syrk_batch#
Computes a group of syrk
operations.
Description
The syrk_batch
routines are batched versions of syrk, performing
multiple syrk
operations in a single call. Each syrk
operation perform a rank-k update with general matrices.
syrk_batch
supports the following precisions.
T
float
double
std::complex<float>
std::complex<double>
syrk_batch (Buffer Version)#
Description
The buffer version of syrk_batch
supports only the strided API.
The strided API operation is defined as:
for i = 0 … batch_size – 1
A and C are matrices at offset i * stridea, i * stridec in a and c.
C := alpha * op(A) * op(A)^T + beta * C
end for
where:
op(X) is one of op(X) = X, or op(X) = XT, or op(X) = XH,
alpha
and beta
are scalars,
A
and C
are matrices,
op(A
) is n
x k
and C
is n
x n
.
The a
and c
buffers contain all the input matrices. The stride
between matrices is given by the stride parameter. The total number
of matrices in a
and c
buffers is given by the batch_size
parameter.
Strided API
Syntax
namespace oneapi::mkl::blas::column_major {
void syrk_batch(sycl::queue &queue,
onemkl::uplo upper_lower,
onemkl::transpose trans,
std::int64_t n,
std::int64_t k,
T alpha,
sycl::buffer<T,1> &a,
std::int64_t lda,
std::int64_t stridea,
T beta,
sycl::buffer<T,1> &c,
std::int64_t ldc,
std::int64_t stridec,
std::int64_t batch_size)
}
namespace oneapi::mkl::blas::row_major {
void syrk_batch(sycl::queue &queue,
onemkl::uplo upper_lower,
onemkl::transpose trans,
std::int64_t n,
std::int64_t k,
T alpha,
sycl::buffer<T,1> &a,
std::int64_t lda,
std::int64_t stridea,
T beta,
sycl::buffer<T,1> &c,
std::int64_t ldc,
std::int64_t stridec,
std::int64_t batch_size)
}
Input Parameters
- queue
The queue where the routine should be executed.
- upper_lower
Specifies whether data in
C
is stored in its upper or lower triangle. For more details, see oneMKL Defined Datatypes.- trans
Specifies op(
A
) the transposition operation applied to the matrixA
. Conjugation is never performed, even if trans = transpose::conjtrans. See oneMKL Defined Datatypes for more details.- n
Number of rows and columns of
C
. Must be at least zero.- k
Number of columns of op(
A
). Must be at least zero.- alpha
Scaling factor for the rank-k update.
- a
Buffer holding the input matrices
A
with sizestridea
*batch_size
.- lda
The leading dimension of the matrices
A
. It must be positive.A
not transposedA
transposedColumn major
lda
must be at leastn
.lda
must be at leastk
.Row major
lda
must be at leastk
.lda
must be at leastn
.- stridea
Stride between different
A
matrices.- beta
Scaling factor for the matrices
C
.- c
Buffer holding input/output matrices
C
with sizestridec
*batch_size
.- ldc
The leading dimension of the matrices
C
. It must be positive and at leastn
.- stridec
Stride between different
C
matrices. Must be at leastldc
*n
.- batch_size
Specifies the number of rank-k update operations to perform.
Output Parameters
- c
Output buffer, overwritten by
batch_size
rank-k update operations of the formalpha
* op(A
)*op(A
)^T +beta
*C
.
syrk_batch (USM Version)#
Description
The USM version of syrk_batch
supports the group API and strided API.
The group API operation is defined as:
idx = 0
for i = 0 … group_count – 1
for j = 0 … group_size – 1
A, B, and C are matrices in a[idx] and c[idx]
C := alpha[i] * op(A) * op(A)^T + beta[i] * C
idx = idx + 1
end for
end for
The strided API operation is defined as
for i = 0 … batch_size – 1
A, B and C are matrices at offset i * stridea, i * stridec in a and c.
C := alpha * op(A) * op(A)^T + beta * C
end for
where:
op(X) is one of op(X) = X, or op(X) = XT, or op(X) = XH,
alpha
and beta
are scalars,
A
and C
are matrices,
op(A
) is n
x k
and C
is n
x n
.
For group API, a
and c
arrays contain the pointers for all the input matrices.
The total number of matrices in a
and c
are given by:
For strided API, a
and c
arrays contain all the input matrices. The total number of matrices
in a
and c
are given by the batch_size
parameter.
Group API
Syntax
namespace oneapi::mkl::blas::column_major {
sycl::event syrk_batch(sycl::queue &queue,
uplo *upper_lower,
transpose *trans,
std::int64_t *n,
std::int64_t *k,
T *alpha,
const T **a,
std::int64_t *lda,
T *beta,
T **c,
std::int64_t *ldc,
std::int64_t group_count,
std::int64_t *group_size,
const std::vector<sycl::event> &dependencies = {})
}
namespace oneapi::mkl::blas::row_major {
sycl::event syrk_batch(sycl::queue &queue,
uplo *upper_lower,
transpose *trans,
std::int64_t *n,
std::int64_t *k,
T *alpha,
const T **a,
std::int64_t *lda,
T *beta,
T **c,
std::int64_t *ldc,
std::int64_t group_count,
std::int64_t *group_size,
const std::vector<sycl::event> &dependencies = {})
}
Input Parameters
- queue
The queue where the routine should be executed.
