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

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gemmt

Computes a matrix-matrix product with general matrices, but updates only the upper or lower triangular part of the result matrix.

Description

The
gemmt
 routines compute a scalar-matrix-matrix product and add the result to the upper or lower part of a scalar-matrix product, with general matrices. The operation is defined as:
LaTex Math image.
where:
  • op(
    X
    ) is one of op(
    X
    ) =
    X
    , or op(
    X
    ) =
    X
    T
    , or op(
    X
    ) =
    X
    H
  • alpha
     and
    beta
     are scalars
  • A
    ,
    B
    , and
    C
     are matrices
  • op(
    A
    ) is
    n
     x
    k
    , op(
    B
    ) is
    k
     x
    n
    , and
    C
     is
    n
     x
    n
gemmt
 supports the following precisions:
T
float
double
std::complex<float>
std::complex<double>

gemmt (Buffer Version)

Syntax
namespace oneapi::mkl::blas::column_major {
    void gemmt(sycl::queue &queue,
               oneapi::mkl::uplo upper_lower,
               oneapi::mkl::transpose transa,
               oneapi::mkl::transpose transb,
               std::int64_t n,
               std::int64_t k,
               T alpha,
               sycl::buffer<T,1> &a,
               std::int64_t lda,
               sycl::buffer<T,1> &b,
               std::int64_t ldb,
               T beta,
               sycl::buffer<T,1> &c,
               std::int64_t ldc)
}
namespace oneapi::mkl::blas::row_major {
    void gemmt(sycl::queue &queue,
               oneapi::mkl::uplo upper_lower,
               oneapi::mkl::transpose transa,
               oneapi::mkl::transpose transb,
               std::int64_t n,
               std::int64_t k,
               T alpha,
               sycl::buffer<T,1> &a,
               std::int64_t lda,
               sycl::buffer<T,1> &b,
               std::int64_t ldb,
               T beta,
               sycl::buffer<T,1> &c,
               std::int64_t ldc)
}
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.
transa
Specifies op(
A
), the transposition operation applied to matrix
A
. See Data Types for more details.
transb
Specifies op(
B
), the transposition operation applied to matrix
B
. See Data Types for more details.
n
Number of rows of matrix op(
A
) and matrix
C
. Must be at least zero.
k
Number of columns of matrix op(
A
) and rows of matrix op(
B
). Must be at least zero.
alpha
Scaling factor for matrix-matrix product.
a
Buffer holding input matrix
A
. See Matrix Storage for more details.
transa
 =
transpose::nontrans
transa
 =
transpose::trans
 or
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.
transa
 =
transpose::nontrans
transa
 =
transpose::trans
 or
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
b
Buffer holding input matrix
B
. See Matrix Storage for more details.
transb
 =
transpose::nontrans
transb
 =
transpose::trans
 or
trans
 =
transpose::conjtrans
Column major
B
 is
k
 x
n
 matrix. Size of array
b
 must be at least
ldb
 *
n
B
 is
n
 x
k
 matrix. Size of array
b
 must be at least
ldb
 *
k
Row major
B
 is
k
 x
n
 matrix. Size of array
b
 must be at least
ldb
 *
k
B
 is
n
 x
k
 matrix. Size of array
b
 must be at least
ldb
 *
n
ldb
Leading dimension of matrix
B
. Must be positive.
transb
 =
transpose::nontrans
transb
 =
transpose::trans
 or
trans
 =
transpose::conjtrans
Column major
Must be at least
k
Must be at least
n
Row major
Must be at least
n
Must be at least
k
beta
Scaling factor for matrix
C
.
c
Buffer holding input/output matrix
C
. See Matrix Storage for more details.
Column major
C
 is
m
 x
n
 matrix. Size of array
c
 must be at least
ldc
 *
n
Row major
C
 is
m
 x
n
 matrix. Size of array
c
 must be at least
ldc
 *
m
ldc
Leading dimension of matrix
C
. Must be positive.
Column major
Must be at least
m
Row major
Must be at least
n
Output Parameters
c
Output buffer overwritten by upper or lower triangular part of
alpha
 * op(
A
)*op(
B
) +
beta
 *
C
.
If
beta
 = 0, matrix
C
 does not need to be initialized before calling
gemmt
.

gemmt (USM Version)

Syntax
namespace oneapi::mkl::blas::column_major {
    sycl::event gemmt(sycl::queue &queue,
                      oneapi::mkl::uplo upper_lower,
                      oneapi::mkl::transpose transa,
                      oneapi::mkl::transpose transb,
                      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 beta,
                      T* c,
                      std::int64_t ldc,
                      const std::vector<sycl::event> &dependencies = {})
}
namespace oneapi::mkl::blas::row_major {
    sycl::event gemmt(sycl::queue &queue,
                      oneapi::mkl::uplo upper_lower,
                      oneapi::mkl::transpose transa,
                      oneapi::mkl::transpose transb,
                      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 beta,
                      T* c,
                      std::int64_t ldc,
                      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.
transa
Specifies op(
A
), the transposition operation applied to matrix
A
. See Data Types for more details.
transb
Specifies op(
B
), the transposition operation applied to matrix
B
. See Data Types for more details.
n
Number of rows of matrix op(
A
) and matrix
C
. Must be at least zero.
k
Number of columns of matrix op(
A
) and rows of matrix op(
B
). Must be at least zero.
alpha
Scaling factor for matrix-matrix product.
a
Pointer to input matrix
A
. See Matrix Storage for more details.
transa
 =
transpose::nontrans
transa
 =
transpose::trans
 or
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.
transa
 =
transpose::nontrans
transa
 =
transpose::trans
 or
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
b
Pointer to input matrix
B
. See Matrix Storage for more details.
transb
 =
transpose::nontrans
transb
 =
transpose::trans
 or
trans
 =
transpose::conjtrans
Column major
B
 is
k
 x
n
 matrix. Size of array
b
 must be at least
ldb
 *
n
B
 is
n
 x
k
 matrix. Size of array
b
 must be at least
ldb
 *
k
Row major
B
 is
k
 x
n
 matrix. Size of array
b
 must be at least
ldb
 *
k
B
 is
n
 x
k
 matrix. Size of array
b
 must be at least
ldb
 *
n
ldb
Leading dimension of matrix
B
. Must be positive.
transb
 =
transpose::nontrans
transb
 =
transpose::trans
 or
trans
 =
transpose::conjtrans
Column major
Must be at least
k
Must be at least
n
Row major
Must be at least
n
Must be at least
k
beta
Scaling factor for matrix
C
.
c
Pointer to input/output matrix
C
. See Matrix Storage for more details.
Column major
C
 is
m
 x
n
 matrix. Size of array
c
 must be at least
ldc
 *
n
Row major
C
 is
m
 x
n
 matrix. Size of array
c
 must be at least
ldc
 *
m
ldc
Leading dimension of matrix
C
. Must be positive.
Column major
Must be at least
m
Row major
Must be at least
n
dependencies
List of events to wait for before starting computation, if any. If omitted, defaults to no dependencies.
Output Parameters
c
Pointer to output matrix
C
 overwritten by upper or lower triangular part of
alpha
 * op(
A
)*op(
B
) +
beta
 *
C
.
If
beta
 = 0, matrix
C
 does not need to be initialized before calling
gemmt
.
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.