JCublasJava/src/main/java/jcuda/jcublas/JCublas.java
public static void cublasSsyrk(char uplo, char trans, int n, int k, float alpha, Pointer A, int lda, float beta, Pointer C, int ldc)
    {
        cublasSsyrkNative(uplo, trans, n, k, alpha, A, lda, beta, C, ldc);
        checkResultBLAS();
    }
public static void cublasSsyrk(char uplo, char trans, int n, int k, float alpha, Pointer A, int lda, float beta, Pointer C, int ldc)
    {
        cublasSsyrkNative(uplo, trans, n, k, alpha, A, lda, beta, C, ldc);
        checkResultBLAS();
    }
<pre>
void
cublasSsyrk (char uplo, char trans, int n, int k, float alpha,
const float *A, int lda, float beta, float *C, int ldc)

performs one of the symmetric rank k operations

C = alpha * A * transpose(A) + beta * C, or
C = alpha * transpose(A) * 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 is a matrix 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 referenced,
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 * transpose(A) + beta * C. If trans == 'T', 't', 'C', or 'c',
C = transpose(A) * 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 applied to A * transpose(A) or
transpose(A) * A.
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 contains 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).
beta   single precision scalar multiplier applied to C. If beta izs 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. It must be at least max(1, n).

Output
------
C      updated according to C = alpha * A * transpose(A) + beta * C, or C =
alpha * transpose(A) * A + beta * C

Reference: http://www.netlib.org/blas/ssyrk.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>