hbmv (USM Version)

Computes a matrix-vector product using a Hermitian band matrix.

Description

The hbmv routines compute a scalar-matrix-vector product and add the result to a scalar-vector product, with a Hermitian 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 Hermitian band matrix, with k super-diagonals,

  • x and y are vectors of length n.

API

Syntax

namespace oneapi::mkl::blas::column_major {
    sycl::event hbmv(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 sycl::vector_class<sycl::event> &dependencies = {})
}
namespace oneapi::mkl::blas::row_major {
    sycl::event hbmv(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 sycl::vector_class<sycl::event> &dependencies = {})
}

The USM version of hbmv supports the following precisions and devices.

T

Devices Supported

std::complex<float>

Host, CPU, and GPU

std::complex<double>

Host, CPU, and GPU

Input Parameters

exec_queue

The queue where the routine should be executed.

upper_lower

Specifies whether A is upper or lower triangular. See Data Types 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 the 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.

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.

dependencies

List of events to wait for before starting computation, if any. If omitted, defaults to no dependencies.

Output Parameters

y

Pointer to the updated vector y.

Return Values

Output event to wait on to ensure computation is complete.