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

multiplies double-precision vector x by double-precision scalar alpha
and adds the result to double-precision vector y; that is, it overwrites
double-precision y with double-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; ly is defined in a
similar way using incy.

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

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

Reference: http://www.netlib.org/blas/daxpy.f

Error status for this function can be retrieved via cublasGetError().

Error Status
------------
CUBLAS_STATUS_NOT_INITIALIZED  if CUBLAS library was not initialized
CUBLAS_STATUS_ARCH_MISMATCH    if invoked on device without DP support
CUBLAS_STATUS_EXECUTION_FAILED if function failed to launch on GPU
</pre>
<pre > void cublasDaxpy ( int n double alpha const double * x int incx double * y int incy )