herk

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

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ID 772045

Date 12/16/2022

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herk

Performs a hermitian rank-k update.

Description

The herk routines compute a rank-k update of a hermitian matrix C by a general matrix A. The operation is defined as:

where:

op(X) is one of op(X) = X or op(X) = X^H
alpha and beta are real scalars
C is n x n hermitian matrix
op(A) is n x k general matrix

herk supports the following precisions:

T	Treal
`std::complex<float>`	`float`
`std::complex<double>`	`double`

herk (Buffer Version)

Syntax

namespace oneapi::mkl::blas::column_major {
    void herk(sycl::queue &queue,
              oneapi::mkl::uplo upper_lower,
              oneapi::mkl::transpose trans,
              std::int64_t n,
              std::int64_t k,
              Treal alpha,
              sycl::buffer<T,1> &a,
              std::int64_t lda,
              Treal beta,
              sycl::buffer<T,1> &c,
              std::int64_t ldc,
              compute_mode mode = compute_mode::unset)
}

namespace oneapi::mkl::blas::row_major {
    void herk(sycl::queue &queue,
              oneapi::mkl::uplo upper_lower,
              oneapi::mkl::transpose trans,
              std::int64_t n,
              std::int64_t k,
              Treal alpha,
              sycl::buffer<T,1> &a,
              std::int64_t lda,
              Treal beta,
              sycl::buffer<T,1> &c,
              std::int64_t ldc,
              compute_mode mode = compute_mode::unset)
}

Input Parameters

queue

The queue where the routine should be executed.

upper_lower

Specifies whether matrix C is upper or lower triangular. See Data Types for more details.

trans

Specifies op(A), the transposition operation applied to matrix A. Supported operations are transpose::nontrans and transpose::conjtrans. See Data Types for more details.

n

Number of rows and columns of matrix C. Must be at least zero.

k

Number of columns of matrix op(A). Must be at least zero.

alpha

Complex scaling factor for the rank-k update.

a

Buffer holding input matrix A. See Matrix Storage for more details.

	`trans` = `transpose::nontrans`	`trans` = `transpose::conjtrans`
Column major	`A` is `n` x `k` matrix. Size of array `a` must be at least `lda` * `k`	`A` is `k` x `n` matrix. Size of array `a` must be at least `lda` * `n`
Row major	`A` is `n` x `k` matrix. Size of array `a` must be at least `lda` * `n`	`A` is `k` x `n` matrix. Size of array `a` must be at least `lda` * `k`

lda

Leading dimension of matrix A. Must be positive.

	`trans` = `transpose::nontrans`	`trans` = `transpose::conjtrans`
Column major	Must be at least `n`	Must be at least `k`
Row major	Must be at least `k`	Must be at least `n`

beta

Real scaling factor for matrix C.

c

Buffer holding input/output matrix C. Size of the buffer must be at least ldc * n. See Matrix Storage for more details.

ldc

Leading dimension of matrix C. Must be positive and at least n.

mode

Optional. Compute mode settings. See Compute Modes for more details.

Output Parameters

c: Output buffer overwritten by alpha * op(A) * op(A)^H + beta * C. The imaginary parts of the diagonal elements are set to zero.

herk (USM Version)

Syntax

namespace oneapi::mkl::blas::column_major {
    sycl::event herk(sycl::queue &queue,
                     oneapi::mkl::uplo upper_lower,
                     oneapi::mkl::transpose trans,
                     std::int64_t n,
                     std::int64_t k,
                     Treal alpha,
                     const T *a,
                     std::int64_t lda,
                     Treal beta,
                     T *c,
                     std::int64_t ldc,
                     compute_mode mode = compute_mode::unset,
                     const std::vector<sycl::event> &dependencies = {})
}

namespace oneapi::mkl::blas::row_major {
    sycl::event herk(sycl::queue &queue,
                     oneapi::mkl::uplo upper_lower,
                     oneapi::mkl::transpose trans,
                     std::int64_t n,
                     std::int64_t k,
                     Treal alpha,
                     const T *a,
                     std::int64_t lda,
                     Treal beta,
                     T *c,
                     std::int64_t ldc,
                     compute_mode mode = compute_mode::unset,
                     const std::vector<sycl::event> &dependencies = {})
}

Input Parameters

queue

The queue where the routine should be executed.

upper_lower

Specifies whether matrix C is upper or lower triangular. See Data Types for more details.

trans

Specifies op(A), the transposition operation applied to matrix A. Supported operations are transpose::nontrans and transpose::conjtrans. See Data Types for more details.

n

Number of rows and columns of matrix C. Must be at least zero.

k

Number of columns of matrix op(A). Must be at least zero.

alpha

Complex scaling factor for the rank-k update.

a

Pointer to input matrix A. See Matrix Storage for more details.

	`trans` = `transpose::nontrans`	`trans` = `transpose::conjtrans`
Column major	`A` is `n` x `k` matrix. Size of array `a` must be at least `lda` * `k`	`A` is `k` x `n` matrix. Size of array `a` must be at least `lda` * `n`
Row major	`A` is `n` x `k` matrix. Size of array `a` must be at least `lda` * `n`	`A` is `k` x `n` matrix. Size of array `a` must be at least `lda` * `k`

lda

Leading dimension of matrix A. Must be positive.

	`trans` = `transpose::nontrans`	`trans` = `transpose::conjtrans`
Column major	Must be at least `n`	Must be at least `k`
Row major	Must be at least `k`	Must be at least `n`

beta

Real scaling factor for matrix C.

c

Pointer to input/output matrix C. Size of the array must be at least ldc * n. See Matrix Storage for more details.

ldc

Leading dimension of matrix C. Must be positive and at least n.

mode

Optional. Compute mode settings. See Compute Modes for more details.

dependencies

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

mode and dependencies may be omitted independently; it is not necessary to specify mode in order to provide dependencies.

Output Parameters

c: Pointer to output matrix overwritten by alpha * op(A) * op(A)^H + beta * C. The imaginary parts of the diagonal elements are set to zero.

Return Values

Output event to wait on to ensure computation is complete.

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