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.

Installation

npm install @stdlib/blas-base-dgemm

Usage

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.

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.
• lda: stride of the first dimension of A (leading dimension of A).
• B: second input matrix stored in linear memory as a 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.
• 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

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 ]

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.

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 views mandate a view offset based on the underlying buffer, the offset parameters support indexing semantics based on starting indices. For example,

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 ]

Notes

• dgemm() corresponds to the BLAS level 3 function dgemm.

Examples

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

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

TODO

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Examples

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Notice

This package is part of 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, see the main project repository.

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License

See LICENSE.

Copyright

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