Public

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

checkResult

Obtain the current CUBLAS status by calling cublasGetErrorNative, and store the
result as the lastRe

checkResultBLAS

cublasAllocNative

cublasCaxpyNative

cublasCcopyNative

cublasCdotcNative

cublasCdotuNative

cublasCgbmvNative

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cublasCgemvNative

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cublasChbmvNative

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cublasChemvNative

cublasCher2Native

cublasCher2kNative

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cublasCherkNative

cublasChpr2Native

Gson

Making http requests using okhttp

The Color class is used to encapsulate colors in the default sRGB color space or
colors in arbitrary

java.awt.Color

HashSet is an implementation of a Set. All optional operations (adding and
removing) are supported. 

java.util.HashSet

A collection designed for holding elements prior to processing. Besides basic
java.util.Collection o

java.util.Queue

Vector is an implementation of List, backed by an array and synchronized. All
optional operations in

java.util.Vector

Factory that creates instances of DateTimeFormatter from patterns and styles. 
Datetime formatting i

org.joda.time.format.DateTimeFormat

The LoggerFactory is a utility class producing Loggers for various logging APIs,
most notably for lo

org.slf4j.LoggerFactory

onRequestPermissionsResult

scheduleAtFixedRate

setContentView

/**
     * <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
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