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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:
where:
op(X) is one of op(X) = X, or op(X) = XT, or op(X) = XH
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,
compute_mode mode = compute_mode::unset)
}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,
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.
- 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
- mode
-
Optional. Compute mode settings. See Compute Modes for more details.
Output Parameters
- c
-
Output buffer overwritten by upper or lower triangular part of alpha * op(A)*op(B) + beta * C.
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,
oneapi::mkl::value_or_pointer<T> alpha,
const T* a,
std::int64_t lda,
const T* b,
std::int64_t ldb,
oneapi::mkl::value_or_pointer<T> 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 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,
oneapi::mkl::value_or_pointer<T> alpha,
const T* a,
std::int64_t lda,
const T* b,
std::int64_t ldb,
oneapi::mkl::value_or_pointer<T> 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.
- 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. See Scalar Arguments for more information on the value_or_pointer data type.
- 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. See Scalar Arguments for more information on the value_or_pointer data type.
- 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
- 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 C overwritten by upper or lower triangular part of alpha * op(A)*op(B) + beta * C.
Return Values
Output event to wait on to ensure computation is complete.