of N. When trans == 'N' or 'n' then ldb must be at
     *        least max(1, n). Otherwise ldb must be at least max(1, k).
     * beta   single precision scalar multiplier applied to C. If beta is zero, C
     *        does not have to be a valid input.
     * C      single precision array of dimensions (ldc, n). If uplo == 'U' or 'u',
     *        the leading n x n triangular part of the array C must contain the
     *        upper triangular part of the symmetric matrix C and the strictly
     *        lower triangular part of C is not referenced. On exit, the upper
     *        triangular part of C is overwritten by the upper triangular part of
     *        the updated matrix. If uplo == 'L' or 'l', the leading n x n
     *        triangular part of the array C must contain the lower triangular part
     *        of the symmetric matrix C and the strictly upper triangular part of C
     *        is not referenced. On exit, the lower triangular part of C is
     *        overwritten by the lower triangular part of the updated matrix.
     * ldc    leading dimension of C. Must be at least max(1, n).
     *
     * Output
     * ------
     * C      updated according to alpha*A*transpose(B) + alpha*B*transpose(A) +
     *        beta*C or alpha*transpose(A)*B + alpha*transpose(B)*A + beta*C
     *
     * Reference:   http://www.netlib.org/blas/ssyr2k.f
     *
     * Error status for this function can be retrieved via cublasGetError().
     *
     * Error Status
     * ------------
     * CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library has not been initialized
     * CUBLAS_STATUS_INVALID_VALUE    if n < 0 or k < 0
     * CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
     * </pre>
     */

/**
   * <pre>
   * void 
   * cublasSsyr2k (char uplo, char trans, int n, int k, float alpha, 
   *               const float *A, int lda, const float *B, int ldb, 
   *               float beta, float *C, int ldc)
   *
   * performs one of the symmetric rank 2k operations
   * 
   *    C = alpha * A * transpose(B) + alpha * B * transpose(A) + beta * C, or 
   *    C = alpha * transpose(A) * B + alpha * transpose(B) * A + beta * C.
   *
   * Alpha and beta are single precision scalars. C is an n x n symmetric matrix
   * consisting of single precision elements and stored in either lower or upper 
   * storage mode. A and B are matrices consisting of single precision elements 
   * with dimension of n x k in the first case, and k x n in the second case.
   *
   * Input
   * -----
   * uplo   specifies whether the symmetric matrix C is stored in upper or lower
   *        storage mode, as follows. If uplo == 'U' or 'u', only the upper 
   *        triangular part of the symmetric matrix is to be referenced, and the
   *        elements of the strictly lower triangular part are to be infered from
   *        those in the upper triangular part. If uplo == 'L' or 'l', only the 
   *        lower triangular part of the symmetric matrix is to be references, 
   *        and the elements of the strictly upper triangular part are to be 
   *        infered from those in the lower triangular part.
   * trans  specifies the operation to be performed. If trans == 'N' or 'n', 
   *        C = alpha * A * transpose(B) + alpha * B * transpose(A) + beta * C, 
   *        If trans == 'T', 't', 'C', or 'c', C = alpha * transpose(A) * B + 
   *        alpha * transpose(B) * A + beta * C.
   * n      specifies the number of rows and the number columns of matrix C. If 
   *        trans == 'N' or 'n', n specifies the number of rows of matrix A. If 
   *        trans == 'T', 't', 'C', or 'c', n specifies the columns of matrix A. 
   *        n must be at least zero.
   * k      If trans == 'N' or 'n', k specifies the number of rows of matrix A. 
   *        If trans == 'T', 't', 'C', or 'c', k specifies the number of rows of 
   *        matrix A. k must be at least zero.
   * alpha  single precision scalar multiplier.
   * A      single precision array of dimensions (lda, ka), where ka is k when 
   *        trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n', 
   *        the leading n x k part of array A must contain the matrix A, 
   *        otherwise the leading k x n part of the array must contain the matrix
   *        A.
   * lda    leading dimension of A. When trans == 'N' or 'n' then lda must be at 
   *        least max(1, n). Otherwise lda must be at least max(1,k).
   * B      single precision array of dimensions (lda, kb), where kb is k when 
   *        trans == 'N' or 'n', and is n otherwise. When trans == 'N' or 'n', 
   *        the leading n x k part of array B must contain the matrix B, 
   *        otherwise the leading k x n part of the array must contain the matrix
   *        B.
   * ldb    leading dimension of N. When trans == 'N' or 'n' then ldb must be at
   *        least max(1, n). Otherwise ldb must be at least max(1, k).
   * beta   single precision scalar multiplier applied to C. If beta is zero, C 
   *        does not have to be a valid input.
   * C      single precision array of dimensions (ldc, n). If uplo == 'U' or 'u',
   *        the leading n x n triangular part of the array C must contain the 
   *        upper triangular part of the symmetric matrix C and the strictly 
   *        lower triangular part of C is not referenced. On exit, the upper 
   *        triangular part of C is overwritten by the upper triangular part of 
   *        the updated matrix. If uplo == 'L' or 'l', the leading n x n 
   *        triangular part of the array C must contain the lower triangular part
   *        of the symmetric matrix C and the strictly upper triangular part of C
   *        is not referenced. On exit, the lower triangular part of C is 
   *        overwritten by the lower triangular part of the updated matrix.
   * ldc    leading dimension of C. Must be at least max(1, n).
   *
   * Output
   * ------
   * C      updated according to alpha*A*transpose(B) + alpha*B*transpose(A) + 
   *        beta*C or alpha*transpose(A)*B + alpha*transpose(B)*A + beta*C
  *
    * Reference:   http://www.netlib.org/blas/ssyr2k.f
    *
    * Error status for this function can be retrieved via cublasGetError().
    *
    * Error Status
    * ------------
    * CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library has not been initialized
    * CUBLAS_STATUS_INVALID_VALUE    if n < 0 or k < 0
    * CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
    * </pre>
    */

How to use cublasSsyr2kNativemethodin jcuda.jcublas.JCublas

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How to use
cublasSsyr2kNative
method
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jcuda.jcublas.JCublas