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:

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

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 m x k matrix
op(B) is k x n matrix
C is m x n matrix

gemm supports the following precisions:

Ts	Ta	Tb	Tc
`sycl::half`	`sycl::half`	`sycl::half`	`sycl::half`
`float`	`sycl::half`	`sycl::half`	`float`
`float`	`oneapi::mkl::bfloat16`	`oneapi::mkl::bfloat16`	`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.

Note

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.

Note

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

BLAS Level 3 Routines hemm

oneAPI Math Kernel Library (oneMKL) documentation

gemm¶

Description¶

gemm (Buffer Version)¶

Syntax¶

Input Parameters¶

Output Parameters¶

Examples¶

gemm (USM Version)¶

Syntax¶

Input Parameters¶

Output Parameters¶

Return Values¶

Examples¶