# dgemm
> 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`.
## 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 => [ 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 => [ 2.0, 5.0, 6.0, 11.0 ]
```
#### 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 => [ 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 => [ 0.0, 0.0, 0.0, 2.0, 6.0, 5.0, 11.0 ]
```
## Notes
- `dgemm()` corresponds to the [BLAS][blas] level 3 function [`dgemm`][blas-dgemm].
## Examples
```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 );
```
* * *
## C APIs
### Usage
```c
#include "stdlib/blas/base/dgemm.h"
```
#### TODO
TODO.
```c
TODO
```
TODO
```c
TODO
```
### Examples
```c
TODO
```
[blas]: http://www.netlib.org/blas
[blas-dgemm]: https://www.netlib.org/lapack/explore-html/dd/d09/group__gemm_ga1e899f8453bcbfde78e91a86a2dab984.html#ga1e899f8453bcbfde78e91a86a2dab984
[mdn-float64array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/Float64Array
[mdn-typed-array]: https://developer.mozilla.org/en-US/docs/Web/JavaScript/Reference/Global_Objects/TypedArray