npm package: @stdlib/blas-base-dgemm
Description: Perform the matrix-matrix operation `C = α*op(A)*op(B) + β*C` where `op(X)` is either `op(X) = X` or `op(X) = X^T`.
Author: The Stdlib Authors
Latest version: 0.1.1

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# dgemm

[![NPM version][npm-image]][npm-url] [![Build Status][test-image]][test-url] [![Coverage Status][coverage-image]][coverage-url] <!-- [![dependencies][dependencies-image]][dependencies-url] -->

> Perform the matrix-matrix operation `C = α*op(A)*op(B) + β*C` where `op(X)` is one of the `op(X) = X`, or `op(X) = X^T`.

<section class="installation">

## Installation

```bash
npm install @stdlib/blas-base-dgemm
```

</section>

<section class="usage">

## Usage

```javascript
var dgemm = require( '@stdlib/blas-base-dgemm' );
```

#### dgemm( ord, ta, tb, M, N, K, α, A, lda, B, ldb, β, C, ldc )

Performs the matrix-matrix operation `C = α*op(A)*op(B) + β*C` where `op(X)` is either `op(X) = X` or `op(X) = X^T`, `α` and `β` are scalars, `A`, `B`, and `C` are matrices, with `op(A)` an `M` by `K` matrix, `op(B)` a `K` by `N` matrix, and `C` an `M` by `N` matrix.

```javascript
var Float64Array = require( '@stdlib/array-float64' );

var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );
var B = new Float64Array( [ 1.0, 1.0, 0.0, 1.0 ] );
var C = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );

dgemm( 'row-major', 'no-transpose', 'no-transpose', 2, 2, 2, 1.0, A, 2, B, 2, 1.0, C, 2 );
// C => <Float64Array>[ 2.0, 5.0, 6.0, 11.0 ]
```

The function has the following parameters:

-   **ord**: storage layout.
-   **ta**: specifies whether `A` should be transposed, conjugate-transposed, or not transposed.
-   **tb**: specifies whether `B` should be transposed, conjugate-transposed, or not transposed.
-   **M**: number of rows in the matrix `op(A)` and in the matrix `C`.
-   **N**: number of columns in the matrix `op(B)` and in the matrix `C`.
-   **K**: number of columns in the matrix `op(A)` and number of rows in the matrix `op(B)`.
-   **α**: scalar constant.
-   **A**: first input matrix stored in linear memory as a [`Float64Array`][mdn-float64array].
-   **lda**: stride of the first dimension of `A` (leading dimension of `A`).
-   **B**: second input matrix stored in linear memory as a [`Float64Array`][mdn-float64array].
-   **ldb**: stride of the first dimension of `B` (leading dimension of `B`).
-   **β**: scalar constant.
-   **C**: third input matrix stored in linear memory as a [`Float64Array`][mdn-float64array].
-   **ldc**: stride of the first dimension of `C` (leading dimension of `C`).

The stride parameters determine how elements in the input arrays are accessed at runtime. For example, to perform matrix multiplication of two subarrays

```javascript
var Float64Array = require( '@stdlib/array-float64' );

var A = new Float64Array( [ 1.0, 2.0, 0.0, 0.0, 3.0, 4.0, 0.0, 0.0 ] );
var B = new Float64Array( [ 1.0, 1.0, 0.0, 0.0, 0.0, 1.0, 0.0, 0.0 ] );
var C = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );

dgemm( 'row-major', 'no-transpose', 'no-transpose', 2, 2, 2, 1.0, A, 4, B, 4, 1.0, C, 2 );
// C => <Float64Array>[ 2.0, 5.0, 6.0, 11.0 ]
```

<!-- lint disable maximum-heading-length -->

#### dgemm.ndarray( ta, tb, M, N, K, α, A, sa1, sa2, oa, B, sb1, sb2, ob, β, C, sc1, sc2, oc )

Performs the matrix-matrix operation `C = α*op(A)*op(B) + β*C`, using alternative indexing semantics and where `op(X)` is either `op(X) = X` or `op(X) = X^T`, `α` and `β` are scalars, `A`, `B`, and `C` are matrices, with `op(A)` an `M` by `K` matrix, `op(B)` a `K` by `N` matrix, and `C` an `M` by `N` matrix.

```javascript
var Float64Array = require( '@stdlib/array-float64' );

var A = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );
var B = new Float64Array( [ 1.0, 1.0, 0.0, 1.0 ] );
var C = new Float64Array( [ 1.0, 2.0, 3.0, 4.0 ] );

dgemm.ndarray( 'no-transpose', 'no-transpose', 2, 2, 2, 1.0, A, 2, 1, 0, B, 2, 1, 0, 1.0, C, 2, 1, 0 );
// C => <Float64Array>[ 2.0, 5.0, 6.0, 11.0 ]
```

The function has the following additional parameters:

-   **sa1**: stride of the first dimension of `A`.
-   **sa2**: stride of the second dimension of `A`.
-   **oa**: starting index for `A`.
-   **sb1**: stride of the first dimension of `B`.
-   **sb2**: stride of the second dimension of `B`.
-   **ob**: starting index for `B`.
-   **sc1**: stride of the first dimension of `C`.
-   **sc2**: stride of the second dimension of `C`.
-   **oc**: starting index for `C`.

