JCublasJava/src/main/java/jcuda/jcublas/JCublas.java
public static void cublasSsymv(char uplo, int n, float alpha, Pointer A, int lda, Pointer x, int incx, float beta, Pointer y, int incy)
    {
        cublasSsymvNative(uplo, n, alpha, A, lda, x, incx, beta, y, incy);
        checkResultBLAS();
    }
public static void cublasSsymv(char uplo, int n, float alpha, Pointer A, int lda, Pointer x, int incx, float beta, Pointer y, int incy)
    {
        cublasSsymvNative(uplo, n, alpha, A, lda, x, incx, beta, y, incy);
        checkResultBLAS();
    }
<pre>
void
cublasSsymv (char uplo, int n, float alpha, const float *A, int lda,
const float *x, int incx, float beta, float *y, int incy)

performs the matrix-vector operation

y = alpha*A*x + beta*y

Alpha and beta are single precision scalars, and x and y are single
precision vectors, each with n elements. A is a symmetric n x n matrix
consisting of single precision elements that is stored in either upper or
lower storage mode.

Input
-----
uplo   specifies whether the upper or lower triangular part of the array A
is to be referenced. If uplo == 'U' or 'u', the symmetric matrix A
is stored in upper storage mode, i.e. only the upper triangular part
of A is to be referenced while the lower triangular part of A is to
be inferred. If uplo == 'L' or 'l', the symmetric matrix A is stored
in lower storage mode, i.e. only the lower triangular part of A is
to be referenced while the upper triangular part of A is to be
inferred.
n      specifies the number of rows and the number of columns of the
symmetric matrix A. n must be at least zero.
alpha  single precision scalar multiplier applied to A*x.
A      single precision array of dimensions (lda, n). If uplo == 'U' or 'u',
the leading n x n upper triangular part of the array A must contain
the upper triangular part of the symmetric matrix and the strictly
lower triangular part of A is not referenced. If uplo == 'L' or 'l',
the leading n x n lower triangular part of the array A must contain
the lower triangular part of the symmetric matrix and the strictly
upper triangular part of A is not referenced.
lda    leading dimension of A. It must be at least max (1, n).
x      single precision array of length at least (1 + (n - 1) * abs(incx)).
incx   storage spacing between elements of x. incx must not be zero.
beta   single precision scalar multiplier applied to vector y.
y      single precision array of length at least (1 + (n - 1) * abs(incy)).
If beta is zero, y is not read.
incy   storage spacing between elements of y. incy must not be zero.

Output
------
y      updated according to y = alpha*A*x + beta*y

Reference: http://www.netlib.org/blas/ssymv.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 if incx or incy == 0
CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
</pre>