public static void cublasSger(int m, int n, float alpha, Pointer x, int incx, Pointer y, int incy, Pointer A, int lda) {
    """
	<pre>
	cublasSger (int m, int n, float alpha, const float *x, int incx,
	const float *y, int incy, float *A, int lda)
	
	performs the symmetric rank 1 operation
	
	A = alpha * x * transpose(y) + A,
	
	where alpha is a single precision scalar, x is an m element single
	precision vector, y is an n element single precision vector, and A
	is an m by n matrix consisting of single precision elements. Matrix A
	is stored in column major format, and lda is the leading dimension of
	the two-dimensional array used to store A.
	
	Input
	-----
	m      specifies the number of rows of the matrix A. It must be at least
	zero.
	n      specifies the number of columns of the matrix A. It must be at
	least zero.
	alpha  single precision scalar multiplier applied to x * transpose(y)
	x      single precision array of length at least (1 + (m - 1) * abs(incx))
	incx   specifies the storage spacing between elements of x. incx must not
	be zero.
	y      single precision array of length at least (1 + (n - 1) * abs(incy))
	incy   specifies the storage spacing between elements of y. incy must not
	be zero.
	A      single precision array of dimensions (lda, n).
	lda    leading dimension of two-dimensional array used to store matrix A
	
	Output
	------
	A      updated according to A = alpha * x * transpose(y) + A
	
	Reference: http://www.netlib.org/blas/sger.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, incx == 0, incy == 0
	CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
	</pre>
    """
        cublasSgerNative(m, n, alpha, x, incx, y, incy, A, lda);
        checkResultBLAS();
    }
public static void cublasSger(int m, int n, float alpha, Pointer x, int incx, Pointer y, int incy, Pointer A, int lda)
    {
        cublasSgerNative(m, n, alpha, x, incx, y, incy, A, lda);
        checkResultBLAS();
    }
public static void cublasSger ( int m , int n , float alpha , Pointer x , int incx , Pointer y , int incy , Pointer A , int lda ) { cublasSgerNative ( m , n , alpha , x , incx , y , incy , A , lda ) ; checkResultBLAS ( ) ; }
<pre>
cublasSger (int m, int n, float alpha, const float *x, int incx,
const float *y, int incy, float *A, int lda)

performs the symmetric rank 1 operation

A = alpha * x * transpose(y) + A,

where alpha is a single precision scalar, x is an m element single
precision vector, y is an n element single precision vector, and A
is an m by n matrix consisting of single precision elements. Matrix A
is stored in column major format, and lda is the leading dimension of
the two-dimensional array used to store A.

Input
-----
m      specifies the number of rows of the matrix A. It must be at least
zero.
n      specifies the number of columns of the matrix A. It must be at
least zero.
alpha  single precision scalar multiplier applied to x * transpose(y)
x      single precision array of length at least (1 + (m - 1) * abs(incx))
incx   specifies the storage spacing between elements of x. incx must not
be zero.
y      single precision array of length at least (1 + (n - 1) * abs(incy))
incy   specifies the storage spacing between elements of y. incy must not
be zero.
A      single precision array of dimensions (lda, n).
lda    leading dimension of two-dimensional array used to store matrix A

Output
------
A      updated according to A = alpha * x * transpose(y) + A

Reference: http://www.netlib.org/blas/sger.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, incx == 0, incy == 0
CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
</pre>
<pre > cublasSger ( int m int n float alpha const float * x int incx const float * y int incy float * A int lda )