While [`typed array`][mdn-typed-array] views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example,

```javascript
var Float64Array = require( '@stdlib/array-float64' );

var A = new Float64Array( [ 0.0, 0.0, 1.0, 3.0, 2.0, 4.0 ] );
var B = new Float64Array( [ 0.0, 1.0, 0.0, 1.0, 1.0 ] );
var C = new Float64Array( [ 0.0, 0.0, 0.0, 1.0, 3.0, 2.0, 4.0 ] );

dgemm.ndarray( 'no-transpose', 'no-transpose', 2, 2, 2, 1.0, A, 1, 2, 2, B, 1, 2, 1, 1.0, C, 1, 2, 3 );
// C => <Float64Array>[ 0.0, 0.0, 0.0, 2.0, 6.0, 5.0, 11.0 ]
```

</section>

<!-- /.usage -->

<section class="notes">

## Notes

-   `dgemm()` corresponds to the [BLAS][blas] level 3 function [`dgemm`][blas-dgemm].

</section>

<!-- /.notes -->

<section class="examples">

## Examples

<!-- eslint no-undef: "error" -->

```javascript
var discreteUniform = require( '@stdlib/random-array-discrete-uniform' );
var dgemm = require( '@stdlib/blas-base-dgemm' );

var opts = {
    'dtype': 'float64'
};

var M = 3;
var N = 4;
var K = 2;

var A = discreteUniform( M*K, 0, 10, opts ); // 3x2
var B = discreteUniform( K*N, 0, 10, opts ); // 2x4
var C = discreteUniform( M*N, 0, 10, opts ); // 3x4

dgemm( 'row-major', 'no-transpose', 'no-transpose', M, N, K, 1.0, A, K, B, N, 1.0, C, N );
console.log( C );

dgemm.ndarray( 'no-transpose', 'no-transpose', M, N, K, 1.0, A, K, 1, 0, B, N, 1, 0, 1.0, C, N, 1, 0 );
console.log( C );
```

</section>

<!-- /.examples -->

<!-- C interface documentation. -->

* * *

<section class="c">

## C APIs

<!-- Section to include introductory text. Make sure to keep an empty line after the intro `section` element and another before the `/section` close. -->

<section class="intro">

</section>

<!-- /.intro -->

<!-- C usage documentation. -->

<section class="usage">

### Usage

```c
#include "stdlib/blas/base/dgemm.h"
```

#### TODO

TODO.

```c
TODO
```

TODO

```c
TODO
```

</section>

<!-- /.usage -->

<!-- C API usage notes. Make sure to keep an empty line after the `section` element and another before the `/section` close. -->

<section class="notes">

</section>

<!-- /.notes -->

<!-- C API usage examples. -->

<section class="examples">

### Examples

```c
TODO
```

</section>

<!-- /.examples -->

</section>

<!-- /.c -->

<!-- Section for related `stdlib` packages. Do not manually edit this section, as it is automatically populated. -->

<section class="related">

</section>

<!-- /.related -->

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* * *

## Notice

This package is part of [stdlib][stdlib], a standard library for JavaScript and Node.js, with an emphasis on numerical and scientific computing. The library provides a collection of robust, high performance libraries for mathematics, statistics, streams, utilities, and more.

For more information on the project, filing bug reports and feature requests, and guidance on how to develop [stdlib][stdlib], see the main project [repository][stdlib].

#### Community

[![Chat][chat-image]][chat-url]

---

## License

See [LICENSE][stdlib-license].


## Copyright

Copyright &copy; 2016-2026. The Stdlib [Authors][stdlib-authors].

</section>

<!-- /.stdlib -->

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[stdlib-license]: https://raw.githubusercontent.com/stdlib-js/blas-base-dgemm/main/LICENSE

[blas]: http://www.netlib.org/blas

[blas-dgemm]: https://www.netlib.org/lapack/explore-html/dd/d09/group__gemm_ga1e899f8453bcbfde78e91a86a2dab984.html#ga1e899f8453bcbfde78e91a86a2dab984

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[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray

</section>

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