Intel® oneAPI Math Kernel Library (oneMKL) - Data Parallel C++ Developer Reference

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hbmv

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:
LaTex Math image.
where:
  • alpha
     and
    beta
     are scalars
  • A
     is
    n
     x
    n
     hermitian band matrix, with
    k
     super-diagonals
  • x
     and
    y
     are vectors of length
    n
hbmv
 supports the following precisions:
T
std::complex<float>
std::complex<double>

hbmv (Buffer Version)

Syntax
namespace oneapi::mkl::blas::column_major {
    void hbmv(sycl::queue &queue,
              oneapi::mkl::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 hbmv(sycl::queue &queue,
              oneapi::mkl::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 matrix
A
 is upper or lower triangular. See Data Types for more details.
n
Number of rows and columns of matrix
A
. Must be at least zero.
k
Number of super-diagonals of matrix
A
. Must be at least zero.
alpha
Scaling factor for the matrix-vector product.
a
Buffer holding input matrix
A
. Size of the buffer must be 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
. Size of the buffer must be 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
Buffer holding input/output vector
y
. Size of the buffer must be at least (1 + (
n
 - 1)*abs(
incy
)). See Matrix Storage for more details.
incy
Stride of vector
y
.
Output Parameters
y
Buffer holding updated vector
y
.

hbmv (USM Version)

Syntax
namespace oneapi::mkl::blas::column_major {
    sycl::event hbmv(sycl::queue &queue,
                     oneapi::mkl::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 hbmv(sycl::queue &queue,
                     oneapi::mkl::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 matrix
A
 is upper or lower triangular. See Data Types for more details.
n
Number of rows and columns of matrix
A
. Must be at least zero.
k
Number of super-diagonals of matrix
A
. Must be at least zero.
alpha
Scaling factor for the matrix-vector product.
a
Pointer to input matrix
A
. Size of the array holding input matrix
A
 must be 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
. Size of the array holding input vector
x
 must be 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
. Size of the array holding input/output vector
y
 must be 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 updated vector
y
.
Return Values
Output event to wait on to ensure computation is complete.

Product and Performance Information

1

Performance varies by use, configuration and other factors. Learn more at www.Intel.com/PerformanceIndex.