gbmv (USM Version)

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

Syntax

event gbmv(queue &exec_queue, transpose trans, std::int64_t m, std::int64_t n, std::int64_t kl, std::int64_t ku, 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 vector_class<event> &dependencies = {})

The USM version ofgbmv supports the following precisions and devices.

T

Devices Supported

float

Host, CPU, and GPU

double

Host, CPU, and GPU

std::complex<float>

Host, CPU, and GPU

std::complex<double>

Host, CPU, and GPU

Description

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

y <- alpha*op(A)*x + beta*y

where:

  • op(A) is one of op(A) = A, or op(A) = AT, or op(A) = AH,

  • alpha and beta are scalars,

  • A is an m-by-n matrix with kl sub-diagonals and ku super-diagonals,

  • x and y are vectors.

Input Parameters

exec_queue

The queue where the routine should be executed.

trans

Specifies op(A), the transposition operation applied to A. See Data Types for more details.

m

Number of rows of A. Must be at least zero.

n

Number of columns of A. Must be at least zero.

kl

Number of sub-diagonals of the matrix A. Must be at least zero.

ku

Number of super-diagonals of the matrix A. Must be at least zero.

alpha

Scaling factor for the matrix-vector product.

a

The array holding input matrix A must have size at least lda*n if column major layout is used, or at least lda*m if row major layout is used.

lda

Leading dimension of matrix A. Must be at least (kl + ku + 1), and positive.

x

Pointer to input vector x. The length len of vector x is n if A is not transposed, and m if A is transposed. The array holding input vector x must be of size at least (1 + (len - 1)*abs(incx)). See Matrix and Vector Storage for more details.

incx

Stride of vector x.

beta

Scaling factor for vector y.

y

Pointer to input/output vector y. The length len of vector y is m, if A is not transposed, and n if A is transposed. The array holding input/output vector y must be of size at least (1 + (len - 1)*abs(incy)) where len is this length. See Matrix and Vector 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.