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:

\[C \leftarrow alpha*op(A)*op(B) + beta*C\]

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>

T
`float`
`double`
`std::complex<float>`
`std::complex<double>`

gemmt (Buffer Version)#

Syntax

namespace oneapi::mkl::blas::column_major {
    void gemmt(sycl::queue &queue,
               onemkl::uplo upper_lower,
               onemkl::transpose transa,
               onemkl::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,
               onemkl::uplo upper_lower,
               onemkl::transpose transa,
               onemkl::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 C’s data is stored in its upper or lower triangle. See oneMKL defined datatypes for more details.

transa

Specifies op(A), the transposition operation applied to A. See oneMKL defined datatypes for more details.

transb

Specifies op(B), the transposition operation applied to B. See oneMKL defined datatypes for more details.

n

Number of rows of op(A), columns of op(B), and columns and rows ofC. Must be at least zero.

k

Number of columns of op(A) and rows of op(B). Must be at least zero.

alpha

Scaling factor for the matrix-matrix product.

a

Buffer holding the input matrix A.

	`A` not transposed	`A` transposed
Column major	`A` is an `n`-by-`k` matrix so the array `a` must have size at least `lda`*`k`.	`A` is an `k`-by-`n` matrix so the array `a` must have size at least `lda`*`n`
Row major	`A` is an `n`-by-`k` matrix so the array `a` must have size at least `lda`*`n`.	`A` is an `k`-by-`n` matrix so the array `a` must have size at least `lda`*`k`.

See Matrix Storage for more details.

lda

The leading dimension of A. It must be positive.

	`A` not transposed	`A` transposed
Column major	`lda` must be at least `n`.	`lda` must be at least `k`.
Row major	`lda` must be at least `k`.	`lda` must be at least `n`.

b

Buffer holding the input matrix B.

	`B` not transposed	`B` transposed
Column major	`B` is an `k`-by-`n` matrix so the array `b` must have size at least `ldb`*`n`.	`B` is an `n`-by-`k` matrix so the array `b` must have size at least `ldb`*`k`
Row major	`B` is an `k`-by-`n` matrix so the array `b` must have size at least `ldb`*`k`.	`B` is an `n`-by-`k` matrix so the array `b` must have size at least `ldb`*`n`.

See Matrix Storage for more details.

ldb

The leading dimension of B. It must be positive.

	`B` not transposed	`B` transposed
Column major	`ldb` must be at least `k`.	`ldb` must be at least `n`.
Row major	`ldb` must be at least `n`.	`ldb` must be at least `k`.

beta

Scaling factor for matrix C.

c

Buffer holding the input/output matrix C. Must have size at least ldc * n. See Matrix Storage for more details.

ldc

Leading dimension of C. Must be positive and at least m.

Output Parameters

c: Output buffer, overwritten by the upper or lower triangular part of alpha * op(A)*op(B) + beta * C.

Notes

If beta = 0, matrix C does not need to be initialized before calling gemmt.

Throws

This routine shall throw the following exceptions if the associated condition is detected. An implementation may throw additional implementation-specific exception(s) in case of error conditions not covered here.

oneapi::mkl::invalid_argument

oneapi::mkl::unsupported_device

oneapi::mkl::host_bad_alloc

oneapi::mkl::device_bad_alloc

oneapi::mkl::unimplemented

gemmt (USM Version)#

Syntax

namespace oneapi::mkl::blas::column_major {
    sycl::event gemmt(sycl::queue &queue,
                      onemkl::uplo upper_lower,
                      onemkl::transpose transa,
                      onemkl::transpose transb,
                      std::int64_t n,
                      std::int64_t k,
                      value_or_pointer<T> alpha,
                      const T *a,
                      std::int64_t lda,
                      const T *b,
                      std::int64_t ldb,
                      value_or_pointer<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,
                      onemkl::uplo upper_lower,
                      onemkl::transpose transa,
                      onemkl::transpose transb,
                      std::int64_t n,
                      std::int64_t k,
                      value_or_pointer<T> alpha,
                      const T *a,
                      std::int64_t lda,
                      const T *b,
                      std::int64_t ldb,
                      value_or_pointer<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 C’s data is stored in its upper or lower triangle. See oneMKL defined datatypes for more details.

transa

Specifies op(A), the transposition operation applied to A. See oneMKL defined datatypes for more details.

transb

Specifies op(B), the transposition operation applied to B. See oneMKL defined datatypes for more details.

n

Number of columns of op(A), columns of op(B), and columns ofC. Must be at least zero.

k

Number of columns of op(A) and rows of op(B). Must be at least zero.

alpha

Scaling factor for the matrix-matrix product. See Scalar Arguments in BLAS for more details.

a

Pointer to input matrix A.

	`A` not transposed	`A` transposed
Column major	`A` is an `n`-by-`k` matrix so the array `a` must have size at least `lda`*`k`.	`A` is an `k`-by-`n` matrix so the array `a` must have size at least `lda`*`n`
Row major	`A` is an `n`-by-`k` matrix so the array `a` must have size at least `lda`*`n`.	`A` is an `k`-by-`n` matrix so the array `a` must have size at least `lda`*`k`

See Matrix Storage for more details.

lda

The leading dimension of A. It must be positive.

	`A` not transposed	`A` transposed
Column major	`lda` must be at least `n`.	`lda` must be at least `k`.
Row major	`lda` must be at least `k`.	`lda` must be at least `n`.

b

Pointer to input matrix B.

	`B` not transposed	`B` transposed
Column major	`B` is an `k`-by-`n` matrix so the array `b` must have size at least `ldb`*`n`.	`B` is an `n`-by-`k` matrix so the array `b` must have size at least `ldb`*`k`
Row major	`B` is an `k`-by-`n` matrix so the array `b` must have size at least `ldb`*`k`.	`B` is an `n`-by-`k` matrix so the array `b` must have size at least `ldb`*`n`