sbmv#

Computes a matrix-vector product with a symmetric band matrix.

Description

The sbmv routines compute a scalar-matrix-vector product and add the result to a scalar-vector product, with a symmetric band matrix. The operation is defined as:

\[y \leftarrow alpha*A*x + beta*y\]

where:

alpha and beta are scalars,

A is an n-by-n symmetric matrix with k super-diagonals,

x and y are vectors of length n.

sbmv supports the following precisions.

T

float

double

T
`float`
`double`

sbmv (Buffer Version)#

Syntax

namespace oneapi::mkl::blas::column_major {
    void sbmv(sycl::queue &queue,
              onemkl::uplo upper_lower,
              std::int64_t n,
              std::int64_t k,
              T alpha,
              sycl::buffer<T,1> &a,
              std::int64_t lda,
              sycl::buffer<T,1> &x,
              std::int64_t incx,
              T beta,
              sycl::buffer<T,1> &y,
              std::int64_t incy)
}

namespace oneapi::mkl::blas::row_major {
    void sbmv(sycl::queue &queue,
              onemkl::uplo upper_lower,
              std::int64_t n,
              std::int64_t k,
              T alpha,
              sycl::buffer<T,1> &a,
              std::int64_t lda,
              sycl::buffer<T,1> &x,
              std::int64_t incx,
              T beta,
              sycl::buffer<T,1> &y,
              std::int64_t incy)
}

Input Parameters

queue: The queue where the routine should be executed.
upper_lower: Specifies whether A is upper or lower triangular. See oneMKL defined datatypes for more details.
n: Number of rows and columns of A. Must be at least zero.
k: Number of super-diagonals of the matrix A. Must be at least zero.
alpha: Scaling factor for the matrix-vector product.
a: Buffer holding input matrix A. Must have size at least lda*n. See Matrix Storage for more details.
lda: Leading dimension of matrix A. Must be at least (k + 1), and positive.
x: Buffer holding input vector x. The buffer must be of size at least (1 + (n - 1)*abs(incx)). See Matrix Storage for more details.
incx: Stride of vector x. Must not be zero.
beta: Scaling factor for vector y.
y: Buffer holding input/output vector y. The buffer must be of size at least (1 + (n - 1)*abs(incy)). See Matrix Storage for more details.
incy: Stride of vector y. Must not be zero.

Output Parameters

y: Buffer holding the updated vector y.

Throws

This routine shall throw the following exceptions if the associated condition is detected. An implementation may throw additional implementation-specific exception(s) in case of error conditions not covered here.

oneapi::mkl::invalid_argument

oneapi::mkl::unsupported_device

oneapi::mkl::host_bad_alloc

oneapi::mkl::device_bad_alloc

oneapi::mkl::unimplemented

sbmv (USM Version)#

Syntax

namespace oneapi::mkl::blas::column_major {
    sycl::event sbmv(sycl::queue &queue,
                     onemkl::uplo upper_lower,
                     std::int64_t n,
                     std::int64_t k,
                     T alpha,
                     const T *a,
                     std::int64_t lda,
                     const T *x,
                     std::int64_t incx,
                     T beta,
                     T *y,
                     std::int64_t incy,
                     const std::vector<sycl::event> &dependencies = {})
}

namespace oneapi::mkl::blas::row_major {
    sycl::event sbmv(sycl::queue &queue,
                     onemkl::uplo upper_lower,
                     std::int64_t n,
                     std::int64_t k,
                     T alpha,
                     const T *a,
                     std::int64_t lda,
                     const T *x,
                     std::int64_t incx,
                     T beta,
                     T *y,
                     std::int64_t incy,
                     const std::vector<sycl::event> &dependencies = {})
}

Input Parameters

queue: The queue where the routine should be executed.
upper_lower: Specifies whether A is upper or lower triangular. See oneMKL defined datatypes for more details.
n: Number of rows and columns of A. Must be at least zero.
k: Number of super-diagonals of the matrix A. Must be at least zero.
alpha: Scaling factor for the matrix-vector product.
a: Pointer to input matrix A. The array holding input matrix A must have size at least lda*n. See Matrix Storage for more details.
lda: Leading dimension of matrix A. Must be at least (k + 1), and positive.
x: Pointer to input vector x. The array holding input vector x must be of size at least (1 + (n - 1)*abs(incx)). See Matrix Storage for more details.
incx: Stride of vector x. Must not be zero.
beta: Scaling factor for vector y.
y: Pointer to input/output vector y. The array holding input/output vector y must be of size at least (1 + (n - 1)*abs(incy)). See Matrix Storage for more details.
incy: Stride of vector y. Must not be zero.
dependencies: List of events to wait for before starting computation, if any. If omitted, defaults to no dependencies.