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gemm

Computes a matrix-matrix product with general matrices.

Description

The
gemm
routines compute a scalar-matrix-matrix product and add the result to 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
    m
    x
    k
    matrix
  • op(
    B
    ) is
    k
    x
    n
    matrix
  • C
    is
    m
    x
    n
    matrix
gemm
supports the following precisions:


Ta
(A matrix)


Tb
(B matrix)


Tc
(C matrix)


Ts
(alpha/beta)

sycl::half
sycl::half
sycl::half
sycl::half
sycl::half
sycl::half
float
float
oneapi::mkl::bfloat16
oneapi::mkl::bfloat16
oneapi::mkl::bfloat16
float
oneapi::mkl::bfloat16
oneapi::mkl::bfloat16
float
float
std::int8_t
std::int8_t
std::int32_t
float
std::int8_t
std::int8_t
float
float
float
float
float
float
double
double
double
double
std::complex<float>
std::complex<float>
std::complex<float>
std::complex<float>
std::complex<double>
std::complex<double>
std::complex<double>
std::complex<double>

gemm (Buffer Version)

Syntax
namespace oneapi::mkl::blas::column_major { void gemm(sycl::queue &queue, oneapi::mkl::transpose transa, oneapi::mkl::transpose transb, std::int64_t m, std::int64_t n, std::int64_t k, Ts alpha, sycl::buffer<Ta,1> &a, std::int64_t lda, sycl::buffer<Tb,1> &b, std::int64_t ldb, Ts beta, sycl::buffer<Tc,1> &c, std::int64_t ldc) }
namespace oneapi::mkl::blas::row_major { void gemm(sycl::queue &queue, oneapi::mkl::transpose transa, oneapi::mkl::transpose transb, std::int64_t m, std::int64_t n, std::int64_t k, Ts alpha, sycl::buffer<Ta,1> &a, std::int64_t lda, sycl::buffer<Tb,1> &b, std::int64_t ldb, Ts beta, sycl::buffer<Tc,1> &c, std::int64_t ldc) }
Input Parameters
queue
The queue where the routine should be executed.
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.
m
Number of rows of matrix op(
A
) and matrix
C
. Must be at least zero.
n
Number of columns of matrix op(
B
) 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
m
x
k
matrix. Size of array
a
must be at least
lda
*
k
A
is
k
x
m
matrix. Size of array
a
must be at least
lda
*
m
Row major
A
is
m
x
k
matrix. Size of array
a
must be at least
lda
*
m
A
is
k
x
m
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
m
Must be at least
k
Row major
Must be at least
k
Must be at least
m
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
alpha
* op(
A
)*op(
B
) +
beta
*
C
.
If
beta
= 0, matrix
C
does not need to be initialized before calling
gemm
.

Examples

An example of how to use buffer version of
gemm
can be found in oneMKL installation directory, under:
examples/dpcpp/blas/source/gemm.cpp

gemm (USM Version)

Syntax
namespace oneapi::mkl::blas::column_major { sycl::event gemm(sycl::queue &queue, oneapi::mkl::transpose transa, oneapi::mkl::transpose transb, std::int64_t m, std::int64_t n, std::int64_t k, Ts alpha, const Ta *a, std::int64_t lda, const Tb *b, std::int64_t ldb, Ts beta, Tc *c, std::int64_t ldc, const std::vector<sycl::event> &dependencies = {}) }
namespace oneapi::mkl::blas::row_major { sycl::event gemm(sycl::queue &queue, oneapi::mkl::transpose transa, oneapi::mkl::transpose transb, std::int64_t m, std::int64_t n, std::int64_t k, Ts alpha, const Ta *a, std::int64_t lda, const Tb *b, std::int64_t ldb, Ts beta, Tc *c, std::int64_t ldc, const std::vector<sycl::event> &dependencies = {}) }
Input Parameters
queue
The queue where the routine should be executed.
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.
m
Number of rows of matrix op(
A
) and matrix
C
. Must be at least zero.
n
Number of columns of matrix op(
B
) 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.
A
not transposed
A
transposed
Column major
A
is
m
x
k
matrix. Size of array
a
must be at least
lda
*
k
A
is
k
x
m
matrix. Size of array
a
must be at least
lda
*
m
Row major
A
is
m
x
k
matrix. Size of array
a
must be at least
lda
*
m
A
is
k
x
m
matrix. Size of array
a
must be at least
lda
*
k
lda
Leading dimension of matrix
A
. Must be positive.
A
not transposed
A
transposed
Column major
Must be at least
m
Must be at least
k
Row major
Must be at least
k
Must be at least
m
b
Pointer to input matrix
B
. See Matrix Storage for more details.
B
not transposed
B
transposed
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.
B
not transposed
B
transposed
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 overwritten by
alpha
* op(
A
)*op(
B
) +
beta
*
C
.
If
beta
= 0, matrix
C
does not need to be initialized before calling
gemm
.
Return Values
Output event to wait on to ensure computation is complete.

Examples

An example of how to use USM version of
gemm
can be found in oneMKL installation directory, under:
examples/dpcpp/blas/source/gemm_usm.cpp

Product and Performance Information

1

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