- upper_lower
Array of
group_count
onemkl::upper_lower
values.upper_lower[i]
specifies whether data in C for every matrix in groupi
is in upper or lower triangle.- trans
Array of
group_count
onemkl::transpose
values.trans[i]
specifies the form of op(A
) used in the rank-k update in groupi
. See oneMKL Defined Datatypes for more details.- n
Array of
group_count
integers.n[i]
specifies the number of rows and columns ofC
for every matrix in groupi
. All entries must be at least zero.- k
Array of
group_count
integers.k[i]
specifies the number of columns of op(A
) for every matrix in groupi
. All entries must be at least zero.- alpha
Array of
group_count
scalar elements.alpha[i]
specifies the scaling factor for every rank-k update in groupi
.- a
Array of pointers to input matrices
A
with sizetotal_batch_count
.See Matrix Storage for more details.
- lda
Array of
group_count
integers.lda[i]
specifies the leading dimension ofA
for every matrix in groupi
. All entries must be positive.A
not transposedA
transposedColumn major
lda[i]
must be at leastn[i]
.lda[i]
must be at leastk[i]
.Row major
lda[i]
must be at leastk[i]
.lda[i]
must be at leastn[i]
.- beta
Array of
group_count
scalar elements.beta[i]
specifies the scaling factor for matrixC
for every matrix in groupi
.- c
Array of pointers to input/output matrices
C
with sizetotal_batch_count
.See Matrix Storage for more details.
- ldc
Array of
group_count
integers.ldc[i]
specifies the leading dimension ofC
for every matrix in groupi
. All entries must be positive andldc[i]
must be at leastn[i]
.- group_count
Specifies the number of groups. Must be at least 0.
- group_size
Array of
group_count
integers.group_size[i]
specifies the number of rank-k update products in groupi
. All entries must be at least 0.- dependencies
List of events to wait for before starting computation, if any. If omitted, defaults to no dependencies.
Output Parameters
- c
Overwritten by the
n[i]
-by-n[i]
matrix calculated by (alpha[i]
* op(A
)*op(A
)^T +beta[i]
*C
) for groupi
.
Return Values
Output event to wait on to ensure computation is complete.
Strided API
Syntax
namespace oneapi::mkl::blas::column_major {
sycl::event syrk_batch(sycl::queue &queue,
uplo upper_lower,
transpose trans,
std::int64_t n,
std::int64_t k,
T alpha,
const T *a,
std::int64_t lda,
std::int64_t stride_a,
T beta,
T *c,
std::int64_t ldc,
std::int64_t stride_c,
std::int64_t batch_size,
const std::vector<sycl::event> &dependencies = {})
}
namespace oneapi::mkl::blas::row_major {
sycl::event syrk_batch(sycl::queue &queue,
uplo upper_lower,
transpose trans,
std::int64_t n,
std::int64_t k,
T alpha,
const T *a,
std::int64_t lda,
std::int64_t stride_a,
T beta,
T *c,
std::int64_t ldc,
std::int64_t stride_c,
std::int64_t batch_size,
const std::vector<sycl::event> &dependencies = {})
}
Input Parameters
- queue
The queue where the routine should be executed.
- upper_lower
Specifies whether data in
C
is stored in its upper or lower triangle. For more details, see oneMKL Defined Datatypes.- trans
Specifies op(
A
) the transposition operation applied to the matricesA
. Conjugation is never performed, even if trans = transpose::conjtrans. See oneMKL Defined Datatypes for more details.- n
Number of rows and columns of
C
. Must be at least zero.- k
Number of columns of op(
A
). Must be at least zero.- alpha
Scaling factor for the rank-k updates.
- a
Pointer to input matrices
A
with sizestridea
*batch_size
.- lda
The leading dimension of the matrices
A
. It must be positive.A
not transposedA
transposedColumn major
lda
must be at leastn
.lda
must be at leastk
.Row major
lda
must be at leastk
.lda
must be at leastn
.- stridea
Stride between different
A
matrices.- beta
Scaling factor for the matrices
C
.- c
Pointer to input/output matrices
C
with sizestridec
*batch_size
.- ldc
The leading dimension of the matrices
C
. It must be positive and at leastn
.- stridec
Stride between different
C
matrices.- batch_size
Specifies the number of rank-k update operations to perform.
- dependencies
List of events to wait for before starting computation, if any. If omitted, defaults to no dependencies.
Output Parameters
- c
Output matrices, overwritten by
batch_size
rank-k update operations of the formalpha
* op(A
)*op(A
)^T +beta
*C
.
Return Values
Output event to wait on to ensure computation is complete.
Parent topic: BLAS-like Extensions