her2k (USM Version)¶
Performs a Hermitian rank-2k update.
Description¶
The her2k
routines perform a rank-2k update of an n
x n
Hermitian matrix C
by general matrices A
and B
. If
trans
= transpose::nontrans
. The operation is defined as
where A
is n
x k
and B
is k
x n
.
If trans
= transpose::conjtrans
, the operation is defined as:
where A
is k
x n
and B
is n
x k
.
In both cases:
alpha
is a complex scalar andbeta
is a real scalar.C
is a Hermitian matrix andA, B
are general matrices.The inner dimension of both matrix multiplications is
k
.
API¶
Syntax¶
namespace oneapi::mkl::blas::column_major {
sycl::event her2k(sycl::queue &queue,
onemkl::uplo upper_lower,
onemkl::transpose trans,
std::int64_t n,
std::int64_t k,
T alpha,
const T* a,
std::int64_t lda,
const T* b,
std::int64_t ldb,
T_real beta,
T* c,
std::int64_t ldc,
const std::vector<sycl::event> &dependencies = {})
}
namespace oneapi::mkl::blas::row_major {
sycl::event her2k(sycl::queue &queue,
onemkl::uplo upper_lower,
onemkl::transpose trans,
std::int64_t n,
std::int64_t k,
T alpha,
const T* a,
std::int64_t lda,
const T* b,
std::int64_t ldb,
T_real beta,
T* c,
std::int64_t ldc,
const std::vector<sycl::event> &dependencies = {})
}
The USM version of her2k
supports the following precisions and devices:
T |
T_real |
Devices Supported |
---|---|---|
|
|
Host, CPU, and GPU |
|
|
Host, CPU, and GPU |
Input Parameters¶
- exec_queue
The queue where the routine should be executed.
- upper_lower
Specifies whether
A
’s data is stored in its upper or lower triangle. See Data Types for more details.- trans
Specifies the operation to apply, as described above. Supported operations are
transpose::nontrans
andtranspose::conjtrans
.- n
The number of rows and columns in
C
. The value ofn
must be at least zero.- k
The inner dimension of matrix multiplications. The value of
k
must be at least equal to zero.- alpha
Complex scaling factor for the rank-2
k
update.- a
Pointer to input matrix
A
. Iftrans
=transpose::nontrans
,A
is ann
-by-k
matrix so the arraya
must have size at leastlda
*k
(respectively,lda
*n
) if column (respectively, row) major layout is used to store matrices. Otherwise,A
is ank
-by-n
matrix so the arraya
must have size at leastlda
*n
(respectively,lda
*k
) if column (respectively, row) major layout is used to store matrices. See Matrix Storage for more details.- lda
Leading dimension of
A
. If matrices are stored using column major layout, lda must be at leastn
iftrans
=transpose::nontrans
, and at leastk
otherwise. If matrices are stored using row major layout, lda must be at leastk
iftrans
=transpose::nontrans
, and at leastn
otherwise. Must be positive.- beta
Real scaling factor for matrix
C
.- b
Pointer to input matrix
B
. Iftrans
=transpose::nontrans
,B
is ank
-by-n
matrix so the arrayb
must have size at leastldb
*n
(respectively,ldb
*k
) if column (respectively, row) major layout is used to store matrices. Otherwise,B
is ann
-by-k
matrix so the arrayb
must have size at leastldb
*k
(respectively,ldb
*n
) if column (respectively, row) major layout is used to store matrices. See Matrix Storage for more details.- ldb
Leading dimension of
B
. If matrices are stored using column major layout, ldb must be at leastk
iftrans
=transpose::nontrans
, and at leastn
otherwise. If matrices are stored using row major layout, ldb must be at leastn
iftrans
=transpose::nontrans
, and at leastk
otherwise. Must be positive.- c
Pointer to input/output matrix
C
. Must have size at leastldc
*n
. See Matrix Storage for more details.- ldc
Leading dimension of
C
. Must be positive and at leastn
.- dependencies
List of events to wait for before starting computation, if any. If omitted, defaults to no dependencies.
Output Parameters¶
- c
Pointer to the output matrix, overwritten by the updated
C
matrix.
Return Values¶
Output event to wait on to ensure computation is complete.