JCublasJava/src/main/java/jcuda/jcublas/JCublas.java
public static void cublasSaxpy(int n, float alpha, Pointer x, int incx, Pointer y, int incy)
    {
        cublasSaxpyNative(n, alpha, x, incx, y, incy);
        checkResultBLAS();
    }
public static void cublasSaxpy(int n, float alpha, Pointer x, int incx, Pointer y, int incy)
    {
        cublasSaxpyNative(n, alpha, x, incx, y, incy);
        checkResultBLAS();
    }
public static void cublasSaxpy ( int n , float alpha , Pointer x , int incx , Pointer y , int incy ) { cublasSaxpyNative ( n , alpha , x , incx , y , incy ) ; checkResultBLAS ( ) ; }
<pre>
void
cublasSaxpy (int n, float alpha, const float *x, int incx, float *y,
int incy)

multiplies single precision vector x by single precision scalar alpha
and adds the result to single precision vector y; that is, it overwrites
single precision y with single precision alpha * x + y. For i = 0 to n - 1,
it replaces y[ly + i * incy] with alpha * x[lx + i * incx] + y[ly + i *
incy], where lx = 1 if incx >= 0, else lx = 1 +(1 - n) * incx, and ly is
defined in a similar way using incy.

Input
-----
n      number of elements in input vectors
alpha  single precision scalar multiplier
x      single precision vector with n elements
incx   storage spacing between elements of x
y      single precision vector with n elements
incy   storage spacing between elements of y

Output
------
y      single precision result (unchanged if n <= 0)

Reference: http://www.netlib.org/blas/saxpy.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_EXECUTION_FAILED if function failed to launch on GPU
</pre>
<pre > void cublasSaxpy ( int n float alpha const float * x int incx float * y int incy )